memra_engine/

hybrid_forward.rs

//! Hybrid forward pass (Stage-1, f32, prefill, single sequence). Per layer dispatches to a
//! linear-attention (Gated DeltaNet) or full-attention mixer, then SwiGLU FFN. Matches
//! llama.cpp src/models/qwen35.cpp node-for-node.

use crate::Engine;
use crate::cache::Cache;
use cudarc::driver::CudaSlice;
use memra_gguf::config::{ModelConfig, SwigluClamp};

/// Resident trunk transients for the eager prime (piecewise-graph foundation; see
/// HybridModel::prime_slabs). Every live buffer prefix is fully overwritten before use per prime;
/// capacity beyond the current token count must never cross a shape-sensitive boundary.
pub struct PrimeSlabs {
    pub t_cap: usize,
    pub h: CudaSlice<f32>,
    pub x1: CudaSlice<f32>,
    pub z: CudaSlice<f32>,
    pub act: CudaSlice<f32>,
    pub xa: CudaSlice<f32>,
    pub xb: CudaSlice<f32>,
    pub h16: CudaSlice<u8>,
    pub z16: CudaSlice<u8>,
    /// piecewise boundary slabs (increment 2): GEMM outputs land here so the
    /// downstream captured segments see fixed addresses.
    pub gate: CudaSlice<f32>, // t * n_ff_max
    pub up: CudaSlice<f32>,      // t * n_ff_max
    pub ffn_out: CudaSlice<f32>, // t * n_embd
    /// piecewise increment 3: per-layer S-glue segment graphs (down-add + next
    /// attn-norm, ALL-slab IO, zero in-graph allocations -> keeperless capture is
    /// clean). Baked at this t_cap; replay only when t == t_cap. seg_glue[il] fires
    /// between layer il and il+1 (ping-pong parity is deterministic per il).
    pub seg_glue: Vec<Option<cudarc::driver::CudaGraph>>,
    /// increment 5 (core-split edition): the mixer out-GEMM writes _into_ `mixed`
    /// directly (no staging copy — the increment-4 copy route was refuted), making
    /// S-mid [add + post-norm] all-slab and capturable.
    pub mixed: CudaSlice<f32>,
    pub seg_mid: Vec<Option<cudarc::driver::CudaGraph>>,
    pub seg_t: usize,
}

// Split prime ranges cannot enter the full-range segment-graph arm, and every slab access
// is serialized by its device mutex after binding that device's CUDA context on the thread.
unsafe impl Send for PrimeSlabs {}

/// Shared-expert gate+up at t==1: NVFP4 fused2 (the ornith15/qwen35moe NVFP4 mints keep
/// gate/up_shexp uniformly NVFP4, so the Q8-only fused2 never fired there and the pair fell
/// to two mr2 singles + two re-quantizes of the same z — 2 of the 8 unfused launches/layer
/// the orndecode B=1 census ranked at 17.1%), else the Q8_0 fused2 (the Q8 35B mint), else
/// two singles. ONE helper for all three shexp dispatch sites — the MEMRA_GDN_MMA
/// three-read-sites defect is the precedent for not inlining this thrice. Fusion law
/// everywhere: per (tensor,row) the fused seg body is verbatim, so fused == singles
/// bit-identically, and the shared (zq, zd) is the same quantize each single recomputes.
fn shexp_gate_up_t1(
    e: &Engine,
    gate_shexp: &crate::model::GpuTensor,
    up_shexp: &crate::model::GpuTensor,
    z: &CudaSlice<f32>,
    zq8: Option<&(CudaSlice<i8>, CudaSlice<f32>)>,
) -> Result<(CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
    let is_nvfp4 = |w: &crate::model::GpuTensor| matches!(w, crate::model::GpuTensor::Quant { qtype, .. } if *qtype == crate::QT_NVFP4);
    if is_nvfp4(gate_shexp) && is_nvfp4(up_shexp) {
        // Reuse the caller's t==1 z-quantize when one exists (the zq8 seam the dev arm
        // already consumes) — the helper's own quantize is the identical kernel on the
        // identical input, so this drops one launch per MoE layer without moving a byte.
        let pair = match zq8 {
            Some((zq, zd)) => e.matmul_nvfp4_fused2(gate_shexp, up_shexp, zq, zd, 1)?,
            None => {
                let (zq, zd) = e.quantize_q8_1(z, 1, gate_shexp.in_features())?;
                e.matmul_nvfp4_fused2(gate_shexp, up_shexp, &zq, &zd, 1)?
            }
        };
        if let Some(pair) = pair {
            return Ok(pair);
        }
    }
    match e.matmul_q8_fused2_x(gate_shexp, up_shexp, z)? {
        Some(pair) => Ok(pair),
        None => Ok((e.matmul(gate_shexp, z, 1)?, e.matmul(up_shexp, z, 1)?)),
    }
}

fn active_matrix_values(
    available: usize,
    rows: usize,
    columns: usize,
    label: &str,
) -> Result<usize, String> {
    let required = rows
        .checked_mul(columns)
        .ok_or_else(|| format!("{label} shape overflows: {rows}x{columns}"))?;
    if available < required {
        return Err(format!(
            "{label} has {available} values, fewer than the active {rows}x{columns} ({required})"
        ));
    }
    Ok(required)
}

fn step_grouped_decode_shape(prefill: bool, tokens: usize) -> bool {
    !prefill && tokens == 1
}

fn parse_step_ep_grouped_prefill(value: Option<&str>) -> Result<bool, String> {
    match value {
        None | Some("") | Some("0") => Ok(false),
        Some("1") => Ok(true),
        Some(value) => Err(format!(
            "MEMRA_STEP_EP_GROUPED_PREFILL={value:?} is invalid; expected 0 or 1"
        )),
    }
}

fn step_ep_grouped_prefill_enabled() -> Result<bool, String> {
    parse_step_ep_grouped_prefill(
        std::env::var("MEMRA_STEP_EP_GROUPED_PREFILL")
            .ok()
            .as_deref(),
    )
}

fn step_grouped_prefill_shape(enabled: bool, prefill: bool, tokens: usize) -> bool {
    enabled && prefill && (PRIME_MIN_T..=crate::cache::PRIME_CHUNK_MAX_TOKENS).contains(&tokens)
}

fn parse_step_tp_prefill(value: Option<&str>) -> Result<bool, String> {
    match value {
        None | Some("") | Some("0") => Ok(false),
        Some("1") => Ok(true),
        Some(value) => Err(format!(
            "MEMRA_STEP_TP_PREFILL={value:?} is invalid; expected 0 or 1"
        )),
    }
}

fn step_tp_prefill_enabled() -> Result<bool, String> {
    parse_step_tp_prefill(std::env::var("MEMRA_STEP_TP_PREFILL").ok().as_deref())
}

fn validate_step_prime_batch_modes(tp_prefill: bool, grouped_prefill: bool) -> Result<(), String> {
    if grouped_prefill && !tp_prefill {
        return Err("MEMRA_STEP_EP_GROUPED_PREFILL=1 requires MEMRA_STEP_TP_PREFILL=1".into());
    }
    if tp_prefill {
        return Err(
            "Step TP4 cross-request prime batching did not clear the live-server performance \
             gate; use per-session grouped prefill"
                .into(),
        );
    }
    Ok(())
}

fn step_tp_prefill_shape(
    enabled: bool,
    tokens: usize,
    ranks: usize,
    native_p2p: bool,
    has_rank_local_attention: bool,
    fp8_kv: bool,
) -> bool {
    // TP2 admitted 2026-08-25 behind the same off-by-default door. The prefill body is
    // rank-count-generic (every geometry check divides by `ranks`); only this shape ever
    // named 4. TP4's high-context NO-GO was a TRANSPORT verdict — token-row column
    // gathers plus remote O blocks issue ~61,440 peer copies per 4K attention layer on
    // that placement — which is not evidence about a 2-card native-P2P placement that
    // reduces O rank-locally. TP2 is UNQUALIFIED until its own prefill argmax + TTFT
    // receipts land; the door stays off by default.
    enabled
        && tokens >= PRIME_MIN_T
        && matches!(ranks, 2 | 4)
        && native_p2p
        && has_rank_local_attention
        && !fp8_kv
}

fn empty_cache_layers<T>(n: usize) -> Vec<Option<T>> {
    std::iter::repeat_with(|| None).take(n).collect()
}

fn prime_cache_stage_for_layer(fence: &[usize], layer: usize) -> usize {
    debug_assert!(fence.len() >= 3);
    match fence[1..fence.len() - 1].binary_search(&layer) {
        Ok(index) => index + 1,
        Err(index) => index,
    }
}

fn move_prime_cache_layers<T>(
    parent: &mut [Option<T>],
    stages: &mut [Vec<Option<T>>],
    fence: &[usize],
) {
    assert_eq!(stages.len() + 1, fence.len());
    assert!(stages.iter().all(|stage| stage.len() == parent.len()));
    for (layer, value) in parent.iter_mut().enumerate() {
        let stage = prime_cache_stage_for_layer(fence, layer);
        debug_assert!(stages[stage][layer].is_none());
        stages[stage][layer] = value.take();
    }
}

#[cfg(test)]
fn restore_prime_cache_layers<T>(
    parent: &mut [Option<T>],
    stages: &mut [Vec<Option<T>>],
    fence: &[usize],
) {
    assert_eq!(stages.len() + 1, fence.len());
    assert!(stages.iter().all(|stage| stage.len() == parent.len()));
    for (layer, value) in parent.iter_mut().enumerate() {
        let stage = prime_cache_stage_for_layer(fence, layer);
        debug_assert!(value.is_none());
        *value = stages[stage][layer].take();
    }
}

/// Temporarily move a PP cache's layer state into independently-owned stage shells. The stage
/// walkers then receive disjoint `&mut Cache` values and can run on separate host threads without
/// aliasing. GPU buffers are moved, not copied; Drop restores every layer and publishes the last
/// position completed by every stage.
struct PrimeCacheStages<'a> {
    parent: &'a mut Cache,
    fence: Vec<usize>,
    stages: Vec<std::sync::Mutex<Cache>>,
    committed: bool,
}

impl<'a> PrimeCacheStages<'a> {
    fn new(parent: &'a mut Cache, fence: &[usize]) -> Self {
        let n = parent.kv.len();
        assert_eq!(parent.recur.len(), n, "cache layer vectors disagree");
        assert_eq!(parent.tp_kv.len(), n, "cache layer vectors disagree");
        assert_eq!(parent.latent.len(), n, "cache layer vectors disagree");
        let n_stages = fence.len().checked_sub(1).expect("PP cache fence is empty");
        assert!((2..=4).contains(&n_stages), "PP cache needs 2..=4 stages");
        assert_eq!(fence[0], 0, "PP cache fence must start at layer zero");
        assert!(
            fence.windows(2).all(|pair| pair[0] < pair[1]),
            "PP cache fence must be strictly increasing"
        );
        assert!(fence[n_stages] <= n, "PP cache fence exceeds {n} layers");

        let mut latent: Vec<_> = (0..n_stages).map(|_| empty_cache_layers(n)).collect();
        let mut g5_recur: Vec<_> = (0..n_stages).map(|_| empty_cache_layers(n)).collect();
        let mut g5_latent: Vec<_> = (0..n_stages).map(|_| empty_cache_layers(n)).collect();
        let mut kv: Vec<_> = (0..n_stages).map(|_| empty_cache_layers(n)).collect();
        let mut tp_kv: Vec<_> = (0..n_stages).map(|_| empty_cache_layers(n)).collect();
        let mut recur: Vec<_> = (0..n_stages).map(|_| empty_cache_layers(n)).collect();
        move_prime_cache_layers(&mut parent.kv, &mut kv, fence);
        move_prime_cache_layers(&mut parent.tp_kv, &mut tp_kv, fence);
        move_prime_cache_layers(&mut parent.recur, &mut recur, fence);

        move_prime_cache_layers(&mut parent.latent, &mut latent, fence);
        move_prime_cache_layers(&mut parent.glm5_tp_recur, &mut g5_recur, fence);
        move_prime_cache_layers(&mut parent.glm5_tp_latent_peer, &mut g5_latent, fence);
        let pos = parent.pos;
        let max_ctx = parent.max_ctx;
        // Indexed rather than zipped: six per-stage layer vectors (main's kv/tp_kv/recur plus
        // this lane's latent/glm5_tp_recur/glm5_tp_latent_peer) do not read as a zip chain, and
        // a nested-tuple pattern is exactly where a field silently lands on the wrong stage.
        let stages = (0..n_stages)
            .map(|stage| {
                std::sync::Mutex::new(Cache {
                    kv: std::mem::take(&mut kv[stage]),
                    tp_kv: std::mem::take(&mut tp_kv[stage]),
                    recur: std::mem::take(&mut recur[stage]),
                    latent: std::mem::take(&mut latent[stage]),
                    glm5_tp_recur: std::mem::take(&mut g5_recur[stage]),
                    glm5_tp_latent_peer: std::mem::take(&mut g5_latent[stage]),
                    pos,
                    max_ctx,
                    tainted: false,
                    last_logits_dev: None,
                    dflash_taps: None,
                    hc_taps: None,
                })
            })
            .collect();
        Self {
            parent,
            fence: fence.to_vec(),
            stages,
            committed: false,
        }
    }

    fn pp2_parts(&mut self) -> (&mut Cache, &mut Cache) {
        assert_eq!(self.stages.len(), 2);
        let (stage0, stage1) = self.stages.split_at_mut(1);
        (
            stage0[0]
                .get_mut()
                .unwrap_or_else(|poisoned| poisoned.into_inner()),
            stage1[0]
                .get_mut()
                .unwrap_or_else(|poisoned| poisoned.into_inner()),
        )
    }

    fn stages(&self) -> &[std::sync::Mutex<Cache>] {
        &self.stages
    }

    fn commit(&mut self) {
        self.committed = true;
    }
}

impl Drop for PrimeCacheStages<'_> {
    fn drop(&mut self) {
        let n = self.parent.kv.len();
        for i in 0..n {
            let stage = prime_cache_stage_for_layer(&self.fence, i);
            let source = self.stages[stage]
                .get_mut()
                .unwrap_or_else(|poisoned| poisoned.into_inner());
            debug_assert!(self.parent.kv[i].is_none());
            debug_assert!(self.parent.tp_kv[i].is_none());
            debug_assert!(self.parent.recur[i].is_none());
            debug_assert!(self.parent.latent[i].is_none());
            self.parent.kv[i] = source.kv[i].take();
            self.parent.tp_kv[i] = source.tp_kv[i].take();
            self.parent.recur[i] = source.recur[i].take();
            self.parent.latent[i] = source.latent[i].take();
            self.parent.glm5_tp_recur[i] = source.glm5_tp_recur[i].take();
            self.parent.glm5_tp_latent_peer[i] = source.glm5_tp_latent_peer[i].take();
        }
        self.parent.pos = self
            .stages
            .iter_mut()
            .map(|stage| {
                stage
                    .get_mut()
                    .unwrap_or_else(|poisoned| poisoned.into_inner())
                    .pos
            })
            .min()
            .unwrap_or(self.parent.pos);
        if !self.committed {
            self.parent.mark_tainted();
        }
    }
}

/// Fail-stop transaction marker for concat-prime paths. These paths mutate several independent
/// caches before their final epilogue can fail; an error must make every member permanently
/// ineligible for retry/reuse rather than replaying a queue over partially advanced state.
struct CacheTaintGuard {
    caches: Vec<*mut Cache>,
    committed: bool,
}

impl CacheTaintGuard {
    fn arm(caches: &mut [&mut Cache]) -> Self {
        Self {
            caches: caches
                .iter_mut()
                .map(|cache| *cache as *mut Cache)
                .collect(),
            committed: false,
        }
    }

    fn commit(&mut self) {
        self.committed = true;
    }
}

impl Drop for CacheTaintGuard {
    fn drop(&mut self) {
        if self.committed {
            return;
        }
        for cache in &self.caches {
            // SAFETY: `arm` receives the function's unique cache references. The guard never
            // escapes that call or dereferences them until unwind/return after active borrows end.
            unsafe { (&mut **cache).mark_tainted() };
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
struct PrimePpWaveSlot {
    wave: usize,
    slot: usize,
}

#[derive(Debug)]
enum PrimePpSignal {
    Slot(PrimePpWaveSlot),
    Error(String),
}

#[derive(Default)]
struct PrimePpWaveCredits {
    next_wave: usize,
    pending: std::collections::VecDeque<PrimePpWaveSlot>,
}

impl PrimePpWaveCredits {
    fn release_required(&self) -> Option<PrimePpWaveSlot> {
        (self.pending.len() == 2).then(|| self.pending[0])
    }

    fn record_release(&mut self, released: PrimePpWaveSlot) -> Result<(), String> {
        let expected =
            self.pending.front().copied().ok_or_else(|| {
                "prime PP received a slot release with no pending wave".to_string()
            })?;
        if released != expected {
            return Err(format!(
                "prime PP slot release {:?} does not match oldest pending {:?}",
                released, expected
            ));
        }
        self.pending.pop_front();
        Ok(())
    }

    fn record_send(&mut self, sent: PrimePpWaveSlot) -> Result<(), String> {
        if sent.wave != self.next_wave {
            return Err(format!(
                "prime PP sent wave {} while wave {} was next",
                sent.wave, self.next_wave
            ));
        }
        if sent.slot >= 2 {
            return Err(format!(
                "prime PP boundary returned invalid slot {}",
                sent.slot
            ));
        }
        if self.pending.iter().any(|pending| pending.slot == sent.slot) {
            return Err(format!(
                "prime PP reused slot {} before its exact-wave release",
                sent.slot
            ));
        }
        self.pending.push_back(sent);
        self.next_wave += 1;
        Ok(())
    }
}

fn recv_prime_pp_signal(
    receiver: &std::sync::mpsc::Receiver<PrimePpSignal>,
    expected: PrimePpWaveSlot,
    exact_slot: bool,
    label: &str,
) -> Result<PrimePpWaveSlot, String> {
    match receiver.recv() {
        Ok(PrimePpSignal::Error(error)) => Err(error),
        Ok(PrimePpSignal::Slot(received))
            if received.wave == expected.wave
                && (!exact_slot || received.slot == expected.slot) =>
        {
            if received.slot >= 2 {
                Err(format!(
                    "{label}: wave {} carried invalid slot {}",
                    received.wave, received.slot
                ))
            } else {
                Ok(received)
            }
        }
        Ok(PrimePpSignal::Slot(received)) => Err(format!(
            "{label}: expected wave/slot {:?}, received {:?}",
            expected, received
        )),
        Err(_) => Err(format!(
            "{label}: channel closed while waiting for wave {}",
            expected.wave
        )),
    }
}

fn send_prime_pp_signal(
    sender: &std::sync::mpsc::Sender<PrimePpSignal>,
    signal: PrimePpSignal,
    label: &str,
) -> Result<(), String> {
    sender
        .send(signal)
        .map_err(|_| format!("{label}: channel closed"))
}

struct PrimePpWave<'a> {
    start: usize,
    end: usize,
    tokens: &'a [u32],
}

struct PrimePpStageChannels {
    incoming: Option<std::sync::mpsc::Receiver<PrimePpSignal>>,
    release_upstream: Option<std::sync::mpsc::Sender<PrimePpSignal>>,
    outgoing: std::sync::mpsc::Sender<PrimePpSignal>,
    released_downstream: std::sync::mpsc::Receiver<PrimePpSignal>,
}

impl PrimePpStageChannels {
    fn notify_failure(&self, error: &str) {
        if let Some(upstream) = &self.release_upstream {
            let _ = upstream.send(PrimePpSignal::Error(error.to_string()));
        }
        let _ = self.outgoing.send(PrimePpSignal::Error(error.to_string()));
    }
}

/// The DSA k-pool indexer's resident state, borrowed for one `mla_attn_core` call.
///
/// TWO PLANES, DIFFERENT LIFETIMES. `state` is the packed `[k_norm | gate]` row per cached token
/// (`LatentKvLayer::index_rows`); it is append-only and grows with the cache. `pool_keys` is the
/// collapsed key per COMPLETE pool of `pool` such rows, and it is the residency win: a pool's key
/// is final the moment its last row lands, so pools `[0, *ready)` are never recomputed and each
/// call builds only the pools its own tokens completed. `ready` is written back through the
/// borrow, so the caller must persist it alongside the buffers.
///
/// `pool_keys` is `Option` because its size needs the indexer's `pool`, which the state plan does
/// not carry — `mla_kpool_indices` allocates it on first use and leaves it resident thereafter.
/// A caller that hands over a fresh `None` every call (the stateless arm) gets the old
/// rebuild-everything behaviour, which is exactly right when the state itself is per-call.
pub struct IndexerPlanes<'a> {
    pub state: &'a mut CudaSlice<f32>,
    pub pool_keys: &'a mut Option<CudaSlice<f32>>,
    pub ready: &'a mut usize,
    /// PHYSICAL rows of `state` when it is a TAIL RING; 0 when the plane is flat (one row per
    /// cached token, absolute addressing). `mla_kpool_indices` rounds this DOWN to a multiple of
    /// the indexer's `pool` — the state plan does not carry `pool`, so the allocator cannot — and
    /// proves the liveness bound against the rounded value before it appends.
    pub state_ring_rows: usize,
    /// Token capacity of the session, which sizes `pool_keys`. It is NOT derivable from
    /// `state.len()` once `state` is a ring: the ring holds one call's tail, the pool-key plane
    /// holds the whole context collapsed `pool`-to-one.
    pub capacity_tokens: usize,
}

/// task #18 (attn side): one sequence's pre-attention outputs (post-rope q/k, v, out-gate).
pub(crate) struct AttnPre {
    pub q: cudarc::driver::CudaSlice<f32>,
    pub k: cudarc::driver::CudaSlice<f32>,
    pub v: cudarc::driver::CudaSlice<f32>,
    pub gate: Option<cudarc::driver::CudaSlice<f32>>,
}

/// task #18: one sequence's GDN prep outputs (the scan inputs).
pub(crate) struct GdnPrep {
    pub hk: usize,
    pub q_l2: cudarc::driver::CudaSlice<f32>,
    pub k_l2: cudarc::driver::CudaSlice<f32>,
    pub v_g: cudarc::driver::CudaSlice<f32>,
    pub beta: cudarc::driver::CudaSlice<f32>,
    pub g_log: cudarc::driver::CudaSlice<f32>,
    pub kb16: Option<cudarc::driver::CudaSlice<u8>>,
    pub qb16: Option<cudarc::driver::CudaSlice<u8>>,
}

/// Device scratch for the burst verify stream (see `verify_stream_scratch`).
pub(crate) struct VerifyStreamScratch {
    pub pos_d: CudaSlice<i32>,
    pub row_ctrs: Vec<CudaSlice<i32>>,
}
use crate::hybrid::{FullAttnLayer, HybridModel, LinearAttnLayer, Mixer, MoeWeights};

struct MoeInputTraceWriter {
    dir: std::path::PathBuf,
    index: std::fs::File,
    payloads: std::collections::HashMap<u16, (std::fs::File, u64)>,
}

static MOE_INPUT_TRACE_WRITER: std::sync::OnceLock<std::sync::Mutex<Option<MoeInputTraceWriter>>> =
    std::sync::OnceLock::new();

/// STAGE-2 GROUPED DECODE gate (MEMRA_MOE_GDEC, default ON; `=0` restores the sequential
/// per-expert launch chain). See `moe_gdec_token`.
fn gdec_enabled() -> bool {
    static E: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
    *E.get_or_init(|| {
        std::env::var("MEMRA_MOE_GDEC")
            .map(|v| v != "0")
            .unwrap_or(true)
    })
}

/// SLAB-LOCAL RESIDENT ARM gate (lane/pp-leverb 2026-08-08, MEMRA_MOE_SLAB, default ON;
/// `=0` restores the SLRU dispatch even when resident slabs exist). Read PER CALL, never
/// memoized — probes A/B the two provenances in one process (the MEMRA_PRIME_PP pattern).
/// See `moe_ffn_sequential_zq8`'s slab_local arm: the sigmoid-router archs (step35/M3/Hy3)
/// are denied every `dev_exps` consumer (pairs/dev route softmax), so before this arm the
/// fits-VRAM resident slabs were UPLOADED for them but never READ — the SLRU kept staging
/// the same bytes beside a dead copy (37 GB H2D per pp4096 prime on the Step SKU, anatomy
/// receipt). The arm reads the SAME bytes through the SAME kernels; only the pointer
/// PROVENANCE changes (slab base + ex*stride vs SLRU slot address) — the bit-identity class
/// `moe_ffn_dev`'s resident arm already documents against its SLRU arm.
fn moe_slab_enabled() -> bool {
    std::env::var("MEMRA_MOE_SLAB").as_deref() != Ok("0")
}

/// `MEMRA_MOE_FUSED_EPI` — the glm5_next fused MoE epilogue (sigmoid-routed, PRE-clamped SwiGLU,
/// per-expert macro fold) collapsed into one launch pair per token-layer.
///
/// DEFAULT OFF, deliberately (docs/FLAGS.md carries the row and the reasons). The arm is proven
/// EXACT against `memra_reference` by `tests/glm5_moe_epilogue_gpu.rs`, but it has no throughput
/// receipt: the rig is correctness-only by law and the 190.7 GB artifact has never been on it, so
/// the launch-count claim is arithmetic from source and nothing has been measured on serving
/// hardware. Unmeasured behavior does not default ON.
///
/// Read PER CALL, not latched in a `OnceLock`: the acceptance gate flips both arms inside one
/// test process (the interleave unit is a model load, not a boot), and a latched flag would make
/// the second arm silently a copy of the first.
fn moe_fused_epi_enabled() -> bool {
    std::env::var("MEMRA_MOE_FUSED_EPI")
        .map(|v| v != "0")
        .unwrap_or(false)
}

/// `MEMRA_HC_DECODE_WS` — the persistent hc-glue decode workspace (lane/glm5-decode-diet
/// lever 2): the T=1 hc decode walk lands its glue transients (mixes, gates, comb, collapse
/// y, both norm scratches, the per-site post output) in one per-engine `HyperDecodeWs`
/// instead of ~12 fresh `cuMemAllocAsync`+free pairs per layer per token (the launch-diet
/// census's 2,358-calls/token class). Same kernels, same call order, same operand bytes —
/// byte identity ON/OFF gated by `tests/hc_decode_ws_gpu.rs`.
///
/// DEFAULT OFF, deliberately (docs/FLAGS.md row): the alloc-call reduction is proven on the
/// rig by counter receipt, but the ms/token value is arithmetic against the box's measured
/// launch/alloc constants — nothing has been measured on serving hardware yet. Unmeasured
/// behavior does not default ON.
///
/// Read PER CALL, not latched (the `MEMRA_MOE_FUSED_EPI` rollback-seam precedent).
fn hyper_decode_ws_on() -> bool {
    std::env::var("MEMRA_HC_DECODE_WS").as_deref() == Ok("1")
}

/// Engagement counter for the workspace walk — the receipt the gate and any box A/B arm
/// must show (engagement lines are receipts, never inferred).
pub static HC_DECODE_WS_DISPATCHES: std::sync::atomic::AtomicU64 =
    std::sync::atomic::AtomicU64::new(0);

/// `MEMRA_MLA_TC_PREFILL` — the glm5_next tensor-core MLA prefill chain
/// (lane/glm5-mla-tc-prefill, 2026-08-30): at prefill widths the three per-position f32
/// kernels the launch-diet census named (`memra_mla_attn_gathered_kernel` 139 ms +
/// `memra_mla_absorb_q_kernel` 44.5 ms + `memra_mla_decompress_v_kernel` 43.6 ms per
/// layer-chunk, 75.8% of a 98%-GPU-busy cold prime) are replaced by two strided-batched
/// bf16 tensor-core GEMMs (absorb / decompress, one launch each) and one gathered
/// flash-attention MMA kernel (`fa_mla_gathered_bf16`). Selection, the latent cache, the
/// q/kv projections, and decode are UNTOUCHED.
///
/// DEFAULT ON (owner acceptance 2026-08-30, "why not? i dont see why not", on the two-box
/// A/B receipts): interleaved x5 fresh boots per arm on BOTH the Server-Edition and
/// Workstation-Edition 4-card boxes, zero violations, zero argmax flips across 20 boots,
/// TTFD -62%..-69% (7.45->2.83 s @4.6k / 6.58->2.51 s), prefill 619-724 -> 1629-2255 tok/s,
/// vendor-default sampled twin -66/-67%, decode untouched, engagement receipted in every ON
/// boot with no cuBLASLt declines (docs/FLAGS.md row carries the pointers). The numeric
/// config remains band-gated (bf16 operands, f32 accumulate — the fa_prefill/MEMRA_PP_BF16
/// class, `tests/mla_tc_prefill_gpu.rs`, never bit). `MEMRA_MLA_TC_PREFILL=0` is the
/// rollback seam.
///
/// Read PER CALL, not latched (the `MEMRA_MOE_FUSED_EPI` rollback-seam precedent): the gate
/// flips both arms inside one test process, and a latched flag would make the second arm
/// silently a copy of the first.
fn mla_tc_prefill_enabled() -> bool {
    std::env::var("MEMRA_MLA_TC_PREFILL")
        .map(|v| v != "0")
        .unwrap_or(true)
}

/// Expert-grouped dispatch remains opt-in after the local 5090 transfer gate rejected the
/// default flip. `=0` selects the established path, while any other explicit value enables the
/// grouped research arm for the current call.
fn moe_grouped_enabled(_cfg: &ModelConfig, _prefill: bool) -> bool {
    std::env::var("MEMRA_MOE_GROUPED")
        .map(|value| value != "0")
        .unwrap_or(false)
}

/// `MEMRA_MOE_GROUPED_PREFILL`: the glm5_next expert-grouped MoE PREFILL arm, token-sort by
/// expert (host CSR, the `moe_align_block_size` shape), then ONE grouped tensor-core GEMM per
/// projection per layer-chunk over the resident NVFP4 bank, with the sigmoid `noaux_tc` routing,
/// the PRE-clamped SwiGLU epilogue and the per-expert `weight_scale_2` macro fold the fused
/// epilogue lane qualified for this family.
///
/// DEFAULT ON since 2026-08-29 (owner acceptance; `=0` is the rollback seam). The flip carries
/// its receipts, per the flag-default law: reference-band + routing-exactness gate green
/// (`tests/glm5_moe_grouped_prefill_gpu.rs`; grouped GEMM is measured non-bit-stable, so byte
/// identity is not the honest bar; routing sel/w stay bit-identical by construction, the same
/// `moe_router_logits` + `moe_route_sigmoid_cfg` invocation as the sequential arm), plus the
/// interleaved x5 box A/B on the serving card class: TTFD 54.2 -> 7.5 s / 65.5 -> 8.9 /
/// 75.9 -> 10.3 at 4.6/5.5/6.5k-token real prompts (85 -> 616-639 tok/s prefill, decode
/// unchanged, sampled vendor-default twin green, engagement 42/42). The one greedy first-token
/// flip (B5550) sits at a position the 8-draw vendor-default census measured as SOFT in both
/// arms (the OFF arm itself draws the ON arm's token there) and was accepted by the OWNER on
/// 2026-08-29, the MEMRA_BF16_MMV acceptance class. Receipts:
/// `research/glm53-flash-bringup-20260827/moe-grouped-prefill-receipts/` (`box-ab-20260829/`).
///
/// Read PER CALL, not latched: the acceptance gate flips both arms inside one test process.
fn moe_grouped_prefill_enabled() -> bool {
    std::env::var("MEMRA_MOE_GROUPED_PREFILL")
        .map(|v| v != "0")
        .unwrap_or(true)
}

/// Deterministic in-token expert prefetch. `MEMRA_MOE_PREFETCH=1` overlaps memory-source H2D on the
/// copy stream; selecting the opt-in worker spill backend enables the same known-next hook for disk.
fn moe_prefetch_enabled() -> bool {
    static E: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
    *E.get_or_init(|| {
        std::env::var("MEMRA_MOE_PREFETCH").as_deref() == Ok("1")
            || crate::spill_pread::worker_enabled()
    })
}

/// Best-effort OS page-cache prefetch distance for mmap-backed expert ranges. Independent of the
/// H2D copy-stream experiment so storage->RAM and RAM->HBM overlap can be measured separately.
/// The opt-in default stays one expert to preserve the original experiment; spill rigs can widen
/// it with `MEMRA_MOE_PAGE_PREFETCH_WINDOW` to cover NVMe latency.
fn moe_page_prefetch_window() -> usize {
    static W: std::sync::OnceLock<usize> = std::sync::OnceLock::new();
    *W.get_or_init(|| {
        page_prefetch_window_from_values(
            std::env::var("MEMRA_MOE_PAGE_PREFETCH").as_deref() == Ok("1"),
            std::env::var("MEMRA_MOE_PAGE_PREFETCH_WINDOW")
                .ok()
                .as_deref(),
        )
    })
}

fn page_prefetch_window_from_values(enabled: bool, raw_window: Option<&str>) -> usize {
    if !enabled {
        return 0;
    }
    raw_window.and_then(|value| value.parse().ok()).unwrap_or(1)
}

/// Return only the newly exposed positions in a rolling lookahead window. Position zero seeds the
/// full window; each later position adds one expert at the far edge. Thus widening the window does
/// not repeatedly issue `MADV_WILLNEED` for the same range.
fn page_prefetch_positions(position: usize, len: usize, window: usize) -> std::ops::Range<usize> {
    if window == 0 || position >= len {
        return len..len;
    }
    let (start, count) = if position == 0 {
        (1, window)
    } else {
        (position.saturating_add(window), 1)
    };
    let start = start.min(len);
    start..start.saturating_add(count).min(len)
}

/// Grouped worker-I/O schedule: prime the first active expert before the loop, then queue exactly
/// one known-next expert at each iteration. Returning positions keeps expert ordering authoritative.
fn grouped_worker_prefetch_position(order_len: usize, current: Option<usize>) -> Option<usize> {
    let position = current.map_or(0, |position| position.saturating_add(1));
    (position < order_len).then_some(position)
}

/// Fill the worker ring with complete experts, retaining one pinned buffer for an unexpected
/// demand miss. Each expert has gate/up/down extents, so depth 16 admits a rolling five-expert
/// window. Position zero primes the current expert too: its three independent reads can run in
/// parallel instead of demand-serializing gate, up, and down before any useful GPU work exists.
fn worker_prefetch_window() -> usize {
    static WINDOW: std::sync::OnceLock<usize> = std::sync::OnceLock::new();
    *WINDOW.get_or_init(|| {
        let automatic = crate::spill_pread::configured_depth().saturating_sub(1) / 3;
        std::env::var("MEMRA_SPILL_WORKER_EXPERT_WINDOW")
            .ok()
            .and_then(|value| value.parse::<usize>().ok())
            .unwrap_or(automatic.max(1))
    })
}

/// Return only positions newly exposed by a rolling worker-I/O window. Unlike mmap page advice,
/// this includes the current expert when the window is seeded so all three current projections
/// enter the CPU pool together.
fn worker_prefetch_positions(position: usize, len: usize, window: usize) -> std::ops::Range<usize> {
    if window == 0 || position >= len {
        return len..len;
    }
    let (start, count) = if position == 0 {
        (0, window)
    } else {
        (position.saturating_add(window).saturating_sub(1), 1)
    };
    let start = start.min(len);
    start..start.saturating_add(count).min(len)
}

/// LAUNCH-STRUCTURE STAGE 3 gate (MEMRA_MOE_DEV, default ON; `=0` restores host routing). The
/// zero-DtoH device-dispatch path for fully-resident layers: router top-k output stays on device,
/// expert weight pointers come from the per-layer device table. Requires the fused router (the
/// dev path consumes the device sel/w directly), so MEMRA_FUSED_ROUTER=0 also disables it.
fn moe_dev_enabled() -> bool {
    static E: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
    *E.get_or_init(|| {
        std::env::var("MEMRA_MOE_DEV")
            .map(|v| v != "0")
            .unwrap_or(true)
            && !matches!(std::env::var("MEMRA_FUSED_ROUTER").as_deref(), Ok("0"))
    })
}

/// Device sigmoid top-k is the default for Step-3.7 / M3 / Hy3 / GLM-DSA. `MEMRA_SIG_ROUTER=0` restores
/// the full-logit DtoH plus `moe_route_sigmoid_host` oracle without changing expert dispatch.
/// Where the verify-rows MoE pair's routed selection lives for one layer-call
/// (lane/glm5-moe-loc door D). `Host` is the shipped arm: the router's pinned readback gave the
/// host `sel`/`w`, and the host builds the pointer/scale tables. `Dev` is door D's arm: the
/// router's own device `sel_idx`/`sel_w` are still live, so the tables are built where they are
/// and the readback (2 DtoH + a full `cuStreamSynchronize` per MoE layer-call) never happens.
/// ONE launch path consumes both — only the table build differs, term-for-term identically.
enum VrowsSel<'a> {
    Host(&'a [u32], &'a [f32]),
    Dev(&'a CudaSlice<i32>, &'a CudaSlice<f32>),
}

fn sigmoid_router_enabled() -> bool {
    static E: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
    *E.get_or_init(|| {
        std::env::var("MEMRA_SIG_ROUTER")
            .map(|v| v != "0")
            .unwrap_or(true)
    })
}

/// MoE EXPERT dp4a gate (MEMRA_MOE_Q8, default ON; `=0` restores the Stage-A f32-dequant expert
/// kernels). Applies when gate/up/down expert qtypes are all in the dp4a body set (IQ3_S/IQ4_XS).
/// FP-order differs from Stage-A (int dp4a + warp tree) — argmax/run-gen/stream-identity gates
/// arbitrate; the sequential and fused q8 paths ship as a matched pair (MEMRA_MOE_GATE contract).
fn moe_q8_enabled() -> bool {
    static E: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
    *E.get_or_init(|| {
        std::env::var("MEMRA_MOE_Q8")
            .map(|v| v != "0")
            .unwrap_or(true)
    })
}

/// gemma4 fast-arm gate: qtypes with an `expert_dot_g` dp4a body (superset used by the gelu
/// dev arm; the qwen q8 arms keep their own battery-gated q8_expert_supported policy).
fn expert_dp4a_supported(qt: i32) -> bool {
    qt == crate::QT_Q4_0
        || qt == crate::QT_IQ3_S
        || qt == crate::QT_IQ4_XS
        || qt == crate::QT_Q3_K
        || qt == crate::QT_Q4_K
        || qt == crate::QT_Q6_K
}

fn q8_expert_supported(qt: i32) -> bool {
    // k-quant arms added 2026-07-06 (Q3_K/Q4_K/Q6_K bodies for the UD tail layers). Briefly
    // default-excluded the same day when they appeared to break 35B real-prompt spec — the
    // ACTUAL culprit was the MoE router's cuBLASLt n-dependence (d994271); with the router
    // decode-exact at verify t, the k-quant arms pass the full spec battery (p1/p2/p3 + raw
    // K=1..8) and are DEFAULT ON again (+9 tok/s: 148.9 -> 157.9). MEMRA_MOE_Q8_KQ=0 excludes.
    static KQ: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
    let kq = *KQ.get_or_init(|| {
        std::env::var("MEMRA_MOE_Q8_KQ")
            .map(|v| v != "0")
            .unwrap_or(true)
    });
    // NVFP4 experts: DEFAULT ON (2026-07-17). The M3-era "decode-vs-verify MISMATCH 3.4e1"
    // that had this excluded was the missing per-expert macro-scale fold, fixed in the
    // dev-kernel epilogues + moe_w_scale_by_expert; the 35B ct-NVFP4 artifact now runs the
    // q8 arm at parity with the IQ4_XS daily (174-178 tok/s, spec K=1..8 exact). M3/Hy3
    // never reach the q8 arms regardless (sigmoid-router cfg gates on pairs/dev/gdec).
    // MEMRA_MOE_Q8_NVFP4=0 restores the f32 arm.
    let nvfp4_q8 = std::env::var("MEMRA_MOE_Q8_NVFP4")
        .map(|v| v != "0")
        .unwrap_or(true);
    qt == crate::QT_IQ3_S
        || qt == crate::QT_IQ4_XS
        || (nvfp4_q8 && qt == crate::QT_NVFP4)
        || (kq && (qt == crate::QT_Q3_K || qt == crate::QT_Q4_K || qt == crate::QT_Q6_K))
}

/// ModelOpt `W4A16_NVFP4` is weight-only: feeding its expert weights a q8_1 activation changes
/// the declared numeric program to W4A8. Keep the established q8 path for every other artifact,
/// but force Hy3 W4A16 experts through the BF16-activation `qmatvec_view` oracle.
fn q8_expert_supported_for_model(cfg: &ModelConfig, qt: i32) -> bool {
    let weight_only_nvfp4 = cfg.hy3.as_ref().is_some_and(|hy3| hy3.weight_only_nvfp4);
    q8_expert_supported(qt) && !(weight_only_nvfp4 && qt == crate::QT_NVFP4)
}

fn moe_q8_enabled_for_model(cfg: &ModelConfig, m: &MoeWeights) -> bool {
    m.has_uniform_expert_layout()
        && moe_q8_enabled()
        && q8_expert_supported_for_model(cfg, m.gate_exps.qtype)
        && q8_expert_supported_for_model(cfg, m.up_exps.qtype)
        && q8_expert_supported_for_model(cfg, m.down_exps.qtype)
}

#[cfg(test)]
mod w4a16_dispatch_tests {
    use super::q8_expert_supported_for_model;
    use memra_gguf::config::{HfConfig, ModelConfig};

    #[test]
    fn hy3_w4a16_never_admits_q8_activations() {
        let hf = HfConfig::parse(
            r#"{"model_type":"hy_v3","num_hidden_layers":2,"hidden_size":8,
            "num_attention_heads":2,"intermediate_size":16,"vocab_size":32,
            "max_position_embeddings":32,
            "quantization_config":{"quant_method":"modelopt","quant_algo":"W4A16_NVFP4"}}"#,
        );
        let cfg = ModelConfig::from_hf(&hf);
        assert!(!q8_expert_supported_for_model(&cfg, crate::QT_NVFP4));
        assert!(q8_expert_supported_for_model(&cfg, crate::QT_IQ4_XS));
    }
}

/// The decode-once (_dec) and IQ-MMA expert kernels dequant via IQ-specific extractors —
/// k-quant tensors must fall to the _em dot path instead.
fn q8_expert_dec_supported(qt: i32) -> bool {
    qt == crate::QT_IQ3_S || qt == crate::QT_IQ4_XS || qt == crate::QT_Q4_0
}

/// Grouped-f16 door (MEMRA_MOE_F16G) per-projection admission: the qtype has a dequant-to-f16
/// kernel in cu/moe_f16_grouped.cu AND the projection's k dimension tiles its block size.
/// Round 49 widened coverage to q35's UD mix (gate/up IQ3_S x39 + Q3_K x1 + IQ4_XS x1; down
/// IQ4_XS x37 + Q6_K x3 + Q4_K x1) — the round-47 IQ4_XS/Q4_0-only table admitted ~1 of 41
/// q35 layers, which is why that cell measured FLAT.
fn f16g_proj_ok(qt: i32, in_f: usize) -> bool {
    match qt {
        crate::QT_Q4_0 => in_f.is_multiple_of(32),
        crate::QT_IQ4_XS | crate::QT_IQ3_S | crate::QT_Q3_K | crate::QT_Q4_K | crate::QT_Q6_K => {
            in_f.is_multiple_of(256)
        }
        // NVFP4 (block 64) added lane/moebatch-q35moe 2026-08-21: the ornith15 expert bank is
        // uniform NVFP4, which passed the pairs q8 gate but missed BOTH batched doors
        // (use_mma's dec set and this table), so 14.7k-token prefill rode the per-pair _em
        // fallback — 88.6% of the prime wall (prime-anatomy receipt).
        crate::QT_NVFP4 => in_f.is_multiple_of(64),
        _ => false,
    }
}

/// STAGE 3 prewarm gate (MEMRA_MOE_PREWARM, default ON; `=0` leaves residency organic). One-shot
/// per layer: force-admit every block while FREE slots cover the whole layer (never evicts).
fn moe_prewarm_enabled() -> bool {
    static E: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
    *E.get_or_init(|| {
        std::env::var("MEMRA_MOE_PREWARM")
            .map(|v| v != "0")
            .unwrap_or(true)
    })
}

/// During a discarded fixed-residency profile, admit CPU-routed misses after their current-token
/// CPU result is complete. The current result and numeric path are unchanged; later warmup tokens
/// can then vote for and exercise those experts on GPU before the cache is frozen.
fn cpu_expert_profile_admit_enabled() -> bool {
    static E: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
    *E.get_or_init(|| std::env::var("MEMRA_CPU_EXPERT_FREEZE_PROFILE_ADMIT").as_deref() == Ok("1"))
}

/// Minimum prompt length for the BATCHED cache prime (`prime_cache`). Below this the tokenwise
/// decode loop wins anyway (the batched path's GEMM dispatch needs m>=16, and the stateful conv
/// kernel needs T >= d_conv-1). Callers: generate / generate_spec.
pub const PRIME_MIN_T: usize = 16;

/// MEMRA_STEP_GEMM_PRIME_SUFFIX: does a CONTINUATION prime (`cache.pos > 0` — a rewound
/// session's suffix, or a prompt remainder split across scheduler ticks) ride the batched
/// GEMM prime, like a fresh prompt does?
///
/// DEFAULT ON since 2026-08-29, by decision, under the flip bar the OFF-era FLAGS row
/// wrote down (never byte identity — a prime-decomposition m-dependence that EVERY
/// measured prime path shares, walk included, bars that gate for all of them):
///  1. vendor-default sampled rows: the blind, rubric-pre-registered 8-turn quality A/B
///     (research/step37-sampled-quality-20260828, 72/72 valid rows, engagement receipts
///     per row) — WARM-GEMM sits inside COLD's own self-spread at t4 and t8 (t8 carried
///     at n=16; the round-1 walk-over-gemm signal collapsed at p~0.91).
///  2. the 8-turn cache-on twin: warm TTFT 0.58 s (door) vs 7.15 s (walk) on the real
///     warm serving shape, zero faults.
///  3. the batched prime's own standard: acceptance 0.80-0.86 across all arms with the
///     door arm highest at t8; interleaved arms; zero ILLEGAL/#87/panics in 19 boots.
///
/// Precondition shipped first: the SWA-ring checkpoint restore fix (c9a617ca99) — real
/// session reuse crosses the grow path before any door question matters.
/// Why it is worth it, measured: the walk continuation costs 5.5978 ms/suffix-token
/// against this path's 0.99 ms/token (five-point sweep, R^2 0.9976), a 7.97x suffix
/// slope collapse.
///
/// The `seq_end` fix beneath is NOT gated on this door — it is unconditional, because
/// the chunk-local `seq_end` it replaced is wrong for a fresh prompt of 4096+k tokens
/// (k in [PRIME_MIN_T, 512)) with no continuation anywhere in sight.
///
/// `=0` is the kill switch (continuations back on the walk, fresh primes keep the fast
/// path); `=1` forces; `MEMRA_STEP_GEMM_PRIME=0` remains the whole-path seam. Read per
/// call, not cached — probes flip it in process.
fn step_gemm_prime_suffix_on() -> bool {
    std::env::var("MEMRA_STEP_GEMM_PRIME_SUFFIX").as_deref() != Ok("0")
}

/// Widest tick the MoE DEV per-token program serves (lane/orndecode-20260822). PRIME_MIN_T
/// doubled as the dev-arm's upper bound on the assumption that t==16 only ever meant real
/// prefill; the exact-16 decode tier broke that assumption — at B=16 the MoE stage crossed
/// onto the t>=MMA_T grouped/kq GEMM program (m_e ~1.6 rows/expert: 52.6% of the tick at
/// ~104 us/launch) or the `_em` per-pair fallback (67.7 us), both catastrophically slower
/// than the dev q8 kernels that serve B<=8 (8.8 us gate_up covering a token's whole expert
/// set). Decode widths 2..=16 now ride dev; the grouped/pairs prefill programs start at 17.
/// gate2/gate3 byte batteries at B=12/16 are the qualification (bit-checked vs isolated).
const MOE_DEV_MAX_T: usize = 16;
const PRIME_PIPE_MICROBATCHES: usize = 8;
const PRIME_PIPE_MIN_CHUNK: usize = 128;
const PRIME_PIPE_EDGE_MIN_CHUNK: usize = 64;
const PRIME_PIPE_LINEAR_WORK: usize = 8;

fn prime_pp2_auto_geometry(n_layers: usize) -> bool {
    crate::pp::prime_pp_on()
        && !crate::pp::pp2_streams_off()
        && crate::pp::pp_cuts(n_layers).is_some_and(|cuts| cuts.len() == 3)
}

fn prime_ppn_wave_auto_geometry(n_layers: usize) -> bool {
    crate::pp::prime_pp_on()
        && !crate::pp::pp2_streams_off()
        && crate::pp::pp_wave_on() == Ok(true)
        && crate::pp::pp_cuts(n_layers).is_some_and(|cuts| matches!(cuts.len(), 4 | 5))
}

fn prime_pipeline_auto_geometry(n_layers: usize) -> bool {
    prime_pp2_auto_geometry(n_layers) || prime_ppn_wave_auto_geometry(n_layers)
}

/// Effective internal prime chunk. An explicit MEMRA_PRIME_CHUNK is authoritative.
/// Pipelined PP primes use the measured PP-2 geometry: up to eight microchunks, never below
/// 128 tokens, while the legacy 4096-token cap remains the long-context bound. PP-3/4 inherit
/// only the geometry when their separate MEMRA_PP_WAVE door is explicitly open.
pub fn prime_chunk_tokens(t: usize, n_layers: usize) -> usize {
    if let Ok(value) = std::env::var("MEMRA_PRIME_CHUNK") {
        let parsed = value
            .parse::<usize>()
            .unwrap_or(crate::cache::PRIME_CHUNK_MAX_TOKENS);
        return if crate::cache::swa_ring_on() {
            if parsed == 0 {
                crate::cache::PRIME_CHUNK_MAX_TOKENS
            } else {
                parsed.min(crate::cache::PRIME_CHUNK_MAX_TOKENS)
            }
        } else {
            parsed
        };
    }
    let chunk = crate::cache::PRIME_CHUNK_MAX_TOKENS;
    if prime_pipeline_auto_geometry(n_layers) && t >= 2 * PRIME_PIPE_MIN_CHUNK {
        chunk.min(
            t.div_ceil(PRIME_PIPE_MICROBATCHES)
                .max(PRIME_PIPE_MIN_CHUNK),
        )
    } else {
        chunk
    }
}

fn fixed_prime_chunk_ranges(t: usize, chunk: usize) -> Vec<(usize, usize)> {
    fixed_prime_chunk_ranges_for_ring(t, chunk, crate::cache::swa_ring_on())
}

fn fixed_prime_chunk_ranges_for_ring(t: usize, chunk: usize, ring_on: bool) -> Vec<(usize, usize)> {
    if chunk == 0 || t <= chunk {
        return vec![(0, t)];
    }
    let mut ranges = Vec::with_capacity(t.div_ceil(chunk));
    let mut start = 0usize;
    while start < t {
        let mut end = (start + chunk).min(t);
        if t - end > 0 && t - end < PRIME_MIN_T {
            if ring_on {
                let shifted = t - PRIME_MIN_T;
                end = if shifted > start { shifted } else { t };
            } else {
                end = t;
            }
        }
        ranges.push((start, end));
        start = end;
    }
    ranges
}

fn prime_chunk_work(prefix: usize, total: usize) -> u128 {
    let prefix = prefix as u128;
    prefix * (prefix + (PRIME_PIPE_LINEAR_WORK as u128) * (total as u128))
}

fn dynamic_prime_chunk_ranges(
    t: usize,
    fixed_chunk: usize,
    fixed: &[(usize, usize)],
) -> Vec<(usize, usize)> {
    let n = fixed.len();
    if n < 3 {
        return fixed.to_vec();
    }

    let max_first = t - (n - 1) * PRIME_MIN_T;
    let first = fixed_chunk
        .div_ceil(2)
        .max(PRIME_PIPE_EDGE_MIN_CHUNK)
        .min(max_first);
    let mut ranges = Vec::with_capacity(n);
    ranges.push((0, first));

    let first_work = prime_chunk_work(first, t);
    let work_span = prime_chunk_work(t, t) - first_work;
    let denominator = (n - 1) as u128;
    let mut previous = first;
    for boundary in 1..n - 1 {
        let target = first_work * denominator + work_span * (boundary as u128);
        let remaining = n - 1 - boundary;
        let mut low = previous + PRIME_MIN_T;
        let mut high = t - remaining * PRIME_MIN_T;
        while low < high {
            let mid = low + (high - low) / 2;
            if prime_chunk_work(mid, t) * denominator >= target {
                high = mid;
            } else {
                low = mid + 1;
            }
        }
        ranges.push((previous, low));
        previous = low;
    }
    ranges.push((previous, t));
    ranges
}

/// Internal prime ranges. A pipelined PP prime defaults to a short-fill, equal-modeled-time
/// schedule; MEMRA_PRIME_CHUNK_SCHED=fixed restores the measured
/// equal-token ranges. An explicit MEMRA_PRIME_CHUNK always retains fixed semantics.
///
/// `gdn_grid`: the model runs the chunked GDN WY scan (`HybridModel::gdn_prime_grid_on`) —
/// AUTO-scheduled internal boundaries are then snapped down to the WY-chunk grid
/// (`align_prime_ranges_to_gdn`; the spec-longctx grid law, extended from serve splits to
/// the PP prime microchunks). Explicit MEMRA_PRIME_CHUNK keeps its operator-authoritative
/// (fixed, unaligned) semantics — the FLAGS caveat documents that identity contract.
pub fn prime_chunk_ranges(t: usize, n_layers: usize, gdn_grid: bool) -> Vec<(usize, usize)> {
    let explicit_chunk = std::env::var_os("MEMRA_PRIME_CHUNK").is_some();
    let chunk = prime_chunk_tokens(t, n_layers);
    let fixed = fixed_prime_chunk_ranges(t, chunk);
    let dynamic = match std::env::var("MEMRA_PRIME_CHUNK_SCHED") {
        Ok(value) => value == "dynamic",
        Err(_) => true,
    };
    if explicit_chunk {
        return fixed;
    }
    let ranges = if !dynamic || !prime_pipeline_auto_geometry(n_layers) {
        fixed
    } else {
        dynamic_prime_chunk_ranges(t, chunk, &fixed)
    };
    // MEMRA_PRIME_GRID_ALIGN=0 is the shared rollback seam of the grid law (same env the
    // worker's serve-boundary alignment honors, read per call so gates can flip it
    // in-process): the legacy off-grid auto schedule — the toothed cell's broken arm.
    if gdn_grid && std::env::var("MEMRA_PRIME_GRID_ALIGN").as_deref() != Ok("0") {
        align_prime_ranges_to_gdn(&ranges, t, Engine::gdn_chunk_size())
    } else {
        ranges
    }
}

/// The mHC prime schedule: how `prime_cache_hyper` SPLITS one prompt into calls.
///
/// THIS IS THE RULE THE CAPACITY GATE ASSERTS ON, and it is separate from
/// [`prime_chunk_ranges`] so the hyper walk's split can be read, gated and changed without
/// touching the serial trunk's. It DELEGATES to the serial schedule rather than deriving a
/// second one: the transient pressure it answers is the same pressure, and two schedules would
/// be two things to keep aligned with `MEMRA_PRIME_CHUNK`.
///
/// WHY THE SPLIT IS SEMANTICALLY INERT, term by term. Read this precisely: it says the split
/// computes the SAME PROGRAM, not that it computes the same BITS. The bit claim is FALSE on this
/// trunk and measured so — see the near-tie note at the end.
///
///   * **The mHC residual is strictly PER TOKEN.** `crate::hyper`'s contract is `mixes[t,:]`, an
///     RMS rescale over that token's own `streams*hidden` slab, a Sinkhorn per token per site,
///     a per-token collapse and a per-token post. The stream state is expanded at the start of a
///     call and collapsed at its end; it carries NOTHING between tokens. Splitting the token
///     axis cannot move a value.
///   * **KDA prefill is a SEQUENTIAL scan** (`kda.rs`: `memra_kda_scan_s128` runs prefill and
///     decode alike, the chunked UT transform is not the shipped path). A sequential recurrence
///     has no fold grid, so the GDN WY grid law has NO KDA analogue to violate and
///     `align_prime_ranges_to_gdn` has nothing to align. The conv ring carries across calls
///     already — it is the seam every decode step uses. **DEBT, named:** if the chunked KDA twin
///     ever becomes the prefill path, it acquires a fold grid and this schedule's internal
///     boundaries must be snapped to it exactly as the GDN ones are, or chunked prime stops
///     being bit-identical. The `gdn_grid` argument is the seam that change lands on.
///   * **The latent KV plane is f32** (`LatentKvLayer::rows`), so a later call reads earlier
///     calls' rows in the SAME numeric class it would have computed them in. There is no
///     analogue of the serial trunk's f32-vs-quantized-KV class edge — the thing that made
///     `MEMRA_PRIME_CHUNK` steer arithmetic until the 2026-08-05 grain-free fix.
///   * **The DSA pool keys are already incremental** (`index_pools_ready`): a pool key is a pure
///     function of its own `pool` state rows and the constant `kpool_ape`, final the instant the
///     pool's last row lands, so no boundary can move one. Selection is per query over resident
///     keys, with visibility keyed on the query's ABSOLUTE cache row.
///
/// NOT BIT-STABLE ACROSS CHUNK SIZES, and the cause is NOT the split. Measured on the rig
/// (`research/glm53-flash-bringup-20260827/1m-context-20260828/02`): the arms diverge at ROW 0,
/// which no cross-token state can reach, and `Engine::linear` — the cuBLASLt f32 `mixes` GEMM in
/// `hyper::pre` — is itself not m-invariant (m=32 vs m=200 moves 9601/12288 output bits at worst
/// 3.815e-6, the same worst the chunked prime reports; m=128 and m=199 vs m=200 are identical).
/// cuBLASLt reselects its algorithm by shape and the reduction order goes with it, which
/// `hyper.rs`'s header already concedes ("a serving trunk, not a byte-parity oracle"). So this
/// is a documented near-tie class the split EXPOSES, not one it creates. It is written into the
/// `MEMRA_PRIME_CHUNK` FLAGS row, and `glm5_chunked_prime_gpu` holds the split to a calibrated
/// band anchored on `memra_reference::execute` rather than on the monolithic sibling.
///
/// One thing the split provably cannot break here: POSITIONS. glm5_next is NoPE end to end
/// (`qk_rope_head_dim = 0`, `mla_use_nope`), and KDA is positionless, so `pos_d` reaches no
/// kernel on this path — a mutation that made it call-local instead of session-absolute moved
/// nothing at all.
///
/// A prompt at or under one chunk takes the monolithic body unchanged, and `MEMRA_PRIME_CHUNK=0`
/// restores the monolithic walk at any length — the rollback seam, and the oracle arm the
/// correctness gate compares against.
pub fn hyper_prime_ranges(t: usize, n_layers: usize, gdn_grid: bool) -> Vec<(usize, usize)> {
    prime_chunk_ranges(t, n_layers, gdn_grid)
}

/// The largest number of token rows any ONE mHC prime call carries under
/// [`hyper_prime_ranges`]. Every per-call transient in the walk — the `t*streams*hidden` stream
/// state, the MLA query planes, and the DSA indexer's `t * n_pools` score plane — is
/// proportional to this, so it is the single number a capacity assertion needs.
pub fn hyper_prime_call_rows(t: usize, n_layers: usize, gdn_grid: bool) -> usize {
    hyper_prime_ranges(t, n_layers, gdn_grid)
        .iter()
        .map(|&(start, end)| end - start)
        .max()
        .unwrap_or(0)
}

/// Per-request prefill WORKSPACE coefficients for a HyperConnections trunk, published to
/// admission (lane/glm5-gpf-workspace, 2026-08-30). `None` for every non-hyper model: their
/// admission arithmetic is byte-identical to the pre-lane behavior.
///
/// These are the FORMULA behind the 262k 2-card cell's measured ~0.8 MiB/token/card prefill
/// wall (`research/glm53-flash-bringup-20260827/262k-2card-20260830/LANE.md`), not the slope
/// itself: each term is the size of a named allocation in the walk, summed per token of ONE
/// prime call. On GLM-5.3-Flash geometry (H=4096, S=4, F=2048, U=8, heads=64, qk=256, v=256,
/// topk=2048, P=4) `chunk_token_bytes` evaluates to ~0.86 MiB — the receipt's slope with the
/// conservative side up. The attribution table naming every term lives in
/// `research/glm53-flash-bringup-20260827/gpf-workspace-20260830/LANE.md` §1.
#[derive(Debug, Clone, Copy)]
pub struct HyperPrimeWorkspaceShape {
    /// Bytes of per-call prefill transients PER TOKEN OF ONE PRIME CALL: the double-buffered
    /// `[t, streams, hidden]` stream state + ppN boundary slots, the pre/norm transients, the
    /// grouped-MoE staging (CSR activations + three f32 partial planes + f16 mirrors + scatter
    /// planes), the MLA query/attention planes, the k-pool idx plane, and the prime-tail
    /// hidden/norm pair. Multiplied by [`hyper_prime_call_rows`] this bounds the workspace of
    /// the CHUNKED prime; on the monolithic rollback (`MEMRA_PRIME_CHUNK=0`) the call rows are
    /// the whole prompt and the same product stays honest.
    pub chunk_token_bytes: usize,
    /// Bytes per PROMPT token that live for the WHOLE prime on the last stage: the returned
    /// pre-output_norm `hiddens` stack (`n_embd` f32), consumed by the MTP-spec `prompt_h`
    /// and the embed capture.
    pub prompt_bytes_per_token: usize,
    /// DSA k-pool group size `P`, or 0 when the model runs no k-pool indexer. The selection
    /// score plane of ONE call is `call_rows * (ctx / P)` f32 — the one prefill transient that
    /// stays COUPLED TO CONTEXT DEPTH after chunking (it is the allocation the 3-card 1M prime
    /// died on at 97.2 GiB).
    pub kpool_score_pool: usize,
    /// Trunk layer count, for re-deriving [`hyper_prime_call_rows`] at admission time with the
    /// same env-sensitive schedule the prime itself will walk.
    pub n_layers: usize,
    /// The model's own GDN grid-alignment input to the schedule.
    pub gdn_grid: bool,
}

impl HyperPrimeWorkspaceShape {
    /// The admission charge for one request: workspace of the LARGEST prime call this request
    /// can produce, plus the ctx-coupled score plane at that call width, plus the prompt-long
    /// hiddens stack.
    ///
    /// Keyed on PROMPT rows, deliberately not on `ctx_cap`: every term here is a function of
    /// what the PRIME walks, and a `max_tokens`-omitted request carries a `ctx_cap` of the
    /// whole server window — charging the window would refuse every vendor-default short
    /// prompt on a deep-window box for workspace it never allocates. A continuation request's
    /// `prompt` is the full rendered conversation (the suffix optimization is internal
    /// reuse), so the score plane's `t_kv` is covered too.
    pub fn admission_bytes(&self, prompt_rows: usize) -> usize {
        let rows = hyper_prime_call_rows(prompt_rows, self.n_layers, self.gdn_grid);
        let chunk = self.chunk_token_bytes.saturating_mul(rows);
        let score = prompt_rows
            .checked_div(self.kpool_score_pool)
            .map(|pools| rows.saturating_mul(pools).saturating_mul(size_of::<f32>()))
            .unwrap_or(0);
        chunk
            .saturating_add(score)
            .saturating_add(self.prompt_bytes_per_token.saturating_mul(prompt_rows))
    }
}

/// Snap AUTO prime-range internal boundaries DOWN to the GDN WY-chunk grid (lane/
/// hermes-perf-fixes, 2026-08-23 — the missing helper the PP-auto-ranges finding names).
///
/// THE LAW THIS EXTENDS (measured, research/multiturn-cache-20260821/
/// LONGCTX-EXACTNESS-20260821.md; the serve-split half already ships as the worker's
/// `grid_align_boundary`): under the chunked WY scan a prompt primed as two calls split at
/// L is bit-identical to the monolithic prime iff `L % gdn_chunk_size() == 0` — an off-grid
/// call start shifts the fold grid and materializes recurrent state at a point the
/// monolithic program never computes. The prime loop walks these ranges as separate
/// `prime_layers` calls, so INTERNAL microchunk boundaries are the same seam: the PP-2
/// auto geometry (`t.div_ceil(8).max(128)` fills, and every dynamic short-fill boundary)
/// lands off the 32-token grid for most prompt lengths, which is exactly the
/// chunk-value bit-identity the GDN lane falsified (FLAGS PRIME_CHUNK/SCHED caveat).
///
/// Boundaries only move DOWN (earlier is always semantically safe — same argument as the
/// worker's alignment); a boundary that collapses onto its predecessor is dropped (ranges
/// merge). The final range always ends at `t`. Aligning down only GROWS the tail
/// remainder, so the fixed-schedule tail-merge rule is never re-violated. Cost bound: at
/// most `c-1` tokens shift per boundary.
pub fn align_prime_ranges_to_gdn(
    ranges: &[(usize, usize)],
    t: usize,
    c: usize,
) -> Vec<(usize, usize)> {
    if c == 0 || ranges.len() < 2 {
        return ranges.to_vec();
    }
    let mut out: Vec<(usize, usize)> = Vec::with_capacity(ranges.len());
    let mut start = 0usize;
    for (i, &(_, end)) in ranges.iter().enumerate() {
        let e = if i + 1 == ranges.len() {
            t
        } else {
            end / c * c
        };
        if e > start {
            out.push((start, e));
            start = e;
        } // else: boundary collapsed onto its predecessor — merge into the next range
    }
    debug_assert_eq!(out.last().map(|&(_, e)| e), Some(t));
    out
}

struct HeadSplit {
    pin: u64,
    w1: CudaSlice<u8>,
    hn1: CudaSlice<f32>,
    y1: CudaSlice<f32>,
    logits_e: CudaSlice<f32>,
    ev_hn: cudarc::driver::CudaEvent,
    ev_done: cudarc::driver::CudaEvent,
    raw_hn1: u64,
    raw_y1: u64,
    raw_logits_hi: u64,
    /// SAMPLED-TAIL scratch (perturbed row + the filter's threshold/z/max slots + the row
    /// index). Allocating these per token cost more than the split head saved: the first
    /// sampled-split measurement came in at 78.25 tok/s against 78.96 for the unsplit head,
    /// which is five allocations per token, not arithmetic.
    samp: Option<SampScratch>,
}

struct SampScratch {
    pb: CudaSlice<f32>,
    th: CudaSlice<f32>,
    z: CudaSlice<f32>,
    mx: CudaSlice<f32>,
    rows: CudaSlice<i32>,
}
/// HEAD-SPLIT workspace (host + device twins share it).
static HEAD_SPLIT_WS: std::sync::Mutex<Option<HeadSplit>> = std::sync::Mutex::new(None);

/// DEV1-LOCAL ROUTER replicas (MEMRA_DEV1_ROUTER): per-layer (gate_inp_f32, exp_probs_b,
/// active_experts) on rank1 + a shared logits scratch. Deterministic kernels on identical
/// input bits — rank1's local selection is bit-equal to the root's.
#[allow(clippy::type_complexity)]
static DEV1_ROUTER_REPS: std::sync::Mutex<
    Option<(
        std::collections::HashMap<u16, (CudaSlice<f32>, CudaSlice<f32>, CudaSlice<u8>)>,
        Option<CudaSlice<f32>>,
    )>,
> = std::sync::Mutex::new(None);

/// SHEXP-ON-DEV1 workspace (MEMRA_SHEXP_DEV1): replica weights + scratch on rank1, the
/// down row lands ROOT-resident over P2P (single store pass), and apply adds it on e
/// behind ev_done. (pins, wg1, wu1, wd1, act1, sh_root, ev_z, ev_done).
#[allow(clippy::type_complexity)]
static SHEXP_D1_REPS: std::sync::Mutex<
    Option<std::collections::HashMap<u16, (CudaSlice<u8>, CudaSlice<u8>, CudaSlice<u8>)>>,
> = std::sync::Mutex::new(None);
#[allow(clippy::type_complexity)]
static SHEXP_D1_WS: std::sync::Mutex<
    Option<(
        (usize, usize),
        CudaSlice<f32>,
        CudaSlice<f32>,
        CudaSlice<f32>,
        cudarc::driver::CudaEvent,
        cudarc::driver::CudaEvent,
    )>,
> = std::sync::Mutex::new(None);

/// SHEXP OVERLAP workspace (issue writes, apply reads): (device, n_embd, n_ff_sh, act, sh).
#[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
static SHEXP_OV_WS: std::sync::Mutex<
    Option<(usize, usize, usize, CudaSlice<f32>, CudaSlice<f32>)>,
> = std::sync::Mutex::new(None);

impl HybridModel {
    /// Does this model's prime schedule live under the GDN WY-chunk grid law? True when the
    /// trunk has GDN (linear-attention) layers AND the chunked scan is on — the regime where
    /// an off-grid prime-call boundary shifts the WY fold grid (see
    /// `align_prime_ranges_to_gdn`). Attention-only models and the sequential scan
    /// (`MEMRA_GDN_CHUNKED=0`) are split-invariant, so the grid is a no-op contract there.
    pub fn gdn_prime_grid_on(&self) -> bool {
        Engine::gdn_chunked_enabled()
            && self
                .layers
                .iter()
                .any(|l| matches!(l.mixer, crate::hybrid::Mixer::Linear(_)))
    }

    /// Can the step TP runtime run the DEVICE-RESIDENT activation path from this serving
    /// engine? Native P2P (peer copies replace the host staging) AND a shared root context
    /// (the device buffers must be addressable on both sides — the TP registry builds its
    /// own Engine per rank, so this is a real seam, not a formality).
    fn full_attn_tp_device_resident(e: &Engine, tp: &crate::hybrid::StepTpQkv) -> bool {
        tp.runtime.native_p2p() && tp.runtime.root_shares_ctx(e)
    }

    pub(crate) fn full_attn_tp_qkv(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        h: &CudaSlice<f32>,
        t: usize,
    ) -> Result<Option<Vec<CudaSlice<f32>>>, Box<dyn std::error::Error>> {
        let Some(tp) = fa.step_tp_qkv.as_ref() else {
            return Ok(None);
        };
        let values = active_matrix_values(
            h.len(),
            t,
            self.cfg.n_embd as usize,
            "Step TP QKV activation",
        )?;
        // DEVICE-RESIDENT NATIVE PATH (lane/hermes-perf-fixes, 2026-08-23 — the host-bounce
        // finding): the native-P2P arm used to dtoh the FULL hidden state per layer, run
        // from a host copy, gather q/k/v to host vectors, and htod all three back — a host
        // round-trip on every execute that the peer transport exists to remove. The
        // device twins are byte-identical by construction (the same bytes travel dtod
        // instead of dtoh+htod; kernels, peer copies, and gather order are shared code).
        // The host arm below remains the transport for !native_p2p (host staging IS that
        // transport) and for a root context this engine cannot address.
        if Self::full_attn_tp_device_resident(e, tp) {
            // Producer fence: h was written on THIS engine's stream; the TP ranks read it
            // on theirs (same context, different streams).
            e.stream().synchronize()?;
            let q = tp
                .runtime
                .bf16_column_parallel_resident_native_device(&tp.q, h, t)?;
            let k = tp
                .runtime
                .bf16_column_parallel_resident_native_device(&tp.k, h, t)?;
            let v = tp
                .runtime
                .bf16_column_parallel_resident_native_device(&tp.v, h, t)?;
            Self::full_attn_tp_log_once(tp, "qkv", "device-resident");
            return Ok(Some(vec![q, k, v]));
        }
        let host = e.dtoh_view(&h.slice(0..values))?;
        let q = if tp.runtime.native_p2p() {
            tp.runtime
                .bf16_column_parallel_resident_native(&tp.q, &host, t)?
        } else {
            tp.runtime
                .bf16_column_parallel_resident(&tp.q, &host, t)?
                .gathered
        };
        let k = if tp.runtime.native_p2p() {
            tp.runtime
                .bf16_column_parallel_resident_native(&tp.k, &host, t)?
        } else {
            tp.runtime
                .bf16_column_parallel_resident(&tp.k, &host, t)?
                .gathered
        };
        let v = if tp.runtime.native_p2p() {
            tp.runtime
                .bf16_column_parallel_resident_native(&tp.v, &host, t)?
        } else {
            tp.runtime
                .bf16_column_parallel_resident(&tp.v, &host, t)?
                .gathered
        };
        Self::full_attn_tp_log_once(tp, "qkv", "host-canonical");
        Ok(Some(vec![e.htod(&q)?, e.htod(&k)?, e.htod(&v)?]))
    }

    /// One transport banner per (projection, transport) — the old per-call eprintln fired
    /// on EVERY layer of EVERY step, itself a decode-rate cost on the path this lane is
    /// unbouncing (the sibling grouped-EP path already learned this).
    fn full_attn_tp_log_once(tp: &crate::hybrid::StepTpQkv, proj: &str, activation: &'static str) {
        use std::sync::atomic::{AtomicBool, Ordering};
        static LOGGED: [AtomicBool; 4] = [
            AtomicBool::new(false),
            AtomicBool::new(false),
            AtomicBool::new(false),
            AtomicBool::new(false),
        ];
        let idx = 2 * usize::from(proj == "o") + usize::from(activation == "device-resident");
        if LOGGED[idx].swap(true, Ordering::Relaxed) {
            return;
        }
        eprintln!(
            "[step-tp-{proj}] execute layer={} devices={:?} projections={proj} \
             tensor_parallel=true attention_local=true kv_local=true transport={} \
             native_p2p={} bulk_p2p={} activation={activation} \
             output={} performance_claim=false (logged once per transport)",
            tp.layer,
            tp.devices,
            tp.runtime.transport_label(),
            tp.runtime.native_p2p(),
            tp.runtime.bulk_p2p(),
            if activation == "device-resident" {
                "root-resident"
            } else {
                "root-readback"
            },
        );
    }

    pub(crate) fn full_attn_tp_o(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        activation: &CudaSlice<f32>,
        tokens: usize,
    ) -> Result<Option<CudaSlice<f32>>, Box<dyn std::error::Error>> {
        let Some(tp) = fa.step_tp_qkv.as_ref() else {
            return Ok(None);
        };
        // DEVICE-RESIDENT NATIVE PATH — the O-projection half of the same finding: no DtoH
        // of the attention output, no host O staging, root-resident reduction consumed in
        // place (byte-identical shared core: `step_bf16_row_native_reduce_from_root`).
        if Self::full_attn_tp_device_resident(e, tp) {
            e.stream().synchronize()?; // producer fence, as the QKV half
            let output = tp
                .runtime
                .step_bf16_row_parallel_resident_native_device(&tp.o, activation, tokens)?;
            Self::full_attn_tp_log_once(tp, "o", "device-resident");
            return Ok(Some(output));
        }
        let host = e.dtoh(activation)?;
        let output = if tp.runtime.native_p2p() {
            tp.runtime
                .step_bf16_row_parallel_resident_native(&tp.o, &host, tokens)?
        } else {
            tp.runtime
                .step_bf16_row_parallel_resident(&tp.o, &host, tokens)?
        };
        Self::full_attn_tp_log_once(tp, "o", "host-canonical");
        Ok(Some(e.htod(&output)?))
    }

    fn full_attn_o(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        activation: &CudaSlice<f32>,
        tokens: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        match self.full_attn_tp_o(e, fa, activation, tokens)? {
            Some(output) => Ok(output),
            None => e.matmul(&fa.wo, activation, tokens),
        }
    }

    /// CHUNK-INVARIANCE BISECT SEAM (lane/chunk-invariance): `MEMRA_PRIME_TRACE=<path>`
    /// appends one JSONL row per (chunk, layer) with a hash of that layer's last-row
    /// post-residual hidden. Diagnostic only — never on in a measured or gated run
    /// (it forces a dtoh + host hash per layer).
    fn prime_trace_path() -> Option<&'static str> {
        static P: std::sync::OnceLock<Option<String>> = std::sync::OnceLock::new();
        P.get_or_init(|| std::env::var("MEMRA_PRIME_TRACE").ok())
            .as_deref()
    }

    /// PRIME ANATOMY (diagnostic): `MEMRA_PRIME_ANATOMY=1` synchronizes the stream around
    /// each prime_layers stage and accumulates wall time per stage class, printed after
    /// every prime_layers call (cumulative across chunks/reps). The per-stage syncs
    /// serialize launch/execute overlap, so the summed total exceeds the naked prime wall —
    /// attribution ratios only, never a measured default run. Non-seg serial arm only.
    fn prime_anatomy_on() -> bool {
        static E: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
        *E.get_or_init(|| std::env::var("MEMRA_PRIME_ANATOMY").as_deref() == Ok("1"))
    }

    fn prime_anatomy_slots() -> &'static [std::sync::atomic::AtomicU64; 5] {
        static S: [std::sync::atomic::AtomicU64; 5] = [
            std::sync::atomic::AtomicU64::new(0), // 0 mixer full-attn
            std::sync::atomic::AtomicU64::new(0), // 1 mixer linear-attn (GDN)
            std::sync::atomic::AtomicU64::new(0), // 2 ffn MoE (router + experts + shexp)
            std::sync::atomic::AtomicU64::new(0), // 3 ffn dense
            std::sync::atomic::AtomicU64::new(0), // 4 norms/adds/glue
        ];
        &S
    }

    /// Fail closed on a path that has not been taught the mHC residual program.
    ///
    /// A serial residual on an hc model is not a degraded answer, it is a DIFFERENT function
    /// computed at full speed and full confidence — the exact failure `crate::hyper` exists to
    /// prevent. Every trunk entry point that has not been converted calls this first, so the
    /// unconverted set is a list of named refusals rather than a list of silent wrong answers.
    pub(crate) fn refuse_hyper(&self, path: &str) -> Result<(), Box<dyn std::error::Error>> {
        if let Some(topology) = self.hyper.as_ref() {
            return Err(format!(
                "{path} runs a serial residual, but this model's ModelPlan declares \
                 ResidualTopology::HyperConnections{{ streams: {}, collapse: {:?} }}. Refusing: \
                 that path would compute a different model. Converted paths: forward, \
                 forward_last, prime_cache, decode_step, and the batched serving chain \
                 decode_step_batch / _sampled / _lean / _masked.",
                topology.streams, topology.collapse
            )
            .into());
        }
        Ok(())
    }

    /// The FFN branch of one hc site, from an already-normed `[t, hidden]` input.
    ///
    /// Split out because under hyper-connections the FFN's input is `rms_norm(hc_pre(x))`, not
    /// `rms_norm(x + attn)` — the fused add+norm+quantize forms the serial paths use have no
    /// residual to fold, so this is the unfused dispatch by construction.
    fn hyper_ffn_branch(
        &self,
        e: &Engine,
        layer: &crate::hybrid::HybridLayer,
        z: &CudaSlice<f32>,
        t: usize,
        il: usize,
        prefill: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        match &layer.ffn {
            crate::hybrid::Ffn::Dense {
                ffn_gate,
                ffn_up,
                ffn_down,
            } => {
                let n_ff = ffn_gate.out_features();
                let mut g2 = e.matmul_group(&[ffn_gate, ffn_up], z, t)?;
                let up = g2.pop().unwrap();
                let gate = g2.pop().unwrap();
                let mut act = e.uninit(t * n_ff)?;
                // A dense FFN reads the SHEXP clamp array — see forward()'s note.
                Self::ffn_act_lim(
                    e,
                    &self.cfg,
                    &gate,
                    &up,
                    1.0,
                    1.0,
                    self.cfg.clamp_shexp_at(il as u32),
                    &mut act,
                    t * n_ff,
                )?;
                e.matmul(ffn_down, &act, t)
            }
            crate::hybrid::Ffn::Moe(m) => {
                if prefill {
                    self.moe_ffn_il_prefill(e, m, z, t, il as u16)
                } else {
                    self.moe_ffn_il_zq8(e, m, z, None, t, il as u16)
                }
            }
        }
    }

    /// Stateless prefill under the mHC residual (`crate::hyper`), the hc twin of `forward` /
    /// `forward_last`. The mixers, the FFNs and the norms are the SAME calls the serial paths
    /// make; only the residual program around them changes, which is the whole point — a mixer
    /// never sees the stream dimension.
    fn forward_hyper(
        &self,
        e: &Engine,
        tokens: &[u32],
        last_only: bool,
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        let topology = *self
            .hyper
            .as_ref()
            .ok_or("forward_hyper on a model with no HyperConnections topology")?;
        // M2 ppN door for the mHC trunk. The generic arm's door lives in `decode_step_h`;
        // this walk is reached BEFORE it (decode.rs routes `hyper.is_some()` first), so the
        // hc walks own their own door. Loud refusal, never silent fallback: an unqualified
        // pipeline rewrite errors here rather than running a single-engine walk over weights
        // the loader has already sharded across devices.
        if let Some(fence) = crate::pp::pp_cuts(self.layers.len()) {
            if !self.rewrite_allowed(memra_gguf::execution_manifest::RewriteSurface::Pipeline) {
                return Err("pipeline rewrite is not qualified for this ModelPlan".into());
            }
            return self.forward_hyper_ppn(e, tokens, last_only, &topology, &fence);
        }
        let n_embd = self.cfg.n_embd as usize;
        let t = tokens.len();
        let eps = self.cfg.rms_eps;
        let pos: Vec<i32> = (0..t as i32).collect();
        let pos_d = e.htod_i32(&pos)?;

        let embedded = self.embed(e, tokens)?;
        let mut x = crate::hyper::expand(e, &topology, &embedded, t, n_embd)?;
        let trace = memra_reference::hidden_trace::enabled();
        if trace {
            memra_reference::hidden_trace::emit_tokens(tokens);
            let streams = x.len() / (t * n_embd);
            memra_reference::hidden_trace::emit_last_row(
                "expand",
                -1,
                t,
                streams * n_embd,
                &e.dtoh(&x)?,
            );
        }

        x = self.hyper_range_forward(e, &topology, x, 0, self.layers.len(), &pos_d, t, trace)?;

        // SHARED EXIT with the ppN twin: one trunk exit, so the split and unsplit arms cannot
        // drift apart in the head. That is what makes `glm5-hyper-ppn-gate`'s bit-identity bar
        // a structural property rather than a coincidence of two maintained copies.
        self.hyper_head_logits(e, &topology, &x, t, n_embd, eps, last_only)
    }

    /// Stateful prefill under the mHC residual: `prime_cache_overlaid`'s contract (leave a
    /// decode-ready cache behind, return last-row logits + the pre-output_norm hidden seed and
    /// stack) over the hc layer program.
    ///
    /// Deliberately UNCHUNKED and UNCAPTURED. The serial prime's chunking, prime slabs, S-mid
    /// graph capture and core-split arms are all keyed to the serial residual's transient set;
    /// re-deriving them for a stream state is a tuning lane, not a correctness one, and this
    /// path is the one the reference gate pins. Long prompts therefore hold `T*streams*hidden`
    /// f32 of stream state — 4x the serial trunk's — and that ceiling is the named cost of the
    /// simple form.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn prime_cache_hyper(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        queued_after: usize,
        overlay: Option<&crate::vision::EmbedOverlay>,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let topology = *self
            .hyper
            .as_ref()
            .ok_or("prime_cache_hyper on a model with no HyperConnections topology")?;
        // M2 ppN door for the mHC prime (see `forward_hyper`'s note). glm5_next has no
        // batched prime arm: the split twin is a straight layer-range split with the
        // [t, streams, hidden] state on the wire.
        //
        // CHUNKED, like the single-engine walk below (lane/glm53-1m-demo, 2026-08-29 — the
        // follow-up the previous note named). The monolithic ppN prime carried the WHOLE
        // prompt as one call, which capped it three independent ways on the 4x96 GB box:
        // per-call transients proportional to t OOM'd from ~32k tokens, and every launch
        // that places t in grid.y (kda_conv_silu, kda_gate, rms_norm over rows, the router)
        // hits the CUDA 65,535 grid.y ceiling from t=65,536 (measured: instant
        // CUDA_ERROR_INVALID_VALUE at a 128,566-token prime, receipts in
        // research/glm53-flash-bringup-20260827/1m-demo-20260829/). The chunk loop reuses
        // the SAME schedule as the single-engine walk (`hyper_prime_ranges`), so per-chunk
        // t is bounded and — because the per-chunk staged walk is bit-identical to the
        // per-chunk unsplit walk (glm5_hyper_ppn_gate arm 2) — the chunked ppN prime
        // composes to bit-identity with the chunked single-engine prime over the same
        // schedule. `queued_after + (t - end)` keeps the REQUEST-level `seq_end` invariant
        // across chunks (each call recomputes pos0+start + (end-start) + rest = pos0 + t +
        // queued_after). A prompt at or under one chunk takes the monolithic ppN body
        // unchanged, and `MEMRA_PRIME_CHUNK=0` restores the monolithic walk — the same
        // rollback seam the single-engine chunk walk documents.
        if let Some(fence) = crate::pp::pp_cuts(self.layers.len()) {
            if !self.rewrite_allowed(memra_gguf::execution_manifest::RewriteSurface::Pipeline) {
                return Err("pipeline rewrite is not qualified for this ModelPlan".into());
            }
            // The mixed-embedding overlay rides the ppN twin too (lane/glm5-vision-default-on,
            // 2026-08-30): the splice is an EMBEDDING-INTAKE transform, and the ppN walk embeds
            // on stage 0 only — every later stage receives the already-expanded stream state.
            // Under the chunked ppN prime each chunk takes the overlay WINDOWED to its own
            // call-relative range (`EmbedOverlay::window`, the same rebase seam the serve
            // prefill tick uses), so splice placement is chunk-schedule-invariant. Gated by
            // glm5-hyper-ppn-gate's overlay arm (bit-identity vs the substituted-token truth,
            // red arm = shifted spans).
            let n_embd = self.cfg.n_embd as usize;
            let t = tokens.len();
            if cache.pos + t > cache.max_ctx {
                return Err("prime_cache: prompt exceeds cache max_ctx".into());
            }
            let ranges = hyper_prime_ranges(t, self.layers.len(), self.gdn_prime_grid_on());
            if ranges.len() == 1 {
                return self.prime_cache_hyper_ppn(
                    e,
                    tokens,
                    cache,
                    queued_after,
                    &topology,
                    &fence,
                    overlay,
                );
            }
            let mut hiddens = e.uninit(t * n_embd)?;
            let mut last: Option<(Vec<f32>, CudaSlice<f32>)> = None;
            for &(start, end) in &ranges {
                let ov = overlay.and_then(|o| o.window(start, end - start));
                let (l, hs, x) = self.prime_cache_hyper_ppn(
                    e,
                    &tokens[start..end],
                    cache,
                    queued_after + (t - end),
                    &topology,
                    &fence,
                    ov.as_ref(),
                )?;
                e.copy_into(&mut hiddens, start * n_embd, &x, (end - start) * n_embd)?;
                last = Some((l, hs));
            }
            let (logits, h_seed) =
                last.expect("hyper_prime_ranges never returns an empty schedule");
            return Ok((logits, h_seed, hiddens));
        }
        let n_embd = self.cfg.n_embd as usize;
        let t = tokens.len();
        if cache.pos + t > cache.max_ctx {
            return Err("prime_cache: prompt exceeds cache max_ctx".into());
        }
        // The REQUEST's absolute end position, computed ONCE before the walk: every chunk sees
        // the same value whatever the chunk size, which is the tick-seg law the serial loop
        // above carries verbatim (`+ queued_after` closes the serve-split axis).
        let seq_end = cache.pos + t + queued_after;
        let ranges = hyper_prime_ranges(t, self.layers.len(), self.gdn_prime_grid_on());
        if ranges.len() == 1 {
            return self.prime_chunk_hyper(e, tokens, cache, seq_end, 0, overlay);
        }
        let mut hiddens = e.uninit(t * n_embd)?;
        let mut last: Option<(Vec<f32>, CudaSlice<f32>)> = None;
        for &(start, end) in &ranges {
            let (l, hs, x) =
                self.prime_chunk_hyper(e, &tokens[start..end], cache, seq_end, start, overlay)?;
            e.copy_into(&mut hiddens, start * n_embd, &x, (end - start) * n_embd)?;
            last = Some((l, hs));
        }
        let (logits, h_seed) = last.expect("hyper_prime_ranges never returns an empty schedule");
        Ok((logits, h_seed, hiddens))
    }

    /// Publish this model's prefill-workspace coefficients to admission
    /// (lane/glm5-gpf-workspace, 2026-08-30). `None` for every non-hyper trunk — the server's
    /// admission arithmetic is then byte-identical to the pre-lane behavior for that family.
    ///
    /// Term-by-term, each anchored on a named allocation of the hyper prime walk (glm5 numbers
    /// in parentheses; the full attribution table is the lane doc's §1):
    ///   * stream state, double-buffered at `hyper::post`, PLUS the ppN boundary tx/rx pair of
    ///     the same `[t, streams, hidden]` payload: `4 * S * H * 4` (256 KiB/token);
    ///   * pre/norm transients (`hyper::pre` y + `rms_norm` h/z + ffn_out): `4 * H * 4`
    ///     (64 KiB/token);
    ///   * prime-tail hidden/norm pair (`collapse` + output-norm stack): `2 * H * 4`
    ///     (32 KiB/token);
    ///   * grouped-MoE prefill staging (`moe_ffn_grouped_prefill_sigmoid`): the f16 CSR
    ///     activations `U*2H`, three f32 partial planes `3*U*4F` (gate/up/act), the f16 down
    ///     mirror `U*2F`, the CSR-order down output + pair-order permute `2*U*4H`, and the
    ///     scatter target `4H` — `U*(10H + 14F) + 4H` (560 KiB/token);
    ///   * MLA query/attention planes: `heads * (qk_head_dim + v_head_dim) * 4`
    ///     (128 KiB/token) and the k-pool idx plane `(topk/P + 1) * 4` (~2 KiB/token).
    ///
    /// Validation against truth: at GLM-5.3-Flash geometry the sum is ~0.92 MiB per call
    /// token, against the 262k cell's MEASURED retained slope of ~0.8 MiB/token/card
    /// (vramwatch.csv: +6.3 GiB across the 8,072-token prime) — the formula sits above the
    /// measurement, never below it. The ctx-coupled score plane and the prompt-long hiddens
    /// stack are separate coefficients on the shape; see [`HyperPrimeWorkspaceShape`].
    pub fn hyper_prime_workspace_shape(&self) -> Option<HyperPrimeWorkspaceShape> {
        let topology = self.hyper.as_ref()?;
        let h = self.cfg.n_embd as usize;
        let s = topology.streams;
        let f32b = std::mem::size_of::<f32>();
        // Stream state (x2) + ppN boundary slots (x2), pre/norm transients, prime tail.
        let mut chunk_token_bytes = 4 * s * h * f32b + 4 * h * f32b + 2 * h * f32b;
        if let Some(moe) = self.cfg.moe.as_ref() {
            let u = moe.expert_used_count as usize;
            let f = moe.expert_ff_length as usize;
            chunk_token_bytes += u * (10 * h + 14 * f) + 4 * h;
        }
        let mut kpool_score_pool = 0;
        if let Some(glm5) = self.cfg.glm5.as_ref() {
            let heads = self.cfg.n_head as usize;
            chunk_token_bytes +=
                heads * (glm5.qk_head_dim as usize + glm5.v_head_dim as usize) * f32b;
            if glm5.index_kpool > 0 {
                chunk_token_bytes += (glm5.index_topk as usize / glm5.index_kpool as usize + 1)
                    * std::mem::size_of::<i32>();
                kpool_score_pool = glm5.index_kpool as usize;
            }
        }
        Some(HyperPrimeWorkspaceShape {
            chunk_token_bytes,
            prompt_bytes_per_token: h * f32b,
            kpool_score_pool,
            n_layers: self.layers.len(),
            gdn_grid: self.gdn_prime_grid_on(),
        })
    }

    /// One T=1 decode step under the mHC residual: `decode_step_h`'s contract over the hc layer
    /// program. The stream state is INTRA-STEP — expanded from the embedded row, collapsed for
    /// the logits — so no cache format changes and the mixers keep their own state exactly as
    /// they do on the serial path.
    pub(crate) fn decode_step_hyper(
        &self,
        e: &Engine,
        token: u32,
        cache: &mut Cache,
    ) -> Result<(Vec<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let topology = *self
            .hyper
            .as_ref()
            .ok_or("decode_step_hyper on a model with no HyperConnections topology")?;
        // M2 ppN door for the mHC decode step (see `forward_hyper`'s note). This is the door
        // the GLM-5.3-Flash residency arc turns on: with it shut, every routed expert has to
        // fit beside card 0's trunk, which 171.2 GB of experts cannot do.
        if let Some(fence) = crate::pp::pp_cuts(self.layers.len()) {
            if !self.rewrite_allowed(memra_gguf::execution_manifest::RewriteSurface::Pipeline) {
                return Err("pipeline rewrite is not qualified for this ModelPlan".into());
            }
            return self.decode_step_hyper_ppn(e, token, cache, &topology, &fence);
        }
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let pos = cache.pos;
        let pos_d = e.htod_i32(&[pos as i32])?;

        let embedded = e.htod(&self.embd.gather(n_embd, &[token]))?;
        let mut x = crate::hyper::expand(e, &topology, &embedded, 1, n_embd)?;

        x = self.hyper_range_decode(e, &topology, x, 0, self.layers.len(), &pos_d, pos, cache)?;

        // SHARED EXIT with the ppN twin (see `forward_hyper`'s note).
        self.hyper_decode_tail(e, &topology, &x, n_embd, eps, cache)
    }

    /// One hc layer RANGE `[lo, hi)` of the STATELESS prefill walk, driven by engine `e`.
    ///
    /// Extracted so the unsplit walk and every pipeline stage run the SAME code over their own
    /// range: the ppN arm's bit-identity claim is then structural, not a coincidence of two
    /// hand-kept-in-sync copies. `x` enters and leaves as the `[t, streams, hidden]` stream
    /// state, which is exactly the payload a stage boundary transports.
    #[allow(clippy::too_many_arguments)]
    fn hyper_range_forward(
        &self,
        e: &Engine,
        topology: &crate::hyper::HyperTopology,
        mut x: CudaSlice<f32>,
        lo: usize,
        hi: usize,
        pos_d: &CudaSlice<i32>,
        t: usize,
        trace: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        for il in lo..hi {
            let layer = &self.layers[il];
            let hyper = layer.hyper.as_ref().ok_or_else(|| {
                format!("layer {il} carries no hyper-connection weights under an hc plan")
            })?;

            let (y, mix) = crate::hyper::pre(e, topology, &hyper.attn, &x, t, n_embd)?;
            let mut h = e.uninit(t * n_embd)?;
            e.rms_norm(&y, layer.attn_norm.float_data(), &mut h, n_embd, t, eps)?;
            let mixed = match &layer.mixer {
                Mixer::Full(fa) => self.full_attn(e, fa, &h, pos_d, t, il)?,
                Mixer::Linear(la) => self.linear_attn(e, la, &h, t)?,
                Mixer::Mla(mla) => self.mla_attn(e, mla, &h, pos_d, t, il)?,
                Mixer::Kda(la) => crate::kda::kda_attn(e, la, &h, t, eps)?,
            };
            x = crate::hyper::post(e, topology, &mixed, &x, &mix, t, n_embd)?;
            if trace {
                let index = il as i64;
                memra_reference::hidden_trace::emit_last_row(
                    "mixer",
                    index,
                    t,
                    n_embd,
                    &e.dtoh(&mixed)?,
                );
                let streams = x.len() / (t * n_embd);
                memra_reference::hidden_trace::emit_last_row(
                    "attn",
                    index,
                    t,
                    streams * n_embd,
                    &e.dtoh(&x)?,
                );
            }

            let (y, mix) = crate::hyper::pre(e, topology, &hyper.mlp, &x, t, n_embd)?;
            let mut z = e.uninit(t * n_embd)?;
            e.rms_norm(
                &y,
                layer.post_attn_norm.float_data(),
                &mut z,
                n_embd,
                t,
                eps,
            )?;
            let ffn_out = self.hyper_ffn_branch(e, layer, &z, t, il, true)?;
            x = crate::hyper::post(e, topology, &ffn_out, &x, &mix, t, n_embd)?;
            if trace {
                let index = il as i64;
                memra_reference::hidden_trace::emit_last_row(
                    "ffn",
                    index,
                    t,
                    n_embd,
                    &e.dtoh(&ffn_out)?,
                );
                let streams = x.len() / (t * n_embd);
                memra_reference::hidden_trace::emit_last_row(
                    "layer",
                    index,
                    t,
                    streams * n_embd,
                    &e.dtoh(&x)?,
                );
            }
        }
        Ok(x)
    }

    /// One hc layer RANGE `[lo, hi)` of the STATEFUL prime walk (see `hyper_range_forward`).
    /// Every mixer writes its own layer's cache state through `e`, so under the ppN door a
    /// stage's KDA conv ring / delta-rule state and its MLA latent rows + kpool indexer plane
    /// are written by the SAME engine `pp::new_cache` allocated them on.
    #[allow(clippy::too_many_arguments)]
    fn hyper_range_prime(
        &self,
        e: &Engine,
        topology: &crate::hyper::HyperTopology,
        mut x: CudaSlice<f32>,
        lo: usize,
        hi: usize,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        seq_end: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        for il in lo..hi {
            let layer = &self.layers[il];
            let hyper = layer.hyper.as_ref().ok_or_else(|| {
                format!("layer {il} carries no hyper-connection weights under an hc plan")
            })?;

            let (y, mix) = crate::hyper::pre(e, topology, &hyper.attn, &x, t, n_embd)?;
            let mut h = e.uninit(t * n_embd)?;
            e.rms_norm(&y, layer.attn_norm.float_data(), &mut h, n_embd, t, eps)?;
            let mixed = match &layer.mixer {
                Mixer::Full(fa) => {
                    self.full_attn_prime(e, fa, &h, None, pos_d, t, cache, il, seq_end)?
                }
                Mixer::Linear(la) => self.linear_attn_prime(e, la, &h, None, t, cache, il)?,
                Mixer::Mla(mla) if mla.tp.is_some() => {
                    self.mla_tp_attn_cached(e, mla, &h, pos_d, t, il, cache, false)?
                }
                Mixer::Mla(mla) => self.mla_attn_cached(e, mla, &h, pos_d, t, il, cache)?,
                Mixer::Kda(la) if la.tp.is_some() => crate::glm5_tp::kda_tp_cached(
                    e,
                    la,
                    &h,
                    t,
                    eps,
                    cache,
                    il,
                    crate::kda::ConvArm::Prefill,
                )?,
                Mixer::Kda(la) => crate::kda::kda_prime_cached(e, la, &h, t, eps, cache, il)?,
            };
            x = crate::hyper::post(e, topology, &mixed, &x, &mix, t, n_embd)?;

            let (y, mix) = crate::hyper::pre(e, topology, &hyper.mlp, &x, t, n_embd)?;
            let mut z = e.uninit(t * n_embd)?;
            e.rms_norm(
                &y,
                layer.post_attn_norm.float_data(),
                &mut z,
                n_embd,
                t,
                eps,
            )?;
            let ffn_out = self.hyper_ffn_branch(e, layer, &z, t, il, true)?;
            x = crate::hyper::post(e, topology, &ffn_out, &x, &mix, t, n_embd)?;
            // glm5 DFlash2 feature tap (lane/glm5-dflash-draft-src): the CONTRACTED
            // completed layer output, host-staged — one Option check when unarmed.
            self.glm5_hc_tap(e, cache, topology, il, &x, t)?;
        }
        Ok(x)
    }

    /// One hc layer RANGE `[lo, hi)` of the T=1 decode step (see `hyper_range_forward`).
    #[allow(clippy::too_many_arguments)]
    fn hyper_range_decode(
        &self,
        e: &Engine,
        topology: &crate::hyper::HyperTopology,
        mut x: CudaSlice<f32>,
        lo: usize,
        hi: usize,
        pos_d: &CudaSlice<i32>,
        pos: usize,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        // MEMRA_HC_DECODE_WS=1 (default OFF, read per call — rollback seam): the persistent-
        // workspace twin of this walk. Same kernels, same call order, same operand bytes;
        // only the hc-glue allocations (mixes/gates/comb/y/h/z/post-out, ~12 alloc+free pairs
        // per layer per token of the census's 2,358) disappear. Byte identity ON/OFF is gated
        // by hc_decode_ws_gpu.rs; refusal shapes fall through to the allocating walk below.
        if hyper_decode_ws_on() {
            return self.hyper_range_decode_ws(e, topology, x, lo, hi, pos_d, pos, cache);
        }
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        for il in lo..hi {
            let layer = &self.layers[il];
            let hyper = layer.hyper.as_ref().ok_or_else(|| {
                format!("layer {il} carries no hyper-connection weights under an hc plan")
            })?;

            let (y, mix) = crate::hyper::pre(e, topology, &hyper.attn, &x, 1, n_embd)?;
            let mut h = e.uninit(n_embd)?;
            e.rms_norm(&y, layer.attn_norm.float_data(), &mut h, n_embd, 1, eps)?;
            let mixed = match &layer.mixer {
                Mixer::Full(fa) => self.full_attn_decode(e, fa, &h, pos_d, pos, cache, il)?,
                Mixer::Linear(la) => self.linear_attn_decode(e, la, &h, cache, il)?,
                Mixer::Mla(mla) if mla.tp.is_some() => {
                    self.mla_tp_attn_cached(e, mla, &h, pos_d, 1, il, cache, false)?
                }
                Mixer::Mla(mla) => self.mla_attn_cached(e, mla, &h, pos_d, 1, il, cache)?,
                Mixer::Kda(la) if la.tp.is_some() => crate::glm5_tp::kda_tp_cached(
                    e,
                    la,
                    &h,
                    1,
                    eps,
                    cache,
                    il,
                    crate::kda::ConvArm::Decode,
                )?,
                Mixer::Kda(la) => crate::kda::kda_decode_cached(e, la, &h, eps, cache, il)?,
            };
            x = crate::hyper::post(e, topology, &mixed, &x, &mix, 1, n_embd)?;

            let (y, mix) = crate::hyper::pre(e, topology, &hyper.mlp, &x, 1, n_embd)?;
            let mut z = e.uninit(n_embd)?;
            e.rms_norm(
                &y,
                layer.post_attn_norm.float_data(),
                &mut z,
                n_embd,
                1,
                eps,
            )?;
            let ffn_out = self.hyper_ffn_branch(e, layer, &z, 1, il, false)?;
            x = crate::hyper::post(e, topology, &ffn_out, &x, &mix, 1, n_embd)?;
        }
        Ok(x)
    }

    /// The persistent-workspace twin of `hyper_range_decode` (MEMRA_HC_DECODE_WS, lever 2 of
    /// the decode diet). One `HyperDecodeWs` per engine (so each ppN stage owns its own,
    /// allocated on its own device); the walk TAKES it from the engine pool, rotates the
    /// stream state against `ws.xb` (an ownership swap, not a copy), and puts it back. The
    /// mixers and the FFN/MoE branches are the SAME calls with the SAME inputs — their
    /// internal allocations are untouched by this lever.
    #[allow(clippy::too_many_arguments)]
    fn hyper_range_decode_ws(
        &self,
        e: &Engine,
        topology: &crate::hyper::HyperTopology,
        x: CudaSlice<f32>,
        lo: usize,
        hi: usize,
        pos_d: &CudaSlice<i32>,
        pos: usize,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let mut ws = match e.hyper_ws_take() {
            Some(ws) if ws.matches(topology, n_embd) => ws,
            _ => crate::hyper::HyperDecodeWs::new(e, topology, n_embd)?,
        };
        if HC_DECODE_WS_DISPATCHES.fetch_add(1, std::sync::atomic::Ordering::Relaxed) == 0 {
            eprintln!(
                "[hc-decode-ws] engaged streams={} hidden={n_embd} (persistent hc-glue \
                 workspace, per-engine pool; MEMRA_HC_DECODE_WS=1)",
                topology.streams
            );
        }
        let out =
            self.hyper_range_decode_ws_body(e, topology, x, lo, hi, pos_d, pos, cache, &mut ws);
        e.hyper_ws_put(ws);
        out
    }

    /// The walk itself — `hyper_range_decode`'s loop with the hc glue landing in `ws`.
    /// KEPT CALL-FOR-CALL IN STEP with the allocating walk above: same kernels, same order
    /// (pre -> rms_norm -> mixer -> post -> pre -> rms_norm -> ffn -> post), so the
    /// byte-identity gate is a structural claim, not a coincidence.
    #[allow(clippy::too_many_arguments)]
    fn hyper_range_decode_ws_body(
        &self,
        e: &Engine,
        topology: &crate::hyper::HyperTopology,
        mut x: CudaSlice<f32>,
        lo: usize,
        hi: usize,
        pos_d: &CudaSlice<i32>,
        pos: usize,
        cache: &mut Cache,
        ws: &mut crate::hyper::HyperDecodeWs,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        for il in lo..hi {
            let layer = &self.layers[il];
            let hyper = layer.hyper.as_ref().ok_or_else(|| {
                format!("layer {il} carries no hyper-connection weights under an hc plan")
            })?;

            crate::hyper::pre_t1_ws(e, topology, &hyper.attn, &x, ws, n_embd)?;
            e.rms_norm(
                &ws.y,
                layer.attn_norm.float_data(),
                &mut ws.h,
                n_embd,
                1,
                eps,
            )?;
            let mixed = match &layer.mixer {
                Mixer::Full(fa) => self.full_attn_decode(e, fa, &ws.h, pos_d, pos, cache, il)?,
                Mixer::Linear(la) => self.linear_attn_decode(e, la, &ws.h, cache, il)?,
                Mixer::Mla(mla) => self.mla_attn_cached(e, mla, &ws.h, pos_d, 1, il, cache)?,
                Mixer::Kda(la) => crate::kda::kda_decode_cached(e, la, &ws.h, eps, cache, il)?,
            };
            crate::hyper::post_t1_ws(e, topology, &mixed, &x, ws, n_embd)?;
            std::mem::swap(&mut x, &mut ws.xb);

            crate::hyper::pre_t1_ws(e, topology, &hyper.mlp, &x, ws, n_embd)?;
            e.rms_norm(
                &ws.y,
                layer.post_attn_norm.float_data(),
                &mut ws.z,
                n_embd,
                1,
                eps,
            )?;
            let ffn_out = self.hyper_ffn_branch(e, layer, &ws.z, 1, il, false)?;
            crate::hyper::post_t1_ws(e, topology, &ffn_out, &x, ws, n_embd)?;
            std::mem::swap(&mut x, &mut ws.xb);
        }
        Ok(x)
    }

    /// One hc layer RANGE `[lo, hi)` of the BATCHED T=1 decode step: B independent sessions
    /// share one walk over the `[B, streams, n_embd]` stream state. The batched twin of
    /// `hyper_range_decode`, and the trunk of `decode_step_batch_hyper` (decode_batch.rs).
    ///
    /// SHAPE — batched where the arithmetic is row-independent, per-session where the state
    /// is, decode-exact where a reduction is width-dependent:
    ///
    ///   * The hc glue (`expand`/`pre_finish` kernels/`post`) is block-per-token by
    ///     construction (grid over t), so t=B batches it with per-row bytes unchanged.
    ///   * The hc mixing GEMM is the ONE width-dependent reduction in the glue
    ///     (cuBLASLt's n-dependent split — the lt_ndep probe), so this walk calls
    ///     `hyper::pre_exact`, which runs each row through the m=1 program the serial step
    ///     runs. rms_norm at m=B is a per-row program.
    ///   * The MIXERS (KDA conv ring + delta rule, MLA latent rows + kpool indexer plane,
    ///     and the Full/Linear classes for completeness) hold per-session recurrent or
    ///     latent state, so each session's row is routed to ITS OWN cache through the SAME
    ///     t=1 call its solo step makes — the per-seq loop is the v1 exactness doctrine
    ///     from this module's sibling (`decode_batch.rs` header), and the row copies in and
    ///     out are arithmetic-free materializations.
    ///   * The FFN batches at t=B: the MoE body's router is the fixed per-row program at
    ///     t < PRIME_MIN_T, expert dispatch is per-token, and the shexp trio rides the
    ///     per-column decode-exact arm at decode widths; the dense branch runs per-row so
    ///     each row executes the serial `hyper_ffn_branch` program verbatim.
    ///
    /// EXACTNESS BAR: row b of a B-row step must be BIT-IDENTICAL to session b decoding
    /// alone through `decode_step_hyper` — full-logit compare, per step. Gate:
    /// `glm5-hyper-batch-gate` (fixture-driven, red-armed with a swapped-row and a
    /// wrong-cache-slot mutation; receipts in
    /// `research/glm53-flash-bringup-20260827/batched-decode-gate/`).
    ///
    /// `pos_rows[bi]` is session bi's single-position device buffer, uploaded by the caller
    /// through THIS range's engine (the per-stage pos_d law under a pp split). `caches[bi]`
    /// advances exactly as its solo step would; `cache.pos` itself is bumped by the caller's
    /// epilogue, never here.
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn hyper_batch_range_decode(
        &self,
        e: &Engine,
        topology: &crate::hyper::HyperTopology,
        mut x: CudaSlice<f32>,
        lo: usize,
        hi: usize,
        pos_rows: &[CudaSlice<i32>],
        caches: &mut [&mut Cache],
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let b_n = caches.len();
        assert_eq!(
            pos_rows.len(),
            b_n,
            "hyper_batch_range_decode: pos_rows built for a different batch width"
        );
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        for il in lo..hi {
            let layer = &self.layers[il];
            let hyper = layer.hyper.as_ref().ok_or_else(|| {
                format!("layer {il} carries no hyper-connection weights under an hc plan")
            })?;

            let (y, mix) = crate::hyper::pre_exact(e, topology, &hyper.attn, &x, b_n, n_embd)?;
            let mut h = e.uninit(b_n * n_embd)?;
            e.rms_norm(&y, layer.attn_norm.float_data(), &mut h, n_embd, b_n, eps)?;
            // ---- mixers: per-session, each row through its OWN cache (the t=1 serial
            // program — cross-session contamination here is the failure mode the gate's
            // swapped-row mutation exists to catch) ----
            let mut mixed = e.uninit(b_n * n_embd)?;
            for bi in 0..b_n {
                let mut h_row = e.uninit(n_embd)?;
                e.dtod_copy_view(&h.slice(bi * n_embd..(bi + 1) * n_embd), &mut h_row)?;
                let cache: &mut Cache = &mut *caches[bi];
                let pos = cache.pos;
                let out_row = match &layer.mixer {
                    Mixer::Full(fa) => {
                        self.full_attn_decode(e, fa, &h_row, &pos_rows[bi], pos, cache, il)?
                    }
                    Mixer::Linear(la) => self.linear_attn_decode(e, la, &h_row, cache, il)?,
                    Mixer::Mla(mla) => {
                        self.mla_attn_cached(e, mla, &h_row, &pos_rows[bi], 1, il, cache)?
                    }
                    Mixer::Kda(la) => crate::kda::kda_decode_cached(e, la, &h_row, eps, cache, il)?,
                };
                e.copy_into(&mut mixed, bi * n_embd, &out_row, n_embd)?;
            }
            x = crate::hyper::post(e, topology, &mixed, &x, &mix, b_n, n_embd)?;

            let (y, mix) = crate::hyper::pre_exact(e, topology, &hyper.mlp, &x, b_n, n_embd)?;
            let mut z = e.uninit(b_n * n_embd)?;
            e.rms_norm(
                &y,
                layer.post_attn_norm.float_data(),
                &mut z,
                n_embd,
                b_n,
                eps,
            )?;
            let ffn_out = self.hyper_ffn_branch_batch(e, layer, &z, b_n, il, false)?;
            x = crate::hyper::post(e, topology, &ffn_out, &x, &mix, b_n, n_embd)?;
        }
        Ok(x)
    }

    /// The FFN branch of the batched hc decode walk (see `hyper_batch_range_decode`).
    ///
    /// MoE batches at t=B — that is the weight win this walk exists for (one stream of the
    /// routed experts serves B rows), and every stage of `moe_ffn_il_zq8` is per-row exact
    /// at decode widths (router: fixed per-row program at t < PRIME_MIN_T; experts:
    /// per-(token,expert) programs; shexp: the per-column decode-exact arm). The DENSE
    /// branch runs PER ROW through the serial `hyper_ffn_branch` instead: its
    /// `matmul_group` dispatch carries no per-row bit-identity contract across widths for
    /// every weight class this walk must serve, and a first-k-dense plan carries one such
    /// layer — per-row costs nothing and each row executes the solo step's program verbatim.
    /// `vrows` (lane/glm5-vrest): the VERIFY walk's batched arm (`MEMRA_GLM5_VERIFY_BATCH`)
    /// passes `true`, which lets the MoE body take the pairs-shaped batched routed-expert
    /// program across the t rows (`moe_vrows_pairs_q8` — bit-identical per row, fail-closed
    /// to the sequential loop for every unqualified shape). The batched DECODE walk
    /// (`decode_step_batch_hyper`) passes `false` — its priced dispatch class stays
    /// byte-stable; porting it is a named follow-up with its own re-price. The DENSE branch
    /// is per-row in both arms (its `matmul_group` dispatch carries no cross-width per-row
    /// bit-identity contract for every weight class this walk must serve; ~3 layers, named
    /// out of scope in the vrest attribution).
    pub(crate) fn hyper_ffn_branch_batch(
        &self,
        e: &Engine,
        layer: &crate::hybrid::HybridLayer,
        z: &CudaSlice<f32>,
        b_n: usize,
        il: usize,
        vrows: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        match &layer.ffn {
            crate::hybrid::Ffn::Dense { .. } => {
                let mut out = e.uninit(b_n * n_embd)?;
                for bi in 0..b_n {
                    let mut z_row = e.uninit(n_embd)?;
                    e.dtod_copy_view(&z.slice(bi * n_embd..(bi + 1) * n_embd), &mut z_row)?;
                    let row = self.hyper_ffn_branch(e, layer, &z_row, 1, il, false)?;
                    e.copy_into(&mut out, bi * n_embd, &row, n_embd)?;
                }
                Ok(out)
            }
            crate::hybrid::Ffn::Moe(m) => {
                if vrows {
                    self.moe_ffn_il_zq8_vrows(e, m, z, b_n, il as u16)
                } else {
                    self.moe_ffn_il_zq8(e, m, z, None, b_n, il as u16)
                }
            }
        }
    }

    // =============================== M2 ppN, mHC arm ===============================
    //
    // The three walks below are the hc twins of `decode_step_h_ppn`. They exist because the
    // GLM-5.3-Flash residency arithmetic does not close on one card: 171.2 GB of routed
    // experts against 2x96 GB means the second card is the only route to full residency, and
    // the pp door is how weights get there. Until these landed, all three hc walks refused
    // the door outright ("the sharded stage handoff is unwired for this residual topology"),
    // which is a loud refusal and was the right behaviour — a single-engine walk over
    // stage-sharded weights dereferences another device's pointers.
    //
    // WHAT IS DIFFERENT FROM THE GENERIC ARM, and it is exactly one thing: the payload on the
    // wire. The serial trunk hands `[n_embd]` (decode) or `[t, n_embd]` (prime) across a
    // boundary; the mHC trunk carries `streams` residual streams between layers, so the
    // boundary payload is `[streams, n_embd]` / `[t, streams, n_embd]`. `pp.rs`'s BoundarySlot
    // buffers are lazily sized from the caller's `n` and grow to the high-water mark, so no
    // slot-sizing change was needed for that — the wider payload just makes them wider.
    // `hyper::expand` runs on stage 0 (it takes no weights) and `hyper::collapse` +
    // output_norm + lm head on the last stage, which is where the loader already put the head
    // (`pp::layer_engine(e, n_trunk, n_trunk - 1)` in `hybrid.rs`) and, under
    // `HcCollapse::GatedHead`, the head trio.
    //
    // Per-layer state placement needed NO new contract: `pp::new_cache` already picks the
    // owning stage's `KvDev` per layer for all three of glm5_next's state classes
    // (`Recurrent` = KDA conv ring + delta-rule state, `LatentKvCache` = MLA rows + the kpool
    // indexer plane, `KvCache` = full attention), and the kpool `index_pool_keys` plane is
    // lazily allocated through the engine the mixer is called with, which under these walks is
    // the stage's engine. `glm5-hyper-ppn-gate` asserts the fence actually separates those
    // classes across stages, so that is a tested property rather than an argued one.
    //
    // NOT WIRED, and refused rather than approximated: the deferred-readback (pipelined) arm.
    // `decode_step_h_ppn_deferred` calls `refuse_hyper`, and this lane did not change that.
    //
    // Gate: `glm5-hyper-ppn-gate` (bit-identical logits vs the unsplit hc walk, decode and
    // prime, at every N/knob combination), receipts in
    // `research/glm53-flash-bringup-20260827/ppn-hyper-gate/`.

    /// ppN twin of `forward_hyper`: the stateless prefill as N stage subgraphs.
    fn forward_hyper_ppn(
        &self,
        e: &Engine,
        tokens: &[u32],
        last_only: bool,
        topology: &crate::hyper::HyperTopology,
        fence: &[usize],
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let t = tokens.len();
        let width = topology.streams * n_embd;
        let trace = memra_reference::hidden_trace::enabled();
        if trace {
            memra_reference::hidden_trace::emit_tokens(tokens);
        }
        let pos: Vec<i32> = (0..t as i32).collect();

        if crate::pp::pp2_streams_off() {
            // Same-stream rollback seam: one engine, one stream, an explicit copy pair per
            // boundary. Structurally identical to the split walk, which is the point — it is
            // the arm that says "the split is the split, not the streams".
            let pos_d = e.htod_i32(&pos)?;
            let embedded = self.embed(e, tokens)?;
            let mut x = crate::hyper::expand(e, topology, &embedded, t, n_embd)?;
            x = self.hyper_range_forward(e, topology, x, fence[0], fence[1], &pos_d, t, trace)?;
            for s in 1..fence.len() - 1 {
                let boundary_tx = e.clone_dtod(&x)?;
                let boundary_rx = e.clone_dtod(&boundary_tx)?;
                x = self.hyper_range_forward(
                    e,
                    topology,
                    boundary_rx,
                    fence[s],
                    fence[s + 1],
                    &pos_d,
                    t,
                    trace,
                )?;
            }
            return self.hyper_head_logits(e, topology, &x, t, n_embd, eps, last_only);
        }

        let rt = crate::pp::PpNRt::get(e)?;
        let n_st = fence.len() - 1;
        assert_eq!(
            rt.n_stages(),
            n_st,
            "PpNRt stage count {} != fence stages {n_st}",
            rt.n_stages()
        );
        // #87 REVERSE PUBLICATION: order every stage stream behind the caller's stream before
        // the first stage allocation (anatomy: `PpNRt::fence_stages_behind`).
        rt.fence_stages_behind(&e.stream())?;

        let mut slot = {
            let _st0 = rt.enter(0);
            let e0 = rt.engine(0, e);
            // PER-STAGE pos_d (M2 pipelining law): each stage uploads its OWN copy on ITS
            // stream, so the buffer is allocated, consumed and freed on one stream.
            let pos_d = e0.htod_i32(&pos)?;
            let embedded = self.embed(e0, tokens)?;
            let x = crate::hyper::expand(e0, topology, &embedded, t, n_embd)?;
            let x =
                self.hyper_range_forward(e0, topology, x, fence[0], fence[1], &pos_d, t, trace)?;
            rt.tx(0, &x, t * width)?
        };
        for s in 1..n_st - 1 {
            let _st = rt.enter(s);
            let es = rt.engine(s, e);
            let pos_d = es.htod_i32(&pos)?;
            let x = rt.rx(s - 1, slot, t * width)?;
            let x = self.hyper_range_forward(
                es,
                topology,
                x,
                fence[s],
                fence[s + 1],
                &pos_d,
                t,
                trace,
            )?;
            slot = rt.tx(s, &x, t * width)?;
        }
        let _stl = rt.enter(n_st - 1);
        let el = rt.engine(n_st - 1, e);
        let pos_d = el.htod_i32(&pos)?;
        let x = rt.rx(n_st - 2, slot, t * width)?;
        let x = self.hyper_range_forward(
            el,
            topology,
            x,
            fence[n_st - 1],
            fence[n_st],
            &pos_d,
            t,
            trace,
        )?;
        self.hyper_head_logits(el, topology, &x, t, n_embd, eps, last_only)
    }

    /// Trunk exit shared by `forward_hyper` and its ppN twin: collapse the stream state, apply
    /// `output_norm`, and project. Runs on the LAST stage's engine under the pp door, which is
    /// where `hybrid.rs` uploaded `output_norm`, the lm head and (under `HcCollapse::GatedHead`)
    /// the head trio.
    #[allow(clippy::too_many_arguments)]
    fn hyper_head_logits(
        &self,
        e: &Engine,
        topology: &crate::hyper::HyperTopology,
        x: &CudaSlice<f32>,
        t: usize,
        n_embd: usize,
        eps: f32,
        last_only: bool,
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        let collapsed =
            crate::hyper::collapse(e, topology, self.hyper_head.as_ref(), x, t, n_embd)?;
        if memra_reference::hidden_trace::enabled() {
            memra_reference::hidden_trace::emit_last_row(
                "collapse",
                -1,
                t,
                n_embd,
                &e.dtoh(&collapsed)?,
            );
        }
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(
            &collapsed,
            self.output_norm.float_data(),
            &mut hn,
            n_embd,
            t,
            eps,
        )?;
        let logits = if last_only {
            let last = e.view(&hn, t * n_embd);
            let last_row = last.slice((t - 1) * n_embd..t * n_embd);
            let mut hlast = e.uninit(n_embd)?;
            e.copy_view_into(&mut hlast, 0, &last_row, n_embd)?;
            e.matmul(&self.output, &hlast, 1)?
        } else {
            e.matmul(&self.output, &hn, t)?
        };
        e.dtoh(&logits)
    }

    /// ppN twin of `prime_cache_hyper`: the monolithic stateful prime as N stage subgraphs.
    /// The returned device buffers (`h_seed`, `hiddens`) are owned by the LAST stage's engine,
    /// the same contract `decode_step_h_ppn` publishes for its `h_seed`.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    #[allow(clippy::too_many_arguments)]
    fn prime_cache_hyper_ppn(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        queued_after: usize,
        topology: &crate::hyper::HyperTopology,
        fence: &[usize],
        overlay: Option<&crate::vision::EmbedOverlay>,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let t = tokens.len();
        let width = topology.streams * n_embd;
        if cache.pos + t > cache.max_ctx {
            return Err("prime_cache: prompt exceeds cache max_ctx".into());
        }
        let seq_end = cache.pos + t + queued_after;
        let pos: Vec<i32> = (cache.pos as i32..(cache.pos + t) as i32).collect();
        // glm5 DFlash2 feature tap: set ONCE per call, before any stage walk — every stage
        // range of this chunk writes the same rows at the chunk's absolute offset.
        if let Some(sink) = cache.hc_taps.as_mut() {
            sink.base = cache.pos;
        }

        if crate::pp::pp2_streams_off() {
            let pos_d = e.htod_i32(&pos)?;
            let mut embedded = self.embed(e, tokens)?;
            if let Some(ov) = overlay {
                // Mixed-embedding splice at embedding intake (the same point the streams-on
                // arm and prime_chunk_hyper use). The caller's overlay is already windowed
                // to THIS call (prefill_tick rebases spans call-relative), so chunk_off = 0.
                ov.splice_into(e, &mut embedded, 0, t, n_embd)?;
            }
            let mut x = crate::hyper::expand(e, topology, &embedded, t, n_embd)?;
            x = self.hyper_range_prime(
                e, topology, x, fence[0], fence[1], &pos_d, t, cache, seq_end,
            )?;
            for s in 1..fence.len() - 1 {
                let boundary_tx = e.clone_dtod(&x)?;
                let boundary_rx = e.clone_dtod(&boundary_tx)?;
                x = self.hyper_range_prime(
                    e,
                    topology,
                    boundary_rx,
                    fence[s],
                    fence[s + 1],
                    &pos_d,
                    t,
                    cache,
                    seq_end,
                )?;
            }
            return self.hyper_prime_tail(e, topology, &x, t, n_embd, eps, cache);
        }

        {
            let rt = crate::pp::PpNRt::get(e)?;
            let n_st = fence.len() - 1;
            assert_eq!(
                rt.n_stages(),
                n_st,
                "PpNRt stage count {} != fence stages {n_st}",
                rt.n_stages()
            );
            rt.fence_stages_behind(&e.stream())?;
            // OVERLAY DEVICE LAW: the overlay rows are built by the caller on the PRIMARY
            // engine (build_vision_overlay runs the tower there). Stage 0 keeps the primary
            // engine whenever it lives on the primary device (pp::PpNRt::build); a placement
            // that moves stage 0 to another device would make the splice a cross-device copy
            // with no receipts — refuse loudly instead of silently peer-reading.
            if overlay.is_some() && !std::ptr::eq(rt.engine(0, e), e) {
                return Err(
                    "vision embedding overlay requires stage 0 on the primary device \
                     (overlay rows are primary-resident); place stage 0 on the primary \
                     or run MEMRA_PP_STREAMS=0"
                        .into(),
                );
            }
            let mut slot = {
                let _st0 = rt.enter(0);
                let e0 = rt.engine(0, e);
                let pos_d = e0.htod_i32(&pos)?;
                let mut embedded = self.embed(e0, tokens)?;
                if let Some(ov) = overlay {
                    // Mixed-embedding splice at stage-0 embedding intake, BEFORE stream
                    // expansion — the reference's execute_multimodal splice point. Stages
                    // s > 0 only ever see the [t, streams, hidden] boundary payload, so no
                    // other stage carries overlay arithmetic. chunk_off = 0: the caller's
                    // overlay is already windowed to this call.
                    ov.splice_into(e0, &mut embedded, 0, t, n_embd)?;
                }
                let x = crate::hyper::expand(e0, topology, &embedded, t, n_embd)?;
                let x = self.hyper_range_prime(
                    e0, topology, x, fence[0], fence[1], &pos_d, t, cache, seq_end,
                )?;
                rt.tx(0, &x, t * width)?
            };
            for s in 1..n_st - 1 {
                let _st = rt.enter(s);
                let es = rt.engine(s, e);
                let pos_d = es.htod_i32(&pos)?;
                let x = rt.rx(s - 1, slot, t * width)?;
                let x = self.hyper_range_prime(
                    es,
                    topology,
                    x,
                    fence[s],
                    fence[s + 1],
                    &pos_d,
                    t,
                    cache,
                    seq_end,
                )?;
                slot = rt.tx(s, &x, t * width)?;
            }
            let _stl = rt.enter(n_st - 1);
            let el = rt.engine(n_st - 1, e);
            let pos_d = el.htod_i32(&pos)?;
            let x = rt.rx(n_st - 2, slot, t * width)?;
            let x = self.hyper_range_prime(
                el,
                topology,
                x,
                fence[n_st - 1],
                fence[n_st],
                &pos_d,
                t,
                cache,
                seq_end,
            )?;
            self.hyper_prime_tail(el, topology, &x, t, n_embd, eps, cache)
        }
    }

    /// ONE call of the mHC prime walk. Carries `tokens.len()` rows of stream state and of every
    /// per-layer transient, appends this call's rows to the mixers' own state, and advances
    /// `cache.pos`. `seq_end` is the REQUEST's absolute end, passed in rather than recomputed,
    /// so no arithmetic here is a function of how the prompt was split.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn prime_chunk_hyper(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        seq_end: usize,
        chunk_off: usize,
        overlay: Option<&crate::vision::EmbedOverlay>,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let topology = *self
            .hyper
            .as_ref()
            .ok_or("prime_chunk_hyper on a model with no HyperConnections topology")?;
        let n_embd = self.cfg.n_embd as usize;
        let t = tokens.len();
        let eps = self.cfg.rms_eps;
        let pos: Vec<i32> = (cache.pos as i32..(cache.pos + t) as i32).collect();
        let pos_d = e.htod_i32(&pos)?;
        // glm5 DFlash2 feature tap: chunked primes write their rows at the chunk's absolute
        // offset (cache.pos advances per chunk — the dflash_taps.base precedent).
        if let Some(sink) = cache.hc_taps.as_mut() {
            sink.base = cache.pos;
        }

        let mut embedded = self.embed(e, tokens)?;
        if let Some(ov) = overlay {
            // Mixed-embedding splice (shared with the ppN twin — EmbedOverlay::splice_into):
            // image rows overwrite placeholder-token embeddings inside this chunk's
            // prompt-relative window [chunk_off, chunk_off+t), BEFORE stream expansion —
            // the reference's splice point (execute_multimodal replaces rows before
            // hc_expand).
            ov.splice_into(e, &mut embedded, chunk_off, t, n_embd)?;
        }
        let mut x = crate::hyper::expand(e, &topology, &embedded, t, n_embd)?;

        for (il, layer) in self.layers.iter().enumerate() {
            let hyper = layer.hyper.as_ref().ok_or_else(|| {
                format!("layer {il} carries no hyper-connection weights under an hc plan")
            })?;

            let (y, mix) = crate::hyper::pre(e, &topology, &hyper.attn, &x, t, n_embd)?;
            let mut h = e.uninit(t * n_embd)?;
            e.rms_norm(&y, layer.attn_norm.float_data(), &mut h, n_embd, t, eps)?;
            let mixed = match &layer.mixer {
                Mixer::Full(fa) => {
                    self.full_attn_prime(e, fa, &h, None, &pos_d, t, cache, il, seq_end)?
                }
                Mixer::Linear(la) => self.linear_attn_prime(e, la, &h, None, t, cache, il)?,
                Mixer::Mla(mla) if mla.tp.is_some() => {
                    self.mla_tp_attn_cached(e, mla, &h, &pos_d, t, il, cache, false)?
                }
                Mixer::Mla(mla) => self.mla_attn_cached(e, mla, &h, &pos_d, t, il, cache)?,
                Mixer::Kda(la) if la.tp.is_some() => crate::glm5_tp::kda_tp_cached(
                    e,
                    la,
                    &h,
                    t,
                    eps,
                    cache,
                    il,
                    crate::kda::ConvArm::Prefill,
                )?,
                Mixer::Kda(la) => crate::kda::kda_prime_cached(e, la, &h, t, eps, cache, il)?,
            };
            x = crate::hyper::post(e, &topology, &mixed, &x, &mix, t, n_embd)?;

            let (y, mix) = crate::hyper::pre(e, &topology, &hyper.mlp, &x, t, n_embd)?;
            let mut z = e.uninit(t * n_embd)?;
            e.rms_norm(
                &y,
                layer.post_attn_norm.float_data(),
                &mut z,
                n_embd,
                t,
                eps,
            )?;
            let ffn_out = self.hyper_ffn_branch(e, layer, &z, t, il, true)?;
            x = crate::hyper::post(e, &topology, &ffn_out, &x, &mix, t, n_embd)?;
            // glm5 DFlash2 feature tap (see hyper_range_prime — the unsplit chunk walk
            // taps the same completed-layer-output contraction).
            self.glm5_hc_tap(e, cache, &topology, il, &x, t)?;
        }

        let hiddens =
            crate::hyper::collapse(e, &topology, self.hyper_head.as_ref(), &x, t, n_embd)?;
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(
            &hiddens,
            self.output_norm.float_data(),
            &mut hn,
            n_embd,
            t,
            eps,
        )?;
        let last = e.view(&hn, t * n_embd);
        let last_row = last.slice((t - 1) * n_embd..t * n_embd);
        let mut hlast = e.uninit(n_embd)?;
        e.copy_view_into(&mut hlast, 0, &last_row, n_embd)?;
        let logits = e.matmul(&self.output, &hlast, 1)?;
        let host = e.dtoh(&logits)?;

        // h_seed is the PRE-output_norm hidden of the last row (MTP-PLAN §A), taken from the
        // collapsed stack so it means the same thing it does on the serial path.
        let stack = e.view(&hiddens, t * n_embd);
        let seed_row = stack.slice((t - 1) * n_embd..t * n_embd);
        let mut h_seed = e.uninit(n_embd)?;
        e.copy_view_into(&mut h_seed, 0, &seed_row, n_embd)?;
        cache.pos += t;
        Ok((host, h_seed, hiddens))
    }

    /// Prime exit shared by `prime_cache_hyper` and its ppN twin: collapse, output_norm, last
    /// row logits, and the pre-output_norm hidden seed taken from the collapsed stack (MTP-PLAN
    /// §A) so it means the same thing it does on the serial path.
    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn hyper_prime_tail(
        &self,
        e: &Engine,
        topology: &crate::hyper::HyperTopology,
        x: &CudaSlice<f32>,
        t: usize,
        n_embd: usize,
        eps: f32,
        cache: &mut Cache,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let hiddens = crate::hyper::collapse(e, topology, self.hyper_head.as_ref(), x, t, n_embd)?;
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(
            &hiddens,
            self.output_norm.float_data(),
            &mut hn,
            n_embd,
            t,
            eps,
        )?;
        let last = e.view(&hn, t * n_embd);
        let last_row = last.slice((t - 1) * n_embd..t * n_embd);
        let mut hlast = e.uninit(n_embd)?;
        e.copy_view_into(&mut hlast, 0, &last_row, n_embd)?;
        let logits = e.matmul(&self.output, &hlast, 1)?;
        let host = e.dtoh(&logits)?;
        let stack = e.view(&hiddens, t * n_embd);
        let seed_row = stack.slice((t - 1) * n_embd..t * n_embd);
        let mut h_seed = e.uninit(n_embd)?;
        e.copy_view_into(&mut h_seed, 0, &seed_row, n_embd)?;
        cache.pos += t;
        Ok((host, h_seed, hiddens))
    }

    /// ppN twin of `decode_step_hyper`: the T=1 step as N stage subgraphs, each on its own
    /// stream (and, under `MEMRA_PP_DEVICES`, its own device/engine), with the
    /// transport-selected boundary handoff of the `[streams, n_embd]` state at each fence cut.
    /// `cache.pos` is snapshotted once and advanced once.
    fn decode_step_hyper_ppn(
        &self,
        e: &Engine,
        token: u32,
        cache: &mut Cache,
        topology: &crate::hyper::HyperTopology,
        fence: &[usize],
    ) -> Result<(Vec<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let pos = cache.pos;
        let width = topology.streams * n_embd;

        if crate::pp::pp2_streams_off() {
            let pos_d = e.htod_i32(&[pos as i32])?;
            let embedded = e.htod(&self.embd.gather(n_embd, &[token]))?;
            let mut x = crate::hyper::expand(e, topology, &embedded, 1, n_embd)?;
            x = self.hyper_range_decode(e, topology, x, fence[0], fence[1], &pos_d, pos, cache)?;
            for s in 1..fence.len() - 1 {
                let boundary_tx = e.clone_dtod(&x)?;
                let boundary_rx = e.clone_dtod(&boundary_tx)?;
                x = self.hyper_range_decode(
                    e,
                    topology,
                    boundary_rx,
                    fence[s],
                    fence[s + 1],
                    &pos_d,
                    pos,
                    cache,
                )?;
            }
            return self.hyper_decode_tail(e, topology, &x, n_embd, eps, cache);
        }

        let rt = crate::pp::PpNRt::get(e)?;
        let n_st = fence.len() - 1;
        assert_eq!(
            rt.n_stages(),
            n_st,
            "PpNRt stage count {} != fence stages {n_st}",
            rt.n_stages()
        );
        rt.fence_stages_behind(&e.stream())?;

        let mut slot = {
            let _st0 = rt.enter(0);
            let e0 = rt.engine(0, e);
            let pos_d = e0.htod_i32(&[pos as i32])?;
            let embedded = e0.htod(&self.embd.gather(n_embd, &[token]))?;
            let x = crate::hyper::expand(e0, topology, &embedded, 1, n_embd)?;
            let x =
                self.hyper_range_decode(e0, topology, x, fence[0], fence[1], &pos_d, pos, cache)?;
            rt.tx(0, &x, width)?
        };
        for s in 1..n_st - 1 {
            let _st = rt.enter(s);
            let es = rt.engine(s, e);
            let pos_d = es.htod_i32(&[pos as i32])?;
            let x = rt.rx(s - 1, slot, width)?;
            let x = self.hyper_range_decode(
                es,
                topology,
                x,
                fence[s],
                fence[s + 1],
                &pos_d,
                pos,
                cache,
            )?;
            slot = rt.tx(s, &x, width)?;
        }
        let _stl = rt.enter(n_st - 1);
        let el = rt.engine(n_st - 1, e);
        let pos_d = el.htod_i32(&[pos as i32])?;
        let x = rt.rx(n_st - 2, slot, width)?;
        let x = self.hyper_range_decode(
            el,
            topology,
            x,
            fence[n_st - 1],
            fence[n_st],
            &pos_d,
            pos,
            cache,
        )?;
        self.hyper_decode_tail(el, topology, &x, n_embd, eps, cache)
    }

    /// Decode exit shared by `decode_step_hyper` and its ppN twin. `h_seed` is the COLLAPSED
    /// hidden (not the pre-collapse stream state and not the post-norm row): that is what the
    /// serial hc step publishes, and the two must not drift.
    fn hyper_decode_tail(
        &self,
        e: &Engine,
        topology: &crate::hyper::HyperTopology,
        x: &CudaSlice<f32>,
        n_embd: usize,
        eps: f32,
        cache: &mut Cache,
    ) -> Result<(Vec<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let h_seed = crate::hyper::collapse(e, topology, self.hyper_head.as_ref(), x, 1, n_embd)?;
        let mut hn = e.uninit(n_embd)?;
        e.rms_norm(
            &h_seed,
            self.output_norm.float_data(),
            &mut hn,
            n_embd,
            1,
            eps,
        )?;
        let logits = e.matmul(&self.output, &hn, 1)?;
        let host = e.dtoh(&logits)?;
        cache.pos += 1;
        Ok((host, h_seed))
    }

    /// Prefill forward over `tokens`; returns logits [T, n_vocab] (host f32).
    pub fn forward(
        &self,
        e: &Engine,
        tokens: &[u32],
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        if self.hyper.is_some() {
            return self.forward_hyper(e, tokens, false);
        }
        if self.is_gemma4_e4b() {
            return self.gemma4_e4b_forward(e, tokens, false);
        }
        if self.uses_gemma_program() {
            return self.gemma4_forward(e, tokens, false);
        }
        let cfg = &self.cfg;
        let n_embd = cfg.n_embd as usize;
        let t = tokens.len();
        let eps = cfg.rms_eps;
        let pos: Vec<i32> = (0..t as i32).collect();
        let pos_d = e.htod_i32(&pos)?;

        let mut x = self.embed(e, tokens)?; // [T, n_embd]

        for (il, layer) in self.layers.iter().enumerate() {
            // attn_norm
            let mut h = e.uninit(t * n_embd)?;
            e.rms_norm(&x, layer.attn_norm.float_data(), &mut h, n_embd, t, eps)?;

            let mixed = match &layer.mixer {
                Mixer::Full(fa) => self.full_attn(e, fa, &h, &pos_d, t, il)?,
                Mixer::Linear(la) => self.linear_attn(e, la, &h, t)?,
                Mixer::Mla(mla) => self.mla_attn(e, mla, &h, &pos_d, t, il)?,
                Mixer::Kda(la) => crate::kda::kda_attn(e, la, &h, t, eps)?,
            };

            // residual 1
            let mut x1 = e.uninit(t * n_embd)?;
            e.add(&x, &mixed, &mut x1, t * n_embd)?;

            // pre-FFN norm (post_attention_norm), FFN (Dense or MoE), residual 2
            let mut z = e.uninit(t * n_embd)?;
            e.rms_norm(
                &x1,
                layer.post_attn_norm.float_data(),
                &mut z,
                n_embd,
                t,
                eps,
            )?;
            let ffn_out = match &layer.ffn {
                crate::hybrid::Ffn::Dense {
                    ffn_gate,
                    ffn_up,
                    ffn_down,
                } => {
                    let n_ff = ffn_gate.out_features();
                    let mut g2 = e.matmul_group(&[ffn_gate, ffn_up], &z, t)?;
                    let up = g2.pop().unwrap();
                    let gate = g2.pop().unwrap();
                    let mut act = e.uninit(t * n_ff)?;
                    // A DENSE FFN reads the SHEXP clamp array: upstream's one `build_ffn` serves
                    // both the dense MLP and the shared expert, and its limit is
                    // `swiglu_clamp_shexp[il]` (llama-graph.cpp:1751). step35's leading dense
                    // blocks 0-2 therefore key off clamp_shexp, not clamp_exp.
                    Self::ffn_act_lim(
                        e,
                        &self.cfg,
                        &gate,
                        &up,
                        1.0,
                        1.0,
                        self.cfg.clamp_shexp_at(il as u32),
                        &mut act,
                        t * n_ff,
                    )?;
                    e.matmul(ffn_down, &act, t)?
                }
                crate::hybrid::Ffn::Moe(m) => self.moe_ffn_il_prefill(e, m, &z, t, il as u16)?,
            };
            let mut x2 = e.uninit(t * n_embd)?;
            e.add(&x1, &ffn_out, &mut x2, t * n_embd)?;
            x = x2;
        }

        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, t, eps)?;
        let logits = e.matmul(&self.output, &hn, t)?;
        e.dtoh(&logits)
    }

    /// Prefill that returns ONLY the last token's logits — the common case (greedy/sample needs
    /// just the final position to start decode). Runs the trunk over all T, then the lm_head
    /// (output.weight, the largest matrix — 248320 rows) on the LAST hidden row ONLY, not all T.
    /// On a 512-token prompt this turns a [512,248320] GEMM into [1,248320] — the dominant prefill
    /// cost (nsys: ~99ms when done for all T). Bit-identical last-row logits to forward()[last].
    pub fn forward_last(
        &self,
        e: &Engine,
        tokens: &[u32],
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        if self.hyper.is_some() {
            return self.forward_hyper(e, tokens, true);
        }
        if self.uses_gemma_program() {
            return self.gemma4_forward(e, tokens, true);
        }
        let cfg = &self.cfg;
        let n_embd = cfg.n_embd as usize;
        let t = tokens.len();
        let eps = cfg.rms_eps;
        let pos: Vec<i32> = (0..t as i32).collect();
        let pos_d = e.htod_i32(&pos)?;

        let mut x = self.embed(e, tokens)?; // [T, n_embd]
        // MEMRA_LAYER_PROBE=1: synchronize + print after every stage — bisects an in-graph
        // ILLEGAL_ADDRESS to (layer, stage) at ~1 line of output per layer (M3 bring-up tool).
        let probe = std::env::var("MEMRA_LAYER_PROBE").is_ok();
        let anat = Self::prime_anatomy_on();
        let mut anat_last = if anat {
            e.stream().synchronize()?;
            Some(std::time::Instant::now())
        } else {
            None
        };
        macro_rules! anat_mark {
            ($slot:expr) => {
                if let Some(ts) = anat_last.as_mut() {
                    e.stream().synchronize()?;
                    Self::prime_anatomy_slots()[$slot].fetch_add(
                        ts.elapsed().as_nanos() as u64,
                        std::sync::atomic::Ordering::Relaxed,
                    );
                    *ts = std::time::Instant::now();
                }
            };
        }
        for (il, layer) in self.layers.iter().enumerate() {
            let mut h = e.uninit(t * n_embd)?;
            e.rms_norm(&x, layer.attn_norm.float_data(), &mut h, n_embd, t, eps)?;
            if probe {
                e.stream().synchronize()?;
                eprintln!("[probe] L{il} norm ok");
            }
            anat_mark!(4);
            let mixed = match &layer.mixer {
                Mixer::Full(fa) => {
                    let y = self.full_attn(e, fa, &h, &pos_d, t, il)?;
                    anat_mark!(0);
                    y
                }
                Mixer::Linear(la) => {
                    let y = self.linear_attn(e, la, &h, t)?;
                    anat_mark!(1);
                    y
                }
                Mixer::Mla(mla) => self.mla_attn(e, mla, &h, &pos_d, t, il)?,
                Mixer::Kda(la) => {
                    let y = crate::kda::kda_attn(e, la, &h, t, eps)?;
                    // KDA shares the linear-mixer anatomy slot: same mixer class, one bucket.
                    anat_mark!(1);
                    y
                }
            };
            if probe {
                e.stream().synchronize()?;
                eprintln!("[probe] L{il} mixer ok");
            }
            let mut x1 = e.uninit(t * n_embd)?;
            e.add(&x, &mixed, &mut x1, t * n_embd)?;
            let mut z = e.uninit(t * n_embd)?;
            e.rms_norm(
                &x1,
                layer.post_attn_norm.float_data(),
                &mut z,
                n_embd,
                t,
                eps,
            )?;
            anat_mark!(4);
            let ffn_out = match &layer.ffn {
                crate::hybrid::Ffn::Dense {
                    ffn_gate,
                    ffn_up,
                    ffn_down,
                } => {
                    let n_ff = ffn_gate.out_features();
                    let mut g2 = e.matmul_group(&[ffn_gate, ffn_up], &z, t)?;
                    let up = g2.pop().unwrap();
                    let gate = g2.pop().unwrap();
                    let mut act = e.uninit(t * n_ff)?;
                    // dense FFN keys off the SHEXP clamp array — see forward()'s note.
                    Self::ffn_act_lim(
                        e,
                        &self.cfg,
                        &gate,
                        &up,
                        1.0,
                        1.0,
                        self.cfg.clamp_shexp_at(il as u32),
                        &mut act,
                        t * n_ff,
                    )?;
                    let y = e.matmul(ffn_down, &act, t)?;
                    anat_mark!(3);
                    y
                }
                crate::hybrid::Ffn::Moe(m) => {
                    let y = self.moe_ffn_il_prefill(e, m, &z, t, il as u16)?;
                    anat_mark!(2);
                    y
                }
            };
            if probe {
                e.stream().synchronize()?;
                eprintln!("[probe] L{il} ffn ok");
            }
            let mut x2 = e.uninit(t * n_embd)?;
            e.add(&x1, &ffn_out, &mut x2, t * n_embd)?;
            x = x2;
        }
        if anat {
            let s = Self::prime_anatomy_slots();
            let ms = |i: usize| s[i].load(std::sync::atomic::Ordering::Relaxed) as f64 / 1.0e6;
            eprintln!(
                "[prime-anatomy] cumulative ms: attn_full={:.1} gdn_linear={:.1} moe={:.1} \
                 dense={:.1} norms_adds={:.1} (t={t}, forward_last)",
                ms(0),
                ms(1),
                ms(2),
                ms(3),
                ms(4)
            );
        }
        // norm over all T, then slice the LAST row and run lm_head on that single row.
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, t, eps)?;
        let last = e.view(&hn, t * n_embd); // [T, n_embd]
        let last_row = last.slice((t - 1) * n_embd..t * n_embd); // [1, n_embd]
        let mut hlast = e.uninit(n_embd)?;
        e.copy_view_into(&mut hlast, 0, &last_row, n_embd)?;
        let logits = e.matmul(&self.output, &hlast, 1)?; // [1, n_vocab] — lm_head on ONE row
        e.dtoh(&logits)
    }

    /// BATCHED PROMPT PRIME (the measured #1 e2e gap, e2e-image-1): `forward_last`'s batched
    /// prefill body EXTENDED to leave a DECODE-READY cache behind — vs the tokenwise prime's
    /// ~102/38 tok/s (9B/27B) decode_step loop, this runs the whole prompt at prefill throughput.
    ///   (a) full-attn layers append their T post-RoPE K/V rows into `cache.kv[il]` via the SAME
    ///       per-row quantize kernel as the decode append (bit-identical cache bytes per row);
    ///   (b) linear layers run STATEFULLY from the cache's current recurrent state (zero at a
    ///       fresh prime): carried-ring conv (ssm_conv1d_tm_state) + ONE gdn_scan(state_in,
    ///       state_out) whose internal sequential t-loop equals T chained T=1 steps — but with
    ///       the NORMAL prefill matmul dispatch (GEMM at m>=16), NOT the decode-exact MMVQ the
    ///       spec verify uses (prime is a prefill-regime pass; the run-gen prefill==decode
    ///       argmax gate is the accuracy authority, exactly as for forward_last);
    ///   (c) `cache.pos`/KV len/len_d advance by T.
    /// Returns (last-row logits host, h_seed = last-row PRE-output_norm hidden [n_embd],
    /// hiddens = the full pre-output_norm hidden stack [T, n_embd] — generate_spec's prompt_h).
    /// FRESH-PROMPT ONLY (cache.pos == 0): the fa_prefill tiles attend within `tokens` alone.
    /// forward_last itself stays untouched (kernel-check / run-gen gate on it).
    ///
    /// `queued_after` (lane/tick-seg, 2026-08-07): the number of prompt tokens of the SAME
    /// REQUEST that the caller will prime in LATER calls — 0 when this call is the whole
    /// request (every single-shot caller). Serve splits a long prompt across SEVERAL
    /// prime_cache calls (one per scheduler tick, plus the prefix-cache LCP split), and the
    /// request's absolute end position `seq_end = cache.pos + t + queued_after` steers step35's
    /// SWA prefill arm — computing it per CALL made the arm a function of the tick budget
    /// (budgets 512/256/64 DIFFER 1.813e0 vs monolithic, greedy diverging at step 6; dark lanes
    /// default to 256 AND cap by live SLO headroom, so identical judge requests primed
    /// differently under load — research/tick-seg-20260807, receipt in
    /// research/step35-chunkfix-20260807 §9). The parameter is what prime_cache structurally
    /// lacked: it cannot know from `tokens` and `cache` alone whether more of the request is
    /// coming. A SESSION CONTINUATION (a NEW user turn primed onto a live cache) is a NEW
    /// request — its arithmetic is keyed to its own extent, so those callers pass 0; only a
    /// caller that SPLITS one request across calls passes the remainder.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    pub fn prime_cache(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        queued_after: usize,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        self.prime_cache_overlaid(e, tokens, cache, queued_after, None)
    }

    /// `prime_cache` with a vision embedding overlay (lane/vision): image merger outputs
    /// replace the `<|image_pad|>` token embeddings at prompt-relative positions before the
    /// trunk walk — the mixed-embedding prime. Text-only callers use `prime_cache` (overlay
    /// None, byte-identical path). Scope: the serial chunk walk, the single-engine hyper walk,
    /// and the hyper ppN twin (splice at stage-0 embedding intake; lane/glm5-vision-default-on,
    /// gated by glm5-hyper-ppn-gate's overlay arm). The serial PP-2 pipelined prime and
    /// gemma4 E4B still refuse loudly.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    pub fn prime_cache_overlaid(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        queued_after: usize,
        overlay: Option<&crate::vision::EmbedOverlay>,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        cache.ensure_usable("prime_cache")?;
        if self.hyper.is_some() {
            // The mixed-embedding splice lands BEFORE stream expansion (the same point
            // the reference's execute_multimodal replaces rows — before hc_expand), so
            // the hyper walk needs no overlay-specific arithmetic (lane/glm5-vision).
            return self.prime_cache_hyper(e, tokens, cache, queued_after, overlay);
        }
        let _pp_walk =
            if crate::pp::pp_cuts(self.layers.len()).is_some() && !crate::pp::pp2_streams_off() {
                let rt = crate::pp::PpNRt::get(e)?;
                Some(rt.acquire_walk("prime_cache")?)
            } else {
                None
            };
        let n_embd = self.cfg.n_embd as usize;
        let t = tokens.len();
        // MEMRA_PRIME_TROWS=1: prefill through the same-session t-row walk (per-row t=1
        // program = the tokenwise-prime ORACLE class) — replaces the host-canonical
        // per-token step-TP prime. Text-only fresh primes; anything else falls through.
        // MEMRA_STEP_GEMM_PRIME: prime the prompt through the batched GEMM path, CHUNKED.
        // The batch entry supplies both halves of the fast prime — the GEMM trunk at m = chunk
        // and the grouped NVFP4 MoE — which is why routing only the MoE through the ordinary
        // chunk loop measured 26.9 s against 3.7 s here. Chunking keeps the transients bounded:
        // a whole 32k prompt in one call would build a 262144-pair CSR and ~4.3 GB of partials
        // per rank, the blow-up the chunked prime exists to prevent.
        //
        // CONTINUATION (lane/gemm-suffix, 2026-08-28): the entry NO LONGER requires
        // `cache.pos == 0`. The batch core has been continuation-capable since 7700e0b6
        // (positions carry each sequence's base; the fresh-prompt guard narrowed to B > 1),
        // and d99b2ea3 named this outer guard as the remaining blocker in its own message.
        // Every multi-turn suffix and every tick remainder was paying the walk's measured
        // ~7.2 ms/token against this path's ~1.0 ms/token, which is why session-affinity
        // reuse measured a 1.012x wash on a growing conversation.
        // ONE DEFECT HAD TO BE FIXED FIRST, and it was LIVE before this lift:
        // `step35_prime_batch_layers` passed `ts[s]` — the CHUNK's length — as `seq_end`.
        // `seq_end` is the REQUEST's absolute end position and it steers step35's SWA arm
        // (`seq_end > win`, win = 512 on step37). A chunk SHORTER than the window at a
        // NONZERO base therefore selected the UNWINDOWED FA arm over a view that the `off`
        // trim leaves at ~win-1+t rows: it attended OUTSIDE the sliding window. That was
        // already reachable with no continuation at all — a fresh prompt of 4096+k for k in
        // [PRIME_MIN_T, 512) ends in a trailing chunk of exactly that shape. `seq_end` is now
        // threaded from here (request-absolute, `+ queued_after`, computed ONCE before the
        // chunk loop, chunk-size-invariant exactly as on the walk), which is what makes the
        // suffix arm expressible at all rather than merely reachable.
        let legacy_calllocal = std::env::var("MEMRA_PRIME_CALLLOCAL").as_deref() == Ok("1");
        let seq_end = if legacy_calllocal {
            cache.pos + t
        } else {
            cache.pos + t + queued_after
        };
        // MEMRA_STEP_GEMM_PRIME_SUFFIX is the SUFFIX-ONLY seam: off (its default in this
        // commit) leaves continuations on the walk while fresh primes stay on the fast path;
        // MEMRA_STEP_GEMM_PRIME=0 is the whole-path seam. The `seq_end` threading above is
        // deliberately NOT behind either door — it is a correctness fix for the fresh path too.
        if overlay.is_none()
            && (cache.pos == 0 || step_gemm_prime_suffix_on())
            && t >= PRIME_MIN_T
            && crate::step_gemm_prime_on()
            && self.uses_sliding_gated_moe_program()
        {
            let n_embd = self.cfg.n_embd as usize;
            let base = cache.pos;
            let width = crate::cache::PRIME_CHUNK_MAX_TOKENS;
            let mut hiddens = e.uninit(t * n_embd)?;
            let mut last: Option<(Vec<f32>, CudaSlice<f32>)> = None;
            let mut start = 0usize;
            while start < t {
                // A trailing chunk below the walk floor folds into the previous one; every chunk
                // this entry sees must clear PRIME_MIN_T on its own.
                let mut end = (start + width).min(t);
                if t - end > 0 && t - end < PRIME_MIN_T {
                    end = t;
                }
                let mut out = self.step35_prime_cache_batch(
                    e,
                    &[&tokens[start..end]],
                    &mut [cache],
                    &[seq_end],
                )?;
                if out.len() != 1 {
                    return Err("B=1 batched prime returned a non-singleton".into());
                }
                let (logits, h_seed, hidden) = out.remove(0);
                e.copy_into(
                    &mut hiddens,
                    start * n_embd,
                    &hidden,
                    (end - start) * n_embd,
                )?;
                last = Some((logits, h_seed));
                start = end;
            }
            let (logits, h_seed) = last.expect("prime produced no chunk");
            // ENGAGEMENT RECEIPT, both directions. `base` is the discriminator: base=0 is a
            // fresh prime (this line existed before the lift), base>0 is a SUFFIX riding the
            // GEMM trunk — the arm this lane added. The declining twin below counts the other
            // direction, so a log that shows neither line is an instrument fault, not a pass.
            eprintln!(
                "[gemm-prime] ENGAGED t={t} base={base} seq_end={seq_end} chunks<={width} (GEMM trunk + grouped MoE)"
            );
            return Ok((logits, h_seed, hiddens));
        }
        if self.uses_sliding_gated_moe_program() {
            eprintln!(
                "[gemm-prime] WALK t={t} base={} seq_end={seq_end} (batched prime declined)",
                cache.pos
            );
        }
        if overlay.is_none()
            && let Some(out) = self.step35_prime_trows(e, tokens, cache)?
        {
            return Ok(out);
        }
        // SESSION CONTINUATION (2026-07-05): cache.pos > 0 = priming a NEW SUFFIX onto a live
        // session cache — every chunk (including the first) takes the continuation arm
        // (fa_prefill_view over the quantized past + this chunk). Fresh prime (pos==0) unchanged.
        assert!(
            t >= PRIME_MIN_T,
            "prime_cache needs T >= {PRIME_MIN_T} (caller gates)"
        );
        assert!(
            cache.pos + t <= cache.max_ctx,
            "prime_cache: prompt exceeds cache max_ctx"
        );

        // CHUNKED PRIME (2026-07-05, the long-ctx OOM fix): the monolithic prime allocates
        // per-layer transients proportional to T (gate/up/act = T*n_ff*4B EACH — 1.5GB apiece at
        // 16k on the 27B), which OOMs a 24GB card around 16k prompt tokens. Chunk the prompt:
        // each chunk runs the full layer stack with transients sized to the chunk, appending its
        // K/V to the resident quantized cache and carrying the GDN conv-ring + recurrent state
        // through `cache.recur` (linear_attn_prime is already stateful — a chunk boundary is
        // exactly the state carry it was built for). Full-attn chunks after the first attend to
        // the QUANTIZED past KV via fa_prefill_view (the spec-verify pattern) — same numeric
        // class as decode reading the cache. Prompts <= one chunk take the ORIGINAL monolithic
        // body byte-for-byte (chunk 0 short-circuits to the f32 fa_prefill path).
        // MEMRA_PRIME_CHUNK sets the chunk size (tokens); 0 disables chunking (monolithic).
        if self.is_gemma4_e4b() || self.uses_gemma_program() {
            if self.is_gemma4_e4b() {
                if overlay.is_some() {
                    return Err(
                        "vision embedding overlay is unsupported on gemma4 E4B (PLE prime)".into(),
                    );
                }
                return self.gemma4_e4b_prime(e, tokens, cache);
            }
            // gemma4 v0: monolithic fresh-prompt prime (chunked/continuation arms later).
            // An overlay takes the masked-prefill arm: image rows splice in unscaled
            // (gemma4.cpp:182 — embd batches skip the sqrt(n_embd) scale) and the image
            // spans become bidirectional attention islands (lane/gemma-vision).
            return self.gemma4_prime(e, tokens, cache, overlay);
        }
        if crate::pp::prime_pipe_on()
            && crate::pp::prime_pp_on()
            && !crate::pp::pp2_streams_off()
            && crate::pp::pp_cuts(self.layers.len())
                .is_some_and(|fence| matches!(fence.len(), 4 | 5))
        {
            crate::pp::pp_wave_on()
                .map_err(|reason| -> Box<dyn std::error::Error> { reason.into() })?;
        }
        let ranges = prime_chunk_ranges(t, self.layers.len(), self.gdn_prime_grid_on());
        // CHUNK-ORDER INVARIANCE (lane/chunk-invariance, 2026-08-05; vLLM #38561 shape).
        // MEMRA_PRIME_CHUNK is documented as a memory-transient knob, but it also decides
        // the prefill's ARITHMETIC, so two rigs with different values produced different
        // greedy text for the same prompt (research/session-affinity-20260805: 97- and
        // 149-token prompts). ROOT CAUSE, measured in research/chunk-invariance-20260805
        // (VERDICT.md) — and it is NOT what docs originally said:
        //   * NOT trunk GEMM m / reduction order. REFUTED: the prefill GEMM is m-INVARIANT
        //     (rows [0,32) bit-identical at m=32 vs m=33..80, both quantized wq and the
        //     output head), so growing a chunk cannot move an existing row's value.
        //   * NOT the GDN scan segmentation. REFUTED: MEMRA_GDN_CHUNKED=0 (sequential scan,
        //     no WY segmentation at all) still diverges, so vLLM's mamba-boundary fix does
        //     not describe our leak.
        //   * IT IS the attention numeric CLASS edge in full_attn_prime_fa_dispatch, which
        //     selects on `base_len == 0`: chunk 0 attends over this batch's f32 K/V
        //     (fa_prefill) while every later chunk attends over the q8_0/q5_1 quantized KV
        //     cache (fa_prefill_view_ws). The chunk size therefore decides WHERE in the
        //     prompt that precision edge falls. Signature: per-row maxdiff is exactly 0.0
        //     before the first boundary and O(1) right after, first_div_pos == chunk size.
        // The grain-free fix (full_attn_prime_fa_dispatch below) removed that class edge at
        // the source — every row is in one numeric class, so the chunk size no longer steers
        // arithmetic and MEMRA_PRIME_CHUNK is a pure memory/transient knob again. The interim
        // MEMRA_PRIME_INVARIANT/MEMRA_PRIME_GRAIN pin-the-boundary door was superseded by that
        // fix and KILLED at v0.71 per the flags doctrine (the jsonl + VERDICT.md are the
        // record); the chunkinv gate asserts byte-identity across chunk sizes naked.
        // The REQUEST's absolute end position, computed ONCE before the loop: every chunk sees the
        // same value, whatever the chunk size. step35's SWA arm selects on it so that kernel
        // selection — and therefore the logits — cannot depend on MEMRA_PRIME_CHUNK
        // (research/step35-chunkfix-20260807; see step35_attn_pre_wo's doc note).
        // `+ queued_after` closes the SECOND axis (lane/tick-seg): when serve splits the request
        // across calls, the request still ends at the same absolute position, whatever the tick
        // budget or LCP split point. MEMRA_PRIME_CALLLOCAL=1 is the ROLLBACK SEAM to the FULL
        // pre-fix arithmetic: the per-call value here AND the unaligned FA view offset in
        // step35_attn_pre_wo. Both halves are required for tickinv35's canary teeth under the FA
        // default. Read per call, not cached (the probe flips it in-process between arms). Never
        // on in a measured default run.
        // `seq_end` (and its MEMRA_PRIME_CALLLOCAL seam) is computed ONCE above the batched
        // entry so both prime arms read the identical request-absolute value.
        if ranges.len() == 1 {
            return self.prime_chunk(e, tokens, cache, seq_end, 0, overlay);
        }
        // PIPELINED PP PRIME. PP-2 retains its independently-qualified two-stage schedule;
        // PP-3/4 require the explicit MEMRA_PP_WAVE=1 persistent-stage wavefront. The serial
        // split stays reachable through MEMRA_PRIME_PIPE=0 and is the exactness oracle.
        if crate::pp::prime_pipe_on() && crate::pp::prime_pp_on() && !crate::pp::pp2_streams_off() {
            if let Some(fence) = crate::pp::pp_cuts(self.layers.len()).filter(|f| f.len() == 3) {
                if overlay.is_some() {
                    return Err(
                        "vision embedding overlay + pipelined PP prime unsupported (v1); \
                         run the serial prime (single device or MEMRA_PRIME_PIPE=0)"
                            .into(),
                    );
                }
                if crate::pp::pp_multi_stream_same_device() {
                    return Err(
                        "prime chunk pipeline refused with 2 stage streams on one device — \
                         that concurrent-stream placement remains quarantined by the deferred \
                         pp flake record. Use one device per stage or MEMRA_PRIME_PIPE=0 for \
                         the serial split."
                            .into(),
                    );
                }
                return self.prime_cache_pp2_pipelined(e, tokens, cache, seq_end, &ranges, &fence);
            }
            if let Some(fence) =
                crate::pp::pp_cuts(self.layers.len()).filter(|f| matches!(f.len(), 4 | 5))
            {
                let wave_on = crate::pp::pp_wave_on()
                    .map_err(|reason| -> Box<dyn std::error::Error> { reason.into() })?;
                let stages = fence.len() - 1;
                if crate::pp::pp_wave_route_enabled(
                    wave_on,
                    crate::pp::pp2_overlap(),
                    stages,
                    ranges.len(),
                ) {
                    if overlay.is_some() {
                        return Err(
                            "vision embedding overlay + pipelined PP prime unsupported; \
                             run the serial prime (MEMRA_PP_WAVE=0 or MEMRA_PRIME_PIPE=0)"
                                .into(),
                        );
                    }
                    let rt = crate::pp::PpNRt::get(e)?;
                    let double_slot = crate::pp::pp2_overlap();
                    crate::pp::pp_wave_eligibility(
                        stages,
                        double_slot,
                        rt.host_bounce_active(),
                        rt.repeated_stage_device(),
                    )
                    .map_err(|reason| -> Box<dyn std::error::Error> { reason.into() })?;
                    return self
                        .prime_cache_ppn_pipelined(e, tokens, cache, seq_end, &ranges, &fence);
                }
            }
        }
        let mut hiddens = e.uninit(t * n_embd)?;
        let mut last: Option<(Vec<f32>, CudaSlice<f32>)> = None;
        for &(start, end) in &ranges {
            // chunked prime writes tap rows at the chunk's absolute offset
            if let Some(taps) = cache.dflash_taps.as_mut() {
                taps.base = start;
            }
            let (l, hs, x) =
                self.prime_chunk(e, &tokens[start..end], cache, seq_end, start, overlay)?;
            e.copy_into(&mut hiddens, start * n_embd, &x, (end - start) * n_embd)?;
            last = Some((l, hs));
        }
        let (logits, h_seed) = last.unwrap();
        Ok((logits, h_seed, hiddens))
    }

    /// PP-2 chunk scheduler: stage 1 of chunk N and stage 0 of chunk N+1 are both queued
    /// before N's epilogue D2H drains the last-stage stream. Arithmetic is unchanged:
    /// every chunk still runs the same two `prime_layers` ranges, boundary copy, output
    /// norm, lm head, and caller hidden-stack copy as the serial split.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn prime_cache_pp2_pipelined(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        seq_end: usize,
        ranges: &[(usize, usize)],
        fence: &[usize],
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        debug_assert_eq!(fence.len(), 3);
        debug_assert!(ranges.len() >= 2);
        let rt = crate::pp::PpNRt::get(e)?;
        assert_eq!(
            rt.n_stages(),
            2,
            "prime pipeline requires exactly two PP stages"
        );
        let n_embd = self.cfg.n_embd as usize;
        let t = tokens.len();
        let initial_base = cache.pos;
        let caller_stream = e.stream();

        // #87 reverse publication before any new stage allocation, then prewarm both
        // boundary slots while the stage streams are otherwise empty. Lazy-growing slot B
        // after stage 1(N) is queued would synchronize that stream and erase the first
        // overlap on a two-chunk prompt.
        rt.fence_stages_behind(&caller_stream)?;
        let max_payload = ranges.iter().map(|(s, e)| (e - s) * n_embd).max().unwrap();
        rt.prepare_overlap_slots(0, max_payload)?;

        let mut hiddens = e.uninit(t * n_embd)?;
        let mut last: Option<(Vec<f32>, CudaSlice<f32>)> = None;
        let mut stage_caches = PrimeCacheStages::new(cache, fence);
        let (cache0, cache1) = stage_caches.pp2_parts();
        let (first_start, first_end) = ranges[0];
        let mut slot = self.prime_pp2_stage0_enqueue(
            e,
            rt,
            &tokens[first_start..first_end],
            cache0,
            seq_end,
            fence,
            initial_base + first_start,
            true,
        )?;
        cache0.pos = initial_base + first_end;

        for (i, &(start, end)) in ranges.iter().enumerate() {
            let base = initial_base + start;
            debug_assert_eq!(
                cache1.pos, base,
                "stage 1 must drain chunks in original position order"
            );
            let (out, next_slot) = if let Some(&(next_start, next_end)) = ranges.get(i + 1) {
                let next_base = initial_base + next_start;
                debug_assert_eq!(
                    cache0.pos, next_base,
                    "stage 0 must issue chunks in original position order"
                );
                let cache0_stage = &mut *cache0;
                // Step's MoE router readback synchronizes once per layer. Two CUDA streams
                // on one host thread therefore serialize even if the calls are ordered as
                // a pipeline. Drive the disjoint stage caches from two scoped host threads:
                // stage 1 consumes slot N while stage 0 produces slot N+1.
                std::thread::scope(|scope| -> Result<_, Box<dyn std::error::Error>> {
                    let stage0 = scope.spawn(move || -> Result<usize, String> {
                        let next = self
                            .prime_pp2_stage0_enqueue(
                                e,
                                rt,
                                &tokens[next_start..next_end],
                                cache0_stage,
                                seq_end,
                                fence,
                                next_base,
                                true,
                            )
                            .map_err(|err| err.to_string())?;
                        cache0_stage.pos = initial_base + next_end;
                        Ok(next)
                    });
                    let x = self.prime_pp2_stage1_enqueue(
                        e,
                        rt,
                        slot,
                        end - start,
                        cache1,
                        seq_end,
                        fence,
                        base,
                        true,
                    )?;
                    let out = {
                        rt.bind_stage(1)?;
                        let _st1 = rt.enter(1);
                        let e1 = rt.engine(1, e);
                        self.prime_chunk_epilogue(e1, x, end - start, cache1)?
                    };
                    let next = match stage0.join() {
                        Ok(result) => {
                            result.map_err(|err| -> Box<dyn std::error::Error> { err.into() })?
                        }
                        Err(payload) => std::panic::resume_unwind(payload),
                    };
                    Ok((out, Some(next)))
                })?
            } else {
                let x = self.prime_pp2_stage1_enqueue(
                    e,
                    rt,
                    slot,
                    end - start,
                    cache1,
                    seq_end,
                    fence,
                    base,
                    true,
                )?;
                let out = {
                    rt.bind_stage(1)?;
                    let _st1 = rt.enter(1);
                    let e1 = rt.engine(1, e);
                    self.prime_chunk_epilogue(e1, x, end - start, cache1)?
                };
                (out, None)
            };

            rt.publish_to(1, &caller_stream)?;
            e.copy_into(&mut hiddens, start * n_embd, &out.2, (end - start) * n_embd)?;
            last = Some((out.0, out.1));
            crate::pp::PRIME_SPLIT_CHUNKS.fetch_add(1, std::sync::atomic::Ordering::Relaxed);

            if let Some(next) = next_slot {
                // The caller copy above reads a stage-1 allocation. Before stage 1 of the
                // next chunk can allocate/reuse blocks, mirror #87's body-entry fence.
                // Stage 0(N+1) is already queued before this wait is appended, so its
                // overlap with stage 1(N) is preserved.
                rt.fence_stages_behind(&caller_stream)?;
                slot = next;
            }
        }

        debug_assert_eq!(cache0.pos, initial_base + t);
        debug_assert_eq!(cache1.pos, initial_base + t);
        let (logits, h_seed) = last.unwrap();
        stage_caches.commit();
        Ok((logits, h_seed, hiddens))
    }

    /// PP-3/4 prime wavefront: one prompt microchunk is one wave. One scoped host worker owns each
    /// non-head stage for the whole walk; the caller thread owns the head stage. Forward boundary
    /// messages preserve wave order, while reverse exact-wave acknowledgements are sent only after
    /// downstream `rx` has recorded `ev_rx`. An upstream stage therefore cannot cycle back to either
    /// shared slot before that slot's current generation has a host-observed release point.
    ///
    /// PP-2 remains on its independently qualified scheduler above. This path is reachable only
    /// through MEMRA_PP_WAVE=1 and the topology gate.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn prime_cache_ppn_pipelined(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        seq_end: usize,
        ranges: &[(usize, usize)],
        fence: &[usize],
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let stages = fence.len().saturating_sub(1);
        debug_assert!((3..=4).contains(&stages));
        debug_assert!(ranges.len() >= 2);
        let rt = crate::pp::PpNRt::get(e)?;
        assert_eq!(
            rt.n_stages(),
            stages,
            "prime wavefront PpNRt/fence stage mismatch"
        );
        let n_embd = self.cfg.n_embd as usize;
        let initial_base = cache.pos;
        let caller_stream = e.stream();
        let primary_context = crate::pp::PrimaryContextRestore::new(e);

        rt.fence_stages_behind(&caller_stream)?;
        let max_payload = ranges
            .iter()
            .map(|(start, end)| (end - start) * n_embd)
            .max()
            .unwrap_or(0);
        for boundary in 0..stages - 1 {
            rt.prepare_overlap_slots(boundary, max_payload)?;
        }

        let mut stage_caches = PrimeCacheStages::new(cache, fence);
        let waves: Vec<_> = ranges
            .iter()
            .map(|&(start, end)| PrimePpWave {
                start,
                end,
                tokens: &tokens[start..end],
            })
            .collect();
        let mut forward_senders = Vec::with_capacity(stages - 1);
        let mut forward_receivers = Vec::with_capacity(stages - 1);
        let mut release_senders = Vec::with_capacity(stages - 1);
        let mut release_receivers = Vec::with_capacity(stages - 1);
        for _ in 0..stages - 1 {
            let (forward_sender, forward_receiver) = std::sync::mpsc::channel();
            let (release_sender, release_receiver) = std::sync::mpsc::channel();
            forward_senders.push(Some(forward_sender));
            forward_receivers.push(Some(forward_receiver));
            release_senders.push(Some(release_sender));
            release_receivers.push(Some(release_receiver));
        }
        let mut stage_channels = Vec::with_capacity(stages - 1);
        for stage in 0..stages - 1 {
            stage_channels.push(Some(PrimePpStageChannels {
                incoming: (stage > 0).then(|| forward_receivers[stage - 1].take().unwrap()),
                release_upstream: (stage > 0).then(|| release_senders[stage - 1].take().unwrap()),
                outgoing: forward_senders[stage].take().unwrap(),
                released_downstream: release_receivers[stage].take().unwrap(),
            }));
        }
        let head_incoming = forward_receivers[stages - 2].take().unwrap();
        let head_release = release_senders[stages - 2].take().unwrap();
        // allow: one-shot composite type; naming it would hide the shape that matters here —
        // this is the head stage's per-wave output, indexed by wave.
        #[allow(clippy::type_complexity)]
        let mut results: Vec<Option<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>)>> =
            std::iter::repeat_with(|| None).take(waves.len()).collect();
        let walk_result = std::thread::scope(|scope| -> Result<(), Box<dyn std::error::Error>> {
            let waves_ref = &waves;
            let mut handles = Vec::with_capacity(stages - 1);
            // `stage` is a STAGE ID, not merely an index: it indexes two different containers
            // (`stage_channels`, `stage_caches.stages()`) and is passed to the worker as the
            // stage it owns. An iterator form would keep only one of the three uses.
            #[allow(clippy::needless_range_loop)]
            for stage in 0..stages - 1 {
                let channels = stage_channels[stage].take().unwrap();
                let cache_state = &stage_caches.stages()[stage];
                handles.push(scope.spawn(move || -> Result<(), String> {
                    let result = self.prime_ppn_wave_worker(
                        e,
                        rt,
                        waves_ref,
                        cache_state,
                        channels.incoming.as_ref(),
                        channels.release_upstream.as_ref(),
                        &channels.outgoing,
                        &channels.released_downstream,
                        stage,
                        seq_end,
                        fence,
                        initial_base,
                    );
                    if let Err(error) = &result {
                        channels.notify_failure(&error.to_string());
                    }
                    result.map_err(|error| error.to_string())
                }));
            }

            let mut head_panic = None;
            let head_result = match std::panic::catch_unwind(std::panic::AssertUnwindSafe(
                || -> Result<(), Box<dyn std::error::Error>> {
                    let mut head_cache = stage_caches.stages()[stages - 1]
                        .lock()
                        .map_err(|_| "prime PP head cache lock poisoned")?;
                    for (wave_index, wave) in waves_ref.iter().enumerate() {
                        let incoming = recv_prime_pp_signal(
                            &head_incoming,
                            PrimePpWaveSlot {
                                wave: wave_index,
                                slot: 0,
                            },
                            false,
                            "prime PP head input",
                        )?;
                        results[wave_index] = Some(self.prime_ppn_wave_final(
                            e,
                            rt,
                            wave,
                            &mut head_cache,
                            incoming,
                            &head_release,
                            seq_end,
                            fence,
                            initial_base,
                        )?);
                    }
                    Ok(())
                },
            )) {
                Ok(result) => result,
                Err(payload) => {
                    head_panic = Some(payload);
                    Err("prime PP head-stage host walker panicked".into())
                }
            };
            if let Err(error) = &head_result {
                let _ = head_release.send(PrimePpSignal::Error(error.to_string()));
            }
            let mut first_error = head_result.err().map(|error| error.to_string());
            let mut worker_panic = None;
            for handle in handles {
                match handle.join() {
                    Ok(Ok(())) => {}
                    Ok(Err(error)) => {
                        first_error.get_or_insert(error);
                    }
                    Err(payload) => {
                        if worker_panic.is_none() {
                            worker_panic = Some(payload);
                        }
                    }
                }
            }
            if let Some(payload) = head_panic {
                std::panic::resume_unwind(payload);
            }
            if let Some(payload) = worker_panic {
                std::panic::resume_unwind(payload);
            }
            if let Some(error) = first_error {
                return Err(error.into());
            }
            Ok(())
        });
        let publish_result = if walk_result.is_ok() {
            Some(rt.publish_to(stages - 1, &caller_stream))
        } else {
            None
        };
        let restore_result = primary_context.restore();
        walk_result?;
        if let Some(result) = publish_result {
            result?;
        }
        restore_result?;
        let mut hiddens = e.uninit(tokens.len() * n_embd)?;
        let mut last: Option<(Vec<f32>, CudaSlice<f32>)> = None;
        for (wave_index, (wave, result)) in waves.iter().zip(results).enumerate() {
            debug_assert_eq!((wave.start, wave.end), ranges[wave_index]);
            let out = result.ok_or("prime PP wavefront completed without a head-stage result")?;
            e.copy_into(
                &mut hiddens,
                wave.start * n_embd,
                &out.2,
                (wave.end - wave.start) * n_embd,
            )?;
            last = Some((out.0, out.1));
            crate::pp::PRIME_SPLIT_CHUNKS.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
        }
        stage_caches.commit();
        drop(stage_caches);

        static LOGGED: std::sync::Once = std::sync::Once::new();
        LOGGED.call_once(|| {
            eprintln!(
                "[pp-wave] PP{stages} prime wavefront engaged: microchunks={} \
                 (experimental, MEMRA_PP_WAVE=1)",
                ranges.len(),
            );
        });
        let (logits, h_seed) = last.expect("prime PP wavefront produced no microchunk");
        crate::pp::record_pp_wave_tick();
        Ok((logits, h_seed, hiddens))
    }

    #[allow(clippy::too_many_arguments)]
    fn prime_ppn_wave_worker(
        &self,
        e: &Engine,
        rt: &crate::pp::PpNRt,
        waves: &[PrimePpWave<'_>],
        cache: &std::sync::Mutex<Cache>,
        incoming: Option<&std::sync::mpsc::Receiver<PrimePpSignal>>,
        release_upstream: Option<&std::sync::mpsc::Sender<PrimePpSignal>>,
        outgoing: &std::sync::mpsc::Sender<PrimePpSignal>,
        released_downstream: &std::sync::mpsc::Receiver<PrimePpSignal>,
        stage: usize,
        seq_end: usize,
        fence: &[usize],
        initial_base: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        debug_assert_eq!(incoming.is_some(), stage > 0);
        debug_assert_eq!(release_upstream.is_some(), stage > 0);
        let mut cache = cache
            .lock()
            .map_err(|_| "prime PP cache stage lock poisoned")?;
        let mut credits = PrimePpWaveCredits::default();
        for (wave_index, wave) in waves.iter().enumerate() {
            let incoming = match incoming {
                Some(receiver) => Some(recv_prime_pp_signal(
                    receiver,
                    PrimePpWaveSlot {
                        wave: wave_index,
                        slot: 0,
                    },
                    false,
                    "prime PP stage input",
                )?),
                None => None,
            };
            let sent = self.prime_ppn_wave_stage(
                e,
                rt,
                wave,
                &mut cache,
                stage,
                incoming,
                release_upstream,
                &mut credits,
                released_downstream,
                seq_end,
                fence,
                initial_base,
            )?;
            send_prime_pp_signal(outgoing, PrimePpSignal::Slot(sent), "prime PP stage output")?;
        }
        while let Some(expected) = credits.pending.front().copied() {
            let released = recv_prime_pp_signal(
                released_downstream,
                expected,
                true,
                "prime PP final slot release",
            )?;
            credits.record_release(released)?;
        }
        Ok(())
    }

    #[allow(clippy::too_many_arguments)]
    fn prime_ppn_wave_stage(
        &self,
        e: &Engine,
        rt: &crate::pp::PpNRt,
        wave: &PrimePpWave<'_>,
        cache: &mut Cache,
        stage: usize,
        incoming: Option<PrimePpWaveSlot>,
        release_upstream: Option<&std::sync::mpsc::Sender<PrimePpSignal>>,
        credits: &mut PrimePpWaveCredits,
        released_downstream: &std::sync::mpsc::Receiver<PrimePpSignal>,
        seq_end: usize,
        fence: &[usize],
        initial_base: usize,
    ) -> Result<PrimePpWaveSlot, Box<dyn std::error::Error>> {
        debug_assert!(stage + 1 < fence.len() - 1);
        let t = wave.end - wave.start;
        let base = initial_base + wave.start;
        debug_assert_eq!(cache.pos, base, "prime PP stage advanced out of order");
        let n_embd = self.cfg.n_embd as usize;
        let payload = t * n_embd;
        let positions: Vec<i32> = (base as i32..(base + t) as i32).collect();
        rt.bind_stage(stage)?;
        let _stage = rt.enter(stage);
        let engine = rt.engine(stage, e);
        let positions_d = engine.htod_i32(&positions)?;
        let x = if stage == 0 {
            debug_assert!(incoming.is_none());
            self.embed(engine, wave.tokens)?
        } else {
            let incoming = incoming.ok_or("prime PP stage has no incoming boundary slot")?;
            let x = rt.rx(stage - 1, incoming.slot, payload)?;
            send_prime_pp_signal(
                release_upstream.ok_or("prime PP stage has no upstream release channel")?,
                PrimePpSignal::Slot(incoming),
                "prime PP upstream slot release",
            )?;
            x
        };
        let x = {
            let _wave_cell = crate::pp::enter_pp_wave_cell();
            let _overlap = crate::pp::enter_prime_pipe_stage();
            self.prime_layers(
                engine,
                x,
                fence[stage],
                fence[stage + 1],
                &positions_d,
                t,
                base,
                cache,
                seq_end,
            )?
        };
        if let Some(expected) = credits.release_required() {
            let released =
                recv_prime_pp_signal(released_downstream, expected, true, "prime PP slot credit")?;
            credits.record_release(released)?;
        }
        let sent = PrimePpWaveSlot {
            wave: credits.next_wave,
            slot: rt.tx_pipelined(stage, &x, payload)?,
        };
        credits.record_send(sent)?;
        cache.pos = base + t;
        Ok(sent)
    }

    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn prime_ppn_wave_final(
        &self,
        e: &Engine,
        rt: &crate::pp::PpNRt,
        wave: &PrimePpWave<'_>,
        cache: &mut Cache,
        incoming: PrimePpWaveSlot,
        release_upstream: &std::sync::mpsc::Sender<PrimePpSignal>,
        seq_end: usize,
        fence: &[usize],
        initial_base: usize,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let stage = fence.len() - 2;
        let t = wave.end - wave.start;
        let base = initial_base + wave.start;
        debug_assert_eq!(cache.pos, base, "prime PP head stage advanced out of order");
        let n_embd = self.cfg.n_embd as usize;
        let payload = t * n_embd;
        let positions: Vec<i32> = (base as i32..(base + t) as i32).collect();
        rt.bind_stage(stage)?;
        let _stage = rt.enter(stage);
        let engine = rt.engine(stage, e);
        let positions_d = engine.htod_i32(&positions)?;
        let x = rt.rx(stage - 1, incoming.slot, payload)?;
        send_prime_pp_signal(
            release_upstream,
            PrimePpSignal::Slot(incoming),
            "prime PP head slot release",
        )?;
        let _wave_cell = crate::pp::enter_pp_wave_cell();
        let _overlap = crate::pp::enter_prime_pipe_stage();
        let x = self.prime_layers(
            engine,
            x,
            fence[stage],
            fence[stage + 1],
            &positions_d,
            t,
            base,
            cache,
            seq_end,
        )?;
        self.prime_chunk_epilogue(engine, x, t, cache)
    }

    /// One prime chunk: the full layer stack over `tokens`, continuing from the cache's current
    /// state (`cache.pos` = tokens already primed; 0 = fresh). Positions/RoPE are absolute
    /// task #21 de-broadcast: the q/k head count PREP must emit so the scan's consumers
    /// agree. Compact (num_k) ONLY when the scan will take the chunked+mma route AND
    /// num_k == num_v/2 (the engine-side hint mirrors this exact formula); everything
    /// else (s128 verify tier, chunked-off, mma-off) keeps the broadcast layout.
    fn gdn_hk(e: &Engine, t: usize, num_v: usize, num_k: usize) -> usize {
        if Engine::gdn_db_on()
            && Engine::gdn_chunked_enabled()
            && t >= 16
            && e.gdn_mma_enabled(Engine::gdn_chunk_size())
            && num_k * 2 == num_v
        {
            num_k
        } else {
            num_v
        }
    }

    /// task #17: gate for the fused fp16-operand epilogues (silu_mul/gated_rmsnorm/sig_mul
    /// `_f16out` twins). Bit-identical class (the twins emit the cvt kernel's exact halves),
    /// but seam-gated (MEMRA_F16OUT=0) so a bit-check can arbitrate, and OFF under verify-exact
    /// (matmul_group skips the f16 lane there; the twins must not resurrect it).
    fn f16out_on(e: &Engine, t: usize) -> bool {
        crate::f16_ffi::pp_f16_enabled()
            && t >= 16
            && !e.verify_exact_on()
            && std::env::var("MEMRA_F16OUT").as_deref() != Ok("0")
    }

    /// (cache.pos + i). Returns (last-row logits, h_seed, this chunk's hidden stack [T, n_embd]).
    /// See HybridModel::prime_slabs — the eager prime's resident trunk transients.
    /// PER-DEVICE since lane/pp-leverb (2026-08-08): the map is keyed by the allocating
    /// engine's CUDA ordinal — under the prime stage split each stage's range walks through
    /// its OWN slabs on its own device (a dev0 slab dereferenced by a dev1 kernel would be
    /// a peer read per GEMM operand, the exact class Lever B removes). Single-device rigs
    /// see one entry, byte-identical behavior.
    pub fn prime_slabs_get(
        &self,
        e: &Engine,
        t: usize,
        n_embd: usize,
        n_ff_max: usize,
    ) -> Result<std::sync::Arc<std::sync::Mutex<PrimeSlabs>>, Box<dyn std::error::Error>> {
        let mut slabs = self.prime_slabs.lock().unwrap();
        let dev = e.ctx().ordinal();
        let need_new = match slabs.get(&dev) {
            None => true,
            Some(sl) => sl.lock().unwrap().t_cap < t,
        };
        if need_new {
            slabs.insert(
                dev,
                std::sync::Arc::new(std::sync::Mutex::new(PrimeSlabs {
                    t_cap: t,
                    h: e.uninit(t * n_embd)?,
                    x1: e.uninit(t * n_embd)?,
                    z: e.uninit(t * n_embd)?,
                    act: e.uninit(t * n_ff_max)?,
                    xa: e.uninit(t * n_embd)?,
                    xb: e.uninit(t * n_embd)?,
                    h16: e.alloc_u8_uninit(t * n_embd * 2)?,
                    z16: e.alloc_u8_uninit(t * n_embd * 2)?,
                    gate: e.uninit(t * n_ff_max)?,
                    up: e.uninit(t * n_ff_max)?,
                    ffn_out: e.uninit(t * n_embd)?,
                    seg_glue: Vec::new(),
                    mixed: e.uninit(t * n_embd)?,
                    seg_mid: Vec::new(),
                    seg_t: 0,
                })),
            );
        }
        Ok(slabs.get(&dev).expect("prime slab inserted").clone())
    }

    /// `seq_end` = the whole REQUEST's absolute end position (`cache.pos + prompt_len` at
    /// `prime_cache` entry), NOT this chunk's end. Chunk-size-invariant by construction; step35's
    /// SWA arm selects on it (see `step35_attn_pre_wo`). Every other arch ignores it.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn prime_chunk(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        seq_end: usize,
        chunk_off: usize,
        overlay: Option<&crate::vision::EmbedOverlay>,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        if crate::pp::pp_host_bounce_active()
            && (self.uses_gemma_program() || !crate::pp::prime_pp_on())
        {
            return Err(
                "prime_chunk: refused with MEMRA_PP_HOST_BOUNCE=1 because this configuration \
                 has no active prime stage split and would peer-read remote weights; keep \
                 MEMRA_PRIME_PP enabled and use a PP-prime-supported model"
                    .into(),
            );
        }
        // LEVER B (lane/pp-leverb, 2026-08-08): the ppN door for the chunked prime. With the
        // door open + per-stage streams, each chunk walks its layer ranges on the OWNING
        // stage's engine/device (the anatomy receipt this kills: dev1 ran ZERO prefill
        // kernels; stage-1 trunk weights were peer-read = 22% of the pp4096 wall).
        // MEMRA_PRIME_PP=0 = the unsplit rollback (also the prime-split-gate's reference
        // arm — prime deliberately keeps NO refuse_unsplit_if_remote, see pp.rs). The
        // MEMRA_PP_STREAMS=0 seam keeps the unsplit walk too: in that regime the sharded
        // loader is off and there is nothing remote to split for.
        if !self.uses_gemma_program()
            && !crate::pp::pp2_streams_off()
            && crate::pp::prime_pp_on()
            && let Some(fence) = crate::pp::pp_cuts(self.layers.len())
        {
            if overlay.is_some() {
                return Err("vision embedding overlay + PP prime unsupported (v1); \
                         run single-device or MEMRA_PRIME_PP=0"
                    .into());
            }
            return self.prime_chunk_ppn(e, tokens, cache, seq_end, &fence);
        }
        if crate::pp::pp_host_bounce_active() {
            return Err(
                "prime_chunk: MEMRA_PP_HOST_BOUNCE=1 found no valid prime stage split; \
                 refusing an unsplit remote-weight walk"
                    .into(),
            );
        }
        let t = tokens.len();
        let base = cache.pos;
        debug_assert!(
            seq_end >= base + t,
            "prime_chunk: seq_end must cover this chunk"
        );
        let pos: Vec<i32> = (base as i32..(base + t) as i32).collect();
        let pos_d = e.htod_i32(&pos)?;

        let mut x_embed = self.embed(e, tokens)?; // [T, n_embd]
        if let Some(ov) = overlay {
            // Mixed-embedding splice: image rows overwrite the pad-token embeddings that
            // fall inside this chunk's prompt-relative window [chunk_off, chunk_off+t).
            // Images larger than one prime chunk straddle boundaries, hence the clipping.
            let n_embd = self.cfg.n_embd as usize;
            for &(pos, row_off, n_rows) in &ov.spans {
                let lo = pos.max(chunk_off);
                let hi = (pos + n_rows).min(chunk_off + t);
                if lo < hi {
                    let src_row = row_off + (lo - pos);
                    let view = ov
                        .rows
                        .slice(src_row * n_embd..(src_row + (hi - lo)) * n_embd);
                    e.copy_view_into(
                        &mut x_embed,
                        (lo - chunk_off) * n_embd,
                        &view,
                        (hi - lo) * n_embd,
                    )?;
                }
            }
        }
        let x = self.prime_layers(
            e,
            x_embed,
            0,
            self.layers.len(),
            &pos_d,
            t,
            base,
            cache,
            seq_end,
        )?;
        self.prime_chunk_epilogue(e, x, t, cache)
    }

    /// PRIME RANGE SUBGRAPH (lane/pp-leverb, 2026-08-08): layers `[lo, hi)` of the chunked
    /// prime walk — `prime_chunk`'s trunk loop extracted verbatim to the
    /// `decode_layers_eager(lo, hi)` / `verify_layers(lo, hi)` contract: enters with a
    /// MATERIALIZED `[T, n_embd]` residual, exits with the range's final residual
    /// materialized (cloned out of the slab). At `lo=0, hi=n_layers` — the unsplit call —
    /// the launch sequence is byte-identical to the pre-extraction body. Range semantics:
    ///   - the cross-layer [down-add + NEXT attn-norm] fusion is range-LOCAL (`il + 1 < hi`):
    ///     layer `hi`'s attn_norm belongs to the NEXT stage's device, so the range ends with
    ///     the plain add (materialize) and the next stage hoists its own first norm — the
    ///     kernel-check-pinned `add_rms_norm == add then rms_norm` identity, the same law
    ///     the decode split rests on (`prime-split-gate` arbitrates end-to-end);
    ///   - prime slabs are PER-DEVICE (`prime_slabs_get` keys on the engine ordinal), so
    ///     each stage walks through its own resident transients;
    ///   - the S-glue/S-mid capture path requires the FULL range (its lookahead fuses
    ///     `self.layers[il+1]` unconditionally) — `use_seg` gains `lo == 0 && hi == n_layers`.
    #[allow(clippy::too_many_arguments)]
    fn prime_layers(
        &self,
        e: &Engine,
        x_in: CudaSlice<f32>,
        lo: usize,
        hi: usize,
        pos_d: &CudaSlice<i32>,
        t: usize,
        base: usize,
        cache: &mut Cache,
        seq_end: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let cfg = &self.cfg;
        let n_embd = cfg.n_embd as usize;
        let eps = cfg.rms_eps;
        // task #14: fuse the fp16 GEMM-operand emission into the trunk norms (kills the
        // standalone convert launches). Only when the f16 lane serves and T reaches the
        // GEMM tier; bit-identical either way.
        let f16fuse = crate::f16_ffi::pp_f16_enabled() && t >= 16;
        // PRIME SLABS (piecewise foundation): trunk transients in resident buffers —
        // ~224 fewer alloc/free calls per prime and FROZEN Lt operand addresses
        // (nvjet's alignment-variant selection becomes run-to-run stable). Every slab is
        // live prefix is fully overwritten before use; x ping-pongs xa<->xb; the inactive
        // capacity tail must stay behind checked views. The hidden-stack return clones the
        // final x (the slab cannot leave). Non-slab fallback: MEMRA_PRIME_SLABS=0.
        let n_ff_max = self
            .layers
            .iter()
            .map(|l| match &l.ffn {
                crate::hybrid::Ffn::Dense { ffn_gate, .. } => ffn_gate.out_features(),
                _ => n_embd,
            })
            .max()
            .unwrap_or(n_embd)
            .max(n_embd);
        let use_slabs = std::env::var("MEMRA_PRIME_SLABS").as_deref() != Ok("0");
        let slab = if use_slabs {
            Some(self.prime_slabs_get(e, t, n_embd, n_ff_max)?)
        } else {
            None
        };
        let mut slab_guard = slab.as_ref().map(|sl| sl.lock().unwrap());
        let mut x_own; // fallback storage when slabs are off
        type SlabRefs<'a> = (
            &'a mut CudaSlice<f32>,
            &'a mut CudaSlice<f32>,
            &'a mut CudaSlice<f32>,
            &'a mut CudaSlice<f32>,
            &'a mut CudaSlice<u8>,
            &'a mut CudaSlice<u8>,
            &'a mut CudaSlice<f32>,
            &'a mut CudaSlice<f32>,
            &'a mut CudaSlice<f32>,
        );
        let (mut x_cur, mut x_nxt, sl): (
            &mut CudaSlice<f32>,
            &mut CudaSlice<f32>,
            Option<SlabRefs>,
        );
        #[allow(clippy::type_complexity)]
        // allow: one-shot composite type; naming it would hide the shape that matters at the call site
        let mut seg: Option<(
            &mut Vec<Option<cudarc::driver::CudaGraph>>,
            &mut Vec<Option<cudarc::driver::CudaGraph>>,
            &mut CudaSlice<f32>,
            &mut usize,
        )> = None;
        let mut x_own2;
        match slab_guard.as_mut() {
            Some(g) => {
                let slabs = &mut **g;
                e.copy_into(&mut slabs.xa, 0, &x_in, t * n_embd)?;
                let PrimeSlabs {
                    xa,
                    xb,
                    h,
                    x1,
                    z,
                    act,
                    h16,
                    z16,
                    gate,
                    up,
                    ffn_out,
                    seg_glue,
                    mixed,
                    seg_mid,
                    seg_t,
                    ..
                } = slabs;
                x_cur = xa;
                x_nxt = xb;
                seg = Some((seg_glue, seg_mid, mixed, seg_t));
                sl = Some((h, x1, z, act, h16, z16, gate, up, ffn_out));
            }
            None => {
                x_own = x_in;
                x_own2 = e.uninit(t * n_embd)?;
                x_cur = &mut x_own;
                x_nxt = &mut x_own2;
                sl = None;
            }
        }
        let mut alloc_h;
        let mut alloc_x1;
        let mut alloc_z;
        let mut alloc_act;
        let mut alloc_h16;
        let mut alloc_z16;
        let mut alloc_gate;
        let mut alloc_up;
        let mut alloc_fo;
        let (h, x1, z, act): (
            &mut CudaSlice<f32>,
            &mut CudaSlice<f32>,
            &mut CudaSlice<f32>,
            &mut CudaSlice<f32>,
        );
        let (h16, z16): (&mut CudaSlice<u8>, &mut CudaSlice<u8>);
        let (sl_gate, sl_up, sl_fo): (
            &mut CudaSlice<f32>,
            &mut CudaSlice<f32>,
            &mut CudaSlice<f32>,
        );
        match sl {
            Some((a, b, c, d, e16, f16b, g, u, fo)) => {
                h = a;
                x1 = b;
                z = c;
                act = d;
                h16 = e16;
                z16 = f16b;
                sl_gate = g;
                sl_up = u;
                sl_fo = fo;
            }
            None => {
                alloc_h = e.uninit(t * n_embd)?;
                alloc_x1 = e.uninit(t * n_embd)?;
                alloc_z = e.uninit(t * n_embd)?;
                alloc_act = e.uninit(t * n_ff_max)?;
                alloc_h16 = e.alloc_u8_uninit(t * n_embd * 2)?;
                alloc_z16 = e.alloc_u8_uninit(t * n_embd * 2)?;
                alloc_gate = e.uninit(t * n_ff_max)?;
                alloc_up = e.uninit(t * n_ff_max)?;
                alloc_fo = e.uninit(t * n_embd)?;
                h = &mut alloc_h;
                x1 = &mut alloc_x1;
                z = &mut alloc_z;
                act = &mut alloc_act;
                h16 = &mut alloc_h16;
                z16 = &mut alloc_z16;
                sl_gate = &mut alloc_gate;
                sl_up = &mut alloc_up;
                sl_fo = &mut alloc_fo;
            }
        }
        // piecewise increment 3: layer-0 norm hoisted; the per-layer tail fuses
        // [down-add + NEXT layer's attn-norm] into one captured S-glue segment
        // (all-slab IO, zero in-graph allocations). Capture happens lazily on the
        // first prime at this t (capture does not execute -> launch right after).
        let n_layers = self.layers.len();
        // OPT-IN (2026-07-26 interleaved verdict): 2-kernel segments measured NET
        // -0.7%-to-neutral under the interleaved A/B protocol — one cuGraphLaunch costs
        // about what two kernel submissions do. The earlier "+0.9%" was cross-run clock
        // drift (the repo's interleaved-A/B law exists for exactly this). Larger segments
        // (S-prep/S-attn, 7-9 kernels) remain the open hypothesis; the core-split
        // machinery stays (byte-identical) as their foundation.
        // step35 is excluded: the core-split path calls `full_attn_prime_core_inner`, which is
        // the GENERIC attn core (uniform n_head, rope_dim_count, no window, no head-wise gate).
        // step35 rides its own mixer through the normal per-layer arm below.
        let use_seg = f16fuse
            && seg.is_some()
            && !self.uses_sliding_gated_moe_program()
            && lo == 0
            && hi == n_layers
            && std::env::var("MEMRA_PRIME_SEG").as_deref() == Ok("1")
            // GRAPH-LAUNCH HEADROOM GUARD (see spec::GRAPH_LAUNCH_MIN_FREE): below the
            // driver-free floor this prime call takes the eager fused else-arm (the
            // byte-identical twin the opt-in was gated against) instead of replaying
            // the S-mid/S-glue segment graphs into an exhausted card. Probe runs only
            // when the opt-in flag is armed (short-circuit order).
            && {
                let ok = crate::spec::graph_launch_headroom_ok(e);
                if !ok {
                    static NOTED: std::sync::Once = std::sync::Once::new();
                    NOTED.call_once(|| crate::spec::graph_replay_suspended_note("prime-seg"));
                }
                ok
            };
        if let Some((sg, sm, _, st)) = seg.as_mut()
            && **st != t
        {
            sg.clear();
            sg.extend((0..n_layers).map(|_| None));
            sm.clear();
            sm.extend((0..n_layers).map(|_| None));
            **st = t;
        }
        {
            let layer_lo = &self.layers[lo];
            if f16fuse {
                e.rms_norm_f16out(
                    x_cur,
                    layer_lo.attn_norm.float_data(),
                    h,
                    h16,
                    n_embd,
                    t,
                    eps,
                )?;
            } else {
                e.rms_norm(x_cur, layer_lo.attn_norm.float_data(), h, n_embd, t, eps)?;
            }
        }
        let anat = Self::prime_anatomy_on();
        let mut anat_last = if anat {
            e.stream().synchronize()?;
            Some(std::time::Instant::now())
        } else {
            None
        };
        // Closes the region that just ENDED into `slot`, restarting the clock.
        macro_rules! anat_mark {
            ($slot:expr) => {
                if let Some(ts) = anat_last.as_mut() {
                    e.stream().synchronize()?;
                    Self::prime_anatomy_slots()[$slot].fetch_add(
                        ts.elapsed().as_nanos() as u64,
                        std::sync::atomic::Ordering::Relaxed,
                    );
                    *ts = std::time::Instant::now();
                }
            };
        }
        for il in lo..hi {
            let layer = &self.layers[il];
            let hx16 = if f16fuse { Some(&*h16) } else { None };
            if use_seg {
                // core-split path: projections -> _inner core -> out-GEMM INTO the mixed
                // slab (no copies) -> S-mid segment [add + post-norm] as one graph launch.
                let (pre, pre16, w_out) = match &layer.mixer {
                    Mixer::Full(fa) => {
                        let g3 = match hx16 {
                            Some(xh) => e.matmul_group_xh(&[&fa.wq, &fa.wk, &fa.wv], h, xh, t)?,
                            None => e.matmul_group(&[&fa.wq, &fa.wk, &fa.wv], h, t)?,
                        };
                        let (pre, pre16) =
                            self.full_attn_prime_core_inner(e, fa, g3, pos_d, t, cache, il)?;
                        (pre, pre16, &fa.wo)
                    }
                    Mixer::Mla(_) => crate::hybrid::mla_path_unimplemented("core-split prime"),
                    Mixer::Kda(_) => {
                        crate::hybrid::kda_path_unimplemented("core-split captured prime")
                    }
                    Mixer::Linear(la) => {
                        let ws = [&la.wqkv, &la.wqkv_gate, &la.ssm_beta, &la.ssm_alpha];
                        let g4 = match hx16 {
                            Some(xh) => e.matmul_group_xh(&ws, h, xh, t)?,
                            None => e.matmul_group(&ws, h, t)?,
                        };
                        let (pre, pre16) =
                            self.linear_attn_prime_core_pad_inner(e, la, g4, t, cache, il, None)?;
                        (pre, pre16, &la.ssm_out)
                    }
                };
                {
                    let (_, sm, mslab, _) = seg.as_mut().unwrap();
                    let pre_n = pre.len() / t;
                    let xh_pre = match pre16 {
                        Some(x) => x,
                        None => e.f16_act(&pre, t * pre_n, pre_n)?,
                    };
                    if !e.try_f16_gemm_pre_into(w_out, &xh_pre, t, mslab)? {
                        let y = e.matmul(w_out, &pre, t)?;
                        e.copy_into(mslab, 0, &y, t * n_embd)?;
                    }
                    if sm[il].is_none() {
                        use cudarc::driver::sys::{CUgraphInstantiate_flags, CUstreamCaptureMode};
                        let w_post = layer.post_attn_norm.float_data();
                        e.stream().synchronize()?;
                        e.stream()
                            .begin_capture(CUstreamCaptureMode::CU_STREAM_CAPTURE_MODE_RELAXED)?;
                        let r = (|| -> Result<(), Box<dyn std::error::Error>> {
                            e.add(x_cur, mslab, x1, t * n_embd)?;
                            e.rms_norm_f16out(x1, w_post, z, z16, n_embd, t, eps)?;
                            Ok(())
                        })();
                        let g = e.stream().end_capture(
                            CUgraphInstantiate_flags::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH);
                        r?;
                        sm[il] = Some(g?.ok_or("S-mid capture produced no graph")?);
                    }
                    sm[il].as_ref().unwrap().launch()?;
                }
            } else {
                let mixed = match &layer.mixer {
                    Mixer::Full(fa) => {
                        let y =
                            self.full_attn_prime(e, fa, h, hx16, pos_d, t, cache, il, seq_end)?;
                        anat_mark!(0);
                        y
                    }
                    Mixer::Linear(la) => {
                        let y = self.linear_attn_prime(e, la, h, hx16, t, cache, il)?;
                        anat_mark!(1);
                        y
                    }
                    Mixer::Mla(mla) => self.mla_attn_cached(e, mla, h, pos_d, t, il, cache)?,
                    Mixer::Kda(la) => {
                        let y = crate::kda::kda_prime_cached(e, la, h, t, eps, cache, il)?;
                        anat_mark!(1);
                        y
                    }
                };
                if f16fuse {
                    // round 28: residual+norm in ONE kernel (add_rms_norm precedent,
                    // bit-identical) — the standalone add pass disappears.
                    e.add_rms_norm_f16out(
                        x_cur,
                        &mixed,
                        layer.post_attn_norm.float_data(),
                        x1,
                        z,
                        z16,
                        n_embd,
                        t,
                        eps,
                    )?;
                } else {
                    e.add(x_cur, &mixed, x1, t * n_embd)?;
                    e.rms_norm(x1, layer.post_attn_norm.float_data(), z, n_embd, t, eps)?;
                }
                anat_mark!(4);
            }
            let zx16 = if f16fuse { Some(&*z16) } else { None };
            match &layer.ffn {
                crate::hybrid::Ffn::Dense {
                    ffn_gate,
                    ffn_up,
                    ffn_down,
                } => {
                    let n_ff = ffn_gate.out_features();
                    // gate/up INTO boundary slabs (piecewise increment 2); fall back to
                    // the allocating group + copy when a mirror is missing.
                    let mut into_ok = false;
                    if let Some(xh) = zx16 {
                        into_ok = e.try_f16_gemm_pre_into(ffn_gate, xh, t, sl_gate)?
                            && e.try_f16_gemm_pre_into(ffn_up, xh, t, sl_up)?;
                    }
                    if !into_ok {
                        let mut g2 = match zx16 {
                            Some(xh) => e.matmul_group_xh(&[ffn_gate, ffn_up], z, xh, t)?,
                            None => e.matmul_group(&[ffn_gate, ffn_up], z, t)?,
                        };
                        let up_y = g2.pop().unwrap();
                        let gate_y = g2.pop().unwrap();
                        e.copy_into(sl_gate, 0, &gate_y, t * n_ff)?;
                        e.copy_into(sl_up, 0, &up_y, t * n_ff)?;
                    }
                    // task #17: the silu arm's f16out twin emits the down GEMM's fp16
                    // operand in-epilogue; non-silu activations keep the standalone convert.
                    // silu_mul_f16out is PLAIN silu(gate)*up — a clamped layer (step35 dense
                    // blocks under a live swiglu_clamp_shexp) must take the ffn_act_lim arm.
                    let d_lim = self.cfg.clamp_shexp_at(il as u32);
                    let act16 = if Self::f16out_on(e, t) && self.cfg.m3.is_none() && d_lim.is_none()
                    {
                        let mut a16 = e.alloc_u8_uninit(t * n_ff * 2)?;
                        e.silu_mul_f16out(sl_gate, sl_up, act, &mut a16, t * n_ff)?;
                        Some(a16)
                    } else {
                        Self::ffn_act_lim(
                            e,
                            &self.cfg,
                            sl_gate,
                            sl_up,
                            1.0,
                            1.0,
                            d_lim,
                            act,
                            t * n_ff,
                        )?;
                        None
                    };
                    // down GEMM into the ffn_out slab (f16 arm; fallback copies)
                    let xh_act = match act16 {
                        Some(x) => x,
                        None => e.f16_act(act, t * n_ff, n_ff)?,
                    };
                    if !e.try_f16_gemm_pre_into(ffn_down, &xh_act, t, sl_fo)? {
                        let y = e.matmul(ffn_down, &*act, t)?;
                        e.copy_into(sl_fo, 0, &y, t * n_embd)?;
                    }
                }
                crate::hybrid::Ffn::Moe(m) => {
                    let y = self.moe_ffn_il_prefill(e, m, z, t, il as u16)?;
                    e.copy_into(sl_fo, 0, &y, t * n_embd)?;
                    anat_mark!(2);
                }
            }
            if let (crate::hybrid::Ffn::Dense { .. }, true) = (&layer.ffn, anat) {
                anat_mark!(3);
            }
            if use_seg && il + 1 < hi {
                // S-glue segment: [add + next attn-norm(+f16out)] — one cuGraphLaunch
                let w_next = self.layers[il + 1].attn_norm.float_data();
                let (sg, _, _, _) = seg.as_mut().unwrap();
                if sg[il].is_none() {
                    use cudarc::driver::sys::{CUgraphInstantiate_flags, CUstreamCaptureMode};
                    e.stream().synchronize()?;
                    e.stream()
                        .begin_capture(CUstreamCaptureMode::CU_STREAM_CAPTURE_MODE_RELAXED)?;
                    let r = (|| -> Result<(), Box<dyn std::error::Error>> {
                        e.add(x1, sl_fo, x_nxt, t * n_embd)?;
                        e.rms_norm_f16out(x_nxt, w_next, h, h16, n_embd, t, eps)?;
                        Ok(())
                    })();
                    let g = e.stream().end_capture(
                        CUgraphInstantiate_flags::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH,
                    );
                    r?;
                    sg[il] = Some(g?.ok_or("S-glue capture produced no graph")?);
                }
                sg[il].as_ref().unwrap().launch()?;
            } else {
                if il + 1 < hi {
                    let w_next = self.layers[il + 1].attn_norm.float_data();
                    if f16fuse {
                        e.add_rms_norm_f16out(x1, sl_fo, w_next, x_nxt, h, h16, n_embd, t, eps)?;
                    } else {
                        e.add(x1, sl_fo, x_nxt, t * n_embd)?;
                        e.rms_norm(x_nxt, w_next, h, n_embd, t, eps)?;
                    }
                } else {
                    e.add(x1, sl_fo, x_nxt, t * n_embd)?;
                }
            }
            anat_mark!(4);
            // CHUNK-INVARIANCE BISECT SEAM (lane/chunk-invariance, 2026-08-05): dump the
            // per-layer post-residual hidden for the LAST row of this chunk, keyed by
            // absolute position, so two runs at different MEMRA_PRIME_CHUNK can be diffed
            // layer-by-layer to find the FIRST diverging layer. Diagnostic only —
            // unset (the default) costs one OnceLock read per layer.
            if let Some(path) = Self::prime_trace_path() {
                let row = base + t - 1;
                let host = e.dtoh(x_nxt)?;
                let last = &host[(t - 1) * n_embd..t * n_embd];
                use std::io::Write as _;
                let mut f = std::fs::OpenOptions::new()
                    .create(true)
                    .append(true)
                    .open(path)?;
                let mut h64: u64 = 0xcbf29ce484222325;
                for v in last {
                    h64 ^= v.to_bits() as u64;
                    h64 = h64.wrapping_mul(0x100000001b3);
                }
                writeln!(
                    f,
                    "{{\"pos\":{row},\"layer\":{il},\"t\":{t},\"base\":{base},\
                             \"hash\":\"{h64:016x}\",\"v0\":{:.9e},\"v1\":{:.9e},\"v2\":{:.9e}}}",
                    last[0], last[1], last[2]
                )?;
            }
            // dflash/dspark tap (no-op when no sink armed): post-layer residual rows for
            // drafter conditioning — the qwen twin of the gemma4 tap sites.
            self.dflash_tap(e, cache, il, x_nxt, t)?;
            std::mem::swap(&mut x_cur, &mut x_nxt);
        }
        if anat {
            let s = Self::prime_anatomy_slots();
            let ms = |i: usize| s[i].load(std::sync::atomic::Ordering::Relaxed) as f64 / 1.0e6;
            eprintln!(
                "[prime-anatomy] cumulative ms: attn_full={:.1} gdn_linear={:.1} moe={:.1} \
                 dense={:.1} norms_adds={:.1} (t={t}, layers {lo}..{hi})",
                ms(0),
                ms(1),
                ms(2),
                ms(3),
                ms(4)
            );
        }
        // hidden-stack return: clone the final x out of the slab
        let mut x = e.uninit(t * n_embd)?;
        e.copy_into(&mut x, 0, x_cur, t * n_embd)?;
        drop(slab_guard);
        Ok(x)
    }

    /// The prime chunk's tail — h_seed + output_norm + one-row lm head + cache.pos advance —
    /// shared verbatim by the unsplit walk and the last stage of the ppN walk (`e` = the
    /// engine that produced `x`, i.e. the last stage's under the split; output_norm/output
    /// were loaded through that engine by the sharded loader, hybrid.rs `e_head`).
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn prime_chunk_epilogue(
        &self,
        e: &Engine,
        x: CudaSlice<f32>,
        t: usize,
        cache: &mut Cache,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        // h_seed = LAST row of x BEFORE output_norm (MTP-PLAN §A default) or AFTER it
        // (MEMRA_SPEC_HPOST — reference convention; hn is computed just below either way, so
        // the post-norm copy happens after hn exists).
        let mut h_seed = e.uninit(n_embd)?;
        if !crate::spec::spec_hpost() {
            e.copy_view_into(
                &mut h_seed,
                0,
                &x.slice((t - 1) * n_embd..t * n_embd),
                n_embd,
            )?;
        }
        // last-row logits, exactly like forward_last (norm all T — per-row op — then lm_head on 1 row).
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, t, eps)?;
        if crate::spec::spec_hpost() {
            e.copy_view_into(
                &mut h_seed,
                0,
                &hn.slice((t - 1) * n_embd..t * n_embd),
                n_embd,
            )?;
        }
        let last = e.view(&hn, t * n_embd);
        let last_row = last.slice((t - 1) * n_embd..t * n_embd);
        let mut hlast = e.uninit(n_embd)?;
        e.copy_view_into(&mut hlast, 0, &last_row, n_embd)?;
        let logits = e.matmul(&self.output, &hlast, 1)?;
        cache.pos += t;
        // Hidden stack handed to generate_spec as prompt_h: pre-norm x (default) or the full
        // post-norm stack hn (MEMRA_SPEC_HPOST).
        Ok((
            e.dtoh(&logits)?,
            h_seed,
            if crate::spec::spec_hpost() { hn } else { x },
        ))
    }

    /// Post-final-norm hidden state of one row of a prime-returned hidden stack — the
    /// embedding-pooling read (lane/embed-serve). `hiddens` is `prime_cache`'s third
    /// return: the pre-norm stack by default, but ALREADY post-norm under
    /// MEMRA_SPEC_HPOST (see `prime_chunk_epilogue`), so the norm is applied only in
    /// the default shape. Returns the host f32 row (`n_embd` wide).
    pub fn hidden_postnorm_row(
        &self,
        e: &Engine,
        hiddens: &CudaSlice<f32>,
        row: usize,
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let mut x1 = e.uninit(n_embd)?;
        e.copy_view_into(
            &mut x1,
            0,
            &hiddens.slice(row * n_embd..(row + 1) * n_embd),
            n_embd,
        )?;
        if crate::spec::spec_hpost() {
            return e.dtoh(&x1);
        }
        let mut hn = e.uninit(n_embd)?;
        e.rms_norm(
            &x1,
            self.output_norm.float_data(),
            &mut hn,
            n_embd,
            1,
            self.cfg.rms_eps,
        )?;
        e.dtoh(&hn)
    }

    /// THE PRIME STAGE SPLIT (lane/pp-leverb, 2026-08-08): one prime chunk as N stage
    /// subgraphs, each range on ITS OWN engine/stream/device, with the `[T, n_embd]`
    /// boundary handoff at every fence cut — the prime twin of `decode_step_h_ppn` /
    /// `decode_step_t_core_ppn`, and the kill for the anatomy's two receipts: stage-1 trunk
    /// weights stop being peer-read (22% of the pp4096 wall) and dev1 stops running zero
    /// prefill kernels. Structure mirrors the verify split exactly:
    ///   stage 0        `rt.enter(0)` → per-stage pos_d + embed (the table lives with
    ///                  stage 0) → `prime_layers(fence[0], fence[1])` → `rt.tx`
    ///   middle stages  `rt.rx` → per-stage pos_d → range → `rt.tx`
    ///   last stage     `rt.rx` → range → the shared epilogue (output_norm + head live
    ///                  there via the sharded loader) → `publish_to`
    /// Laws inherited (not relearned): `fence_stages_behind` at entry (#87 — a previous
    /// round's stage-freed buffers must not be reused under the caller's queued reads);
    /// per-stage `pos_d` (allocated/consumed/freed on one stream); per-stage Engines from
    /// `PpNRt` (shared-scratch race); EXIT PUBLICATION for the device-resident returns
    /// (h_seed + hidden stack live on the last stage — the caller's stream must wait).
    /// KV/MoE locality falls out: each range's KV appends run on the owning stage
    /// (`pp::new_cache` placed the buffers there), each stage engine owns its own SLRU
    /// pool (per-Engine `moe_cache`, sized on ITS device — the SGLang #33666 law), and the
    /// slab-local MoE arm's `DevExps.dev` gate now matches on stage-1 layers too.
    /// EXACTNESS: the split adds zero deviation by construction (same kernels, same bytes,
    /// boundary = straight f32 copy); `prime-split-gate` (ppsplit) arbitrates bit-for-bit
    /// and its liveness counter is bumped here — the gate goes green with this function.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn prime_chunk_ppn(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        seq_end: usize,
        fence: &[usize],
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let rt = crate::pp::PpNRt::get(e)?;
        let n_st = fence.len() - 1;
        assert_eq!(
            rt.n_stages(),
            n_st,
            "PpNRt stage count {} != fence stages {n_st}",
            rt.n_stages()
        );
        let n_embd = self.cfg.n_embd as usize;
        let t = tokens.len();
        let base = cache.pos;
        debug_assert!(
            seq_end >= base + t,
            "prime_chunk_ppn: seq_end must cover this chunk"
        );
        let payload = t * n_embd;
        // The caller's ambient stream, captured BEFORE any enter() pushes a stage stream
        // (inside a stage scope e.stream() IS the stage stream and the exit wait would
        // self-order into a no-op) — the decode_step_t_core_ppn pattern.
        let caller_stream = e.stream();
        rt.fence_stages_behind(&caller_stream)?;

        if n_st == 2 {
            let slot =
                self.prime_pp2_stage0_enqueue(e, rt, tokens, cache, seq_end, fence, base, false)?;
            let x =
                self.prime_pp2_stage1_enqueue(e, rt, slot, t, cache, seq_end, fence, base, false)?;
            let out = {
                rt.bind_stage(1)?;
                let _st1 = rt.enter(1);
                let e1 = rt.engine(1, e);
                self.prime_chunk_epilogue(e1, x, t, cache)?
            };
            rt.publish_to(1, &caller_stream)?;
            crate::pp::PRIME_SPLIT_CHUNKS.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
            return Ok(out);
        }

        let pos: Vec<i32> = (base as i32..(base + t) as i32).collect();

        // ---- STAGE 0: embed + layers [0, fence[1]) + boundary-0 TX ----
        let mut slot = {
            let _st0 = rt.enter(0);
            let e0 = rt.engine(0, e);
            let pos_d = e0.htod_i32(&pos)?;
            let x = self.embed(e0, tokens)?;
            let x =
                self.prime_layers(e0, x, fence[0], fence[1], &pos_d, t, base, cache, seq_end)?;
            rt.tx(0, &x, payload)?
            // x + pos_d drop here: freed stream-ordered on stage-0's stream after use.
        };

        // ---- MIDDLE STAGES: RX boundary s-1 -> range -> TX boundary s ----
        for s in 1..n_st - 1 {
            let _st = rt.enter(s);
            let es = rt.engine(s, e);
            let pos_d = es.htod_i32(&pos)?;
            let x = rt.rx(s - 1, slot, payload)?;
            let x = self.prime_layers(
                es,
                x,
                fence[s],
                fence[s + 1],
                &pos_d,
                t,
                base,
                cache,
                seq_end,
            )?;
            slot = rt.tx(s, &x, payload)?;
        }

        // ---- LAST STAGE: RX + final range + the shared epilogue ----
        let _stl = rt.enter(n_st - 1);
        let el = rt.engine(n_st - 1, e);
        let pos_d = el.htod_i32(&pos)?;
        let x = rt.rx(n_st - 2, slot, payload)?;
        let x = self.prime_layers(
            el,
            x,
            fence[n_st - 1],
            fence[n_st],
            &pos_d,
            t,
            base,
            cache,
            seq_end,
        )?;
        let out = self.prime_chunk_epilogue(el, x, t, cache)?;
        // EXIT PUBLICATION: h_seed + the hidden stack are device-resident on the last
        // stage's stream; the caller resumes on its own stream (chunk-loop copy_into /
        // generate_spec's prompt_h consumer). The logits dtoh above already drained the
        // stage stream host-side, but the law is stated in events, not in a dtoh side
        // effect a later deferred form would remove.
        rt.publish_to(n_st - 1, &caller_stream)?;
        crate::pp::PRIME_SPLIT_CHUNKS.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
        Ok(out)
    }

    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn prime_pp2_stage0_enqueue(
        &self,
        e: &Engine,
        rt: &crate::pp::PpNRt,
        tokens: &[u32],
        cache: &mut Cache,
        seq_end: usize,
        fence: &[usize],
        base: usize,
        pipelined: bool,
    ) -> Result<usize, Box<dyn std::error::Error>> {
        let t = tokens.len();
        let n_embd = self.cfg.n_embd as usize;
        let pos: Vec<i32> = (base as i32..(base + t) as i32).collect();
        rt.bind_stage(0)?;
        let _st0 = rt.enter(0);
        let e0 = rt.engine(0, e);
        let pos_d = e0.htod_i32(&pos)?;
        let x = self.embed(e0, tokens)?;
        let _overlap = pipelined.then(crate::pp::enter_prime_pipe_stage);
        let x = self.prime_layers(e0, x, fence[0], fence[1], &pos_d, t, base, cache, seq_end)?;
        if pipelined {
            rt.tx_pipelined(0, &x, t * n_embd)
        } else {
            rt.tx(0, &x, t * n_embd)
        }
    }

    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn prime_pp2_stage1_enqueue(
        &self,
        e: &Engine,
        rt: &crate::pp::PpNRt,
        slot: usize,
        t: usize,
        cache: &mut Cache,
        seq_end: usize,
        fence: &[usize],
        base: usize,
        pipelined: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let pos: Vec<i32> = (base as i32..(base + t) as i32).collect();
        rt.bind_stage(1)?;
        let _st1 = rt.enter(1);
        let e1 = rt.engine(1, e);
        let pos_d = e1.htod_i32(&pos)?;
        let x = rt.rx(0, slot, t * n_embd)?;
        let _overlap = pipelined.then(crate::pp::enter_prime_pipe_stage);
        self.prime_layers(e1, x, fence[1], fence[2], &pos_d, t, base, cache, seq_end)
    }

    /// CAPTURE-SAFE prime trunk (task #14 increment 1, ARCHITECTURE-H100.md design v2):
    /// prime_chunk's layer stack with every capture hazard hoisted — `x` is the
    /// PRE-EMBEDDED input in a STABLE graph-input buffer (embed + its 8MB htod stay
    /// eager, one launch), `pos_d` is a baked device param (fresh prime = 0..T, constant
    /// per bucket), logits/h_seed/hidden stay DEVICE-resident (no dtoh), and cache.pos is
    /// NOT advanced (host state — the replay wrapper owns it). Body mirrors prime_chunk
    /// (the prime-graph-gate pins them together). KNOWN smoke-scope gap: append host-len
    /// bookkeeping still runs on the host per call — the real replay path moves the write
    /// slot to the len_d device counter (increment 3).
    /// GRAPH-OUTPUT CONTRACT: results are COPIED into caller-provided stable buffers
    /// (`logits_out` [n_vocab], `h_seed_out` [n_embd]) — every internal allocation drops
    /// INSIDE the capture region (alloc+free node pairs). Retaining an in-capture
    /// allocation across end_capture makes instantiate throw INVALID_VALUE (smoke finding
    /// 2026-07-26), and under AUTO_FREE_ON_LAUNCH its address wouldn't survive a launch
    /// anyway — the decode GraphSession's pre-allocated-output pattern is the law here.
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    pub fn prime_chunk_captured(
        &self,
        e: &Engine,
        x_in: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        len_d: &CudaSlice<i32>,
        logits_out: &mut CudaSlice<f32>,
        h_seed_out: &mut CudaSlice<f32>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        self.refuse_hyper("prime_chunk_captured")?;
        cache.ensure_usable("prime_chunk_captured")?;
        let cfg = &self.cfg;
        let n_embd = cfg.n_embd as usize;
        let eps = cfg.rms_eps;
        let f16fuse = crate::f16_ffi::pp_f16_enabled() && t >= 16;
        let mut x = e.uninit(t * n_embd)?;
        e.copy_into(&mut x, 0, x_in, t * n_embd)?;
        for (il, layer) in self.layers.iter().enumerate() {
            let mut h = e.uninit(t * n_embd)?;
            let mut hx16: Option<CudaSlice<u8>> = None;
            if f16fuse {
                let mut b16 = e.alloc_u8_uninit(t * n_embd * 2)?;
                e.rms_norm_f16out(
                    &x,
                    layer.attn_norm.float_data(),
                    &mut h,
                    &mut b16,
                    n_embd,
                    t,
                    eps,
                )?;
                hx16 = Some(b16);
            } else {
                e.rms_norm(&x, layer.attn_norm.float_data(), &mut h, n_embd, t, eps)?;
            }
            let mixed = match &layer.mixer {
                // The captured prime is ONE unchunked bucket over a FRESH cache (pos == 0), so the
                // request ends at t. If bucketed capture ever composes with chunking, seq_end must
                // come from the caller (see step35_attn_pre_wo's doc note).
                Mixer::Full(fa) => {
                    self.full_attn_prime(e, fa, &h, hx16.as_ref(), pos_d, t, cache, il, t)?
                }
                Mixer::Mla(_) => crate::hybrid::mla_path_unimplemented("captured-graph prime"),
                Mixer::Kda(_) => crate::hybrid::kda_path_unimplemented("captured prime chunk"),
                Mixer::Linear(la) => {
                    let ws = [&la.wqkv, &la.wqkv_gate, &la.ssm_beta, &la.ssm_alpha];
                    let g4 = match hx16.as_ref() {
                        Some(xh) => e.matmul_group_xh(&ws, &h, xh, t)?,
                        None => e.matmul_group(&ws, &h, t)?,
                    };
                    self.linear_attn_prime_core_pad(e, la, g4, t, cache, il, Some(len_d))?
                }
            };
            let mut x1 = e.uninit(t * n_embd)?;
            e.add(&x, &mixed, &mut x1, t * n_embd)?;
            let mut z = e.uninit(t * n_embd)?;
            let mut zx16: Option<CudaSlice<u8>> = None;
            if f16fuse {
                let mut b16 = e.alloc_u8_uninit(t * n_embd * 2)?;
                e.rms_norm_f16out(
                    &x1,
                    layer.post_attn_norm.float_data(),
                    &mut z,
                    &mut b16,
                    n_embd,
                    t,
                    eps,
                )?;
                zx16 = Some(b16);
            } else {
                e.rms_norm(
                    &x1,
                    layer.post_attn_norm.float_data(),
                    &mut z,
                    n_embd,
                    t,
                    eps,
                )?;
            }
            let ffn_out = match &layer.ffn {
                crate::hybrid::Ffn::Dense {
                    ffn_gate,
                    ffn_up,
                    ffn_down,
                } => {
                    let n_ff = ffn_gate.out_features();
                    let mut g2 = match &zx16 {
                        Some(xh) => e.matmul_group_xh(&[ffn_gate, ffn_up], &z, xh, t)?,
                        None => e.matmul_group(&[ffn_gate, ffn_up], &z, t)?,
                    };
                    let up = g2.pop().unwrap();
                    let gate = g2.pop().unwrap();
                    let mut act = e.uninit(t * n_ff)?;
                    // dense FFN keys off the SHEXP clamp array — see forward()'s note.
                    Self::ffn_act_lim(
                        e,
                        &self.cfg,
                        &gate,
                        &up,
                        1.0,
                        1.0,
                        self.cfg.clamp_shexp_at(il as u32),
                        &mut act,
                        t * n_ff,
                    )?;
                    e.matmul(ffn_down, &act, t)?
                }
                crate::hybrid::Ffn::Moe(m) => self.moe_ffn_il_prefill(e, m, &z, t, il as u16)?,
            };
            let mut x2 = e.uninit(t * n_embd)?;
            e.add(&x1, &ffn_out, &mut x2, t * n_embd)?;
            x = x2;
        }
        // device-indexed TRUE last row (pads sit past it in a bucketed graph)
        if !crate::spec::spec_hpost() {
            e.row_gather_dev(&x, h_seed_out, len_d, n_embd)?;
        }
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, t, eps)?;
        if crate::spec::spec_hpost() {
            e.row_gather_dev(&hn, h_seed_out, len_d, n_embd)?;
        }
        let mut hlast = e.uninit(n_embd)?;
        e.row_gather_dev(&hn, &mut hlast, len_d, n_embd)?;
        let logits = e.matmul(&self.output, &hlast, 1)?;
        let nv = logits.len();
        e.copy_into(logits_out, 0, &logits, nv)?;
        Ok(())
    }

    fn step35_prime_batch_on() -> bool {
        std::env::var("MEMRA_STEP35_PRIME_BATCH").as_deref() != Ok("0")
    }

    /// Step35 cross-request prime range: weight-streaming work runs once at `m=sum(T)`;
    /// sequence-scoped attention/KV work stays on each request's own cache and positions.
    #[allow(clippy::too_many_arguments)]
    /// `seq_ends[s]`: sequence s's REQUEST-absolute end position — NOT `ts[s]`. It is the
    /// only thing step35's SWA arm keys on, so a chunk-local value here decides the attention
    /// kernel from the chunk size (and, below the 512-row window at a nonzero base, drops the
    /// window mask entirely). See the batched entry's note in `prime_cache_overlaid`.
    #[allow(clippy::too_many_arguments)]
    fn step35_prime_batch_layers(
        &self,
        e: &Engine,
        mut x: CudaSlice<f32>,
        lo: usize,
        hi: usize,
        ts: &[usize],
        offs: &[usize],
        seq_ends: &[usize],
        pos_ds: &[CudaSlice<i32>],
        caches: &mut [&mut Cache],
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let cfg = &self.cfg;
        let n_embd = cfg.n_embd as usize;
        let eps = cfg.rms_eps;
        let b = ts.len();
        let total: usize = ts.iter().sum();
        let f16fuse = crate::f16_ffi::pp_f16_enabled() && total >= 16;

        let split = |e: &Engine,
                     y: &CudaSlice<f32>,
                     dim: usize|
         -> Result<Vec<CudaSlice<f32>>, Box<dyn std::error::Error>> {
            let mut out = Vec::with_capacity(b);
            for s in 0..b {
                let mut ys = e.uninit(ts[s] * dim)?;
                e.copy_view_into(
                    &mut ys,
                    0,
                    &y.slice(offs[s] * dim..(offs[s] + ts[s]) * dim),
                    ts[s] * dim,
                )?;
                out.push(ys);
            }
            Ok(out)
        };

        // MEMRA_PRIME_PROF=1: per-phase wall inside the prime, sync-bounded (absolute time
        // inflates; the SPLIT is the signal). Two inspection passes failed to find where a
        // 3.8 s/4096-token chunk goes against a ~0.55 s compute budget, and nsys cannot capture
        // through the server's worker, so the walk measures itself.
        let prof = std::env::var("MEMRA_PRIME_PROF").as_deref() == Ok("1");
        let mut ph = [0f64; 4]; // 0 norm+qkv, 1 attn, 2 o_proj+norm, 3 moe
        let mark = |e: &Engine, acc: usize, t0: &mut std::time::Instant, ph: &mut [f64; 4]| {
            if prof {
                let _ = e.stream().synchronize();
                ph[acc] += t0.elapsed().as_secs_f64() * 1e3;
                *t0 = std::time::Instant::now();
            }
        };
        let mut pt = std::time::Instant::now();
        for il in lo..hi {
            let layer = &self.layers[il];
            let Mixer::Full(fa) = &layer.mixer else {
                return Err(format!("step35 layer {il} is not full-attn — corrupt config").into());
            };

            let mut h = e.uninit(total * n_embd)?;
            let mut hx16 = e.alloc_u8_uninit(total * n_embd * 2)?;
            if f16fuse {
                e.rms_norm_f16out(
                    &x,
                    layer.attn_norm.float_data(),
                    &mut h,
                    &mut hx16,
                    n_embd,
                    total,
                    eps,
                )?;
            } else {
                e.rms_norm(&x, layer.attn_norm.float_data(), &mut h, n_embd, total, eps)?;
            }

            // Q/K/V + gate projections and wo are batched weight streams. The step35 core
            // remains per sequence, so its partial RoPE, SWA view, KV append, and gate
            // application stay verbatim.
            let gate_w = fa
                .attn_gate
                .as_ref()
                .ok_or("step35 layer is missing attn_gate.weight (head-wise attention gate)")?;
            let mut g4 = if f16fuse {
                e.matmul_group_xh(&[&fa.wq, &fa.wk, &fa.wv, gate_w], &h, &hx16, total)?
            } else {
                e.matmul_group(&[&fa.wq, &fa.wk, &fa.wv, gate_w], &h, total)?
            };
            let gate = g4.pop().unwrap();
            let mut parts: Vec<Vec<CudaSlice<f32>>> =
                (0..b).map(|_| Vec::with_capacity(3)).collect();
            for (w, y) in [&fa.wq, &fa.wk, &fa.wv].iter().zip(g4) {
                for (s, ys) in split(e, &y, w.out_features())?.into_iter().enumerate() {
                    parts[s].push(ys);
                }
            }
            let gates = split(e, &gate, gate_w.out_features())?;
            let geometry = self.step35_geom(il);
            let hd = geometry.head_dim_k as usize;
            let nh = geometry.n_head as usize;
            let mut ag_cat = e.uninit(total * nh * hd)?;
            for (s, (g3s, gate)) in parts.into_iter().zip(gates).enumerate() {
                mark(e, 0, &mut pt, &mut ph);
                let ag = self.step35_attn_pre_wo(
                    e,
                    fa,
                    g3s,
                    None,
                    Some(&gate),
                    &pos_ds[s],
                    ts[s],
                    Some(&mut *caches[s]),
                    il,
                    seq_ends[s],
                )?;
                e.copy_into(&mut ag_cat, offs[s] * nh * hd, &ag, ts[s] * nh * hd)?;
            }
            mark(e, 1, &mut pt, &mut ph);
            let mixed = e.matmul(&fa.wo, &ag_cat, total)?;

            let mut x1 = e.uninit(total * n_embd)?;
            let mut z = e.uninit(total * n_embd)?;
            let mut zx16 = e.alloc_u8_uninit(total * n_embd * 2)?;
            if f16fuse {
                e.add_rms_norm_f16out(
                    &x,
                    &mixed,
                    layer.post_attn_norm.float_data(),
                    &mut x1,
                    &mut z,
                    &mut zx16,
                    n_embd,
                    total,
                    eps,
                )?;
            } else {
                e.add(&x, &mixed, &mut x1, total * n_embd)?;
                e.rms_norm(
                    &x1,
                    layer.post_attn_norm.float_data(),
                    &mut z,
                    n_embd,
                    total,
                    eps,
                )?;
            }

            mark(e, 2, &mut pt, &mut ph);
            let ffn_out = match &layer.ffn {
                crate::hybrid::Ffn::Dense {
                    ffn_gate,
                    ffn_up,
                    ffn_down,
                } => {
                    let n_ff = ffn_gate.out_features();
                    let mut g2 = if f16fuse {
                        e.matmul_group_xh(&[ffn_gate, ffn_up], &z, &zx16, total)?
                    } else {
                        e.matmul_group(&[ffn_gate, ffn_up], &z, total)?
                    };
                    let up = g2.pop().unwrap();
                    let gate = g2.pop().unwrap();
                    let mut act = e.uninit(total * n_ff)?;
                    let d_lim = cfg.clamp_shexp_at(il as u32);
                    if Self::f16out_on(e, total) && cfg.m3.is_none() && d_lim.is_none() {
                        let mut a16 = e.alloc_u8_uninit(total * n_ff * 2)?;
                        e.silu_mul_f16out(&gate, &up, &mut act, &mut a16, total * n_ff)?;
                        match e.try_f16_gemm_pre(ffn_down, &a16, total)? {
                            Some(y) => y,
                            None => e.matmul(ffn_down, &act, total)?,
                        }
                    } else {
                        Self::ffn_act_lim(
                            e,
                            cfg,
                            &gate,
                            &up,
                            1.0,
                            1.0,
                            d_lim,
                            &mut act,
                            total * n_ff,
                        )?;
                        e.matmul(ffn_down, &act, total)?
                    }
                }
                crate::hybrid::Ffn::Moe(m) => self.moe_ffn_il(e, m, &z, total, il as u16)?,
            };
            let mut x2 = e.uninit(total * n_embd)?;
            e.add(&x1, &ffn_out, &mut x2, total * n_embd)?;
            x = x2;
            mark(e, 3, &mut pt, &mut ph);
        }
        if prof {
            eprintln!(
                "[prime-prof] t={total} layers={} norm+qkv={:.0}ms attn={:.0}ms o_proj={:.0}ms moe={:.0}ms",
                hi - lo,
                ph[0],
                ph[1],
                ph[2],
                ph[3]
            );
        }
        Ok(x)
    }

    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn step35_prime_batch_epilogue(
        &self,
        e: &Engine,
        x: CudaSlice<f32>,
        ts: &[usize],
        offs: &[usize],
        caches: &mut [&mut Cache],
    ) -> Result<Vec<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>)>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let total: usize = ts.iter().sum();
        let mut hn = e.uninit(total * n_embd)?;
        e.rms_norm(
            &x,
            self.output_norm.float_data(),
            &mut hn,
            n_embd,
            total,
            self.cfg.rms_eps,
        )?;

        let hidden_src = if crate::spec::spec_hpost() { &hn } else { &x };
        let mut out = Vec::with_capacity(ts.len());
        for s in 0..ts.len() {
            let mut hidden = e.uninit(ts[s] * n_embd)?;
            e.copy_view_into(
                &mut hidden,
                0,
                &hidden_src.slice(offs[s] * n_embd..(offs[s] + ts[s]) * n_embd),
                ts[s] * n_embd,
            )?;
            let last0 = (offs[s] + ts[s] - 1) * n_embd;
            let mut h_seed = e.uninit(n_embd)?;
            e.copy_view_into(
                &mut h_seed,
                0,
                &hidden_src.slice(last0..last0 + n_embd),
                n_embd,
            )?;
            // Exactness-first: the serial reference runs the output head at m=1.
            let mut hlast = e.uninit(n_embd)?;
            e.copy_view_into(&mut hlast, 0, &hn.slice(last0..last0 + n_embd), n_embd)?;
            let logits = e.dtoh(&e.matmul(&self.output, &hlast, 1)?)?;
            caches[s].pos += ts[s];
            out.push((logits, h_seed, hidden));
        }
        Ok(out)
    }

    /// `seq_ends[s]` = sequence s's REQUEST-absolute end position (`cache.pos + prompt_len
    /// + queued_after`, computed once before any chunk loop). Only step35's SWA arm reads it,
    /// and it must NOT be this chunk's own length: see the note on the batched entry in
    /// `prime_cache_overlaid` for the window the chunk-local value opened.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn step35_prime_cache_batch(
        &self,
        e: &Engine,
        prompts: &[&[u32]],
        caches: &mut [&mut Cache],
        seq_ends: &[usize],
    ) -> Result<Vec<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>)>, Box<dyn std::error::Error>> {
        assert_eq!(
            seq_ends.len(),
            prompts.len(),
            "step35 batched prime: one seq_end per sequence"
        );
        validate_step_prime_batch_modes(
            step_tp_prefill_enabled()?,
            step_ep_grouped_prefill_enabled()?,
        )?;
        if crate::pp::pp_host_bounce_active()
            && (!crate::pp::prime_pp_on() || crate::pp::pp_cuts(self.layers.len()).is_none())
        {
            return Err(
                "step35_prime_cache_batch: MEMRA_PP_HOST_BOUNCE=1 requires a valid prime \
                 stage split; refusing an unsplit remote-weight walk"
                    .into(),
            );
        }
        if !Self::step35_prime_batch_on() {
            return Err("step35 batched prime is disabled (MEMRA_STEP35_PRIME_BATCH=0)".into());
        }
        // Continuation chunks are admitted (positions above carry each sequence's base). The
        // remaining restriction is genuine: a CROSS-REQUEST batch mixing sequences at different
        // positions still needs per-request queued_after to place its KV, so B > 1 keeps the
        // fresh-prompt rule.
        if prompts.len() > 1 && caches.iter().any(|c| c.pos != 0) {
            return Err(
                "step35 batched prime supports continuation only at B=1; a cross-request batch \
                 at mixed positions requires per-request queued_after"
                    .into(),
            );
        }

        let ts: Vec<usize> = prompts.iter().map(|p| p.len()).collect();
        for &t in &ts {
            assert!(
                t >= PRIME_MIN_T,
                "step35 batched prime needs T >= {PRIME_MIN_T}"
            );
        }
        for (s, c) in caches.iter().enumerate() {
            // POS-INCLUSIVE, like the walk's assert: a continuation chunk's rows land at
            // c.pos.., so the fresh-only `ts[s] <= max_ctx` form under-checked it.
            assert!(
                c.pos + ts[s] <= c.max_ctx,
                "step35 batched prime exceeds cache max_ctx"
            );
            assert!(
                seq_ends[s] >= c.pos + ts[s],
                "step35 batched prime: seq_end must cover this chunk"
            );
        }
        let mut transaction = CacheTaintGuard::arm(caches);
        // MEMRA_STEP35_PRIME_BATCH_TSEND=1: CANARY SEAM restoring the pre-fix chunk-local
        // `seq_end` (this chunk's own length, which `ts[s]` used to supply here). It is suffix-
        // and chunk-VARIANT by construction, so the suffix byte-identity gate MUST break under
        // it. That is how the defect is DEMONSTRATED rather than argued: one binary, one seam,
        // the legacy arm fails cold-vs-rewound identity and the default arm passes. Read per
        // call; never on in a measured default run.
        let legacy_tsend = std::env::var("MEMRA_STEP35_PRIME_BATCH_TSEND").as_deref() == Ok("1");
        let seq_ends_eff: Vec<usize> = if legacy_tsend {
            ts.clone()
        } else {
            seq_ends.to_vec()
        };
        let offs: Vec<usize> = ts
            .iter()
            .scan(0usize, |a, &t| {
                let o = *a;
                *a += t;
                Some(o)
            })
            .collect();
        let total: usize = ts.iter().sum();
        let payload = total * self.cfg.n_embd as usize;
        let cat_tokens: Vec<u32> = prompts.iter().flat_map(|p| p.iter().copied()).collect();
        // Positions start at each sequence's CURRENT cache position, not 0, so this entry can
        // prime a continuation chunk. The attention core already supports it: step35_attn_pre_wo
        // with Some(cache) is PRIME mode — it appends this chunk's post-rope K / raw V and
        // attends THROUGH the cache view — so only the hardcoded 0..t and the guard below ever
        // restricted it to fresh prompts.
        let positions: Vec<Vec<i32>> = ts
            .iter()
            .zip(caches.iter())
            .map(|(&t, c)| {
                let base = c.pos as i32;
                (0..t as i32).map(|i| base + i).collect()
            })
            .collect();
        let upload_positions =
            |e: &Engine| -> Result<Vec<CudaSlice<i32>>, Box<dyn std::error::Error>> {
                positions
                    .iter()
                    .map(|p| e.htod_i32(p))
                    .collect::<Result<_, _>>()
            };

        static ONCE: std::sync::Once = std::sync::Once::new();
        ONCE.call_once(|| {
            eprintln!(
                "[step35-prime-batch] first concat prime: B={} tokens={total}",
                prompts.len()
            );
        });

        let out = if !crate::pp::pp2_streams_off() && crate::pp::prime_pp_on() {
            if let Some(fence) = crate::pp::pp_cuts(self.layers.len()) {
                let rt = crate::pp::PpNRt::get(e)?;
                let n_st = fence.len() - 1;
                assert_eq!(
                    rt.n_stages(),
                    n_st,
                    "step35 prime batch stage count mismatch"
                );
                let caller_stream = e.stream();
                rt.fence_stages_behind(&caller_stream)?;

                let mut slot = {
                    let _st0 = rt.enter(0);
                    let e0 = rt.engine(0, e);
                    let pos_ds = upload_positions(e0)?;
                    let x = self.embed(e0, &cat_tokens)?;
                    let x = self.step35_prime_batch_layers(
                        e0,
                        x,
                        fence[0],
                        fence[1],
                        &ts,
                        &offs,
                        &seq_ends_eff,
                        &pos_ds,
                        caches,
                    )?;
                    rt.tx(0, &x, payload)?
                };
                for s in 1..n_st - 1 {
                    let _st = rt.enter(s);
                    let es = rt.engine(s, e);
                    let pos_ds = upload_positions(es)?;
                    let x = rt.rx(s - 1, slot, payload)?;
                    let x = self.step35_prime_batch_layers(
                        es,
                        x,
                        fence[s],
                        fence[s + 1],
                        &ts,
                        &offs,
                        &seq_ends_eff,
                        &pos_ds,
                        caches,
                    )?;
                    slot = rt.tx(s, &x, payload)?;
                }

                let _stl = rt.enter(n_st - 1);
                let el = rt.engine(n_st - 1, e);
                let pos_ds = upload_positions(el)?;
                let x = rt.rx(n_st - 2, slot, payload)?;
                let x = self.step35_prime_batch_layers(
                    el,
                    x,
                    fence[n_st - 1],
                    fence[n_st],
                    &ts,
                    &offs,
                    &seq_ends_eff,
                    &pos_ds,
                    caches,
                )?;
                let out = self.step35_prime_batch_epilogue(el, x, &ts, &offs, caches)?;
                rt.publish_to(n_st - 1, &caller_stream)?;
                crate::pp::STEP35_PRIME_BATCH_SPLITS
                    .fetch_add(1, std::sync::atomic::Ordering::Relaxed);
                out
            } else {
                let pos_ds = upload_positions(e)?;
                let x = self.embed(e, &cat_tokens)?;
                let x = self.step35_prime_batch_layers(
                    e,
                    x,
                    0,
                    self.layers.len(),
                    &ts,
                    &offs,
                    &seq_ends_eff,
                    &pos_ds,
                    caches,
                )?;
                self.step35_prime_batch_epilogue(e, x, &ts, &offs, caches)?
            }
        } else {
            let pos_ds = upload_positions(e)?;
            let x = self.embed(e, &cat_tokens)?;
            let x = self.step35_prime_batch_layers(
                e,
                x,
                0,
                self.layers.len(),
                &ts,
                &offs,
                &seq_ends_eff,
                &pos_ds,
                caches,
            )?;
            self.step35_prime_batch_epilogue(e, x, &ts, &offs, caches)?
        };
        crate::pp::STEP35_PRIME_BATCHES.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
        transaction.commit();
        Ok(out)
    }

    /// Cross-request BATCHED fresh prime (task #13, design in ARCHITECTURE-H100.md): the
    /// trunk's token-parallel ops (embed, norms, adds, ffn, projection GROUPS) run once on
    /// the CONCATENATION of B sequences — GEMMs at m = sum_T, the continuous-batching win
    /// the serving-lane bench attributed the remaining vLLM gap to. The stateful mixer
    /// CORES (QK-norm/RoPE/FA/append, conv/GDN scans) run per sequence on split projection
    /// buffers (D2D row copies; the mixers' own out-projections stay per-seq this
    /// increment). CONTINUATION primes (increment (b), 2026-07-30): cache.pos > 0 seqs
    /// batch the projections/FFN/lm_head exactly like fresh; the mixer cores take the
    /// per-seq CONTINUATION arms (Full: core_inner with carried pos_d + fa_prefill_view
    /// over the quantized past; Linear: the stateful pad_view twin — the same state
    /// carry the chunked single-seq prime rides). The fresh-only favl/gdn-vl fast paths
    /// stay byte-identical (gated on !carried). gemma4 models have no continuation
    /// prime (v0 monolithic fresh) — carried gemma4 batches return Err (caller falls
    /// back to single-chunk serving).
    /// NUMERIC CONFIG: a concat GEMM tiles K differently than per-seq GEMMs — same class
    /// as every prefill GEMM change; prime_batch_gate arbitrates (argmax + stream battery).
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    pub fn prime_cache_batch(
        &self,
        e: &Engine,
        prompts: &[&[u32]],
        caches: &mut [&mut Cache],
    ) -> Result<Vec<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>)>, Box<dyn std::error::Error>> {
        self.refuse_hyper("prime_cache_batch")?;
        for cache in caches.iter() {
            cache.ensure_usable("prime_cache_batch")?;
        }
        if crate::pp::pp_cuts(self.layers.len()).is_some()
            && !self.rewrite_allowed(memra_gguf::execution_manifest::RewriteSurface::Pipeline)
        {
            return Err("pipeline rewrite is not qualified for batched prime".into());
        }
        if !self.rewrite_allowed(memra_gguf::execution_manifest::RewriteSurface::CarriedPrime) {
            if !self.rewrite_allowed(memra_gguf::execution_manifest::RewriteSurface::DecodeEager) {
                return Err("neither batched-prime nor eager rewrite is qualified".into());
            }
            if prompts.len() != caches.len() {
                return Err("prime fallback prompt/cache shape mismatch".into());
            }
            static ONCE: std::sync::Once = std::sync::Once::new();
            ONCE.call_once(|| {
                eprintln!(
                    "[rewrite] carried-prime.v1 unqualified; using individual native eager primes"
                );
            });
            let mut transaction = CacheTaintGuard::arm(caches);
            let result: Result<Vec<_>, Box<dyn std::error::Error>> = prompts
                .iter()
                .copied()
                .zip(caches.iter_mut())
                .map(|(prompt, cache)| self.prime_cache(e, prompt, cache, 0))
                .collect();
            if result.is_ok() {
                transaction.commit();
            }
            return result;
        }
        let _pp_walk =
            if crate::pp::pp_cuts(self.layers.len()).is_some() && !crate::pp::pp2_streams_off() {
                let rt = crate::pp::PpNRt::get(e)?;
                Some(rt.acquire_walk("prime_cache_batch")?)
            } else {
                None
            };
        let cfg = &self.cfg;
        let n_embd = cfg.n_embd as usize;
        let eps = cfg.rms_eps;
        let b = prompts.len();
        assert!(b >= 1 && b == caches.len());
        let pos0s: Vec<usize> = caches.iter().map(|c| c.pos).collect();
        let carried = pos0s.iter().any(|&p| p > 0);
        // gemma4: refuse UNCONDITIONALLY (2026-08-07, lane/gemma4-serve-gaps). The old guard
        // covered only `carried` — two concurrent FRESH gemma4 prompts batched into the
        // generic concat attn core below (uniform geometry, no per-layer swa window, no
        // softcapped head): compiles, runs, wrong logits. Same silent-wrong class as the
        // step35 refusal beneath. The per-sequence `gemma4_prime` is the supported prefill.
        if self.uses_gemma_program() {
            return Err(
                "prime_cache_batch: gemma4 has no batched prime core (per-layer \
                        swa/global geometry, softcapped head) — use gemma4_prime per sequence"
                    .into(),
            );
        }
        // Step35 has a dedicated concat walk: the generic core below cannot express its
        // per-layer geometry/SWA/head gate, and under PP the dedicated path is stage-scoped.
        if self.uses_sliding_gated_moe_program() {
            // The cross-request driver hands whole requests (no chunk loop of its own), so each
            // sequence's request-absolute end IS its base plus its prompt length — the value
            // `ts[s]` happened to equal for the fresh B>=1 batches this caller admits, which is
            // why this arm is bit-for-bit unchanged by the seq_end threading.
            let seq_ends: Vec<usize> = caches
                .iter()
                .zip(prompts.iter())
                .map(|(c, p)| c.pos + p.len())
                .collect();
            return self.step35_prime_cache_batch(e, prompts, caches, &seq_ends);
        }
        if crate::pp::pp_cuts(self.layers.len()).is_some() && !crate::pp::pp2_streams_off() {
            let rt = crate::pp::PpNRt::get(e)?;
            if rt.cross_device() {
                return Err(
                    "prime_cache_batch: generic dense concat prime has no cross-device PP split; use individual prime_cache calls"
                        .into(),
                );
            }
        }
        let ts: Vec<usize> = prompts.iter().map(|p| p.len()).collect();
        for &t in &ts {
            assert!(
                t >= PRIME_MIN_T,
                "prime_cache_batch needs T >= {PRIME_MIN_T}"
            );
        }
        for (s, c) in caches.iter().enumerate() {
            assert!(
                c.pos + ts[s] <= c.max_ctx,
                "prime_cache_batch: prompt exceeds cache max_ctx"
            );
        }
        let mut transaction = CacheTaintGuard::arm(caches);
        let total: usize = ts.iter().sum();
        let offs: Vec<usize> = ts
            .iter()
            .scan(0usize, |a, &t| {
                let o = *a;
                *a += t;
                Some(o)
            })
            .collect();
        // per-seq positions (fresh: 0..T_s; continuation: pos0..pos0+T_s)
        let pos_ds: Vec<CudaSlice<i32>> = ts
            .iter()
            .zip(&pos0s)
            .map(|(&t, &p0)| e.htod_i32(&(p0 as i32..(p0 + t) as i32).collect::<Vec<_>>()))
            .collect::<Result<_, _>>()?;
        // split a concat [total, dim] buffer into per-seq copies
        let split = |e: &Engine,
                     y: &CudaSlice<f32>,
                     dim: usize|
         -> Result<Vec<CudaSlice<f32>>, Box<dyn std::error::Error>> {
            let mut out = Vec::with_capacity(b);
            for s in 0..b {
                let mut ys = e.uninit(ts[s] * dim)?;
                e.copy_view_into(
                    &mut ys,
                    0,
                    &y.slice(offs[s] * dim..(offs[s] + ts[s]) * dim),
                    ts[s] * dim,
                )?;
                out.push(ys);
            }
            Ok(out)
        };

        let cat_tokens: Vec<u32> = prompts.iter().flat_map(|p| p.iter().copied()).collect();
        let mut x = self.embed(e, &cat_tokens)?; // [total, n_embd]
        for (il, layer) in self.layers.iter().enumerate() {
            let mut h = e.uninit(total * n_embd)?;
            let mut hx16 = e.alloc_u8_uninit(total * n_embd * 2)?;
            e.rms_norm_f16out(
                &x,
                layer.attn_norm.float_data(),
                &mut h,
                &mut hx16,
                n_embd,
                total,
                eps,
            )?;
            // mixer: projection GROUP on the concat (m = total), stateful core per seq
            let mut mixed = e.uninit(total * n_embd)?;
            match &layer.mixer {
                Mixer::Full(fa) => {
                    let g3 = e.matmul_group_xh(&[&fa.wq, &fa.wk, &fa.wv], &h, &hx16, total)?;
                    // task #18 (attn side): the WHOLE attn core is varlen for fresh gated
                    // batches — split/QK-norm/RoPE/append (attn_pre_vl8, view inputs: the
                    // q/k/v split copies vanish) + ONE varlen FA. Per-block math identical
                    // everywhere (bit-gateable); MEMRA_FA_VL=0 or a non-bf16kv config falls
                    // back to the per-seq dispatch.
                    let geometry = self.cfg.full_attention_geometry_at(il as u32);
                    let (n_head, n_head_kv, head_dim) = (
                        geometry.n_head as usize,
                        geometry.n_head_kv as usize,
                        geometry.head_dim_k as usize,
                    );
                    let fa_scale = geometry.attention_scale();
                    let use_favl = !carried
                        && (2..=8).contains(&b)
                        && (head_dim == 256 || head_dim == 128)
                        && geometry.attention_gate == memra_gguf::config::AttentionGateKind::FusedQ
                        && std::env::var("MEMRA_NOFA").is_err()
                        && std::env::var("MEMRA_FA_FLOOR").is_err()
                        && std::env::var("MEMRA_FA_PP_W2").as_deref() != Ok("1")
                        && std::env::var("MEMRA_FA_BF16KV").as_deref() != Ok("0")
                        && std::env::var("MEMRA_FA_VL").as_deref() != Ok("0");
                    if use_favl {
                        let (qf_w, kf_w, vf_w) = (
                            fa.wq.out_features(),
                            fa.wk.out_features(),
                            fa.wv.out_features(),
                        );
                        // Same bounds contract as `Engine::q_gate_split`, applied to the varlen
                        // twin's PER-TOKEN stride. `attn_pre_vl8` takes raw device pointers so it
                        // cannot check its own extents; `qf_w` is the wq out-features that set
                        // them, and `q_gate_split_vl` reads 2*head_dim per head out of it.
                        memra_gguf::config::check_fused_q_gate_extent(qf_w, head_dim, n_head, 1)?;
                        struct APre {
                            q: CudaSlice<f32>,
                            gate: Option<CudaSlice<f32>>,
                            qn: CudaSlice<f32>,
                            kn: CudaSlice<f32>,
                        }
                        let mut aps = Vec::with_capacity(b);
                        for &t in ts.iter().take(b) {
                            aps.push(APre {
                                q: e.uninit(t * n_head * head_dim)?,
                                gate: Some(e.uninit(t * n_head * head_dim)?),
                                qn: e.uninit(t * n_head * head_dim)?,
                                kn: e.uninit(t * n_head_kv * head_dim)?,
                            });
                        }
                        let (kv_dim_k, kv_dim_v, ktb, vtb) = {
                            let kvl = caches[0].kv[il].as_ref().unwrap();
                            (kvl.kv_dim_k, kvl.kv_dim_v, kvl.k_tok_bytes, kvl.v_tok_bytes)
                        };
                        let pargs: Vec<crate::AttnPreVl> = (0..b)
                            .map(|s| {
                                let (o, t) = (offs[s], ts[s]);
                                let kvl = caches[s].kv[il].as_ref().unwrap();
                                assert!(
                                    kvl.len == 0 && kvl.len + t <= caches[s].max_ctx,
                                    "prime_cache_batch attn vl: fresh + capacity"
                                );
                                crate::AttnPreVl {
                                    qf: e.addr_f32v(&g3[0].slice(o * qf_w..(o + t) * qf_w)),
                                    kf: e.addr_f32v(&g3[1].slice(o * kf_w..(o + t) * kf_w)),
                                    vf: e.addr_f32v(&g3[2].slice(o * vf_w..(o + t) * vf_w)),
                                    q: e.addr_f32(&aps[s].q),
                                    gate: e.addr_f32(aps[s].gate.as_ref().unwrap()),
                                    qn: e.addr_f32(&aps[s].qn),
                                    kn: e.addr_f32(&aps[s].kn),
                                    kc: e.addr_u8(&kvl.k),
                                    vc: e.addr_u8(&kvl.v),
                                    t: t as i32,
                                    pad: 0,
                                }
                            })
                            .collect();
                        e.attn_pre_vl8(
                            &pargs,
                            fa.q_norm.float_data(),
                            fa.k_norm.float_data(),
                            head_dim,
                            geometry.n_rot as usize,
                            n_head,
                            n_head_kv,
                            self.cfg.rms_eps,
                            geometry.rope_base,
                            1.0,
                            kv_dim_k,
                            kv_dim_v,
                            ktb,
                            vtb,
                        )?;
                        for s in 0..b {
                            let kvl = caches[s].kv[il].as_mut().unwrap();
                            kvl.len += ts[s];
                            let new_len = kvl.len as i32;
                            e.set_i32_one(&mut kvl.len_d, new_len)?;
                        }
                        let mut attns = Vec::with_capacity(b);
                        let mut mirrors = Vec::with_capacity(b);
                        for &t in ts.iter().take(b) {
                            attns.push(e.uninit(t * n_head * head_dim)?);
                            let n = t * n_head_kv * head_dim;
                            mirrors.push((e.alloc_u8_uninit(n * 2)?, e.alloc_u8_uninit(n * 2)?));
                        }
                        // FA3 batched twin (round 31): TMA-swizzled wgmma vl when the
                        // promoted single-seq config is on; else the mma favl.
                        let fa3_on = match std::env::var("MEMRA_FA3").as_deref() {
                            Ok("0") => false,
                            // Same refusal as the single-seq twin (lib.rs fa_prefill): the
                            // batched bf16 stage reaches func("f32_to_bf16_bulk"), absent on a
                            // portable build.
                            Ok("1") => {
                                crate::refuse_portable_force(
                                    "MEMRA_FA3=1",
                                    "the sm_90a fa3/bf16 kernels",
                                );
                                true
                            }
                            _ => cfg!(memra_hopper_mma),
                        };
                        if fa3_on {
                            let mut q16s = Vec::with_capacity(b);
                            let mut v16s = Vec::with_capacity(b);
                            for s in 0..b {
                                let t = ts[s];
                                let mut q16 = e.alloc_u8_uninit(t * n_head * head_dim * 2)?;
                                e.f32_to_bf16_into(&aps[s].qn, &mut q16, t * n_head * head_dim)?;
                                let mut k16 = e.alloc_u8_uninit(t * n_head_kv * head_dim * 2)?;
                                e.f32_to_bf16_into(&aps[s].kn, &mut k16, t * n_head_kv * head_dim)?;
                                let mut v16 = e.alloc_u8_uninit(t * n_head_kv * head_dim * 2)?;
                                e.f32_to_bf16_v(
                                    &g3[2].slice(offs[s] * vf_w..(offs[s] + t) * vf_w),
                                    &mut v16,
                                    t * n_head_kv * head_dim,
                                )?;
                                q16s.push(q16);
                                v16s.push((k16, v16));
                            }
                            let mut qp = [core::ptr::null::<core::ffi::c_void>(); 8];
                            let mut kp = qp;
                            let mut vp = qp;
                            let mut op = [core::ptr::null_mut::<f32>(); 8];
                            let mut tsv = [0i32; 8];
                            for s in 0..b {
                                qp[s] = e.addr_u8(&q16s[s]) as *const core::ffi::c_void;
                                kp[s] = e.addr_u8(&v16s[s].0) as *const core::ffi::c_void;
                                vp[s] = e.addr_u8(&v16s[s].1) as *const core::ffi::c_void;
                                op[s] = e.addr_f32(&attns[s]) as *mut f32;
                                tsv[s] = ts[s] as i32;
                            }
                            let rc = unsafe {
                                crate::fa3_vl_raw(
                                    qp.as_ptr(),
                                    kp.as_ptr(),
                                    vp.as_ptr(),
                                    op.as_ptr(),
                                    tsv.as_ptr(),
                                    b as i32,
                                    n_head as i32,
                                    n_head_kv as i32,
                                    head_dim as i32,
                                    fa_scale,
                                    e.stream().cu_stream() as *mut core::ffi::c_void,
                                )
                            };
                            if rc != 0 {
                                return Err(format!("memra_fa3_vl rc={rc}").into());
                            }
                        } else {
                            let fargs: Vec<crate::FaSeqVl> = (0..b)
                                .map(|s| crate::FaSeqVl {
                                    q: e.addr_f32(&aps[s].qn),
                                    k16: e.addr_u8(&mirrors[s].0),
                                    v16: e.addr_u8(&mirrors[s].1),
                                    o: e.addr_f32(&attns[s]),
                                    kf: e.addr_f32(&aps[s].kn),
                                    vf: e.addr_f32v(
                                        &g3[2].slice(offs[s] * vf_w..(offs[s] + ts[s]) * vf_w),
                                    ),
                                    t: ts[s] as i32,
                                    pad: 0,
                                })
                                .collect();
                            e.fa_prefill_vl8(&fargs, head_dim, n_head, n_head_kv, fa_scale)?;
                        }
                        for (s, attn) in attns.into_iter().enumerate() {
                            let (attn_g, ag16) = self.full_attn_prime_post_fa(
                                e,
                                attn,
                                &aps[s].gate,
                                ts[s],
                                n_head,
                                head_dim,
                            )?;
                            let mut done = false;
                            if let Some(xh) = &ag16 {
                                done = e.try_f16_gemm_pre_into_off(
                                    &fa.wo,
                                    xh,
                                    ts[s],
                                    &mut mixed,
                                    offs[s] * n_embd,
                                )?;
                            }
                            if !done {
                                let m = e.matmul(&fa.wo, &attn_g, ts[s])?;
                                e.copy_into(&mut mixed, offs[s] * n_embd, &m, ts[s] * n_embd)?;
                            }
                        }
                    } else {
                        let mut parts: Vec<Vec<CudaSlice<f32>>> =
                            (0..b).map(|_| Vec::new()).collect();
                        for (w, y) in [&fa.wq, &fa.wk, &fa.wv].iter().zip(g3) {
                            for (s, ys) in split(e, &y, w.out_features())?.into_iter().enumerate() {
                                parts[s].push(ys);
                            }
                        }
                        for (s, g3s) in parts.into_iter().enumerate() {
                            // task #16 gather removal: wo writes into `mixed` at offs[s] directly.
                            let (attn_g, ag16) = self.full_attn_prime_core_inner(
                                e, fa, g3s, &pos_ds[s], ts[s], caches[s], il,
                            )?;
                            let mut done = false;
                            if let Some(xh) = &ag16 {
                                done = e.try_f16_gemm_pre_into_off(
                                    &fa.wo,
                                    xh,
                                    ts[s],
                                    &mut mixed,
                                    offs[s] * n_embd,
                                )?;
                            }
                            if !done {
                                let m = e.matmul(&fa.wo, &attn_g, ts[s])?;
                                e.copy_into(&mut mixed, offs[s] * n_embd, &m, ts[s] * n_embd)?;
                            }
                        }
                    }
                }
                Mixer::Mla(_) => crate::hybrid::mla_path_unimplemented("batched cache prime"),
                Mixer::Kda(_) => crate::hybrid::kda_path_unimplemented("batched prime"),
                Mixer::Linear(la) => {
                    // task #16: NO split copies (cores read row-offset views of the concat
                    // outputs; out-GEMMs write into `mixed` at offs[s]). task #18: the core
                    // itself is BATCHED — per-seq prep/K1-K3, then ONE varlen K4 + ONE
                    // varlen K5 launch for all sequences.
                    let ws = [&la.wqkv, &la.wqkv_gate, &la.ssm_beta, &la.ssm_alpha];
                    let g4 = e.matmul_group_xh(&ws, &h, &hx16, total)?;
                    let outs =
                        self.linear_attn_prime_core_batch(e, la, &g4, &offs, &ts, caches, il)?;
                    for (s, (gn, gn16)) in outs.into_iter().enumerate() {
                        let (o, t) = (offs[s], ts[s]);
                        let mut done = false;
                        if let Some(xh) = &gn16 {
                            done = e.try_f16_gemm_pre_into_off(
                                &la.ssm_out,
                                xh,
                                t,
                                &mut mixed,
                                o * n_embd,
                            )?;
                        }
                        if !done {
                            let m = e.matmul(&la.ssm_out, &gn, t)?;
                            e.copy_into(&mut mixed, o * n_embd, &m, t * n_embd)?;
                        }
                    }
                }
            }
            let mut x1 = e.uninit(total * n_embd)?;
            let mut z = e.uninit(total * n_embd)?;
            let mut zx16 = e.alloc_u8_uninit(total * n_embd * 2)?;
            e.add_rms_norm_f16out(
                &x,
                &mixed,
                layer.post_attn_norm.float_data(),
                &mut x1,
                &mut z,
                &mut zx16,
                n_embd,
                total,
                eps,
            )?;
            let ffn_out = match &layer.ffn {
                crate::hybrid::Ffn::Dense {
                    ffn_gate,
                    ffn_up,
                    ffn_down,
                } => {
                    let n_ff = ffn_gate.out_features();
                    let mut g2 = e.matmul_group_xh(&[ffn_gate, ffn_up], &z, &zx16, total)?;
                    let up = g2.pop().unwrap();
                    let gate = g2.pop().unwrap();
                    let mut act = e.uninit(total * n_ff)?;
                    // task #17 (batch trunk): silu twin emits the down GEMM's fp16 operand
                    // in-epilogue (nsys round-26: this trunk still paid 32 cvt passes).
                    // A clamped layer must skip the plain-SiLU twin (see prime_chunk's note).
                    let d_lim = self.cfg.clamp_shexp_at(il as u32);
                    if Self::f16out_on(e, total) && self.cfg.m3.is_none() && d_lim.is_none() {
                        let mut a16 = e.alloc_u8_uninit(total * n_ff * 2)?;
                        e.silu_mul_f16out(&gate, &up, &mut act, &mut a16, total * n_ff)?;
                        match e.try_f16_gemm_pre(ffn_down, &a16, total)? {
                            Some(y) => y,
                            None => e.matmul(ffn_down, &act, total)?,
                        }
                    } else {
                        Self::ffn_act_lim(
                            e,
                            &self.cfg,
                            &gate,
                            &up,
                            1.0,
                            1.0,
                            d_lim,
                            &mut act,
                            total * n_ff,
                        )?;
                        e.matmul(ffn_down, &act, total)?
                    }
                }
                crate::hybrid::Ffn::Moe(m) => {
                    self.moe_ffn_il_prefill(e, m, &z, total, il as u16)?
                }
            };
            let mut x2 = e.uninit(total * n_embd)?;
            e.add(&x1, &ffn_out, &mut x2, total * n_embd)?;
            x = x2;
        }
        // epilogue per seq (identical math to prime_chunk: norm all rows, lm_head on last row)
        let mut hn = e.uninit(total * n_embd)?;
        e.rms_norm(
            &x,
            self.output_norm.float_data(),
            &mut hn,
            n_embd,
            total,
            eps,
        )?;
        // batched lm_head (nsys round-26: B sequential m=1 matvecs re-read the 600MB
        // lm_head weight B times — 2.2ms at B=6): gather the B last rows and run ONE
        // m=B GEMM (f16 lane; falls back to the per-seq matvec when no mirror). The
        // f16-vs-mmvq logits delta is the prefill GEMM numeric class — prime_batch_gate's
        // argmax battery arbitrates, same as every other prefill GEMM change.
        let mut hcat = e.uninit(b * n_embd)?;
        for s in 0..b {
            let last0 = (offs[s] + ts[s] - 1) * n_embd;
            e.copy_view_into(
                &mut hcat,
                s * n_embd,
                &hn.slice(last0..last0 + n_embd),
                n_embd,
            )?;
        }
        let logits_cat = if b >= 2 {
            e.try_f16_gemm(&self.output, &hcat, b)?
        } else {
            None
        };
        let logits_host: Option<Vec<f32>> = match &logits_cat {
            Some(lc) => Some(e.dtoh(lc)?),
            None => None,
        };
        let n_vocab = self.output.out_features();
        let mut hidden_all = if crate::spec::spec_hpost() {
            split(e, &hn, n_embd)?
        } else {
            split(e, &x, n_embd)?
        };
        let mut out = Vec::with_capacity(b);
        for s in 0..b {
            let last0 = (offs[s] + ts[s] - 1) * n_embd;
            let mut h_seed = e.uninit(n_embd)?;
            if !crate::spec::spec_hpost() {
                e.copy_view_into(&mut h_seed, 0, &x.slice(last0..last0 + n_embd), n_embd)?;
            } else {
                e.copy_view_into(&mut h_seed, 0, &hn.slice(last0..last0 + n_embd), n_embd)?;
            }
            let logits = match &logits_host {
                Some(lh) => lh[s * n_vocab..(s + 1) * n_vocab].to_vec(),
                None => {
                    let mut hlast = e.uninit(n_embd)?;
                    e.copy_view_into(&mut hlast, 0, &hn.slice(last0..last0 + n_embd), n_embd)?;
                    e.dtoh(&e.matmul(&self.output, &hlast, 1)?)?
                }
            };
            caches[s].pos += ts[s];
            out.push((logits, h_seed, hidden_all.remove(0)));
        }
        transaction.commit();
        Ok(out)
    }

    /// `full_attn` (batched prefill mixer) + the cache side-effect: append the T post-RoPE K/V
    /// rows into the resident quantized KV cache (q8_0 K / q5_1 V) and advance len/len_d. Row
    /// bytes are BIT-IDENTICAL to the decode append (same per-warp quant kernel per row; the
    /// batched `append_kv_quantized_rows` runs that exact warp math on a (block, token) grid).
    /// The attention itself is unchanged prefill math (fa_prefill over the f32 K/V).
    ///
    /// `seq_end` = the ABSOLUTE end position of the WHOLE prime request (`cache.pos + prompt_len`
    /// measured BEFORE the chunk loop starts), i.e. a chunk-size-invariant property of the request.
    /// Only step35's SWA arm reads it (`step35_attn_pre_wo`'s doc note explains why keying on the
    /// chunk's own `t_kv` made the output depend on MEMRA_PRIME_CHUNK); every other arch ignores it.
    #[allow(clippy::too_many_arguments)]
    fn full_attn_prime(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        h: &CudaSlice<f32>,
        hx: Option<&CudaSlice<u8>>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        il: usize,
        seq_end: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        if self.uses_sliding_gated_moe_program() {
            return self.step35_attn_prime(e, fa, h, hx, pos_d, t, cache, il, seq_end);
        }
        // PROJ/CORE SPLIT (task #13, 2026-07-26): the q/k/v group projection is hoisted so
        // the cross-request batch driver can run it at m = sum_T over concatenated tokens;
        // this single-seq path composes proj+core identically (byte-for-byte the old body).
        // task #14: `hx` = the norm-fused fp16 twin of `h` (skips the convert launch).
        let g3 = match hx {
            Some(xh) => e.matmul_group_xh(&[&fa.wq, &fa.wk, &fa.wv], h, xh, t)?,
            None => e.matmul_group(&[&fa.wq, &fa.wk, &fa.wv], h, t)?,
        };
        self.full_attn_prime_core(e, fa, g3, pos_d, t, cache, il)
    }

    /// Everything after the q/k/v projections (split/QK-norm/RoPE/FA/gate/append + wo).
    /// `g3` = matmul_group([wq, wk, wv]) output rows for THIS sequence.
    /// Composes inner + wo (== the pre-split core; every existing caller unchanged).
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn full_attn_prime_core(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        g3: Vec<CudaSlice<f32>>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        il: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let (attn_g, ag16) = self.full_attn_prime_core_inner(e, fa, g3, pos_d, t, cache, il)?;
        if let Some(xh) = &ag16
            && let Some(y) = e.try_f16_gemm_pre(&fa.wo, xh, t)?
        {
            return Ok(y);
        }
        e.matmul(&fa.wo, &attn_g, t)
    }

    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn full_attn_prime_core_inner(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        g3: Vec<CudaSlice<f32>>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        il: usize,
    ) -> Result<(CudaSlice<f32>, Option<CudaSlice<u8>>), Box<dyn std::error::Error>> {
        let cfg = &self.cfg;
        let geometry = cfg.full_attention_geometry_at(il as u32);
        let n_head = geometry.n_head as usize;
        let n_head_kv = geometry.n_head_kv as usize;
        let head_dim = geometry.head_dim_k as usize;
        let scale = geometry.attention_scale();
        let (pre, base_len) = self.full_attn_prime_pre_fa(e, fa, g3, pos_d, t, cache, il)?;
        let AttnPre { q, k, v, gate } = pre;
        let mut attn = e.uninit(t * n_head * head_dim)?;
        self.full_attn_prime_fa_dispatch(
            e, &q, &k, &v, &mut attn, base_len, t, cache, il, head_dim, n_head, n_head_kv, scale,
        )?;
        self.full_attn_prime_post_fa(e, attn, &gate, t, n_head, head_dim)
    }

    /// task #18 (attn side): projections tail through KV append — everything before the
    /// attention kernel. Returns the post-rope q/k, v, optional out-gate, and the KV rows
    /// present BEFORE this chunk's append (base_len; 0 == fresh).
    #[allow(clippy::type_complexity)]
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn full_attn_prime_pre_fa(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        mut g3: Vec<CudaSlice<f32>>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        il: usize,
    ) -> Result<(AttnPre, usize), Box<dyn std::error::Error>> {
        let cfg = &self.cfg;
        let geometry = cfg.full_attention_geometry_at(il as u32);
        let n_head = geometry.n_head as usize;
        let n_head_kv = geometry.n_head_kv as usize;
        let head_dim = geometry.head_dim_k as usize;
        let eps = cfg.rms_eps;

        // qwen35 fuses [q|gate] per head in wq (2*head_dim stride); M3/Hy3 have NO output gate
        // (attention_output_gate=false) — wq out = n_head*head_dim exactly, and q_gate_split
        // would read 2x out of bounds. `gated` keys both the split and the sigmoid epilogue.
        let gated = geometry.attention_gate == memra_gguf::config::AttentionGateKind::FusedQ;
        let v = g3.pop().unwrap();
        let mut k = g3.pop().unwrap();
        let qf = g3.pop().unwrap();
        let (mut q, gate) = if gated {
            let mut q = e.uninit(t * n_head * head_dim)?;
            let mut gate = e.uninit(t * n_head * head_dim)?;
            e.q_gate_split(&qf, &mut q, &mut gate, head_dim, n_head, t)?;
            (q, Some(gate))
        } else {
            (qf, None)
        };

        let mut qn = e.uninit(t * n_head * head_dim)?;
        e.rms_norm(
            &q,
            fa.q_norm.float_data(),
            &mut qn,
            head_dim,
            n_head * t,
            eps,
        )?;
        q = qn;
        let mut kn = e.uninit(t * n_head_kv * head_dim)?;
        e.rms_norm(
            &k,
            fa.k_norm.float_data(),
            &mut kn,
            head_dim,
            n_head_kv * t,
            eps,
        )?;
        k = kn;
        let rope_dims = geometry.n_rot as usize;
        e.rope_neox(
            &mut q,
            pos_d,
            head_dim,
            rope_dims,
            n_head,
            t,
            geometry.rope_base,
            1.0,
        )?;
        e.rope_neox(
            &mut k,
            pos_d,
            head_dim,
            rope_dims,
            n_head_kv,
            t,
            geometry.rope_base,
            1.0,
        )?;

        // CACHE SIDE-EFFECT: append the T post-rope K/V token rows (token-major [T, kv_dim] ==
        // the cache row layout) quantized into cache.kv[il], then advance len + device len_d.
        {
            let kvl = cache.kv[il].as_mut().unwrap();
            assert!(kvl.len + t <= cache.max_ctx, "prime_cache: KV overflow");
            e.append_kv_quantized_rows(
                &k,
                &v,
                &mut kvl.k,
                &mut kvl.v,
                kvl.len,
                t,
                kvl.kv_dim_k,
                kvl.kv_dim_v,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                crate::Engine::kv_fp8_on(),
            )?;
            kvl.len += t;
            let new_len = kvl.len as i32;
            e.set_i32_one(&mut kvl.len_d, new_len)?;
        }

        let base_len = {
            let kvl = cache.kv[il].as_ref().unwrap();
            kvl.len - t // KV rows present BEFORE this chunk's append above
        };
        Ok((AttnPre { q, k, v, gate }, base_len))
    }

    /// batched prefill attention. FRESH prime (no past KV): unchanged forward_last math over
    /// the f32 K/V of this batch. CONTINUATION chunk (past KV present): the chunk's queries
    /// must attend to [0 .. base+t) — run fa_prefill_view over the resident QUANTIZED cache
    /// (the spec-verify pattern; kernel's causal mask offsets by T_kv-T). Numerically this
    /// reads q8_0/q5_1-dequantized K/V for the past AND the current chunk — the same class as
    /// decode reading the cache; the run-gen/first-16 battery is the accuracy authority.
    #[allow(clippy::too_many_arguments)]
    fn full_attn_prime_fa_dispatch(
        &self,
        e: &Engine,
        q: &CudaSlice<f32>,
        k: &CudaSlice<f32>,
        v: &CudaSlice<f32>,
        attn: &mut CudaSlice<f32>,
        base_len: usize,
        t: usize,
        cache: &mut Cache,
        il: usize,
        head_dim: usize,
        n_head: usize,
        n_head_kv: usize,
        scale: f32,
    ) -> Result<(), Box<dyn std::error::Error>> {
        // GRAIN-FREE CHUNK-INVARIANCE FIX (lane/chunkinv-flip, 2026-08-05): the base_len == 0
        // f32 special case (fa_prefill over this batch's f32 K/V) is DROPPED. pre_fa appends
        // the chunk's quantized rows into cache.kv[il] BEFORE this dispatch, so chunk 0 can
        // attend through the quantized cache exactly like every later chunk (quantize-then-
        // attend). One numeric class for every row => the chunk size cannot decide where a
        // precision edge falls, so chunked prefill is reduction-order-stable with NO grain
        // knob (the stronger fix VERDICT.md filed; supersedes the MEMRA_PRIME_INVARIANT door's
        // pin-the-boundary approach).
        // MEMRA_PRIME_F32CHUNK0=1 is the ROLLBACK SEAM to the old arithmetic (chunk 0 attends
        // f32 K/V) — flags-doctrine rollback door AND the chunkinv gate's canary injection:
        // with the fix unconditional, only re-introducing the class edge can prove the gate
        // still detects the mechanism. Never on in a measured default run.
        if base_len == 0 && std::env::var("MEMRA_PRIME_F32CHUNK0").as_deref() == Ok("1") {
            if std::env::var("MEMRA_NOFA").is_ok() || !(head_dim == 256 || head_dim == 128) {
                e.sdpa_naive(
                    q, k, v, attn, head_dim, n_head, n_head_kv, t, t, scale, true,
                )?;
            } else {
                e.fa_prefill(
                    q, k, v, attn, head_dim, n_head, n_head_kv, t, t, scale, true,
                )?;
            }
            return Ok(());
        }
        let kvl = cache.kv[il].as_ref().unwrap();
        let t_kv = base_len + t;
        let k_view = e.view_u8(&kvl.k, t_kv * kvl.k_tok_bytes);
        let v_view = e.view_u8(&kvl.v, t_kv * kvl.v_tok_bytes);
        // fa_prefill_q/_qw twins are stamped for head_dim 256 (qwen35) and 128 (M3) only;
        // other dims (and MEMRA_NOFA) take the naive quantized-view SDPA — SAME cache bytes,
        // same numeric class, so the uniform contract holds on the fallback too.
        if std::env::var("MEMRA_NOFA").is_ok() || !(head_dim == 256 || head_dim == 128) {
            e.sdpa_naive_quantized_view(
                q,
                &k_view,
                &v_view,
                attn,
                head_dim,
                n_head,
                n_head_kv,
                t,
                t_kv,
                scale,
                true,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
            )?;
            return Ok(());
        }
        // ARC B (2026-07-05): dequant-once workspace, DEFAULT ON. fa_prefill_q's inline
        // dequant re-reads+re-dequants the whole quantized KV stream from every one of the
        // T/64 x n_head CTAs (64x+ redundant at chunk=4096; 30.5% of the 32k prime wall).
        // fa_prefill_view_ws dequants K/V ONCE into a resident bf16 workspace then runs the
        // bit-identical bf16 twin (fa_prefill_qw) — same staged values, same FP order, token-
        // identical output (gate: MEMRA_PRIME_CHUNK=4096 ws-on vs ws-off vs monolithic).
        // MEMRA_PRIME_DEQW=0 reverts to the inline-dequant kernel.
        let deqw = std::env::var("MEMRA_PRIME_DEQW")
            .map(|v| v != "0")
            .unwrap_or(true);
        if deqw {
            e.fa_prefill_view_ws(
                q,
                &k_view,
                &v_view,
                attn,
                head_dim,
                n_head,
                n_head_kv,
                t,
                t_kv,
                scale,
                true,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                crate::Engine::kv_fp8_on(),
            )?;
        } else {
            e.fa_prefill_view(
                q,
                &k_view,
                &v_view,
                attn,
                head_dim,
                n_head,
                n_head_kv,
                t,
                t_kv,
                scale,
                true,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                crate::Engine::kv_fp8_on(),
            )?;
        }
        Ok(())
    }

    /// task #17: sig_mul_f16out fuses [sigmoid + mul + f16 convert] into one launch
    /// (bit-identical composition) and hands wo its fp16 operand directly.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn full_attn_prime_post_fa(
        &self,
        e: &Engine,
        attn: CudaSlice<f32>,
        gate: &Option<CudaSlice<f32>>,
        t: usize,
        n_head: usize,
        head_dim: usize,
    ) -> Result<(CudaSlice<f32>, Option<CudaSlice<u8>>), Box<dyn std::error::Error>> {
        let (attn_g, ag16) = match gate {
            Some(gate) => {
                let n = t * n_head * head_dim;
                let mut ag = e.uninit(n)?;
                if Self::f16out_on(e, t) {
                    let mut a16 = e.alloc_u8_uninit(n * 2)?;
                    e.sig_mul_f16out(&attn, gate, &mut ag, &mut a16, n)?;
                    (ag, Some(a16))
                } else {
                    let mut gsig = e.uninit(n)?;
                    e.sigmoid(gate, &mut gsig, n)?;
                    e.mul(&attn, &gsig, &mut ag, n)?;
                    (ag, None)
                }
            }
            None => (attn, None),
        };
        Ok((attn_g, ag16))
    }

    /// STATEFUL batched linear-attention prime: `linear_attn`'s prefill-dispatch pass (normal
    /// `e.matmul` — GEMM at m>=16 — plus the prefill repack/L2/glog kernels) but with the state
    /// carried THROUGH the cache like the spec verify does: carried-ring conv
    /// (ssm_conv1d_tm_state writes the final ring back) + ONE gdn_scan from cache.recur[il]'s
    /// current state (zero at a fresh prime) whose final state ping-pongs back into the cache.
    /// Wiring mirrors `linear_attn_verify_t` (spec.rs); dispatch mirrors `linear_attn` (prefill).
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn linear_attn_prime(
        &self,
        e: &Engine,
        la: &LinearAttnLayer,
        h: &CudaSlice<f32>,
        hx: Option<&CudaSlice<u8>>,
        t: usize,
        cache: &mut Cache,
        il: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        // PROJ/CORE SPLIT (task #13): see full_attn_prime — same hoist for the GDN 4-tuple.
        let ws = [&la.wqkv, &la.wqkv_gate, &la.ssm_beta, &la.ssm_alpha];
        let g4 = match hx {
            Some(xh) => e.matmul_group_xh(&ws, h, xh, t)?,
            None => e.matmul_group(&ws, h, t)?,
        };
        self.linear_attn_prime_core(e, la, g4, t, cache, il)
    }

    /// Everything after the GDN 4-tuple projections (conv/chunk stack/gated norm + ssm_out).
    fn linear_attn_prime_core(
        &self,
        e: &Engine,
        la: &LinearAttnLayer,
        mut g4: Vec<CudaSlice<f32>>,
        t: usize,
        cache: &mut Cache,
        il: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        self.linear_attn_prime_core_pad(e, la, std::mem::take(&mut g4), t, cache, il, None)
    }

    /// task #14: `pad_len` = device true length for PADDED prime graphs. Pads become
    /// identity GDN steps (gdn_pad_mask zeroes beta/g_log past the true length) and the
    /// conv ring writes back from the true tail. None = classic path, byte-identical.
    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn linear_attn_prime_core_pad_inner(
        &self,
        e: &Engine,
        la: &LinearAttnLayer,
        mut g4: Vec<CudaSlice<f32>>,
        t: usize,
        cache: &mut Cache,
        il: usize,
        pad_len: Option<&CudaSlice<i32>>,
    ) -> Result<(CudaSlice<f32>, Option<CudaSlice<u8>>), Box<dyn std::error::Error>> {
        // shim over the view twin (task #16): full-range views of the owned buffers.
        let geometry = la.geometry;
        let d_state = geometry.key_head_dim as usize;
        let num_k = geometry.key_heads as usize;
        let num_v = geometry.value_heads as usize;
        let key_dim = d_state * num_k;
        let value_dim = geometry.value_head_dim as usize * num_v;
        let conv_dim = key_dim * 2 + value_dim;
        let alpha = g4.pop().unwrap(); // [T, num_v]
        let beta_raw = g4.pop().unwrap(); // [T, num_v]
        let z = g4.pop().unwrap(); // [T, value_dim]
        let qkv_mixed = g4.pop().unwrap(); // [T, conv_dim] token-major
        self.linear_attn_prime_core_pad_view(
            e,
            la,
            &qkv_mixed.slice(0..t * conv_dim),
            &z.slice(0..t * value_dim),
            &beta_raw.slice(0..t * num_v),
            &alpha.slice(0..t * num_v),
            t,
            cache,
            il,
            pad_len,
        )
    }

    /// task #18: the GDN prep stage (conv + repack + l2 x2 + sigmoid + glog + pad-mask) —
    /// shared verbatim by the per-seq scan path and the varlen batched path.
    #[allow(clippy::too_many_arguments)]
    fn linear_attn_gdn_prep(
        &self,
        e: &Engine,
        la: &LinearAttnLayer,
        qkv_mixed: &cudarc::driver::CudaView<f32>,
        beta_raw: &cudarc::driver::CudaView<f32>,
        alpha: &cudarc::driver::CudaView<f32>,
        t: usize,
        cache: &mut Cache,
        il: usize,
        pad_len: Option<&CudaSlice<i32>>,
    ) -> Result<GdnPrep, Box<dyn std::error::Error>> {
        let cfg = &self.cfg;
        let geometry = la.geometry;
        let d_state = geometry.key_head_dim as usize;
        let num_k = geometry.key_heads as usize;
        let num_v = geometry.value_heads as usize;
        let d_conv = geometry.conv_kernel as usize;
        let key_dim = d_state * num_k; // 2048
        let value_dim = geometry.value_head_dim as usize * num_v;
        let conv_dim = key_dim * 2 + value_dim; // 8192
        let eps = cfg.rms_eps;
        debug_assert!(
            t >= d_conv - 1,
            "stateful conv needs T >= pad (PRIME_MIN_T gates)"
        );

        // conv with CARRIED ring state + ring roll (state read + final-window write-back).
        // task #18 conv-fuse (default ON): conv + SiLU + repack in one pass — the 67MB
        // conv_out intermediate and its transposed re-read disappear (values bit-identical).
        // MEMRA_CONV_FUSE=0 reverts to the two-kernel chain.
        let rl = cache.recur[il].as_mut().unwrap();
        let hk = Self::gdn_hk(e, t, num_v, num_k);
        let conv_fuse = std::env::var("MEMRA_CONV_FUSE").as_deref() != Ok("0");
        let hk = if conv_fuse { hk } else { num_v }; // de-broadcast rides the fused conv
        let mut q_g = e.uninit(d_state * hk * t)?;
        let mut k_g = e.uninit(d_state * hk * t)?;
        let mut v_g = e.uninit(d_state * num_v * t)?;
        if conv_fuse {
            e.ssm_conv1d_gdn_state_pad(
                qkv_mixed,
                &mut rl.conv_state,
                la.ssm_conv1d.float_data(),
                &mut q_g,
                &mut k_g,
                &mut v_g,
                conv_dim,
                t,
                d_conv,
                d_state,
                num_v,
                num_k,
                key_dim,
                hk,
                pad_len,
            )?;
        } else {
            let mut conv_out = e.uninit(conv_dim * t)?; // [conv_dim, T] channel-major, SiLU
            e.ssm_conv1d_tm_state_pad_v(
                qkv_mixed,
                &mut rl.conv_state,
                la.ssm_conv1d.float_data(),
                &mut conv_out,
                conv_dim,
                t,
                d_conv,
                pad_len,
            )?;
            e.qkv_to_gdn_repack(
                &conv_out, &mut q_g, &mut k_g, &mut v_g, d_state, num_v, num_k, key_dim, t,
            )?;
        }
        let mut q_l2 = e.uninit(d_state * hk * t)?;
        // mirror-fold (round 35): q's bf16 twin (wgmma K45/K2 A-operand) in-epilogue too.
        // Emitted only where a consumer exists (the wgmma config) — on other arches the
        // alloc + epilogue stores would be pure waste.
        let qb16 = if Engine::l2_v2_on(d_state) && e.gdn_wgmma_on(32) {
            let mut qb = e.alloc_u8_uninit(d_state * hk * t * 2)?;
            e.l2_norm_pp(&q_g, &mut q_l2, Some(&mut qb), d_state, hk * t, eps)?;
            Some(qb)
        } else {
            e.l2_norm_pp(&q_g, &mut q_l2, None, d_state, hk * t, eps)?;
            None
        };
        let mut k_l2 = e.uninit(d_state * hk * t)?;
        // mirror-fold: emit k's bf16 twin (the chunked-scan kb16 mirror) in-epilogue
        let kb16 = if Engine::l2_v2_on(d_state) {
            let mut kb = e.alloc_u8_uninit(d_state * hk * t * 2)?;
            e.l2_norm_pp(&k_g, &mut k_l2, Some(&mut kb), d_state, hk * t, eps)?;
            Some(kb)
        } else {
            e.l2_norm_pp(&k_g, &mut k_l2, None, d_state, hk * t, eps)?;
            None
        };
        let mut beta = e.uninit(t * num_v)?;
        e.sigmoid_v(beta_raw, &mut beta, t * num_v)?;
        let mut g_log = e.uninit(t * num_v)?;
        e.gdn_glog_v(
            alpha,
            la.ssm_dt.float_data(),
            la.ssm_a.float_data(),
            &mut g_log,
            num_v,
            t,
        )?;
        if let Some(len_d) = pad_len {
            e.gdn_pad_mask(&mut beta, &mut g_log, len_d, num_v, t)?;
        }
        Ok(GdnPrep {
            hk,
            q_l2,
            k_l2,
            v_g,
            beta,
            g_log,
            kb16,
            qb16,
        })
    }

    /// task #18: batched GDN mixer core — per-seq prep + K1-K3, then ONE varlen K4 and
    /// ONE varlen K5 launch for all B sequences (full-machine occupancy vs B underfilled
    /// per-seq trains). Per-block math identical to the per-seq path (bit-gateable);
    /// MEMRA_GDN_VL=0 or a non-mma/non-C32 config falls back to the per-seq loop.
    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn linear_attn_prime_core_batch(
        &self,
        e: &Engine,
        la: &LinearAttnLayer,
        g4: &[CudaSlice<f32>],
        offs: &[usize],
        ts: &[usize],
        caches: &mut [&mut Cache],
        il: usize,
    ) -> Result<Vec<(CudaSlice<f32>, Option<CudaSlice<u8>>)>, Box<dyn std::error::Error>> {
        let geometry = la.geometry;
        let d_state = geometry.key_head_dim as usize;
        let num_k = geometry.key_heads as usize;
        let num_v = geometry.value_heads as usize;
        let d_conv = geometry.conv_kernel as usize;
        let key_dim = d_state * num_k;
        let value_dim = geometry.value_head_dim as usize * num_v;
        let conv_dim = key_dim * 2 + value_dim;
        let eps = self.cfg.rms_eps;
        let scale = 1.0 / (d_state as f32).sqrt();
        let b = ts.len();
        let c = Engine::gdn_chunk_size();
        // CONTINUATION batches (increment (b)) take the per-seq stateful pad_view twin
        // below — the varlen K4/K5 chain is fresh-only (zero initial state assumed).
        let carried = caches.iter().any(|c| c.pos > 0);
        let use_vl = !carried
            && (2..=8).contains(&b)
            && Engine::gdn_chunked_enabled()
            && ts.iter().all(|&t| t >= 16)
            && e.gdn_mma_enabled(c)
            && std::env::var("MEMRA_GDN_VL").as_deref() != Ok("0");
        if !use_vl {
            return (0..b)
                .map(|s| {
                    let (o, t) = (offs[s], ts[s]);
                    self.linear_attn_prime_core_pad_view(
                        e,
                        la,
                        &g4[0].slice(o * conv_dim..(o + t) * conv_dim),
                        &g4[1].slice(o * value_dim..(o + t) * value_dim),
                        &g4[2].slice(o * num_v..(o + t) * num_v),
                        &g4[3].slice(o * num_v..(o + t) * num_v),
                        t,
                        caches[s],
                        il,
                        None,
                    )
                })
                .collect();
        }
        // increment 3: the ENTIRE core is varlen — allocs only per seq, then 13 launches
        // total for the whole batch: [conv, ring, repack, l2(q+k), gate-prep] +
        // [k-mirror, K1, K2, K3, w-mirror, K4, K5] + [gated-norm tail].
        struct SeqBufs {
            conv_out: CudaSlice<f32>,
            q_g: CudaSlice<f32>,
            k_g: CudaSlice<f32>,
            v_g: CudaSlice<f32>,
            q_l2: CudaSlice<f32>,
            k_l2: CudaSlice<f32>,
            beta: CudaSlice<f32>,
            g_log: CudaSlice<f32>,
            gn: CudaSlice<f32>,
            gn16: CudaSlice<u8>,
        }
        let f16o = Self::f16out_on(e, 16);
        let hk = Self::gdn_hk(e, 16, num_v, num_k); // vl path is always chunked+mma
        let mut sb = Vec::with_capacity(b);
        let mut pres = Vec::with_capacity(b);
        for &t in ts.iter().take(b) {
            sb.push(SeqBufs {
                conv_out: e.uninit(conv_dim * t)?,
                q_g: e.uninit(d_state * hk * t)?,
                k_g: e.uninit(d_state * hk * t)?,
                v_g: e.uninit(d_state * num_v * t)?,
                q_l2: e.uninit(d_state * hk * t)?,
                k_l2: e.uninit(d_state * hk * t)?,
                beta: e.uninit(t * num_v)?,
                g_log: e.uninit(t * num_v)?,
                gn: e.uninit(d_state * num_v * t)?,
                gn16: e.alloc_u8_uninit(d_state * num_v * t * 2)?,
            });
            pres.push(e.gdn_chunk_alloc(num_v, t, c, hk)?);
        }
        let prep_args: Vec<crate::GdnPrepVl> = (0..b)
            .map(|s| {
                let (o, t) = (offs[s], ts[s]);
                let rl = caches[s].recur[il].as_ref().unwrap();
                crate::GdnPrepVl {
                    qkv: e.addr_f32v(&g4[0].slice(o * conv_dim..(o + t) * conv_dim)),
                    conv_state: e.addr_f32(&rl.conv_state),
                    conv_out: e.addr_f32(&sb[s].conv_out),
                    q_g: e.addr_f32(&sb[s].q_g),
                    k_g: e.addr_f32(&sb[s].k_g),
                    v_g: e.addr_f32(&sb[s].v_g),
                    q_l2: e.addr_f32(&sb[s].q_l2),
                    k_l2: e.addr_f32(&sb[s].k_l2),
                    beta_raw: e.addr_f32v(&g4[2].slice(o * num_v..(o + t) * num_v)),
                    alpha: e.addr_f32v(&g4[3].slice(o * num_v..(o + t) * num_v)),
                    beta: e.addr_f32(&sb[s].beta),
                    g_log: e.addr_f32(&sb[s].g_log),
                    o: e.addr_f32(&pres[s].o),
                    z: e.addr_f32v(&g4[1].slice(o * value_dim..(o + t) * value_dim)),
                    gn: e.addr_f32(&sb[s].gn),
                    gn16: e.addr_u8(&sb[s].gn16),
                    kb16: if Engine::l2_v2_on(d_state) {
                        e.addr_u8(&pres[s].kb16)
                    } else {
                        0
                    },
                    qb16: if Engine::l2_v2_on(d_state) && e.gdn_wgmma_on(c) {
                        e.addr_u8(&pres[s].qb16)
                    } else {
                        0
                    },
                    t: t as i32,
                    pad: 0,
                }
            })
            .collect();
        let args: Vec<crate::GdnSeqVl> = (0..b)
            .map(|s| {
                let rl = caches[s].recur[il].as_ref().unwrap();
                crate::GdnSeqVl {
                    kb16: e.addr_u8(&pres[s].kb16),
                    gcum: e.addr_f32(&pres[s].gcum),
                    beta: e.addr_f32(&sb[s].beta),
                    u: e.addr_f32(&pres[s].u),
                    wb16: e.addr_u8(&pres[s].wb16),
                    y: e.addr_u8(&pres[s].y16),
                    ssnap: e.addr_u8(&pres[s].ssnap16),
                    state_in: e.addr_f32(&rl.ssm_state),
                    state_out: e.addr_f32(&rl.ssm_state_alt),
                    q: e.addr_f32(&sb[s].q_l2),
                    p: e.addr_f32(&pres[s].p),
                    o: e.addr_f32(&pres[s].o),
                    k: e.addr_f32(&sb[s].k_l2),
                    v: e.addr_f32(&sb[s].v_g),
                    g: e.addr_f32(&sb[s].g_log),
                    a: e.addr_f32(&pres[s].a),
                    w: e.addr_f32(&pres[s].w),
                    t: ts[s] as i32,
                    nc: pres[s].nc as i32,
                }
            })
            .collect();
        e.gdn_prep_vl8(
            &prep_args,
            la.ssm_conv1d.float_data(),
            la.ssm_dt.float_data(),
            la.ssm_a.float_data(),
            conv_dim,
            d_conv,
            d_state,
            num_v,
            num_k,
            key_dim,
            hk,
            eps,
        )?;
        // mirror-fold (round 27): l2 v2 emits kb16 in-epilogue; K3's store emits wb16 —
        // both standalone mirror launches vanish on the default config.
        if !Engine::l2_v2_on(d_state) {
            e.gdn_mirror_vl8(&args, num_v, 0, hk)?;
        }
        // task #22: wgmma-fused vl twins — qb16 mirrors + GdnWVl8 side-struct.
        let wq8: Option<crate::GdnWVl8> = if e.gdn_wgmma_on(c) {
            // qb16 emitted by the vl l2 mirror-fold when l2-v2 serves; bulk cvt otherwise
            if !Engine::l2_v2_on(d_state) {
                for s in 0..b {
                    e.f32_to_bf16_into(&sb[s].q_l2, &mut pres[s].qb16, d_state * hk * ts[s])?;
                }
            }
            let mut wa = [crate::GdnWVl::default(); 8];
            for s in 0..b {
                wa[s] = crate::GdnWVl {
                    qb16: e.addr_u8(&pres[s].qb16),
                    pb16: e.addr_u8(&pres[s].pb16),
                };
            }
            Some(crate::GdnWVl8(wa))
        } else {
            None
        };
        e.gdn_chunk_k123_vl8(&args, num_v, hk, wq8.as_ref())?;
        e.gdn_chunk_vl8(&args, num_v, scale, hk, wq8.as_ref())?;
        if f16o {
            e.gdn_tail_vl8(&prep_args, la.ssm_norm.float_data(), d_state, num_v, eps)?;
        }
        // per-seq state swap (+ non-f16out tail fallback)
        let mut out = Vec::with_capacity(b);
        for (s, bufs) in sb.into_iter().enumerate() {
            let rl = caches[s].recur[il].as_mut().unwrap();
            std::mem::swap(&mut rl.ssm_state, &mut rl.ssm_state_alt);
            let (o, t) = (offs[s], ts[s]);
            let SeqBufs { mut gn, gn16, .. } = bufs;
            if f16o {
                out.push((gn, Some(gn16)));
            } else {
                let z_v = g4[1].slice(o * value_dim..(o + t) * value_dim);
                e.gated_rmsnorm_zv(
                    &pres[s].o,
                    la.ssm_norm.float_data(),
                    &z_v,
                    &mut gn,
                    d_state,
                    num_v * t,
                    eps,
                )?;
                out.push((gn, None));
            }
        }
        Ok(out)
    }

    /// task #16: view-consuming GDN prime core — the batched prime hands row-offset
    /// views of the CONCAT projection outputs directly (no per-seq split copies).
    /// Same kernels, same values, byte-identical to the Vec shim above.
    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn linear_attn_prime_core_pad_view(
        &self,
        e: &Engine,
        la: &LinearAttnLayer,
        qkv_mixed: &cudarc::driver::CudaView<f32>,
        z: &cudarc::driver::CudaView<f32>,
        beta_raw: &cudarc::driver::CudaView<f32>,
        alpha: &cudarc::driver::CudaView<f32>,
        t: usize,
        cache: &mut Cache,
        il: usize,
        pad_len: Option<&CudaSlice<i32>>,
    ) -> Result<(CudaSlice<f32>, Option<CudaSlice<u8>>), Box<dyn std::error::Error>> {
        let cfg = &self.cfg;
        let geometry = la.geometry;
        let d_state = geometry.key_head_dim as usize;
        let num_v = geometry.value_heads as usize;
        let eps = cfg.rms_eps;
        let scale = 1.0 / (d_state as f32).sqrt();

        let prep =
            self.linear_attn_gdn_prep(e, la, qkv_mixed, beta_raw, alpha, t, cache, il, pad_len)?;

        // ONE gdn_scan over T from the cache's CURRENT state (zero at fresh prime); the final
        // state lands in the spare buffer and ping-pongs back (stable resident pointers, the
        // decode-determinism discipline from linear_attn_decode_inner). A4: `gdn_scan_prefill`
        // dispatches the chunked WY form under MEMRA_GDN_CHUNKED (prefill-only seam; decode +
        // verify keep the sequential kernel).
        let mut o = e.uninit(d_state * num_v * t)?;
        let rl = cache.recur[il].as_mut().unwrap();
        {
            let crate::cache::RecurLayer {
                ssm_state,
                ssm_state_alt,
                ..
            } = rl;
            e.gdn_scan_prefill(
                &prep.q_l2,
                &prep.k_l2,
                &prep.v_g,
                &prep.g_log,
                &prep.beta,
                prep.kb16.as_ref(),
                prep.qb16.as_ref(),
                ssm_state,
                ssm_state_alt,
                &mut o,
                num_v,
                t,
                scale,
                prep.hk,
            )?;
        }
        std::mem::swap(&mut rl.ssm_state, &mut rl.ssm_state_alt);

        // gated RMSNorm + out projection (prefill dispatch). task #17: the f16out twin also
        // emits the ssm_out GEMM's fp16 operand in-epilogue (kills the standalone convert).
        let mut gn = e.uninit(d_state * num_v * t)?;
        let gn16 = if Self::f16out_on(e, t) {
            let mut g16 = e.alloc_u8_uninit(d_state * num_v * t * 2)?;
            e.gated_rmsnorm_f16out_zv(
                &o,
                la.ssm_norm.float_data(),
                z,
                &mut gn,
                &mut g16,
                d_state,
                num_v * t,
                eps,
            )?;
            Some(g16)
        } else {
            e.gated_rmsnorm_zv(
                &o,
                la.ssm_norm.float_data(),
                z,
                &mut gn,
                d_state,
                num_v * t,
                eps,
            )?;
            None
        };
        Ok((gn, gn16))
    }

    /// Task #15 core-split wrapper: composes inner + ssm_out (== the old core).
    #[allow(clippy::too_many_arguments)]
    fn linear_attn_prime_core_pad(
        &self,
        e: &Engine,
        la: &LinearAttnLayer,
        g4: Vec<CudaSlice<f32>>,
        t: usize,
        cache: &mut Cache,
        il: usize,
        pad_len: Option<&CudaSlice<i32>>,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let (gn, gn16) = self.linear_attn_prime_core_pad_inner(e, la, g4, t, cache, il, pad_len)?;
        if let Some(xh) = &gn16
            && let Some(y) = e.try_f16_gemm_pre(&la.ssm_out, xh, t)?
        {
            return Ok(y);
        }
        e.matmul(&la.ssm_out, &gn, t)
    }

    /// Full-attention mixer with QK-norm, partial RoPE, sigmoid output gate (qwen35 :257-336).
    ///
    /// `il` = layer index: step35 needs it (per-layer n_head / rope width / window / gate) and
    /// routes to its own mixer. Every other arch ignores it (uniform geometry).
    pub fn full_attn(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        if self.uses_sliding_gated_moe_program() {
            return self.step35_attn(e, fa, h, pos_d, t, il);
        }
        let cfg = &self.cfg;
        let _n_embd = cfg.n_embd as usize;
        let geometry = cfg.full_attention_geometry_at(il as u32);
        let n_head = geometry.n_head as usize;
        let n_head_kv = geometry.n_head_kv as usize;
        let head_dim = geometry.head_dim_k as usize;
        let eps = cfg.rms_eps;
        let scale = geometry.attention_scale();

        // qwen35: wq output = head_dim*2*n_head (fused [q|gate] per head). M3/Hy3: NO output
        // gate — wq out = n_head*head_dim, no split (see prime-path note).
        let gated = geometry.attention_gate == memra_gguf::config::AttentionGateKind::FusedQ;
        // A load-time full-attention TP plan owns the same Q/K/V projections for every
        // architecture. Fall back to the original grouped owner-device projection when this
        // layer has no TP sidecar.
        let mut g3 = match self.full_attn_tp_qkv(e, fa, h, t)? {
            Some(g3) => g3,
            None => e.matmul_group(&[&fa.wq, &fa.wk, &fa.wv], h, t)?,
        };
        let v = g3.pop().unwrap();
        let mut k = g3.pop().unwrap();
        let qf = g3.pop().unwrap();
        let (mut q, gate) = if gated {
            let mut q = e.uninit(t * n_head * head_dim)?;
            let mut gate = e.uninit(t * n_head * head_dim)?;
            e.q_gate_split(&qf, &mut q, &mut gate, head_dim, n_head, t)?;
            (q, Some(gate))
        } else {
            (qf, None)
        };

        // QK-norm (per head_dim row), then partial RoPE.
        let mut qn = e.uninit(t * n_head * head_dim)?;
        e.rms_norm(
            &q,
            fa.q_norm.float_data(),
            &mut qn,
            head_dim,
            n_head * t,
            eps,
        )?;
        q = qn;
        let mut kn = e.uninit(t * n_head_kv * head_dim)?;
        e.rms_norm(
            &k,
            fa.k_norm.float_data(),
            &mut kn,
            head_dim,
            n_head_kv * t,
            eps,
        )?;
        k = kn;
        let rope_dims = geometry.n_rot as usize;
        e.rope_neox(
            &mut q,
            pos_d,
            head_dim,
            rope_dims,
            n_head,
            t,
            geometry.rope_base,
            1.0,
        )?;
        e.rope_neox(
            &mut k,
            pos_d,
            head_dim,
            rope_dims,
            n_head_kv,
            t,
            geometry.rope_base,
            1.0,
        )?;

        // SDPA
        let mut attn = e.uninit(t * n_head * head_dim)?;
        // hand-written FlashAttention prefill (head_dim 256/128 stamped twins). MEMRA_NOFA
        // falls back to naive sdpa.
        if std::env::var("MEMRA_NOFA").is_ok() || !(head_dim == 256 || head_dim == 128) {
            // head_dim gate: see prime-path note (fa_prefill is stamped at 256 and 128 only).
            e.sdpa_naive(
                &q, &k, &v, &mut attn, head_dim, n_head, n_head_kv, t, t, scale, true,
            )?;
        } else {
            e.fa_prefill(
                &q, &k, &v, &mut attn, head_dim, n_head, n_head_kv, t, t, scale, true,
            )?;
        }

        // output gate: attn * sigmoid(gate) — qwen35 only (M3 has no gate).
        let attn_g = match &gate {
            Some(gate) => {
                let mut gsig = e.uninit(t * n_head * head_dim)?;
                e.sigmoid(gate, &mut gsig, t * n_head * head_dim)?;
                let mut ag = e.uninit(t * n_head * head_dim)?;
                e.mul(&attn, &gsig, &mut ag, t * n_head * head_dim)?;
                ag
            }
            None => attn,
        };

        // O follows the same generic load-time TP plan as Q/K/V.
        self.full_attn_o(e, fa, &attn_g, t)
    }

    /// The absorb/decompress operands (`attn_k_b` / `attn_v_b`) are 3D and are ALWAYS the Float
    /// arm — on every checkpoint dtype, not just an f32 fixture. Two guards upstream make that a
    /// property rather than a hope: `GpuTensor::load_from_source` refuses any quantized non-2D
    /// tensor by name (`row_bytes` is derived from `ne[1]`, the MIDDLE axis of a 3D tensor), and
    /// `MlaAttnLayer::load` audits residency at load. A quantized `kv_b_proj` is dequantized at
    /// the source by `TransformKind::MlaKeyUpSplit`/`MlaValueUpSplit`. This is the last backstop:
    /// fail NAMING the constraint rather than through `float_data()`'s norm-flavoured panic.
    fn mla_split_operand<'w>(
        w: &'w crate::model::GpuTensor,
        name: &str,
        il: usize,
    ) -> &'w CudaSlice<f32> {
        match w {
            crate::model::GpuTensor::Float { data, .. } => data,
            _ => panic!(
                "layer {il}: MLA conversion-split operand {name} is not f32-resident. The 3D \
                 (d_nope|kv_rank, kv_rank|d_v, n_head) splits have no quantized resident layout: \
                 a quantized 3D tensor mis-derives row_bytes in the generic 2D Quant arm, so the \
                 source must dequantize the fused kv_b_proj (TensorTransform::SplitMlaKv). \
                 Reaching this means both the loader rank guard and MlaAttnLayer::load's \
                 residency audit were bypassed"
            ),
        }
    }

    /// MLA (multi-head latent attention) mixer core, ABSORBED form — the one arm that serves
    /// prefill, chunked prefill and decode (see `cu/mla_attn.cu` FORM CHOICE).
    ///
    /// `latent` is the layer's latent KV plane; this call APPENDS its own `t` rows at row
    /// `slot` and then attends rows `0..slot + t`, which is exactly the oracle's convention
    /// that the queries are the LAST `t_q` rows of the cache (`crate::mla::MlaInputs`).
    /// Returns the post-`wo` block output [t, n_embd].
    #[allow(clippy::too_many_arguments)]
    // allow: the parameter list mirrors the kernel/FFI/call contract (rows_exact is the
    // verify-batch matmul-class selector, lane/glm5-verify-batch); bundling into a struct
    // is a refactor, not a lint fix
    fn mla_attn_core(
        &self,
        e: &Engine,
        mla: &crate::hybrid::MlaAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
        latent: &mut CudaSlice<f32>,
        index_plane: Option<IndexerPlanes<'_>>,
        slot: usize,
        rows_exact: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let attn = self.mla_attn_core_pre_wo(
            e,
            mla,
            h,
            pos_d,
            t,
            il,
            latent,
            index_plane,
            slot,
            rows_exact,
        )?;
        // Verify-batch wo seam (lane/glm5-verify-batch): the rows arm routes the output
        // projection decode-exact, same as every projection inside the core — the wo
        // dispatch moved here with the TP split, its routing did not change.
        if rows_exact {
            e.matmul_rows_exact(&mla.wo, &attn, t)
        } else {
            e.matmul(&mla.wo, &attn, t)
        }
    }

    /// [`mla_attn_core`] up to (and excluding) the output projection: returns the
    /// per-head attention output `[t, n_head * d_v]`. Split out for the glm5 TP-2 seam,
    /// whose column-parallel `wo` runs over the cross-rank GATHERED heads — the plain path
    /// is the wrapper above, byte-for-byte the pre-split body (the wo matmul moved,
    /// nothing else).
    #[allow(clippy::too_many_arguments)]
    fn mla_attn_core_pre_wo(
        &self,
        e: &Engine,
        mla: &crate::hybrid::MlaAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
        latent: &mut CudaSlice<f32>,
        index_plane: Option<IndexerPlanes<'_>>,
        slot: usize,
        rows_exact: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let g = mla.geom;
        let cfg = &self.cfg;
        let eps = cfg.rms_eps;
        let base = cfg.rope_freq_base;
        let (nh, dn, dr, dv, r) = (g.n_head, g.d_nope, g.d_rope, g.d_v, g.kv_rank);
        assert_eq!(
            g.latent_dim,
            r + dr,
            "layer {il}: MlaGeom latent_dim disagrees with kv_rank + d_rope"
        );
        let t_kv = slot + t;
        // Verify-batch matmul seam (lane/glm5-verify-batch): rows_exact routes every
        // projection through the decode-exact classes so each of the t rows is
        // bit-identical to the t=1 decode program (matmul_rows_exact contract); false =
        // the unchanged dispatch for every other caller (prime keeps its classes).
        let mm = |w: &crate::model::GpuTensor,
                  x: &CudaSlice<f32>|
         -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
            if rows_exact {
                e.matmul_rows_exact(w, x, t)
            } else {
                e.matmul(w, x, t)
            }
        };

        // --- q path: wq_a -> q_a_norm -> wq_b -> per-head [nope | rope] ---
        let q_a = mm(&mla.wq_a, h)?;
        let q_lora = mla.wq_b.in_features();
        let mut q_an = e.uninit(t * q_lora)?;
        e.rms_norm(&q_a, mla.q_a_norm.float_data(), &mut q_an, q_lora, t, eps)?;
        let q = mm(&mla.wq_b, &q_an)?;
        // Per head the row is [nope | rope] contiguous, so t*nh rows of width dn+dr split with
        // the same kernel the latent row uses — the two layouts are the same shape.
        let mut q_nope = e.uninit(t * nh * dn)?;
        let mut q_pe = e.uninit((t * nh * dr).max(1))?;
        e.mla_split_latent(&q, &mut q_nope, &mut q_pe, t * nh, dn, dr)?;
        // NoPE (glm5_next, rope_head_dim 0): no rope plane exists. The launcher is a no-op, but
        // the allocation above is still non-empty so nothing downstream holds a null slice.
        e.mla_rope_interleaved(&mut q_pe, pos_d, t, nh, dr, base)?;

        // --- kv path: wkv_a -> [c_kv (rms-normed) | k_pe (roped, NOT normed)] ---
        let kv = mm(&mla.wkv_a, h)?;
        let mut c_kv = e.uninit(t * r)?;
        let mut k_pe = e.uninit((t * dr).max(1))?;
        e.mla_split_latent(&kv, &mut c_kv, &mut k_pe, t, r, dr)?;
        let mut c_kv_n = e.uninit(t * r)?;
        e.rms_norm(&c_kv, mla.kv_a_norm.float_data(), &mut c_kv_n, r, t, eps)?;
        e.mla_rope_interleaved(&mut k_pe, pos_d, t, 1, dr, base)?;
        e.mla_append_latent(latent, &c_kv_n, &k_pe, slot, t, r, dr)?;

        // --- DSA k-pool selection, BEFORE attending: the indexer's own state row for each of
        // this call's tokens is appended first, so a query sees itself exactly as the latent
        // plane already lets it (the reference concatenates into the indexer cache, then scores).
        let gathered = match (&mla.index, index_plane) {
            (Some(indexer), Some(plane)) => {
                Some(self.mla_kpool_select(e, indexer, h, &q_an, plane, t, slot, il, rows_exact)?)
            }
            (Some(_), None) => {
                return Err(format!(
                    "layer {il} declares a DSA k-pool indexer but no indexer state plane was \
                     supplied — the ModelPlan must declare StatePlan::LatentKvCache with a \
                     non-zero index_width for it"
                )
                .into());
            }
            (None, _) => None,
        };

        // --- absorbed MLA core over the latent plane ---
        let wk_b = Self::mla_split_operand(&mla.wk_b, "attn_k_b", il);
        let wv_b = Self::mla_split_operand(&mla.wv_b, "attn_v_b", il);

        // MEMRA_MLA_TC_PREFILL door (default OFF; flag read per call — the rollback seam).
        // Engagement conditions, every one load-bearing:
        //   * a gathered selection exists — the door serves the DSA arm only; the dense
        //     absorbed arm (GLM-5.2, no indexer) keeps the f32 kernel it was gated on;
        //   * d_rope == 0 (NoPE) — the TC kernel treats the latent row as both K and V,
        //     which is only the whole truth when there is no rope plane;
        //   * kv_rank == 512 — the kernel's stamped head dim (glm5_next / GLM-5.2 class);
        //   * t >= 16 — prefill widths only. Decode (t == 1) and short resumes NEVER enter,
        //     which is what the decode byte-identity gate proves rather than assumes.
        // Anything else falls through to the unchanged f32 kernels below — behavior identical
        // to the flag being off.
        // A chain returning Ok(None) is a cuBLASLt shape DECLINE (announced once per shape);
        // the let-chain then simply does not match and the f32 kernels below serve the call.
        if let Some((idx, slots)) = &gathered
            && dr == 0
            && r == 512
            && t >= 16
            && !rows_exact // verify-batch stays on the decode-exact classes (t <= 15 anyway)
            && !crate::portable_mma_gated()
            && mla_tc_prefill_enabled()
            && let Some(attn) = self.mla_tc_prefill_chain(
                e, wk_b, wv_b, &q_nope, latent, idx, *slots, t, t_kv, nh, dn, dv, r, g.scale,
            )?
        {
            return Ok(attn);
        }

        let mut q_lat = e.uninit(t * nh * r)?;
        e.mla_absorb_q(&q_nope, wk_b, &mut q_lat, t, nh, dn, r)?;
        let mut o_lat = e.uninit(t * nh * r)?;
        match &gathered {
            Some((idx, slots)) => e.mla_attn_gathered(
                &q_lat, &q_pe, latent, idx, &mut o_lat, nh, r, dr, t, *slots, g.scale,
            )?,
            None => e.mla_attn_absorbed(
                &q_lat, &q_pe, latent, &mut o_lat, nh, r, dr, t, t_kv, g.scale,
            )?,
        }
        let mut attn = e.uninit(t * nh * dv)?;
        e.mla_decompress_v(&o_lat, wv_b, &mut attn, t, nh, dv, r)?;

        Ok(attn)
    }

    /// The MEMRA_MLA_TC_PREFILL chain: absorb and decompress as strided-batched bf16
    /// tensor-core GEMMs, attention as the gathered bf16 MMA kernel. Returns `Ok(None)` when
    /// cuBLASLt declines a GEMM shape (announced once per shape) so the caller falls back to
    /// the f32 kernels; every other failure is a hard error.
    ///
    /// FORM CHOICE, stated for the record (the dual-form MLA law): every fast engine runs
    /// MATERIALIZED (per-head MHA) attention at DENSE prefill and absorbed MQA at decode.
    /// glm5_next prefill is NOT dense: the DSA indexer caps every query at topk+tail rows and
    /// selects ONE list per query SHARED ACROSS ALL 64 HEADS. That shared list is what makes
    /// the ABSORBED form the GEMM-shaped one here — the head axis is the MMA m, the shared
    /// latent rows are one B operand per tile — while materializing K/V would give every head
    /// its own K plane and destroy exactly that sharing (back to per-(query,head) matvecs on
    /// the gathered walk). It is also FlashMLA's own sparse-prefill geometry (q 576/512 over
    /// gathered latent rows). Queries whose selection is trivial (visible <= topk: the lists
    /// ARE the full causal prefix, emitted by the selector itself) ride the SAME kernel with
    /// the identity gather — there is no separate dense program to gate.
    ///
    /// Transient cost per (layer, chunk) at the census shape (t=2313, t_kv=4626): bf16 q_lat
    /// 152 MB + bf16 latent window 4.7 MB + bf16 q_nope/o_lat copies ~230 MB — all freed with
    /// the call. (The materialized-K/V alternative would have been 4096 x 64 x (256+256) x 2B
    /// = 256 MB/layer-chunk of K/V ALONE, plus the per-head-K program cost above.) Weight
    /// bf16 converts (wk_b/wv_b, 8.4M elems each) run per call, ~50 us class; a resident
    /// mirror is a later diet, not correctness.
    #[allow(clippy::too_many_arguments)]
    fn mla_tc_prefill_chain(
        &self,
        e: &Engine,
        wk_b: &CudaSlice<f32>,
        wv_b: &CudaSlice<f32>,
        q_nope: &CudaSlice<f32>,
        latent: &CudaSlice<f32>,
        idx: &CudaSlice<i32>,
        width: usize,
        t: usize,
        t_kv: usize,
        nh: usize,
        dn: usize,
        dv: usize,
        r: usize,
        scale: f32,
    ) -> Result<Option<CudaSlice<f32>>, Box<dyn std::error::Error>> {
        // Once-per-shape decline announce (the bf16_tc_gemm pattern): a door that quietly
        // stops engaging reads exactly like a door that never helped.
        fn declined(stage: &str, m: usize, n: usize, k: usize, batch: usize) {
            type ShapeSet = std::collections::HashSet<(usize, usize, usize, usize)>;
            static SAID: std::sync::Mutex<Option<ShapeSet>> = std::sync::Mutex::new(None);
            let mut g = SAID.lock().unwrap();
            if g.get_or_insert_with(std::collections::HashSet::new)
                .insert((m, n, k, batch))
            {
                eprintln!(
                    "[mla-tc-prefill] DECLINED at {stage} m={m} n={n} k={k} batch={batch} \
                     (no cuBLASLt heuristic) — this call falls back to the f32 MLA kernels"
                );
            }
        }
        // Weights and activations to bf16. The converts require n % 4 == 0; every operand here
        // is a multiple of the head dims (dn/dv/r all >= 16 and % 4 == 0 on the shapes the door
        // admits), asserted rather than assumed.
        for (name, n) in [
            ("wk_b", nh * r * dn),
            ("wv_b", nh * dv * r),
            ("q_nope", t * nh * dn),
            ("latent", t_kv * r),
        ] {
            debug_assert!(
                n.is_multiple_of(4),
                "mla-tc-prefill: {name} elems {n} % 4 != 0"
            );
            let _ = (name, n);
        }
        let wk_bf = e.f32_to_bf16(wk_b, nh * r * dn)?;
        let wv_bf = e.f32_to_bf16(wv_b, nh * dv * r)?;
        let qn_bf = e.f32_to_bf16(q_nope, t * nh * dn)?;
        // absorb: per head h, q_lat[:,h,:] [t, r] = q_nope[:,h,:] [t, dn] @ W_uk[h] [r, dn]^T.
        // wk_b is the conversion-split (h, l, p) plane, contiguous in p == the reduction axis:
        // per head it IS the [n=r, k=dn] row-major operand. bf16 out feeds the attention kernel.
        let mut q_lat_bf = e.alloc_u8_uninit(t * nh * r * 2)?;
        if !e.mla_bf16_gemm_sb_bf16out(
            &wk_bf,
            &qn_bf,
            &mut q_lat_bf,
            t,
            r,
            dn,
            nh * dn,
            dn,
            nh * r,
            r,
            nh,
        )? {
            declined("absorb", t, r, dn, nh);
            return Ok(None);
        }
        // The latent window rows 0..t_kv (this call's rows were appended above), bf16.
        let cache_bf = e.f32_to_bf16(latent, t_kv * r)?;
        let mut o_lat = e.uninit(t * nh * r)?;
        e.mla_attn_gathered_tc(
            &q_lat_bf, &cache_bf, idx, &mut o_lat, nh, r, t, width, scale,
        )?;
        // decompress: per head h, attn[:,h,:] [t, dv] = o_lat[:,h,:] [t, r] @ W_uv[h] [dv, r]^T.
        // wv_b is (h, j, l), contiguous in l == the reduction axis: per head [n=dv, k=r].
        let o_bf = e.f32_to_bf16(&o_lat, t * nh * r)?;
        let mut attn = e.uninit(t * nh * dv)?;
        if !e.mla_bf16_gemm_sb_f32out(
            &wv_bf,
            &o_bf,
            &mut attn,
            t,
            dv,
            r,
            nh * r,
            r,
            nh * dv,
            dv,
            nh,
        )? {
            declined("decompress", t, dv, r, nh);
            return Ok(None);
        }
        crate::MLA_TC_PREFILL_DISPATCHES.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
        {
            static ANNOUNCED: std::sync::Once = std::sync::Once::new();
            ANNOUNCED.call_once(|| {
                eprintln!(
                    "[mla-tc-prefill] engaged: absorb/decompress = strided-batched bf16 TC \
                     GEMMs, attention = fa_mla_gathered_bf16 (t={t}, t_kv={t_kv}, nh={nh}, \
                     width={width}); dispatches counted in MLA_TC_PREFILL_DISPATCHES"
                );
            });
        }
        Ok(Some(attn))
    }

    /// Layer-scoped wrapper: names the layer in any selection failure.
    #[allow(clippy::too_many_arguments)]
    fn mla_kpool_select(
        &self,
        e: &Engine,
        indexer: &crate::hybrid::MlaIndexer,
        h: &CudaSlice<f32>,
        q_resid: &CudaSlice<f32>,
        plane: IndexerPlanes<'_>,
        t: usize,
        slot: usize,
        il: usize,
        rows_exact: bool,
    ) -> Result<(CudaSlice<i32>, usize), Box<dyn std::error::Error>> {
        Self::mla_kpool_indices_ex(e, indexer, h, q_resid, plane, t, slot, rows_exact).map_err(
            |source| -> Box<dyn std::error::Error> {
                format!("layer {il}: DSA k-pool selection failed: {source}").into()
            },
        )
    }

    /// DSA k-pool indexer: append this call's packed indexer state, then select the cache rows
    /// each query may attend. Returns the per-query position list and its width (`-1` padded).
    ///
    /// The program is `Glm5NextTextIndexer.forward`
    /// (research/glm53-flash-bringup-20260827/modular_glm5_next-ref.py:771), transcribed in
    /// `memra_reference::kpool_allowed_tokens`, which is this path's oracle:
    ///   1. `k = LayerNorm_affine(wk(x))` — LayerNorm WITH BIAS at eps 1e-5, NOT the model's
    ///      RMSNorm at `rms_norm_eps`; `gate = index_kpool_compress_gate(x)`. Both are cached.
    ///   2. Every COMPLETE pool of `pool` consecutive cached tokens collapses to one key by a
    ///      per-channel softmax over (gate + positional embedding).
    ///   3. `score[i][p] = sum_h relu(q[i][h] . pool_key[p] * d^-1/2) * weights_proj(x)[i][h] *
    ///      heads^-1/2`, with pools whose last token is invisible to the query masked out.
    ///   4. Top `top_k / pool` pools expand back to raw rows; the incomplete tail is appended raw.
    ///
    /// `q_resid` is `q_a_layernorm(q_a_proj(x))` — the SAME tensor the MLA query up-projection
    /// consumes, which is why the indexer is scored here rather than before the core.
    #[allow(clippy::too_many_arguments)]
    pub fn mla_kpool_indices(
        e: &Engine,
        indexer: &crate::hybrid::MlaIndexer,
        h: &CudaSlice<f32>,
        q_resid: &CudaSlice<f32>,
        plane: IndexerPlanes<'_>,
        t: usize,
        slot: usize,
    ) -> Result<(CudaSlice<i32>, usize), Box<dyn std::error::Error>> {
        Self::mla_kpool_indices_ex(e, indexer, h, q_resid, plane, t, slot, false)
    }

    /// [`Self::mla_kpool_indices`] with the verify-batch matmul-class selector
    /// (lane/glm5-verify-batch): `rows_exact` routes the indexer's four projections
    /// through the decode-exact classes so each of the t rows is bit-identical to the
    /// t=1 decode program; `false` is the unchanged dispatch.
    #[allow(clippy::too_many_arguments)]
    pub fn mla_kpool_indices_ex(
        e: &Engine,
        indexer: &crate::hybrid::MlaIndexer,
        h: &CudaSlice<f32>,
        q_resid: &CudaSlice<f32>,
        plane: IndexerPlanes<'_>,
        t: usize,
        slot: usize,
        rows_exact: bool,
    ) -> Result<(CudaSlice<i32>, usize), Box<dyn std::error::Error>> {
        let mm = |w: &crate::model::GpuTensor,
                  x: &CudaSlice<f32>|
         -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
            if rows_exact {
                e.matmul_rows_exact(w, x, t)
            } else {
                e.matmul(w, x, t)
            }
        };
        /// `nn.LayerNorm` default epsilon. The indexer's k_norm is a LayerNorm, so it does NOT
        /// take the model's `rms_norm_eps` (census: "eps 1e-5, NOT rms_norm_eps"); they coincide
        /// numerically on GLM-5.3-Flash and the constant keeps them from being coupled.
        const INDEX_NORM_EPS: f32 = 1e-5;

        let ig = indexer.geom;
        let d = ig.head_dim;
        let t_kv = slot + t;
        let IndexerPlanes {
            state: plane,
            pool_keys: pool_key_plane,
            ready: pools_ready,
            state_ring_rows,
            capacity_tokens,
        } = plane;

        // TAIL RING. The plane's rows are read EXACTLY ONCE, by the pool-key build of the pool
        // each row belongs to, so a ring of `ring` rows holds everything still live. The state
        // plan does not carry `pool`, so the allocator books physical rows and the EFFECTIVE ring
        // is rounded down here: a ring that is not a whole number of pools would split a pool
        // across the wrap. ONE POOL is the whole correctness floor (lane/glm53-ring-sizing): the
        // drain below serves any `t` from any ring at or above it, so `ring` never bounds a
        // prompt.
        let ring = if state_ring_rows == 0 {
            0
        } else {
            state_ring_rows / ig.pool * ig.pool
        };
        if state_ring_rows > 0 && ring == 0 {
            return Err(format!(
                "indexer tail ring of {state_ring_rows} rows cannot hold one pool of {}; \
                 raise MEMRA_DSA_INDEX_RING or set it to 0 for the flat plane",
                ig.pool
            )
            .into());
        }
        // RESIDENCY TRIPWIRE. `*pools_ready` counts pools whose keys were built over rows that are
        // now history. If the cache ever rewound past `slot` without clamping it (see
        // `LatentKvLayer::truncate_index_pool_keys`), those keys were built over rows this call is
        // about to overwrite — a silent wrong selection. Fail here instead.
        if *pools_ready > slot / ig.pool {
            return Err(format!(
                "resident k-pool key plane claims {} finished pools but the cache holds only {} \
                 complete pools before this call ({slot} rows / pool {}) — a rewind reduced the \
                 latent length without clamping index_pools_ready",
                *pools_ready,
                slot / ig.pool,
                ig.pool
            )
            .into());
        }

        // 1. packed state rows [k | gate], appended at `slot` — the same [a|b] row shape the
        //    latent plane uses, so `mla_append_latent` packs it with no new kernel.
        let k_raw = mm(&indexer.wk, h)?;
        let mut k_norm = e.uninit(t * d)?;
        e.layer_norm_bias(
            &k_raw,
            indexer.k_norm_w.float_data(),
            indexer.k_norm_b.float_data(),
            &mut k_norm,
            d,
            t,
            INDEX_NORM_EPS,
        )?;
        let gate = mm(&indexer.kpool_gate, h)?;

        // 2. pool keys over every COMPLETE pool in the cache — INCREMENTALLY. A pool's key is a
        //    function of its own `pool` state rows (append-only, never rewritten) and the constant
        //    `kpool_ape`, so it is final the instant the pool's last row lands. Only pools
        //    `[*pools_ready, n_pools)` are built; the rest are already resident and bit-identical
        //    to a rebuild. This turns the old O(t_kv * d) per-call pass into O(t * d).
        let n_pools = t_kv / ig.pool;
        let select_k = ig.select_k(n_pools);
        let width = ig.index_width(n_pools);
        // Sized to the SESSION's capacity, so a session that primes and then decodes never
        // reallocates (a fresh buffer would drop every resident key, and under the ring the rows
        // to rebuild them from are gone). `capacity_tokens` is that capacity; a pool covers
        // `ig.pool` tokens, so the key plane is `pool` times SHORTER than a flat state plane —
        // 32 f32 per token against 256. It is NOT read off `plane.len()` any more: once the state
        // plane is a ring, its length is one call's tail, not the context.
        // `.max(1)` keeps the slice non-null at t_kv < pool, where no complete pool exists yet.
        let capacity_pools = capacity_tokens / ig.pool;
        let need = (capacity_pools * d).max(n_pools * d).max(1);
        if pool_key_plane.as_ref().is_none_or(|k| k.len() < need) {
            *pool_key_plane = Some(e.uninit(need)?);
            *pools_ready = 0;
        }
        let pool_keys = pool_key_plane
            .as_mut()
            .expect("resident pool-key plane just allocated");

        // THE DRAIN. The state plane is written by exactly one kernel and read by exactly one,
        // and a row's single read is the pool-key build of the pool that row belongs to. So the
        // rows that must be live at any instant are `[*pools_ready * pool, cur)`: everything
        // below has been read, everything above is not written yet, and the two kernels can be
        // interleaved in sub-ranges of the call instead of run once each over the whole call.
        //
        // That is what makes the ring size a WORKING-SET choice rather than a bound on `t`:
        // `index_ring_take` hands back how many rows fit before the ring must be drained, the
        // build drains it, and the loop continues. `k_norm`/`gate` are computed ONCE for the
        // whole call above and walked by source-row offset, so the values, their order, and the
        // ring addresses they land on are exactly what a single whole-call append produced.
        // A flat plane (`ring == 0`) takes the whole call in one iteration, byte for byte.
        let ape = indexer.kpool_ape.float_data();
        let mut cur = slot;
        let mut appended = 0usize;
        while appended < t {
            let take =
                crate::cache::index_ring_take(ring, ig.pool, *pools_ready, cur, t - appended)
                    .ok_or_else(|| -> Box<dyn std::error::Error> {
                        format!(
                            "indexer tail ring lapped: {ring} rows cannot hold the {} rows still \
                         owed to unbuilt pools at row {cur} (pools_ready {}, pool {}, slot \
                         {slot}, t {t}). The pool-key plane was reset or the cache rewound \
                         without clamping index_pools_ready, so rows this call must read were \
                         already overwritten. Raise MEMRA_DSA_INDEX_RING, or set \
                         MEMRA_DSA_INDEX_RING=0 for the flat plane",
                            cur.saturating_sub((*pools_ready).saturating_mul(ig.pool)),
                            *pools_ready,
                            ig.pool
                        )
                        .into()
                    })?;
            debug_assert!(take > 0 && appended + take <= t);
            e.mla_index_append(plane, &k_norm, &gate, appended, cur, take, d, d, ring)?;
            cur += take;
            appended += take;
            let ready_now = cur / ig.pool;
            e.mla_kpool_pool_keys(
                plane,
                ape,
                pool_keys,
                (*pools_ready).min(ready_now),
                ready_now,
                ig.pool,
                d,
                ring,
            )?;
            *pools_ready = ready_now;
        }
        debug_assert!(t == 0 || *pools_ready == n_pools);
        let pool_keys = &*pool_keys;

        // 3. score + head mix, 4. top-k -> raw rows + tail.
        let q_index = mm(&indexer.wq_b, q_resid)?;
        let head_weights = mm(&indexer.weights_proj, h)?;
        let mut score = e.uninit((t * n_pools).max(1))?;
        e.mla_kpool_score(
            &q_index,
            pool_keys,
            &head_weights,
            &mut score,
            t,
            ig.heads,
            d,
            n_pools,
            ig.pool,
            slot,
            (d as f32).powf(-0.5),
            (ig.heads as f32).powf(-0.5),
        )?;
        let mut idx = e.uninit_i32(t * width)?;
        e.mla_kpool_select(
            &score,
            &mut idx,
            t,
            n_pools,
            ig.pool,
            select_k,
            width,
            slot,
            ig.always_select_tail,
        )?;
        Ok((idx, width))
    }

    /// STATELESS MLA arm (`HybridModel::forward`): the latent plane lives for this call only,
    /// sized to the request. Same math as the cached arm — it is the same core with slot 0.
    pub fn mla_attn(
        &self,
        e: &Engine,
        mla: &crate::hybrid::MlaAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        if mla.tp.is_some() {
            return Err(format!(
                "layer {il}: MLA layer is glm5-TP-sharded (MEMRA_GLM5_TP): the stateless \
                 mixer path is unwired for a head shard"
            )
            .into());
        }
        let mut latent = e.uninit(t * mla.geom.latent_dim)?;
        let mut index_plane = match mla.index.as_ref() {
            Some(indexer) => Some(e.uninit(t * indexer.geom.state_width())?),
            None => None,
        };
        // No residency across calls here — the planes die with the call, so `ready` starts at 0 and
        // every pool is built exactly once, which is what the cached arm also does on its prime.
        let mut pool_keys = None;
        let mut pools_ready = 0usize;
        let planes = index_plane.as_mut().map(|state| IndexerPlanes {
            state,
            pool_keys: &mut pool_keys,
            ready: &mut pools_ready,
            // Per-call plane, sized to the request: no ring, capacity is the request itself.
            state_ring_rows: 0,
            capacity_tokens: t,
        });
        self.mla_attn_core(e, mla, h, pos_d, t, il, &mut latent, planes, 0, false)
    }

    /// STATEFUL MLA arm (prime and T=1 decode): appends into the session's latent plane and
    /// attends the whole history. `cache.latent[il]` is allocated by the `LatentKvCache` arm of
    /// the cache allocator; a `None` here means the ModelPlan and the loaded mixer disagree.
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    pub fn mla_attn_cached(
        &self,
        e: &Engine,
        mla: &crate::hybrid::MlaAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        self.mla_attn_cached_inner(e, mla, h, pos_d, t, il, cache, false)
    }

    /// [`Self::mla_attn_cached`] on the VERIFY-BATCH matmul classes (lane/glm5-verify-batch):
    /// the SAME core at t=K+1 rows with every internal projection routed decode-exact
    /// (`matmul_rows_exact`), so row r of the batched call is bit-identical to the t=1
    /// `mla_attn_cached` call the per-row verify walk makes at position pos0+r. Causality
    /// within the batch is per-query by construction: the kpool selection masks pools
    /// invisible to each query and appends each query's OWN raw tail
    /// (`first_pos + t + 1`), and the gathered attention walks each query's own idx list
    /// (-1 padding arithmetic-invariant). Held by `glm5_tparallel_verify_gpu` gates 1+2
    /// running the batched arm. ONLY the glm5 verify-batch walk calls this.
    #[allow(clippy::too_many_arguments)] // allow: mirrors mla_attn_cached's contract
    pub fn mla_attn_cached_rows_exact(
        &self,
        e: &Engine,
        mla: &crate::hybrid::MlaAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        self.mla_attn_cached_inner(e, mla, h, pos_d, t, il, cache, true)
    }

    #[allow(clippy::too_many_arguments)] // allow: mirrors mla_attn_cached's contract
    fn mla_attn_cached_inner(
        &self,
        e: &Engine,
        mla: &crate::hybrid::MlaAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
        cache: &mut Cache,
        rows_exact: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        // glm5 TP fail-closed choke point, covering BOTH plain entries (decode/prime AND
        // the verify-batch rows arm): a TP-sharded layer holds heads/2 and a per-rank
        // latent replica — running it on the plain path would compute a silently-halved
        // mixer against the wrong plane, so it refuses by name instead.
        if mla.tp.is_some() {
            return Err(format!(
                "layer {il}: MLA layer is glm5-TP-sharded (MEMRA_GLM5_TP): the plain mixer \
                 path is unwired for a head shard — only the TP decode/prime walk may \
                 execute it (rows_exact={rows_exact})"
            )
            .into());
        }
        // Read before the layer borrow: it sizes the resident pool-key plane, which the ring'd
        // state plane's own length can no longer stand in for.
        let max_ctx = cache.max_ctx;
        let layer = cache.latent[il].as_mut().ok_or_else(|| {
            format!(
                "layer {il} is Mixer::Mla but the cache has no latent plane — the ModelPlan \
                 must declare StatePlan::LatentKvCache for it"
            )
        })?;
        let attn =
            self.mla_attn_cached_pre_wo(e, mla, h, pos_d, t, il, layer, max_ctx, rows_exact)?;
        // Verify-batch wo seam: the rows arm keeps its decode-exact output projection —
        // the wo dispatch moved here with the TP split, its routing did not change.
        if rows_exact {
            e.matmul_rows_exact(&mla.wo, &attn, t)
        } else {
            e.matmul(&mla.wo, &attn, t)
        }
    }

    /// The stateful MLA call against ONE latent plane, up to (and excluding) the output
    /// projection. The plain path wraps it above (canonical plane + `wo`); the glm5 TP-2
    /// walk calls it once per rank (root shard on the canonical plane, peer shard on the
    /// replicated peer plane) and joins the halves through the column-parallel `wo`.
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn mla_attn_cached_pre_wo(
        &self,
        e: &Engine,
        mla: &crate::hybrid::MlaAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
        layer: &mut memra_kv::LatentKvLayer,
        max_ctx: usize,
        rows_exact: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let slot = layer.len;
        let width = layer.width;
        assert_eq!(
            width, mla.geom.latent_dim,
            "layer {il}: cache latent width {width} != MlaGeom latent_dim {}",
            mla.geom.latent_dim
        );
        let capacity = layer.rows.len() / width;
        if slot + t > capacity {
            return Err(format!(
                "layer {il}: latent cache overflow — {slot} + {t} rows exceeds capacity {capacity}"
            )
            .into());
        }
        if mla.index.is_some() && layer.index_rows.is_none() {
            return Err(format!(
                "layer {il} loaded a DSA k-pool indexer but its latent cache carries no indexer \
                 state plane — StatePlan::LatentKvCache declared index_width 0 for a layer whose \
                 SparseIndexPlan is Own {{ kpool: Some(..) }}"
            )
            .into());
        }
        // Both planes are borrowed for the whole core call, so len bookkeeping happens after.
        // ONE `len` covers both: they are appended in the same call and must never drift.
        // The resident pool-key plane rides along: it is state that must SURVIVE the call, so the
        // core writes `ready` back through the borrow and it is restored with the buffers.
        let mut rows = std::mem::replace(&mut layer.rows, e.uninit(0)?);
        let mut index_rows = layer.index_rows.take();
        let mut pool_keys = layer.index_pool_keys.take();
        let mut pools_ready = layer.index_pools_ready;
        let index_ring_rows = layer.index_ring_rows.unwrap_or(0);
        let planes = index_rows.as_mut().map(|state| IndexerPlanes {
            state,
            pool_keys: &mut pool_keys,
            ready: &mut pools_ready,
            state_ring_rows: index_ring_rows,
            capacity_tokens: max_ctx,
        });
        let out =
            self.mla_attn_core_pre_wo(e, mla, h, pos_d, t, il, &mut rows, planes, slot, rows_exact);
        layer.rows = rows;
        layer.index_rows = index_rows;
        layer.index_pool_keys = pool_keys;
        // A FAILED core leaves `len` where it was, so the resident plane must go back too: it may
        // have advanced over pools built from rows a retry is about to rewrite with different
        // inputs. Clamping here (rather than letting the next call's tripwire fire) makes
        // retry-after-error correct instead of merely loud.
        layer.index_pools_ready = if out.is_ok() {
            pools_ready
        } else if let Some(indexer) = mla.index.as_ref() {
            pools_ready.min(layer.len / indexer.geom.pool)
        } else {
            pools_ready
        };
        let out = out?;
        // Resolve the layer's RESIDENT pool copy (the state plan does not carry `pool`; the
        // latent-plane snapshot path reads this field to address the tail ring). A nonzero
        // resident value that disagrees with the loaded geometry is corruption, not a race:
        // there is exactly one geometry per loaded layer.
        if let Some(indexer) = mla.index.as_ref() {
            let pool = indexer.geom.pool;
            if layer.index_pool != 0 && layer.index_pool != pool {
                return Err(format!(
                    "layer {il}: resident indexer pool {} != loaded geometry pool {pool}",
                    layer.index_pool,
                )
                .into());
            }
            layer.index_pool = pool;
        }
        layer.len = slot + t;
        let len_i32 = i32::try_from(layer.len).map_err(|_| "latent length exceeds i32 mirror")?;
        // Door H (`MEMRA_GLM5_HTOD_DIET`): the async `i32_set_k` launch instead of this
        // SYNCHRONIZING pageable 4-byte copy — 11 of these per round, one per MLA trunk layer.
        e.i32_mirror_store(&mut layer.len_d, len_i32)?;
        Ok(out)
    }

    /// The glm5 TP MLA walk for one prime/decode call (`mla` is the ROOT head shard; its
    /// sidecar carries the peer shards + runtime). Replicated per-token work runs on EVERY
    /// rank from identical inputs (wq_a/wkv_a/indexer/k-pool selection — identical bytes by
    /// determinism on uniform hardware, gate-held); each rank attends with its heads over
    /// its OWN latent replica; the attention parts are gathered through the armed transport
    /// and each rank's COLUMN-parallel `wo` slice computes its slice of the output with the
    /// plain matvec kernel — no cross-rank arithmetic anywhere.
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn mla_tp_attn_cached(
        &self,
        e: &Engine,
        mla: &crate::hybrid::MlaAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
        cache: &mut Cache,
        rows_exact: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let tp = mla
            .tp
            .as_ref()
            .ok_or("mla_tp_attn_cached called on an unsharded layer")?;
        let rt = &tp.rt;
        let ranks = tp.ranks();
        let g = mla.geom; // SHARD geometry: n_head = full/ranks
        let hl = g.n_head;
        let dv = g.d_v;
        let full_heads = tp.full_heads;
        let n_embd = tp.n_embd;
        let hh = n_embd / ranks;
        let max_ctx = cache.max_ctx;

        // HOP 1 — fan-out of the mixer input and positions to every peer rank. Both move the
        // WHOLE buffer, exactly as the v1 arm did, so the transport arms move identical
        // byte ranges (lane/glm5-tp-transport).
        let hop = rt.hop(e);
        let h_peers = crate::glm5_tp_transport::fanout_f32(&hop, h, h.len())?;
        let pos_peers = crate::glm5_tp_transport::fanout_i32(&hop, pos_d, pos_d.len())?;

        // Peer replica planes (lazily geometry-cloned from the canonical plane).
        {
            let canonical = cache.latent[il].as_ref().ok_or_else(|| {
                format!("layer {il}: glm5 TP MLA walk found no canonical latent plane")
            })?;
            crate::glm5_tp::ensure_mla_peer_latent(
                rt,
                canonical,
                &mut cache.glm5_tp_latent_peer[il],
            )?;
        }

        // Peer passes first (each rank's heads over its replica), then root (canonical
        // plane unchanged) — v1's issue order at two ranks. `rows_exact` threads the
        // caller's matmul class through every rank: false = the prime/decode walk
        // (byte-for-byte the pre-composition arm), true = the spec x TP verify walk
        // (lane/glm5-composition) riding the same rows-exact classes as the unsharded
        // verify walk.
        let mut attn: Vec<Option<CudaSlice<f32>>> = (0..ranks).map(|_| None).collect();
        for r in 1..ranks {
            let layer = &mut cache.glm5_tp_latent_peer[il].as_mut().unwrap()[r - 1];
            attn[r] = Some(self.mla_attn_cached_pre_wo(
                &rt.peers[r - 1],
                &tp.peers[r - 1],
                &h_peers[r - 1],
                &pos_peers[r - 1],
                t,
                il,
                layer,
                max_ctx,
                rows_exact,
            )?);
        }
        attn[0] = {
            let layer = cache.latent[il].as_mut().unwrap();
            Some(self.mla_attn_cached_pre_wo(e, mla, h, pos_d, t, il, layer, max_ctx, rows_exact)?)
        };

        // HOP 2 — gather the per-head parts into the FULL [t, full_heads*dv] layout on
        // every rank. `full_heads * dv == ranks * (hl * dv)` by the shard map.
        let part = hl * dv;
        debug_assert_eq!(full_heads * dv, ranks * part);
        let attn_refs: Vec<&CudaSlice<f32>> = attn
            .iter()
            .map(|a| a.as_ref().expect("filled above"))
            .collect();
        let fulls = crate::glm5_tp_transport::gather_parts(&hop, &attn_refs, t, part)?;

        // Column-parallel wo slices + output concat (pure movement). The verify walk's
        // wo rides the rows-exact class, exactly like the unsharded verify walk's wo.
        let mut ys = Vec::with_capacity(ranks);
        if rows_exact {
            ys.push(e.matmul_rows_exact(&mla.wo, &fulls[0], t)?);
            for r in 1..ranks {
                ys.push(rt.peers[r - 1].matmul_rows_exact(&tp.peers[r - 1].wo, &fulls[r], t)?);
            }
        } else {
            ys.push(e.matmul(&mla.wo, &fulls[0], t)?);
            for r in 1..ranks {
                ys.push(rt.peers[r - 1].matmul(&tp.peers[r - 1].wo, &fulls[r], t)?);
            }
        }
        // HOP 3 — concat the column parts into the mixer output on ROOT.
        debug_assert_eq!(n_embd, ranks * hh);
        let y_refs: Vec<&CudaSlice<f32>> = ys.iter().collect();
        crate::glm5_tp_transport::concat_parts_on_root(&hop, &y_refs, t, hh)
    }

    /// Linear-attention (Gated DeltaNet) mixer (qwen35 :338-470).
    pub fn linear_attn(
        &self,
        e: &Engine,
        la: &LinearAttnLayer,
        h: &CudaSlice<f32>,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let cfg = &self.cfg;
        let _n_embd = cfg.n_embd as usize;
        let geometry = la.geometry;
        let d_state = geometry.key_head_dim as usize;
        let num_k = geometry.key_heads as usize;
        let num_v = geometry.value_heads as usize;
        let d_conv = geometry.conv_kernel as usize;
        let head_k = d_state;
        let head_v = geometry.value_head_dim as usize;
        let key_dim = head_k * num_k; // 2048
        let value_dim = head_v * num_v; // 4096
        let conv_dim = key_dim * 2 + value_dim; // 8192
        let eps = cfg.rms_eps;
        let scale = 1.0 / (d_state as f32).sqrt();

        // projections
        // grouped: one f16 activation convert feeds all four projections (matmul_group)
        let mut g4 = e.matmul_group(
            &[&la.wqkv, &la.wqkv_gate, &la.ssm_beta, &la.ssm_alpha],
            h,
            t,
        )?;
        let alpha = g4.pop().unwrap(); // [T, num_v]
        let beta_raw = g4.pop().unwrap(); // [T, num_v]
        let z = g4.pop().unwrap(); // [T, value_dim]
        let qkv_mixed = g4.pop().unwrap(); // [T, conv_dim] token-major

        // conv + GDN repack, FUSED (2026-07-03): ssm_conv1d_gdn reads qkv_mixed [T, conv_dim]
        // token-major DIRECTLY (causal window rows t-pad..t, rows<0 = zero prefill state), applies
        // the 8-tap conv + SiLU, and scatters straight into the GDN [d_state, num_v, T] q/k/v
        // layout with the modulo head-repeat. Replaces transpose + zeros + conv_left_pad +
        // ssm_conv1d + qkv_to_gdn_repack (5 launches, conv_in/conv_out scratch + a 16MB@T=512
        // round-trip). BIT-IDENTICAL accumulation and scatter mapping.
        let _ = (head_k, head_v);
        let mut q_g = e.uninit(d_state * num_v * t)?;
        let mut k_g = e.uninit(d_state * num_v * t)?;
        let mut v_g = e.uninit(d_state * num_v * t)?;
        e.ssm_conv1d_gdn(
            &qkv_mixed,
            la.ssm_conv1d.float_data(),
            &mut q_g,
            &mut k_g,
            &mut v_g,
            conv_dim,
            t,
            d_conv,
            d_state,
            num_v,
            num_k,
            key_dim,
        )?;
        // L2-norm q,k per (head_dim) row — rows are contiguous d_state in q_g.
        let mut q_l2 = e.uninit(d_state * num_v * t)?;
        e.l2_norm(&q_g, &mut q_l2, d_state, num_v * t, eps)?;
        let mut k_l2 = e.uninit(d_state * num_v * t)?;
        e.l2_norm(&k_g, &mut k_l2, d_state, num_v * t, eps)?;
        let v_gd = v_g;

        // beta = sigmoid(beta_raw) ; g_log = a * softplus(alpha + dt). Both need [num_v, T] layout
        // (g[t*num_v + h]). beta_raw/alpha are [T, num_v] token-major == that layout already.
        let mut beta = e.uninit(t * num_v)?;
        e.sigmoid(&beta_raw, &mut beta, t * num_v)?;
        // gdn_glog expects alpha [H,T] with alpha[t*H+h] and dt_bias/a [H] — matches token-major [T,num_v].
        let mut g_log = e.uninit(t * num_v)?;
        e.gdn_glog(
            &alpha,
            la.ssm_dt.float_data(),
            la.ssm_a.float_data(),
            &mut g_log,
            num_v,
            t,
        )?;

        // GDN scan (A4: gdn_scan_prefill dispatches chunked WY under MEMRA_GDN_CHUNKED)
        let state_in = e.zeros(d_state * d_state * num_v)?; // zero state (prefill)
        let mut state_out = e.zeros(d_state * d_state * num_v)?;
        let mut o = e.uninit(d_state * num_v * t)?;
        e.gdn_scan_prefill(
            &q_l2,
            &k_l2,
            &v_gd,
            &g_log,
            &beta,
            None,
            None,
            &state_in,
            &mut state_out,
            &mut o,
            num_v,
            t,
            scale,
            num_v,
        )?;

        // gated RMSNorm: dst = RMSNorm(o, ssm_norm[head_v]) * silu(z). o is [d_state, num_v, T];
        // rows of head_v=d_state, nrows = num_v*T. z must match row layout: z is [T, value_dim] token-major
        // = [T, num_v*head_v]; per (t, vh) the head_v slice is contiguous -> rows align as (t*num_v+vh).
        // o rows are (t*num_v+vh) too. Good.
        let mut gn = e.uninit(d_state * num_v * t)?;
        e.gated_rmsnorm(
            &o,
            la.ssm_norm.float_data(),
            &z,
            &mut gn,
            d_state,
            num_v * t,
            eps,
        )?;

        // ssm_out projection: gn is [d_state, num_v, T] = [value_dim, T] viewed token-major as [T, value_dim]?
        // gn layout: (t*num_v+vh)*d_state + i  == token t, then (vh,i) = channel vh*d_state+i. That's
        // token-major [T, value_dim]. linear wants [T, in=value_dim]. Good.
        let out = e.matmul(&la.ssm_out, &gn, t)?;
        Ok(out)
    }
}

impl HybridModel {
    /// MoE FFN (EDGE-1). z: [T, n_embd] (already post-attention-normed). Returns moe_out [T, n_embd].
    /// Node-for-node vs llama.cpp build_moe_ffn + qwen35moe::build_layer_ffn.
    ///
    /// `il` is the trunk layer index — the residency-cache key prefix (a gate-expert of layer 3 is a
    /// different 860160-byte block than the same expert of layer 7).
    ///
    /// Routing: host softmax+sort (default) OR the fused router kernel (MEMRA_FUSED_ROUTER).
    /// Dispatch: stage-every-token into 3 scratch slots (default) OR the SLRU residency cache
    /// (MEMRA_MOE_CACHE). The cache-HIT weight path is bit-identical to stage-every-token (§B.3).
    /// Convenience wrapper used by the hybrid trunk/MTP loops: pulls dims + max-block from `self`.
    pub fn moe_ffn_il(
        &self,
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        il: u16,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        Self::moe_ffn_inner(
            e,
            m,
            z,
            None,
            t,
            &self.cfg,
            il,
            self.max_moe_block(),
            false,
            None,
            self.uses_sliding_gated_moe_program(),
            false,
        )
    }

    /// Prefill twin: Step35 promotes expert-grouped dispatch by default while decode/spec callers
    /// keep `moe_ffn_il` and therefore retain their existing dispatch class.
    pub fn moe_ffn_il_prefill(
        &self,
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        il: u16,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        Self::moe_ffn_inner(
            e,
            m,
            z,
            None,
            t,
            &self.cfg,
            il,
            self.max_moe_block(),
            true,
            Some(&self.step_grouped_prefill),
            self.uses_sliding_gated_moe_program(),
            false,
        )
    }

    /// Decode-path twin with a PRE-QUANTIZED z (from add_rms_norm_zq8): threads (zq, zd) into the
    /// t=1 dev arm so the per-layer standalone quantize_q8_1 launch folds away. Identical bytes
    /// (the fused kernel reproduces quantize_q8_1 exactly); every other path ignores the pair.
    pub fn moe_ffn_il_zq8(
        &self,
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        zq8: Option<&(CudaSlice<i8>, CudaSlice<f32>)>,
        t: usize,
        il: u16,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        Self::moe_ffn_inner(
            e,
            m,
            z,
            zq8,
            t,
            &self.cfg,
            il,
            self.max_moe_block(),
            false,
            None,
            self.uses_sliding_gated_moe_program(),
            false,
        )
    }

    /// Verify-rows twin of [`Self::moe_ffn_il_zq8`] (lane/glm5-vrest): the SAME routing and
    /// dispatch decisions with the pairs-shaped batched routed-expert arm armed. Only the
    /// verify walk's batched arm (`MEMRA_GLM5_VERIFY_BATCH`, t>=2) calls this; every
    /// unqualified shape inside falls closed to the byte-identical sequential loop.
    pub(crate) fn moe_ffn_il_zq8_vrows(
        &self,
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        il: u16,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        Self::moe_ffn_inner(
            e,
            m,
            z,
            None,
            t,
            &self.cfg,
            il,
            self.max_moe_block(),
            false,
            None,
            self.uses_sliding_gated_moe_program(),
            true,
        )
    }

    /// MoE FFN (EDGE-1), source-/model-agnostic. z: [T, n_embd] (already post-attention-normed).
    /// Returns moe_out [T, n_embd]. Node-for-node vs llama.cpp build_moe_ffn. Shared by the hybrid
    /// (qwen35moe, shared expert present) and the dense-attention MoE (OLMoE, no shared expert) paths;
    /// `cfg.moe` supplies the dims and the optional shexp fields decide whether step 3 runs.
    ///
    /// `il` is the layer index — the residency-cache key prefix. `max_block` is the global max expert
    /// stride (fixed cache-slot size); pass `self.max_moe_block()`.
    pub(crate) fn moe_ffn(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        max_block: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        Self::moe_ffn_inner(
            e, m, z, None, t, cfg, il, max_block, false, None, false, false,
        )
    }

    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::map_entry)] // allow: the init body is fallible (`?`); Entry::or_insert_with cannot propagate errors
    pub(crate) fn moe_ffn_inner(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        zq8: Option<&(CudaSlice<i8>, CudaSlice<f32>)>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        max_block: usize,
        prefill: bool,
        grouped_prefill: Option<&std::sync::Mutex<crate::hybrid::StepEpGroupedPrefill>>,
        sliding_gated_moe: bool,
        vrows: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let worker_io = crate::spill_pread::worker_enabled();
        let epoch_lfu = std::env::var_os("MEMRA_MOE_LFU_DECAY").is_some();
        if Engine::moe_cache_enabled() && (worker_io || epoch_lfu) {
            e.with_moe_cache(max_block, |cache, _| {
                cache.begin_forward_epoch(il, t);
                if worker_io {
                    cache.begin_worker_scope();
                }
                Ok(())
            })?;
        }
        if let Some(ep) = &m.glm5_ep {
            // glm5 TP-2 EP walk (MEMRA_GLM5_TP): whole-expert halves, root router, slot-ordered
            // canonical combine. Every other arm of this function is unreachable for an
            // EP-armed layer by construction. `prefill` keys the EP grouped-prime arm
            // (MEMRA_GLM5_EP_GROUPED_PRIME) exactly as it keys the plain grouped arm below.
            return Self::moe_ffn_glm5_ep(e, m, ep, z, zq8, t, cfg, il, prefill);
        }
        if m.step_ep.is_some() || m.step_tp.is_some() {
            let moe = cfg
                .moe
                .as_ref()
                .ok_or("Step distributed execution requires MoE model metadata")?;
            let n_embd = cfg.n_embd as usize;
            let n_expert = moe.expert_count as usize;
            let n_used = moe.expert_used_count as usize;
            let sigmoid = cfg
                .sigmoid_router()
                .ok_or("Step distributed execution requires the Step sigmoid router")?;
            let logits = Self::moe_router_logits(e, m, z, t, cfg)?;
            Self::trace_sigmoid_router_logits(e, il, t, n_expert, n_used, &logits, m, sigmoid)?;
            let grouped_prefill_requested = prefill && step_ep_grouped_prefill_enabled()?;
            if grouped_prefill_requested && !step_tp_prefill_enabled()? {
                return Err(
                    "MEMRA_STEP_EP_GROUPED_PREFILL=1 requires MEMRA_STEP_TP_PREFILL=1".into(),
                );
            }
            if grouped_prefill_requested && !step_grouped_prefill_shape(true, prefill, t) {
                return Err(format!(
                    "Step grouped prefill tokens {t} are outside the qualified {}..={} range",
                    PRIME_MIN_T,
                    crate::cache::PRIME_CHUNK_MAX_TOKENS,
                )
                .into());
            }
            let grouped_decode_shape = step_grouped_decode_shape(prefill, t);
            let grouped_prefill_shape =
                step_grouped_prefill_shape(grouped_prefill_requested, prefill, t);
            if let Some(ep) = m.step_ep.as_ref().filter(|ep| {
                ep.grouped_decode.is_some() && (grouped_decode_shape || grouped_prefill_shape)
            }) {
                let (selected, route_weights) =
                    Self::moe_route_sigmoid_cfg(e, &logits, t, n_expert, n_used, m, sigmoid)?;
                crate::moesd::record_host_routes(il, n_expert, n_used, &selected)?;
                Self::trace_moe_routes(il, t, &selected, &route_weights)?;
                Self::trace_moe_input(e, il, t, n_embd, z)?;
                let selected = selected
                    .iter()
                    .map(|&expert| expert as usize)
                    .collect::<Vec<_>>();

                // The narrow route readback above orders the owning-stage producer. The grouped
                // runtime then copies the resident root activation into its persistent rank inputs.
                e.stream().synchronize()?;
                let execute = |state: &mut crate::hybrid::StepEpGroupedDecode| {
                    state.projection.set_activation_limit(ep.activation_limit)?;
                    ep.runtime
                        .refresh_step_grouped_expert_parallel_gate_from_root_device(
                            ep.experts.e4m3()?,
                            &mut state.projection,
                            z,
                            t,
                            &selected,
                        )?;
                    ep.runtime.refresh_step_grouped_expert_parallel_combine(
                        &state.projection,
                        &mut state.combine,
                        &route_weights,
                    )?;
                    ep.runtime.execute_step_grouped_expert_parallel_gate(
                        ep.experts.e4m3()?,
                        &mut state.projection,
                    )?;
                    ep.runtime.execute_step_grouped_expert_parallel_combine(
                        &state.projection,
                        &mut state.combine,
                    )?;
                    let mut output = ep.runtime.copy_step_grouped_expert_parallel_combine_root(
                        &state.projection,
                        &state.combine,
                        e,
                    )?;
                    Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut output)?;
                    if prefill {
                        // A shared plan may be reused by the next layer on a different runtime
                        // stream. Complete the owning-stage copy before its source is overwritten.
                        e.stream().synchronize()?;
                    }
                    eprintln!(
                        "[step-tp-ep-grouped] execute layer={il} tokens={t} devices={:?} \
                         attention_layout=tensor-parallel expert_layout=expert-parallel \
                         expert_transport={} native_p2p=true route_control=host-narrow \
                         input=root-device projection_workspaces=persistent \
                         combine=root-device output=owning-stage-device \
                         prefill={prefill} batched_decode=false capacity={} \
                         performance_claim=false",
                        ep.devices,
                        ep.runtime.transport_label(),
                        state.projection.max_tokens(),
                    );
                    Ok::<_, Box<dyn std::error::Error>>(output)
                };

                if grouped_prefill_shape {
                    let grouped_prefill = grouped_prefill
                        .ok_or("Step grouped prefill has no model-scoped executor")?;
                    let mut shared = grouped_prefill
                        .lock()
                        .map_err(|_| "Step grouped prefill state lock is poisoned")?;
                    let needs_prepare = shared.state.as_ref().is_none_or(|state| {
                        state.devices != ep.devices
                            || state.grouped.projection.max_tokens() < t
                            || state.grouped.projection.input_width() != n_embd
                            || state.grouped.projection.expert_width()
                                != moe.expert_ff_length as usize
                    });
                    if needs_prepare {
                        let seed_input = vec![0.0f32; n_embd];
                        let seed_selected = &selected[..n_used];
                        let seed_weights = &route_weights[..n_used];
                        let projection = ep
                            .runtime
                            .prepare_step_grouped_expert_parallel_gate_with_capacity(
                                ep.experts.e4m3()?,
                                &seed_input,
                                1,
                                seed_selected,
                                ep.activation_limit,
                                t,
                            )?;
                        let combine = ep.runtime.prepare_step_grouped_expert_parallel_combine(
                            &projection,
                            seed_weights,
                        )?;
                        shared.state = Some(crate::hybrid::StepEpGroupedPrefillState {
                            devices: ep.devices.clone(),
                            grouped: crate::hybrid::StepEpGroupedDecode {
                                projection,
                                combine,
                            },
                        });
                        eprintln!(
                            "[step-tp-ep-grouped-prefill] prepare capacity={t} devices={:?} \
                             shared_across_layers=true performance_claim=false",
                            ep.devices,
                        );
                    }
                    return execute(
                        &mut shared
                            .state
                            .as_mut()
                            .expect("Step grouped prefill state prepared above")
                            .grouped,
                    );
                }

                let mut grouped = ep
                    .grouped_decode
                    .as_ref()
                    .expect("grouped decode presence checked above")
                    .lock()
                    .map_err(|_| "Step grouped decode state lock is poisoned")?;
                return execute(&mut grouped);
            }
            if grouped_prefill_shape {
                return Err(
                    "Step grouped prefill requires native-P2P expert-owner device arithmetic"
                        .into(),
                );
            }
            // MEMRA_STEP_TP_DEV_ROUTER=1 (t=1): route on device and feed the device-routed
            // expert program — the per-layer host logits readback (the last per-layer host
            // sync) disappears. Selection tie-breaking may differ from the host router:
            // numeric-class door, run-gen argmax gate + boot battery.
            // STEP TP2 GEMM PRIME (2026-08-27, TTFT lane): a prime chunk's routed MoE goes
            // through ONE grouped f16 GEMM per projection over the resident NVFP4 banks —
            // the per-token device routes below cost 240 s at m=4092 (measured), the grouped
            // lane's sizing rows run 170-270 TFLOP/s. Router selections come from the same
            // sigmoid host oracle the EP arm uses; shexp rides the canonical grouped add.
            // t>=16 alone keys the branch: the batch prime reaches here through moe_ffn_il,
            // whose `prefill` is FALSE (only the _prefill twin sets it), and no other step37
            // route runs t>=16 — verify walks t<=8, decode t=1. Requiring `prefill` made the
            // first gate arm skip this branch entirely and wake the generic f16g arm instead
            // (48 s + kq_gemm_sk rc=1001, 2026-08-27).
            if t >= 16
                && crate::step_gemm_prime_on()
                && let Some(tp) = &m.step_tp
                && let crate::hybrid::StepTpExpertBank::Nvfp4(bank) = &tp.experts
            {
                // MEMRA_PRIME_PROF=1 sub-split of the moe bucket. The phase timer put
                // 1788 ms of a 3093 ms chunk here, but forcing the 32-row tile form (4x
                // more weight dequant) moved it only 5% — so the grouped GEMM is not
                // obviously what dominates. The router below is a HOST oracle: sigmoid +
                // top-8 over 288 experts for every one of 4096 tokens, per layer, which
                // is a D2H copy and a full pipeline drain 42 times per chunk. Attribute
                // it before optimizing the kernel it sits in front of.
                let mprof = std::env::var("MEMRA_PRIME_PROF").as_deref() == Ok("1") && t >= 16;
                let mut mt = std::time::Instant::now();
                let (selected, route_weights) =
                    Self::moe_route_sigmoid_cfg(e, &logits, t, n_expert, n_used, m, sigmoid)?;
                let sel_i32: Vec<i32> = selected.iter().map(|&x| x as i32).collect();
                let d_router = if mprof {
                    let _ = e.stream().synchronize();
                    let v = mt.elapsed().as_secs_f64() * 1e3;
                    mt = std::time::Instant::now();
                    v
                } else {
                    0.0
                };
                // MEMRA_MOE_DETERM=1: run the WHOLE grouped routine twice on identical
                // inputs and diff. The standalone harness cleared the grouped GEMM kernels
                // (8 invocations, both lanes, maxdiff 0.0 over 20.9M elements) but it does
                // not model the cross-device join/scatter or the o_proj-style reduction,
                // and the loader refuses both topologies (TP1, same-device) that would
                // isolate those by env. This tests the un-excluded region directly, in
                // the place it actually runs.
                //
                // The prime is nondeterministic: same prompt, one forward, temperature=0,
                // max_tokens=1, and the first token varies across reps. That blocks
                // MEMRA_PP_BF16's correctness receipt and invalidates every byte-identity
                // gate taken through the server. This probe also yields the jitter
                // MAGNITUDE, which any tolerance band needs.
                let mdet =
                    std::env::var("MEMRA_MOE_DETERM").as_deref() == Ok("1") && t >= 16 && il < 4;
                if mdet {
                    let a = tp.runtime.run_tensor_parallel_routes_nvfp4_prime_grouped(
                        bank,
                        e,
                        z,
                        t,
                        &sel_i32,
                        &route_weights,
                        n_used,
                        tp.activation_limit,
                    )?;
                    let b = tp.runtime.run_tensor_parallel_routes_nvfp4_prime_grouped(
                        bank,
                        e,
                        z,
                        t,
                        &sel_i32,
                        &route_weights,
                        n_used,
                        tp.activation_limit,
                    )?;
                    let (ha, hb) = (e.dtoh(&a)?, e.dtoh(&b)?);
                    let mut md = 0.0f32;
                    let mut ndiff = 0usize;
                    for (x, y) in ha.iter().zip(hb.iter()) {
                        let d = (x - y).abs();
                        if d > 0.0 {
                            ndiff += 1;
                        }
                        if d > md {
                            md = d;
                        }
                    }
                    eprintln!(
                        "[moe-determ] il={il} t={t} maxdiff={md:.3e} \
                                 differing={ndiff}/{} -> {}",
                        ha.len(),
                        if ndiff == 0 {
                            "IDENTICAL"
                        } else {
                            "NONDETERMINISTIC"
                        }
                    );
                }
                let mut output = tp.runtime.run_tensor_parallel_routes_nvfp4_prime_grouped(
                    bank,
                    e,
                    z,
                    t,
                    &sel_i32,
                    &route_weights,
                    n_used,
                    tp.activation_limit,
                )?;
                let d_gemm = if mprof {
                    let _ = e.stream().synchronize();
                    let v = mt.elapsed().as_secs_f64() * 1e3;
                    mt = std::time::Instant::now();
                    v
                } else {
                    0.0
                };
                Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut output)?;
                if mprof {
                    let _ = e.stream().synchronize();
                    let d_shared = mt.elapsed().as_secs_f64() * 1e3;
                    // Per LAYER, not accumulated: the four trunk phases already carry the
                    // per-chunk totals, and one line per layer is what shows whether the
                    // cost is flat across layers or concentrated in a few.
                    eprintln!(
                        "[moe-prof] il={il} t={t} router={d_router:.1}ms \
                                 gemm={d_gemm:.1}ms shared={d_shared:.1}ms"
                    );
                }
                return Ok(output);
            }
            if t == 1
                && crate::tp::step_nvfp4_dev_routes_enabled()?
                && crate::tp::step_tp_dev_router_enabled()?
                && let Some(tp) = &m.step_tp
                && let crate::hybrid::StepTpExpertBank::Nvfp4(bank) = &tp.experts
            {
                let (sf, route_norm) = sigmoid;
                // MEMRA_ROUTES_PRESTAGE=1: issue the rank input pull + quantize NOW,
                // before the router — the rank streams overlap the gemv+topk.
                // MEMRA_DEV1_ROUTER=1 rides the prestage hook: rank1 routes LOCALLY
                // from its own z copy (replicated deterministic router — identical
                // bits in, identical sel/w out) and starts its sweep without
                // waiting the root's sel broadcast.
                static D1_ROUTER: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
                let d1_router = *D1_ROUTER
                    .get_or_init(|| std::env::var("MEMRA_DEV1_ROUTER").as_deref() == Ok("1"));
                if d1_router {
                    let (sf_h, rn_h) = sigmoid;
                    let n_ex = m.gate_exps.n_expert;
                    let act_ct = m.active_count();
                    let _ = tp.runtime.nvfp4_routes_prestage_with(
                        bank,
                        e,
                        z,
                        |rank1, in1, sel1, w1| {
                            let mut guard = DEV1_ROUTER_REPS
                                .lock()
                                .map_err(|_| "dev1 router replica lock")?;
                            let (reps, scratch) =
                                guard.get_or_insert_with(|| (Default::default(), None));
                            if !reps.contains_key(&il) {
                                use cudarc::driver::DevicePtr;
                                let (g1, p1, a1) = (
                                    rank1.htod(&vec![0.0f32; n_ex * n_embd])?,
                                    rank1.htod(&vec![0.0f32; n_ex])?,
                                    rank1.alloc_u8_uninit(n_ex)?,
                                );
                                for (src, dst_len, dst) in [
                                    (
                                        {
                                            let s = e.stream();
                                            let (p, _g) = m.gate_inp.float_data().device_ptr(&s);
                                            p
                                        },
                                        n_ex * n_embd * 4,
                                        {
                                            let s = rank1.stream();
                                            let (p, _g) = g1.device_ptr(&s);
                                            p
                                        },
                                    ),
                                    (
                                        {
                                            let s = e.stream();
                                            let (p, _g) = m.exp_probs_b_dev.device_ptr(&s);
                                            p
                                        },
                                        n_ex * 4,
                                        {
                                            let s = rank1.stream();
                                            let (p, _g) = p1.device_ptr(&s);
                                            p
                                        },
                                    ),
                                    (
                                        {
                                            let s = e.stream();
                                            let (p, _g) = m.active_experts_dev.device_ptr(&s);
                                            p
                                        },
                                        n_ex,
                                        {
                                            let s = rank1.stream();
                                            let (p, _g) = a1.device_ptr(&s);
                                            p
                                        },
                                    ),
                                ] {
                                    crate::tp::raw_copy_bytes(dst, src, dst_len, rank1)?;
                                }
                                rank1.stream().synchronize()?;
                                reps.insert(il, (g1, p1, a1));
                            }
                            if scratch.is_none() {
                                *scratch = Some(rank1.htod(&vec![0.0f32; n_ex])?);
                            }
                            let (g1, p1, a1) = reps.get(&il).expect("armed above");
                            let logits1 = scratch.as_mut().expect("armed above");
                            rank1.router_gemv_into(g1, in1, logits1, n_embd, n_ex, 1)?;
                            rank1.moe_router_sigmoid_topk_into(
                                logits1, 1, n_ex, n_used, act_ct, p1, a1, sf_h, rn_h, sel1, w1,
                            )?;
                            Ok(true)
                        },
                    )?;
                } else {
                    let _ = tp.runtime.nvfp4_routes_prestage(bank, e, z)?;
                }
                // Persistent selection buffers: the allocating topk built two fresh
                // slices per layer; sel/w land in process-static rows instead
                // (host-op diet — same kernel, same bytes).
                #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
                static SELW: std::sync::Mutex<Option<(usize, CudaSlice<i32>, CudaSlice<f32>)>> =
                    std::sync::Mutex::new(None);
                let mut selw = SELW.lock().map_err(|_| "selw lock poisoned")?;
                if selw.as_ref().is_none_or(|(d, ..)| *d != e.ctx().ordinal()) {
                    *selw = Some((
                        e.ctx().ordinal(),
                        e.htod_i32(&vec![0i32; n_used])?,
                        e.htod(&vec![0.0f32; n_used])?,
                    ));
                }
                let (_, sel_d, w_d) = selw.as_mut().expect("armed above");
                e.moe_router_sigmoid_topk_into(
                    &logits,
                    t,
                    n_expert,
                    n_used,
                    m.active_count(),
                    &m.exp_probs_b_dev,
                    &m.active_experts_dev,
                    sf,
                    route_norm,
                    sel_d,
                    w_d,
                )?;
                crate::moesd::record_device_routes(e, il, n_expert, n_used, sel_d)?;
                // FAIL-CLOSED for the route taps: this walk keeps the selection
                // device-side, so `trace_moe_routes` (MEMRA_MOE_TRACE /
                // MEMRA_MOE_WEIGHT_TRACE) never sees its rows. Every other MoE walk is
                // either host-routed (the taps ride the existing readback) or forced to
                // the host-visible path by observation mode — this one is neither. A
                // trace that silently misses whole layers poisons any placement mint
                // built on it (LAW:coactivation-expert-placement measurement leg), so an
                // armed tap refuses by name instead of dropping rows.
                if std::env::var("MEMRA_MOE_TRACE").is_ok()
                    || std::env::var("MEMRA_MOE_WEIGHT_TRACE").is_ok()
                {
                    return Err("MEMRA_MOE_TRACE/MEMRA_MOE_WEIGHT_TRACE cannot trace the \
                         device-routed step TP walk (selection never returns to host; \
                         tracing would add a new sync). Route through the host-router \
                         arm — refused rather than silently dropping rows"
                        .into());
                }
                // MEMRA_SHEXP_OVERLAP=1: issue the shared expert from the routes
                // PREJOIN hook so it executes while the peer rank drains its sweep
                // (fills dev0's join wait); apply adds the identical values after.
                static SHEXP_OV: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
                let shexp_ov = *SHEXP_OV
                    .get_or_init(|| std::env::var("MEMRA_SHEXP_OVERLAP").as_deref() == Ok("1"));
                // MEMRA_SHEXP_DEV1=1 (supersedes the dev0 overlap): the shared
                // expert runs on rank1 — the idle device — same kernels, same
                // split program, down row root-resident: bit-identical.
                static SHEXP_D1: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
                let shexp_d1 = *SHEXP_D1
                    .get_or_init(|| std::env::var("MEMRA_SHEXP_DEV1").as_deref() == Ok("1"))
                    && tp.runtime.rank_engine(1).is_some();
                // MOE TAIL FUSION M1 (MEMRA_TAIL_ADD3=0 reverts): pre-arm the
                // overlap ws + ones row and hand their RAW pointers to the routed
                // run — the join add folds the shexp apply into one launch.
                static TAIL3: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
                let tail3 =
                    *TAIL3.get_or_init(|| std::env::var("MEMRA_TAIL_ADD3").as_deref() != Ok("0"));
                let mut ov_issued = false;
                let mut d1_issued = false;
                let mut tail_folded = false;
                let mut output = if shexp_d1 {
                    let rank1 = tp.runtime.rank_engine(1).expect("checked above");
                    tp.runtime
                        .run_tensor_parallel_routes_nvfp4_device_routed_prejoin(
                            bank,
                            e,
                            z,
                            sel_d,
                            w_d,
                            n_used,
                            tp.activation_limit,
                            || {
                                d1_issued =
                                    Self::shexp_dev1_issue(e, rank1, m, z, cfg, il, n_embd)?;
                                Ok(())
                            },
                        )?
                } else if shexp_ov {
                    // Raw sh/ones pointers for the fused tail (persistent statics;
                    // pointers stable, no lock held across the routed call). The
                    // sh CONTENT is written by the prejoin-issued kernels earlier
                    // on e's stream — stream order covers the fused add.
                    let post_add = if tail3 {
                        Self::shexp_overlap_tail_ptrs(e, m, cfg, n_embd)?
                    } else {
                        None
                    };
                    let used_post = post_add.is_some();
                    let out = tp
                        .runtime
                        .run_tensor_parallel_routes_nvfp4_device_routed_prejoin_add3(
                            bank,
                            e,
                            z,
                            sel_d,
                            w_d,
                            n_used,
                            tp.activation_limit,
                            || {
                                ov_issued = Self::shexp_overlap_issue(e, m, z, cfg, il, n_embd)?;
                                Ok(())
                            },
                            post_add,
                        )?;
                    // ov_issued false with post_add armed = an early-return arm
                    // (the GRAPH door) skipped the prejoin AND ignored post_add —
                    // fall through to the normal shexp add (battery v22 receipt:
                    // the strict error here failed every graph-door boot).
                    if used_post && ov_issued {
                        tail_folded = true; // apply folded into the join add
                    }
                    out
                } else {
                    tp.runtime.run_tensor_parallel_routes_nvfp4_device_routed(
                        bank,
                        e,
                        z,
                        sel_d,
                        w_d,
                        n_used,
                        tp.activation_limit,
                    )?
                };
                if output.len() != t * n_embd {
                    return Err(format!(
                        "Step tp routed output has {} values, expected {t}x{n_embd}",
                        output.len()
                    )
                    .into());
                }
                if tail_folded {
                    // shexp already folded into the join add (MOE TAIL FUSION M1)
                } else if d1_issued {
                    Self::shexp_dev1_apply(e, &mut output, n_embd)?;
                } else if ov_issued {
                    Self::shexp_overlap_apply(e, &mut output, n_embd)?;
                } else {
                    Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut output)?;
                }
                static DR_LOGGED: std::sync::atomic::AtomicU64 =
                    std::sync::atomic::AtomicU64::new(0);
                let layer_bit = 1u64 << (il as u64 % 64);
                if DR_LOGGED.fetch_or(layer_bit, std::sync::atomic::Ordering::Relaxed) & layer_bit
                    == 0
                {
                    eprintln!(
                        "[step-tp] execute layer={il} tokens={t} devices={:?} \
                                 expert_transport={} native_p2p={} router=device \
                                 activation=host-canonical accumulation=host-canonical \
                                 output=e-device io=device performance_claim=false \
                                 (logged once per layer)",
                        tp.devices,
                        tp.runtime.transport_label(),
                        tp.runtime.native_p2p(),
                    );
                }
                return Ok(output);
            }
            let automatic_ep_device_router = crate::tp::parallel_ep_device_router_enabled()?;
            let automatic_ep_q8_act = crate::tp::parallel_ep_q8_act_enabled()?;
            let automatic_ep_q8_scope = crate::tp::parallel_ep_q8_scope()?;
            let automatic_ep_q8_active =
                automatic_ep_q8_act && t <= crate::tp::NVFP4_EP_Q8_BATCH_CAP;
            if automatic_ep_q8_scope.is_some() && !automatic_ep_q8_act {
                return Err(
                    "MEMRA_PARALLEL_EP_Q8_SCOPE requires MEMRA_PARALLEL_EP_Q8_ACT=1".into(),
                );
            }
            if automatic_ep_q8_act && !automatic_ep_device_router {
                return Err(
                    "MEMRA_PARALLEL_EP_Q8_ACT=1 requires MEMRA_PARALLEL_EP_DEVICE_ROUTER=1".into(),
                );
            }
            if automatic_ep_q8_act && m.step_ep.as_ref().is_none_or(|ep| !ep.nvfp4_device_routes) {
                return Err(
                    "MEMRA_PARALLEL_EP_Q8_ACT=1 requires automatic W4A16 whole-expert EP".into(),
                );
            }
            if t <= crate::tp::NVFP4_EP_DEVICE_ROUTER_BATCH_CAP
                && automatic_ep_device_router
                && let Some(ep) = &m.step_ep
                && ep.nvfp4_device_routes
            {
                let bank = match &ep.experts {
                    crate::hybrid::StepEpExpertBank::Nvfp4(bank) => bank,
                    crate::hybrid::StepEpExpertBank::E4m3(_) => {
                        return Err("W4A16 device-routed EP reached an E4M3 expert bank".into());
                    }
                };
                let pairs = t
                    .checked_mul(n_used)
                    .ok_or("W4A16 device-routed EP pair count overflow")?;
                let capacity = crate::tp::NVFP4_EP_DEVICE_BATCH_CAP * n_used;
                /// Per-device persistent route scratch: device ordinal -> (armed capacity in
                /// pairs, selected-expert rows, route-weight rows). Named because the nested
                /// form is unreadable at this depth, not to hide it.
                type EpSelwByDevice =
                    std::collections::HashMap<usize, (usize, CudaSlice<i32>, CudaSlice<f32>)>;
                static EP_SELW: std::sync::Mutex<Option<EpSelwByDevice>> =
                    std::sync::Mutex::new(None);
                let mut selw = EP_SELW
                    .lock()
                    .map_err(|_| "automatic EP device-router workspace lock poisoned")?;
                let device = e.ctx().ordinal();
                let workspaces = selw.get_or_insert_with(Default::default);
                if workspaces
                    .get(&device)
                    .is_none_or(|(cap, ..)| *cap < capacity)
                {
                    workspaces.insert(
                        device,
                        (
                            capacity,
                            e.htod_i32(&vec![0i32; capacity])?,
                            e.htod(&vec![0.0f32; capacity])?,
                        ),
                    );
                }
                let (_, sel_d, w_d) = workspaces.get_mut(&device).expect("armed above");
                let (sf, route_norm) = sigmoid;
                e.moe_router_sigmoid_topk_into(
                    &logits,
                    t,
                    n_expert,
                    n_used,
                    m.active_count(),
                    &m.exp_probs_b_dev,
                    &m.active_experts_dev,
                    sf,
                    route_norm,
                    sel_d,
                    w_d,
                )?;
                crate::moesd::record_device_routes(e, il, n_expert, n_used, sel_d)?;
                static SHEXP_OV_AUTO: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
                let shexp_ov = t == 1
                    && *SHEXP_OV_AUTO
                        .get_or_init(|| std::env::var("MEMRA_SHEXP_OVERLAP").as_deref() == Ok("1"));
                let mut ov_issued = false;
                let mut output = if shexp_ov {
                    ep.runtime
                        .run_routed_experts_nvfp4_w4a16_device_routed_prejoin(
                            bank,
                            e,
                            z,
                            sel_d,
                            w_d,
                            t,
                            n_used,
                            ep.activation_limit,
                            || {
                                ov_issued = Self::shexp_overlap_issue(e, m, z, cfg, il, n_embd)?;
                                Ok(())
                            },
                        )?
                } else {
                    ep.runtime.run_routed_experts_nvfp4_w4a16_device_routed(
                        bank,
                        e,
                        z,
                        sel_d,
                        w_d,
                        t,
                        n_used,
                        ep.activation_limit,
                    )?
                };
                if output.len() != t * n_embd {
                    return Err(format!(
                        "W4A16 device-routed EP output has {} values, expected \
                         {t}x{n_embd}={}",
                        output.len(),
                        t * n_embd,
                    )
                    .into());
                }
                if ov_issued {
                    Self::shexp_overlap_apply(e, &mut output, n_embd)?;
                } else {
                    Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut output)?;
                }
                static DEVICE_ROUTER_LOGGED: std::sync::atomic::AtomicU64 =
                    std::sync::atomic::AtomicU64::new(0);
                let layer_bit = 1u64 << (il as u64 % 64);
                if DEVICE_ROUTER_LOGGED.fetch_or(layer_bit, std::sync::atomic::Ordering::Relaxed)
                    & layer_bit
                    == 0
                {
                    eprintln!(
                        "[parallel-ep] execute layer={il} tokens={t} devices={:?} \
                         router=device expert_transport={} native_p2p={} \
                         activation=bf16-rounded accumulation={} output=e-device \
                         performance_claim=false (logged once per layer)",
                        ep.devices,
                        ep.runtime.transport_label(),
                        ep.runtime.native_p2p(),
                        if automatic_ep_q8_active {
                            "token-slot-order-q8"
                        } else {
                            "token-slot-order"
                        },
                    );
                }
                debug_assert!(pairs <= capacity);
                return Ok(output);
            }
            // MEMRA_STEP_TP_TIMING=1: cumulative cost of the host routing seam (the dtoh here
            // drains every e-stream op queued since the layer's FFN entry, so this bills the
            // router matmul + glue too — the decode-bucket ffn residue decomposes here).
            static ROUTE_NS: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
            static ROUTE_CALLS: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
            let route_timing = std::env::var("MEMRA_STEP_TP_TIMING").as_deref() == Ok("1");
            let route_started = route_timing.then(std::time::Instant::now);
            let (selected, route_weights, input) = Self::moe_route_sigmoid_with_input(
                e,
                &logits,
                z,
                t,
                n_embd,
                n_expert,
                n_used,
                m.exp_probs_b.as_deref(),
                sigmoid,
                m.active_experts.as_deref(),
            )?;
            if let Some(started) = route_started {
                use std::sync::atomic::Ordering;
                let ns = ROUTE_NS.fetch_add(started.elapsed().as_nanos() as u64, Ordering::Relaxed)
                    + started.elapsed().as_nanos() as u64;
                let calls = ROUTE_CALLS.fetch_add(1, Ordering::Relaxed) + 1;
                if calls.is_multiple_of(430) {
                    eprintln!(
                        "[moe-route-timing] calls={calls} total_ms={:.1} avg_us={:.1}",
                        ns as f64 / 1.0e6,
                        ns as f64 / calls as f64 / 1.0e3,
                    );
                }
            }
            crate::moesd::record_host_routes(il, n_expert, n_used, &selected)?;
            Self::trace_moe_routes(il, t, &selected, &route_weights)?;
            Self::trace_moe_input(e, il, t, n_embd, z)?;
            let selected = selected
                .iter()
                .map(|&expert| expert as usize)
                .collect::<Vec<_>>();
            if t <= crate::tp::NVFP4_EP_DEVICE_BATCH_CAP
                && let Some(ep) = &m.step_ep
                && ep.nvfp4_device_routes
            {
                let bank = match &ep.experts {
                    crate::hybrid::StepEpExpertBank::Nvfp4(bank) => bank,
                    crate::hybrid::StepEpExpertBank::E4m3(_) => {
                        return Err("W4A16 NVFP4 device EP reached an E4M3 expert bank".into());
                    }
                };
                let mut output = ep.runtime.run_routed_experts_nvfp4_w4a16_device_io(
                    bank,
                    e,
                    z,
                    t,
                    &selected,
                    &route_weights,
                    n_used,
                    ep.activation_limit,
                )?;
                if output.len() != t * n_embd {
                    return Err(format!(
                        "W4A16 NVFP4 EP routed output has {} values, expected \
                         {t}x{n_embd}={}",
                        output.len(),
                        t * n_embd,
                    )
                    .into());
                }
                Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut output)?;
                static W4A16_EP_LOGGED: std::sync::atomic::AtomicU64 =
                    std::sync::atomic::AtomicU64::new(0);
                let layer_bit = 1u64 << (il as u64 % 64);
                if W4A16_EP_LOGGED.fetch_or(layer_bit, std::sync::atomic::Ordering::Relaxed)
                    & layer_bit
                    == 0
                {
                    eprintln!(
                        "[step-ep] execute layer={il} tokens={t} devices={:?} \
                         expert_transport={} native_p2p={} activation=bf16-rounded \
                        accumulation={} output=e-device \
                         performance_claim=false (logged once per layer)",
                        ep.devices,
                        ep.runtime.transport_label(),
                        ep.runtime.native_p2p(),
                        if t == 1 {
                            "owner-grouped-rank-order"
                        } else {
                            "token-slot-order"
                        },
                    );
                }
                return Ok(output);
            }
            // Device-IO routes (t=1): the layer input goes to the ranks as a device row and the
            // combined output comes back as an e-context row — no host round-trip, no host
            // stream sync. Program bytes identical to the host-IO twin (dtoh/htod and dtod
            // both preserve f32 bits), gated by greedy token identity.
            if t == 1
                && crate::tp::step_nvfp4_dev_routes_enabled()?
                && let Some(tp) = &m.step_tp
                && let crate::hybrid::StepTpExpertBank::Nvfp4(bank) = &tp.experts
            {
                let mut output = tp.runtime.run_tensor_parallel_routes_nvfp4_device_io(
                    bank,
                    e,
                    z,
                    &selected,
                    &route_weights,
                    n_used,
                    tp.activation_limit,
                )?;
                if output.len() != t * n_embd {
                    return Err(format!(
                        "Step tp routed output has {} values, expected {t}x{n_embd}",
                        output.len()
                    )
                    .into());
                }
                Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut output)?;
                static IO_LOGGED: std::sync::atomic::AtomicU64 =
                    std::sync::atomic::AtomicU64::new(0);
                let layer_bit = 1u64 << (il as u64 % 64);
                if IO_LOGGED.fetch_or(layer_bit, std::sync::atomic::Ordering::Relaxed) & layer_bit
                    == 0
                {
                    eprintln!(
                        "[step-tp] execute layer={il} tokens={t} devices={:?} \
                                 expert_transport={} native_p2p={} activation=host-canonical \
                                 accumulation=host-canonical output=e-device io=device \
                                 performance_claim=false (logged once per layer)",
                        tp.devices,
                        tp.runtime.transport_label(),
                        tp.runtime.native_p2p(),
                    );
                }
                return Ok(output);
            }
            let (routed, mode, devices, transport, native_p2p) = if let Some(tp) = &m.step_tp {
                (
                    match &tp.experts {
                        crate::hybrid::StepTpExpertBank::E4m3(bank) => {
                            tp.runtime.run_tensor_parallel_routes(
                                bank,
                                &input,
                                t,
                                &selected,
                                &route_weights,
                                n_used,
                            )?
                        }
                        crate::hybrid::StepTpExpertBank::Nvfp4(bank) => {
                            if t == 1 && crate::tp::step_nvfp4_dev_routes_enabled()? {
                                tp.runtime.run_tensor_parallel_routes_nvfp4_device(
                                    bank,
                                    &input,
                                    &selected,
                                    &route_weights,
                                    n_used,
                                    tp.activation_limit,
                                )?
                            } else {
                                tp.runtime.run_tensor_parallel_routes_nvfp4(
                                    bank,
                                    &input,
                                    t,
                                    &selected,
                                    &route_weights,
                                    n_used,
                                    tp.activation_limit,
                                )?
                            }
                        }
                    },
                    "tp",
                    &tp.devices,
                    tp.runtime.transport_label(),
                    tp.runtime.native_p2p(),
                )
            } else {
                let ep = m
                    .step_ep
                    .as_ref()
                    .ok_or("Step distributed runtime has no EP or TP state")?;
                (
                    match &ep.experts {
                        crate::hybrid::StepEpExpertBank::E4m3(bank) => {
                            ep.runtime.run_routed_experts(
                                bank,
                                &input,
                                t,
                                &selected,
                                &route_weights,
                                n_used,
                                ep.activation_limit,
                            )?
                        }
                        crate::hybrid::StepEpExpertBank::Nvfp4(bank) => {
                            ep.runtime.run_routed_experts_nvfp4(
                                bank,
                                &input,
                                t,
                                &selected,
                                &route_weights,
                                n_used,
                                ep.activation_limit,
                            )?
                        }
                    },
                    if ep.configured_by_tp { "tp-ep" } else { "ep" },
                    &ep.devices,
                    ep.runtime.transport_label(),
                    ep.runtime.native_p2p(),
                )
            };
            if routed.len() != t * n_embd {
                return Err(format!(
                    "Step {mode} routed output has {} values, expected {t}x{n_embd}",
                    routed.len()
                )
                .into());
            }
            let mut output = e.htod(&routed)?;
            Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut output)?;
            // Once per layer per process: the topology contract line is a boot receipt, not a
            // per-token trace — 4520 of these per 64-token run measured as real decode wall.
            static STEP_LOGGED: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
            let layer_bit = 1u64 << (il as u64 % 64);
            if STEP_LOGGED.fetch_or(layer_bit, std::sync::atomic::Ordering::Relaxed) & layer_bit
                == 0
            {
                eprintln!(
                    "[step-{mode}] execute layer={il} tokens={t} devices={devices:?} \
                     expert_transport={transport} native_p2p={native_p2p} \
                     activation={} accumulation={} output={} \
                     performance_claim=false (logged once per layer)",
                    if let Some(ep) = &m.step_ep {
                        ep.runtime.expert_activation_label()
                    } else {
                        "host-canonical"
                    },
                    if let Some(ep) = &m.step_ep {
                        ep.runtime.expert_accumulation_label()
                    } else {
                        "host-canonical"
                    },
                    if let Some(ep) = &m.step_ep {
                        ep.runtime.expert_output_label()
                    } else {
                        "host-accumulated"
                    },
                );
                if let Some(ep) = &m.step_ep
                    && let Some(limit) = ep.activation_limit
                {
                    eprintln!(
                        "[step-ep-clamp] execute layer={il} tokens={t} routed_clamp={limit} \
                             formula=min-silu-times-clamped-up performance_claim=false"
                    );
                }
            }
            return Ok(output);
        }
        if Self::sigmoid_resident_dev_eligible(e, m, cfg, sliding_gated_moe) {
            let moe = cfg.moe.as_ref().unwrap();
            let n_expert = moe.expert_count as usize;
            let n_used = moe.expert_used_count as usize;
            let sigmoid = cfg.sigmoid_router().unwrap();
            let logits = Self::moe_router_logits(e, m, z, t, cfg)?;
            Self::trace_sigmoid_router_logits(e, il, t, n_expert, n_used, &logits, m, sigmoid)?;
            return Self::moe_ffn_sigmoid_dev(e, m, z, zq8, &logits, t, cfg, il, sigmoid);
        }
        // GROUPED MoE PREFILL, sigmoid-router class (glm5_next), MEMRA_MOE_GROUPED_PREFILL
        // default ON since 2026-08-29 (owner-accepted flip; =0 rollback seam; receipts on the
        // flag helper). The prefill-gap attribution (research/glm53-flash-bringup-20260827/
        // prefill-gap-20260829/PREFILL-GAP.md §1.1) measured this arch prefilling every prompt
        // token through the decode program: 49 launches per token-layer, ~8.4M launches and
        // 4.76 GB of expert-weight VRAM re-reads per token across 42 layers per 4096-token
        // chunk, because every batched arm is predicate-denied for sigmoid-router archs. This
        // arm is the composition of qualified ingredients: the m-invariant router + sigmoid
        // host oracle (routing sel/w BIT-identical to the sequential arm by construction),
        // host token-sort by expert (the moe_align_block_size shape), one grouped NVFP4
        // tensor-core GEMM per projection (the step37 grouped-prime kernel class via
        // `moe_f16_grouped`, generalized to the single-device resident slab), the PRE-clamped
        // SwiGLU epilogue and the per-expert weight_scale_2 macro fold the fused-epilogue lane
        // gated for this family. Keyed on `prefill` (only the _prefill twin sets it) so decode,
        // spec verify and the exact-16 batched-decode tier keep their dispatch class, and on
        // `t > MOE_DEV_MAX_T` so t<=16 stays on the per-token program (grouped/pairs prefill
        // classes start at 17, same seam as the softmax pairs arm).
        // ENGAGEMENT RECEIPT: the announce below prints in BOTH arms (flag on and off), once
        // per process, so an A/B grep distinguishes engagement without the line itself being
        // an arm-local cost (the step37 engagement-receipt trap: prove the path RAN before
        // attributing a number to it).
        if prefill && t > MOE_DEV_MAX_T && cfg.sigmoid_router().is_some() && cfg.glm5.is_some() {
            // Once per process PER FLAG VALUE (bit 0 = off, bit 1 = on): a server boot prints
            // exactly one line, and a gate process that flips the flag shows both arms.
            static GPF_ANNOUNCED: std::sync::atomic::AtomicU8 = std::sync::atomic::AtomicU8::new(0);
            let enabled = moe_grouped_prefill_enabled();
            let bit = 1u8 << u8::from(enabled);
            if GPF_ANNOUNCED.fetch_or(bit, std::sync::atomic::Ordering::Relaxed) & bit == 0 {
                eprintln!(
                    "[moe-grouped-prefill] flag={} t={t} il={il} (announce printed in both \
                     arms; engagement is the per-layer execute line + the dispatch counter)",
                    if enabled { "on" } else { "off" },
                );
            }
            if enabled
                && let Some(out) = Self::moe_ffn_grouped_prefill_sigmoid(e, m, z, t, cfg, il)?
            {
                return Ok(out);
            }
        }
        // Expert-grouped dispatch for prefill (T>1). MEMRA_MOE_GROUPED explicitly opts the
        // current caller into this research arm; the naked default stays on the established path.
        if t > 1 && moe_grouped_enabled(cfg, prefill) {
            let grouped_out = Self::moe_ffn_grouped(e, m, z, t, cfg, il, max_block)?;
            // MEMRA_MOE_GATE: byte-identity comparison vs sequential path.
            // The grouped q8 path uses the same row-wise quantize/dot/activation/FMA programs as
            // sequential dispatch; mixed or q8-disabled layouts fall back to the matching f32
            // path. A mismatch is therefore a correctness failure, not an accepted numeric class.
            if std::env::var("MEMRA_MOE_GATE").is_ok() {
                let seq_out = Self::moe_ffn_sequential(e, m, z, t, cfg, il, max_block)?;
                let g_host = e.dtoh(&grouped_out)?;
                let s_host = e.dtoh(&seq_out)?;
                let g_bytes: &[u8] = unsafe {
                    std::slice::from_raw_parts(g_host.as_ptr() as *const u8, g_host.len() * 4)
                };
                let s_bytes: &[u8] = unsafe {
                    std::slice::from_raw_parts(s_host.as_ptr() as *const u8, s_host.len() * 4)
                };
                if g_bytes == s_bytes {
                    println!("moe-gate il={il} t={t} BYTE-IDENTICAL");
                } else {
                    let diffs = g_host
                        .iter()
                        .zip(s_host.iter())
                        .enumerate()
                        .filter(|(_, (a, b))| a != b)
                        .count();
                    let maxdiff = g_host
                        .iter()
                        .zip(s_host.iter())
                        .map(|(a, b)| (a - b).abs())
                        .fold(0.0f32, f32::max);
                    panic!(
                        "moe-gate il={il} t={t} MISMATCH: {diffs}/{} elems differ, maxdiff={maxdiff:.6e}",
                        g_host.len()
                    );
                }
            }
            return Ok(grouped_out);
        }
        Self::moe_ffn_sequential_zq8(e, m, z, zq8, t, cfg, il, max_block, vrows)
    }

    fn sigmoid_resident_dev_eligible(
        e: &Engine,
        m: &MoeWeights,
        cfg: &ModelConfig,
        sliding_gated_moe: bool,
    ) -> bool {
        let Some(moe) = cfg.moe.as_ref() else {
            return false;
        };
        // Cached once per process: this predicate runs per MoE layer per decode step, and five
        // uncached env reads were ~300+ environ scans/step on the exact path this arm de-hosts.
        static OBSERVATION_MODE: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
        let observation_mode = *OBSERVATION_MODE.get_or_init(|| {
            std::env::var("MEMRA_MOE_STATS").is_ok()
                || std::env::var("MEMRA_MOE_TRACE").is_ok()
                || std::env::var("MEMRA_MOE_WEIGHT_TRACE").is_ok()
                || std::env::var("MEMRA_MOE_INPUT_TRACE_DIR").is_ok()
                || std::env::var("MEMRA_MOE_GATE").is_ok()
        });
        let resident_layout_supported = m.dev_exps.as_ref().is_some_and(|dev| {
            if dev.dev != e.ctx().ordinal() {
                return false;
            }
            let q8 = moe_q8_enabled_for_model(cfg, m);
            let fp8 = dev.fp8_blk.is_some()
                && m.gate_exps.qtype == crate::QT_F8_E4M3_BLK
                && m.up_exps.qtype == crate::QT_F8_E4M3_BLK
                && m.down_exps.qtype == crate::QT_F8_E4M3_BLK;
            q8 || fp8
        });
        sliding_gated_moe
            && sigmoid_router_enabled()
            && moe_dev_enabled()
            && moe_slab_enabled()
            && !observation_mode
            && moe.expert_used_count <= 8
            && m.has_uniform_expert_layout()
            && m.gate_exps.macros.is_none()
            && m.up_exps.macros.is_none()
            && m.down_exps.macros.is_none()
            && !m.has_macros
            && resident_layout_supported
    }

    /// Sequential (per-token) MoE FFN -- the original path. Factored out for the gate comparison.
    pub(crate) fn moe_ffn_sequential(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        max_block: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        Self::moe_ffn_sequential_zq8(e, m, z, None, t, cfg, il, max_block, false)
    }

    /// One router-logit selector shared by sequential and grouped dispatch. Real prefill must use
    /// the row-wise GEMV when exact routing is enabled: cuBLASLt's reduction changes with `m`,
    /// which makes expert selection depend on the caller's chunk or concat-batch shape.
    fn moe_router_logits(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        if t < PRIME_MIN_T {
            // Decode and speculative verify use one fixed per-row reduction program.
            if crate::router_kernel_on() {
                e.router_gemv(
                    m.gate_inp.float_data(),
                    z,
                    cfg.n_embd as usize,
                    m.gate_exps.n_expert,
                    t,
                )
            } else {
                e.matmul_decode_exact(&m.gate_inp, z, t)
            }
        } else if crate::router_prefill_exact_on() && crate::router_kernel_on() {
            e.router_gemv(
                m.gate_inp.float_data(),
                z,
                cfg.n_embd as usize,
                m.gate_exps.n_expert,
                t,
            )
        } else {
            e.matmul(&m.gate_inp, z, t)
        }
    }

    /// Append the host-visible router selection for one layer/forward when calibration tracing is
    /// enabled. Both sequential and expert-grouped prefill must call this after routing so the
    /// trace is independent of the dispatch optimization selected for the forward.
    /// `MEMRA_MOE_WEIGHT_TRACE` is also the co-activation measurement input of
    /// LAW:coactivation-expert-placement (lane/glm5-ep-place: rows ride this existing host
    /// readback — zero new device syncs; `glm5-tp-gate` arm T holds the ON-identity +
    /// row-count bar on the glm5 walks).
    fn trace_moe_routes(
        il: u16,
        t: usize,
        sel_all: &[u32],
        weights: &[f32],
    ) -> Result<(), Box<dyn std::error::Error>> {
        use std::io::Write as _;
        if let Ok(path) = std::env::var("MEMRA_MOE_TRACE") {
            let mut f = std::fs::OpenOptions::new()
                .create(true)
                .append(true)
                .open(path)?;
            let ids: Vec<String> = sel_all.iter().map(|s| s.to_string()).collect();
            writeln!(f, "{} {} {}", il, t, ids.join(","))?;
        }
        if let Ok(path) = std::env::var("MEMRA_MOE_WEIGHT_TRACE") {
            let mut f = std::fs::OpenOptions::new()
                .create(true)
                .append(true)
                .open(path)?;
            let pairs: Vec<String> = sel_all
                .iter()
                .zip(weights)
                .map(|(expert, weight)| format!("{expert}:{weight:.9}"))
                .collect();
            writeln!(f, "{} {} {}", il, t, pairs.join(","))?;
        }
        Ok(())
    }

    #[allow(clippy::too_many_arguments)]
    fn trace_sigmoid_router_logits(
        e: &Engine,
        il: u16,
        t: usize,
        n_expert: usize,
        n_used: usize,
        logits: &CudaSlice<f32>,
        m: &MoeWeights,
        (scaling_factor, route_norm): (f32, bool),
    ) -> Result<(), Box<dyn std::error::Error>> {
        if !crate::sigrouter_contract::served_logit_trace_enabled() || t != 1 {
            return Ok(());
        }
        let logits = e.dtoh(logits)?;
        let active: Vec<u8> = m
            .active_experts
            .as_ref()
            .map(|mask| mask.iter().map(|&enabled| u8::from(enabled)).collect())
            .unwrap_or_else(|| vec![1; n_expert]);
        let bias = m.exp_probs_b.clone().unwrap_or_else(|| vec![0.0; n_expert]);
        crate::sigrouter_contract::capture_served_logits(
            il as u32,
            t,
            n_expert,
            n_used,
            scaling_factor,
            route_norm,
            &active,
            &bias,
            &logits,
        )?;
        Ok(())
    }

    /// Append the f32 input to one MoE layer for offline, layerwise calibration. This diagnostic
    /// intentionally performs a DtoH copy and is therefore disabled unless an explicit fresh trace
    /// directory is supplied. Each layer owns one payload file; index.jsonl records byte offsets so
    /// a validator can prove request/layer coverage before the trace is used for pruning or healing.
    fn trace_moe_input(
        e: &Engine,
        il: u16,
        t: usize,
        n_embd: usize,
        z: &CudaSlice<f32>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        use std::io::Write as _;
        let Ok(dir) = std::env::var("MEMRA_MOE_INPUT_TRACE_DIR") else {
            return Ok(());
        };
        let values = active_matrix_values(z.len(), t, n_embd, "MoE input trace activation")?;
        let host = e.dtoh_view(&z.slice(0..values))?;
        let bytes = unsafe {
            std::slice::from_raw_parts(
                host.as_ptr().cast::<u8>(),
                host.len() * std::mem::size_of::<f32>(),
            )
        };
        let state = MOE_INPUT_TRACE_WRITER.get_or_init(|| std::sync::Mutex::new(None));
        let mut state = state
            .lock()
            .map_err(|_| "MoE input trace writer lock is poisoned")?;
        if state.is_none() {
            let dir = std::path::PathBuf::from(&dir);
            std::fs::create_dir_all(&dir)?;
            let index = std::fs::OpenOptions::new()
                .create(true)
                .append(true)
                .open(dir.join("index.jsonl"))?;
            *state = Some(MoeInputTraceWriter {
                dir,
                index,
                payloads: std::collections::HashMap::new(),
            });
        }
        let writer = state.as_mut().unwrap();
        if writer.dir != std::path::Path::new(&dir) {
            return Err("MEMRA_MOE_INPUT_TRACE_DIR changed after capture started".into());
        }
        let file_name = format!("layer-{il:03}.f32");
        if !writer.payloads.contains_key(&il) {
            let payload = std::fs::OpenOptions::new()
                .create(true)
                .append(true)
                .open(writer.dir.join(&file_name))?;
            let offset = payload.metadata()?.len();
            writer.payloads.insert(il, (payload, offset));
        }
        let (payload, offset) = writer.payloads.get_mut(&il).unwrap();
        let row_offset = *offset;
        payload.write_all(bytes)?;
        *offset += bytes.len() as u64;
        writeln!(
            writer.index,
            "{{\"format\":\"memra-moe-input-trace-v1\",\"layer\":{il},\"tokens\":{t},\
             \"hidden_size\":{n_embd},\"file\":\"{file_name}\",\"offset\":{row_offset},\
             \"payload_bytes\":{}}}",
            bytes.len()
        )?;
        Ok(())
    }

    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::too_many_arguments)]
    // allow: the parameter list mirrors its moe_ffn_inner caller's dispatch contract
    pub(crate) fn moe_ffn_sequential_zq8(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        zq8: Option<&(CudaSlice<i8>, CudaSlice<f32>)>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        max_block: usize,
        vrows: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, PROJ_DOWN, PROJ_GATE, PROJ_UP};
        let moe = cfg.moe.as_ref().unwrap();
        let n_embd = cfg.n_embd as usize; // 2048 (gate/up in_f, down out_f)
        let n_expert = moe.expert_count as usize; // 256
        let n_used = moe.expert_used_count as usize; // 8
        let n_ff_exp = moe.expert_ff_length as usize; // 512 (gate/up out_f, down in_f)

        // verify the HostExps dims match cfg (catches a wrong-file / transpose mixup)
        debug_assert_eq!(m.gate_exps.in_f, n_embd);
        debug_assert_eq!(m.gate_exps.out_f, n_ff_exp);
        debug_assert_eq!(m.down_exps.in_f, n_ff_exp); // down is TRANSPOSED: in=512
        debug_assert_eq!(m.down_exps.out_f, n_embd); //                     out=2048
        debug_assert_eq!(m.gate_exps.n_expert, n_expert);

        // step35 PER-LAYER SwiGLU clamp (None on every other arch and on every unclamped layer).
        // Routed experts and the shared expert read SEPARATE arrays — never share one value.
        let lim_exp = cfg.clamp_exp_at(il as u32);
        let lim_shexp = cfg.clamp_shexp_at(il as u32);
        let use_cache = Engine::moe_cache_enabled();
        let uniform_experts = m.has_uniform_expert_layout();
        let moe_q8 = uniform_experts && moe_q8_enabled_for_model(cfg, m);
        // Experimental secondary backend: complete experts already resident in the SLRU stay on
        // CUDA; any expert missing one or more projections runs from the original host GGUF bytes
        // through llama.cpp CPU quant dots. Small-t only keeps decode and speculative verification
        // in the same numeric/dispatch class; real prefill remains on the established GPU path and
        // seeds the residency cache. An explicit library path is the build/runtime gate, so naked
        // commands and CI have no llama.cpp or OpenMP dependency.
        let cpu_expert_requested = crate::cpu_experts::configured();
        if cpu_expert_requested && (cfg.hy3.is_none() || cfg.m3.is_some()) {
            return Err(std::io::Error::other(
                "MEMRA_CPU_EXPERT_LIB is experimental and currently gated to Hy3",
            )
            .into());
        }
        let cpu_hybrid = cpu_expert_requested && t < PRIME_MIN_T && m.dev_exps.is_none();
        // A dynamic SLRU changes which complete experts run on CUDA versus llama.cpp CPU dots.
        // Those backends are each deterministic but are different numeric configurations, so a
        // later prefill eviction can change greedy output. Freeze after the first real prefill;
        // decode/spec reads the fixed resident set, while later prefill misses use transient GPU
        // staging below and cannot change backend assignment.
        let freeze_cpu_residency = cpu_expert_requested
            && std::env::var("MEMRA_CPU_EXPERT_FREEZE_CACHE").as_deref() == Ok("1");
        let caller_warms_before_freeze = std::env::var("MEMRA_CPU_EXPERT_FREEZE_WARMUP_TOKENS")
            .ok()
            .and_then(|value| value.parse::<usize>().ok())
            .is_some_and(|tokens| tokens > 0);
        if cpu_hybrid && freeze_cpu_residency && !caller_warms_before_freeze {
            e.freeze_moe_cache();
        }
        let cache_frozen = use_cache && e.moe_cache_frozen();
        let cache_dispatch = use_cache && (!cache_frozen || cpu_hybrid);

        // 1. ROUTER: one selector is shared with expert-grouped prefill so changing dispatch
        // cannot change logits, selected expert ids, or routing weights.
        let logits = Self::moe_router_logits(e, m, z, t, cfg)?;
        if let Some(sig) = cfg.sigmoid_router() {
            Self::trace_sigmoid_router_logits(e, il, t, n_expert, n_used, &logits, m, sig)?;
        }

        // LAUNCH-STRUCTURE STAGE 3 (2026-07-05, MEMRA_MOE_DEV default ON, =0 rollback): ZERO-DtoH
        // device-dispatch when this layer's expert blocks are ALL cache-resident. The fused
        // router's sel/w stay ON DEVICE; the expert weight pointers come from the per-layer
        // device table of fixed slot addresses; gate/up/silu + down/fma run as the same TWO
        // launches per token as gdec. Removes the per-layer router DtoH + stream sync — the
        // per-token host stall that dominated the 35B decode wall after stages 1+2.
        // BIT-IDENTITY: the router kernel is selection-exact vs the host oracle (kernel-check
        // tie gate) and the _dev matvec twins reproduce the gdec kernels' exact FP chains; the
        // only difference is where sel/w/pointers are READ from (device instead of params).
        // Residency: one-shot PREWARM force-admits the layer while free slots cover it
        // (MEMRA_MOE_PREWARM=0 -> organic residency, dev path fires when the SLRU fills).
        // Any non-resident layer falls through to host routing + the gdec/sequential path.
        // FITS-VRAM RESIDENT EXPERTS (2026-07-06): the layer's expert slabs are device-resident
        // (load-time decision) — fire the zero-DtoH dev path unconditionally with the prebuilt
        // pointer row. No cache, no dispatch, no residency check: the llama full-offload regime
        // (it measured 169.55 vs the cache path's 28.5 on the local 35B — the residency-gate
        // all-or-nothing fallback was the 6x). BIT-IDENTITY: same _dev kernels, same math; only
        // the pointer table's provenance differs (slab base+stride vs SLRU slot addresses).
        // MoE PREFILL PAIR-BATCH (2026-07-06, the 16x pp hole): t>1 on resident experts — ONE
        // launch per proj covers ALL (token,expert) pairs (grid.y=pair, warp-per-row), replacing
        // the per-expert loop (256 experts x 3-4 launches x tiny m_e). Scatter is slot-ordered
        // per token (the sequential-axpy bit-identity class). Requires q8-supported qtypes +
        // resident slabs. MEMRA_MOE_PAIRS=0 rollback.
        // t >= PRIME_MIN_T only (2026-07-06 exactness fix, verify-probe proof): spec VERIFY
        // batches (t = 2..K+2) previously rode these pairs kernels while T=1 decode rode the
        // dev_q8 loop — different FP chains, verify-T2 logit maxdiff 2.6e-1 vs eager -> greedy
        // flips at tight margins -> 35B real-prompt spec self-consistency FAIL (the 27B
        // "verify must be kernel-DISPATCH-identical to decode" lesson, MoE edition). Small-t
        // now rides the dev loop below (same kernels per token as decode); pairs serves real
        // prefill (t >= 16, where spec never verifies).
        // sigmoid-router archs must NOT enter the pairs/dev arms: those dispatch arms still route
        // via the fused SOFTMAX device router (moe_router_topk) — silently wrong experts (the
        // M3 gate-MISMATCH 74602-vs-92 lesson, 2026-07-07). Sigmoid routing has its own device
        // top-k kernel; MEMRA_SIG_ROUTER=0 selects the host sigmoid oracle instead.
        // Per-expert macro-scales (compressed-tensors NVFP4 ST class, e.g. unsloth 35B-A3B):
        // the device-dispatch kernels (pairs/dev) do NOT fold them — those checkpoints must
        // ride the macro-aware sequential/staged paths below or every expert output is off by
        // its global scale (~3e4x, measured garbage 2026-07-16). GGUF experts: macros None.
        let no_exp_macros = m.gate_exps.macros.is_none()
            && m.up_exps.macros.is_none()
            && m.down_exps.macros.is_none();
        // A clamped-SwiGLU layer (step35 43/44) also cannot ride pairs: moe_pairs_silu_mul's
        // epilogue is plain silu(gate)*up with no clamp form, and moe_ffn_pairs takes no `il`
        // so it cannot even see the per-layer limit.
        if cfg.sigmoid_router().is_none()
            && cfg.m3.is_none()
            && cfg.hy3.is_none()
            && !cfg.swiglu_clamped_at(il as u32)
            && no_exp_macros
            // > MOE_DEV_MAX_T, not >= PRIME_MIN_T: t==16 is a decode width under the
            // exact-16 tier and rides the dev per-token program (see MOE_DEV_MAX_T);
            // pairs serves real prefill from 17 up.
            && t > MOE_DEV_MAX_T
            && m.dev_exps.is_some()
            && moe_q8_enabled_for_model(cfg, m)
            && std::env::var("MEMRA_MOE_PAIRS")
                .map(|v| v != "0")
                .unwrap_or(true)
            && std::env::var("MEMRA_MOE_STATS").is_err()
        {
            return Self::moe_ffn_pairs(e, m, z, &logits, t, cfg);
        }

        // t < PRIME_MIN_T: moe_ffn_dev loops tokens serially (1 launch-pair per token) — the
        // decode path AND the spec-verify path (dispatch parity = exactness; see pairs gate
        // above). Serial launches are fine at t<=10 (K+2); real prefill never lands here.
        // moe_ffn_dev routes via the FUSED SOFTMAX device router (moe_router_topk). Sigmoid
        // routing has a separate device top-k kernel, but this dev arm cannot consume its sel/w
        // contract, so sigmoid-router arches must NOT enter it: with MOE_CACHE=1 M3 silently
        // routed softmax = wrong experts (gate MISMATCH 74602 vs 92, caught 2026-07-07).
        // MEMRA_SIG_ROUTER=0 selects the host sigmoid oracle for the supported sigmoid path.
        // macro-carrying experts are handled inside the dev path now (epilogue fold + w-scale);
        // pairs/gdec/csr keep their macro gates until their kernels grow the fold.
        // step35 (2026-08-06) is the third sigmoid-router arch and the deny above was written as
        // two arch names, not as the mechanism — so `dev_ok` let it into moe_ffn_dev's
        // moe_router_topk (softmax, no exp_probs_b bias, no expert_weights_scale) = silently
        // wrong experts, the same failure the pairs gate at :2254 already blocks by predicate.
        // Keyed off sigmoid_router() so arch #4 is denied by construction.
        // The clamped-SwiGLU layers must also fall through: every moe_ffn_dev kernel's fused
        // epilogue is plain silu(gate)*up (moe_gate_up_silu8_dev_*), which has no clamp form.
        let dev_ok = uniform_experts
            && cfg.sigmoid_router().is_none()
            && cfg.m3.is_none()
            && cfg.hy3.is_none()
            && !cfg.swiglu_clamped_at(il as u32);
        // Observation modes must route through the host-visible selection below. Otherwise a fully
        // resident layer returns through device dispatch before its trace/stats row is recorded,
        // silently biasing calibration toward only non-resident layers on large-VRAM machines.
        let observe_routes = std::env::var("MEMRA_MOE_STATS").is_ok()
            || std::env::var("MEMRA_MOE_TRACE").is_ok()
            || std::env::var("MEMRA_MOE_WEIGHT_TRACE").is_ok()
            || std::env::var("MEMRA_MOE_INPUT_TRACE_DIR").is_ok();
        if dev_ok
            && t <= MOE_DEV_MAX_T
            && m.dev_exps.is_some()
            && n_used <= 8
            && moe_dev_enabled()
            && !observe_routes
        {
            return Self::moe_ffn_dev(e, m, z, zq8, &logits, t, cfg, il, max_block);
        }
        if dev_ok && use_cache && n_used <= 8 && moe_dev_enabled() && !observe_routes {
            let row_ok = e.with_moe_cache(max_block, |c, eng| {
                if moe_prewarm_enabled() {
                    c.prewarm_layer(il, m, eng)?;
                }
                Ok(c.layer_dev_row(il, n_expert, eng)?.is_some())
            })?;
            if row_ok {
                return Self::moe_ffn_dev(e, m, z, zq8, &logits, t, cfg, il, max_block);
            }
        }

        // SLAB-LOCAL RESIDENT ARM bases, hoisted above the router (lane/glm5-moe-loc door D):
        // whether the layer can run the DEVICE vrows table build decides HOW it routes, and
        // that has to be settled before the router runs. Pure immutable pointer reads with no
        // side effects, so the hoist changes nothing for any other arm; the full rationale for
        // the arm itself is at the `slab_fused_may_fire` predicate below.
        let slab_local = m
            .dev_exps
            .as_ref()
            .filter(|d| !d.gu_il && moe_slab_enabled() && d.dev == e.ctx().ordinal());
        let slab_bases = slab_local.map(|d| {
            use cudarc::driver::DevicePtr;
            let s = e.stream();
            let (pg, _g0) = d.gate.device_ptr(&s);
            let (pu, _g1) = d.up.device_ptr(&s);
            let (pd, _g2) = d.down.device_ptr(&s);
            (pg, pu, pd)
        });
        // DOOR D (`MEMRA_MOE_VROWS_DEV_TABLES`, default OFF): route WITHOUT the pinned sel/w
        // readback and build the pair's pointer/scale tables on device instead. On the serving
        // shape the host table build is the selection's ONLY consumer, and it costs a full
        // `cuStreamSynchronize` + 2 DtoH + 2 pageable HtoD + 2 host Vecs per MoE layer-call —
        // 42 device-wide drains, 84 DtoH and 84 HtoD per ship round (the decode-gap
        // attribution's "43 cuStreamSynchronize/token ... the per-layer router-admission sync
        // structure", and 44.6% of the unattributed 71.6 HtoD calls/token).
        //
        // The extra conjuncts beyond `vrows_fires` (asserted equal at the dispatch) are exactly
        // the host-visible consumers of `sel_all` between here and there, each of which would
        // silently read an empty selection: `moesd::record_host_routes`, `hidden_trace`,
        // `MEMRA_MOE_TRACE`/`MEMRA_MOE_STATS`/the other `observe_routes` modes. Plus
        // `sigmoid_router_enabled()`, because `MEMRA_SIG_ROUTER=0` is a full-logit HOST oracle
        // with no device selection to read. `promote_worker_h2d` needs no conjunct: it requires
        // t == 1 and this arm requires t >= 2. Any miss falls closed to the host readback.
        let vrows_dev = vrows
            && t >= 2
            && crate::moe_vrows_dev_tables_on()
            && slab_bases.is_some()
            && moe_q8
            && uniform_experts
            && n_used <= 8
            && cfg.sigmoid_router().is_some()
            && matches!(lim_exp, Some(SwigluClamp::Pre(l)) if l > 1e-6)
            && !cpu_hybrid
            && sigmoid_router_enabled()
            && !observe_routes
            && !memra_reference::hidden_trace::enabled()
            && !crate::moesd::capture_active();
        // Per-token (sel[8], w[8]) — sigmoid host oracle (M3/Hy3), else fused-router/softmax.
        // Door D adds a fourth arm returning the router's DEVICE sel/w with no readback.
        let mut sel_dev: Option<(CudaSlice<i32>, CudaSlice<f32>)> = None;
        let (sel_all, w_all, routed_cpu_input) = if vrows_dev {
            let (sf, route_norm) = cfg
                .sigmoid_router()
                .expect("vrows_dev carries cfg.sigmoid_router().is_some()");
            sel_dev = Some(e.moe_router_sigmoid_topk(
                &logits,
                t,
                n_expert,
                n_used,
                m.active_count(),
                &m.exp_probs_b_dev,
                &m.active_experts_dev,
                sf,
                route_norm,
            )?);
            crate::MOE_VROWS_ROUTER_SYNCS_AVOIDED
                .fetch_add(1, std::sync::atomic::Ordering::Relaxed);
            (Vec::new(), Vec::new(), None)
        } else if let Some(sig) = cfg.sigmoid_router() {
            if cpu_hybrid {
                let (sel, w, input) = Self::moe_route_sigmoid_with_input(
                    e,
                    &logits,
                    z,
                    t,
                    n_embd,
                    n_expert,
                    n_used,
                    m.exp_probs_b.as_deref(),
                    sig,
                    m.active_experts.as_deref(),
                )?;
                (sel, w, Some(input))
            } else {
                let (sel, w) =
                    Self::moe_route_sigmoid_cfg(e, &logits, t, n_expert, n_used, m, sig)?;
                (sel, w, None)
            }
        } else {
            let (sel, w) =
                Self::moe_route_cfg(e, &logits, t, n_expert, n_used, m.active_experts.as_deref())?;
            (sel, w, None)
        };
        crate::moesd::record_host_routes(il, n_expert, n_used, &sel_all)?;
        if memra_reference::hidden_trace::enabled() {
            memra_reference::hidden_trace::emit_last_row(
                "router",
                il as i64,
                t,
                n_expert,
                &e.dtoh(&logits)?,
            );
            let last = (t - 1) * n_used;
            let mut route = Vec::with_capacity(n_used * 2);
            for slot in 0..n_used {
                route.push(sel_all[last + slot] as f32);
                route.push(w_all[last + slot]);
            }
            memra_reference::hidden_trace::emit_last_row("route", il as i64, 1, n_used * 2, &route);
        }

        // MEMRA_MOE_TRACE=<path>: append one line per (layer, step) with the selected expert ids —
        // offline analysis derives the decode working set + step-to-step reuse (the go/no-go
        // measurement for resident-expert tiering; see rig5090.jsonl 2026-07-07 pinned-tier row).
        Self::trace_moe_routes(il, t, &sel_all, &w_all)?;
        Self::trace_moe_input(e, il, t, n_embd, z)?;

        // Worker reads ordinarily overlap NVMe with the current expert but leave their H2D copies
        // demand-serialized on the compute stream. Hy3 decode already has a safe owner-thread
        // boundary here: sigmoid routing read `logits` back to the host, which synchronized all
        // earlier-layer compute. Submit the complete selected set first, then reserve only victims
        // outside that set and queue its H2D copies on the copy stream. Dispatch inserts an event
        // wait for each pending block, so later copies can overlap the earlier expert kernels while
        // the selected weights and numerical kernels remain unchanged. Restrict this experiment to
        // T=1; batched forwards can have token-local consumers still in flight between selections.
        // The CPU backend reads mmap-backed model bytes directly. Positioned-read worker buffers
        // are CUDA write-combined staging allocations and are intentionally not CPU-compute inputs;
        // do not spend NVMe bandwidth filling them for a layer whose misses go to CPU.
        let worker_disk_prefetch =
            cache_dispatch && crate::spill_pread::worker_enabled() && !cpu_hybrid;
        let promote_worker_h2d =
            t == 1 && worker_disk_prefetch && crate::spill_pread::copy_h2d_enabled();
        if promote_worker_h2d {
            let mut selected_blocks = Vec::with_capacity(n_used * 3);
            for &ex in sel_all.iter().take(n_used) {
                let ex = ex as u16;
                selected_blocks.extend([
                    BlockId::new(il, PROJ_GATE, ex),
                    BlockId::new(il, PROJ_UP, ex),
                    BlockId::new(il, PROJ_DOWN, ex),
                ]);
            }
            for &ex in sel_all.iter().take(n_used) {
                Self::moe_prefetch_disk_expert(e, il, ex as usize, m, max_block, &selected_blocks)?;
            }
            e.with_moe_cache(max_block, |cache, eng| {
                cache.promote_worker_reads_at_safe_boundary(
                    &selected_blocks,
                    &selected_blocks,
                    eng,
                )?;
                Ok(())
            })?;
        }

        // MEMRA_MOE_STATS: per-layer routing stats for the A2 (expert-grouped prefill) baseline —
        // per-token expert-id entropy, active-expert coverage, tokens-per-expert group sizes.
        if t > 1 && std::env::var("MEMRA_MOE_STATS").is_ok() {
            let mut cnt = vec![0u32; n_expert];
            for &s in sel_all.iter() {
                cnt[s as usize] += 1;
            }
            let total = sel_all.len() as f64;
            let mut h = 0.0f64;
            let mut active = 0usize;
            for &c in &cnt {
                if c > 0 {
                    active += 1;
                    let p = c as f64 / total;
                    h -= p * p.log2();
                }
            }
            let maxc = cnt.iter().copied().max().unwrap_or(0);
            println!(
                "moe-stats il={} t={} assignments={} active={}/{} entropy={:.3}b (max {:.3}b) mean_tok_per_active={:.2} max_tok_per_expert={}",
                il,
                t,
                sel_all.len(),
                active,
                n_expert,
                h,
                (n_expert as f64).log2(),
                total / active.max(1) as f64,
                maxc
            );
        }

        // LAUNCH-STRUCTURE STAGE 2 (2026-07-05): moe_out memset elision on the gdec path.
        // moe_down8_fma_f32 FULLY overwrites its token row (dst[o] = the in-kernel FMA chain that
        // starts at 0.0f — numerically the axpy-into-zeroed-row chain), so when the grouped-decode
        // path fires the upfront `e.zeros(t*n_embd)` memset is pure launch churn. Allocate uninit
        // when gdec CAN fire (any t — decode t=1 AND the spec verify t=K+1 route here per token)
        // and lazily zero ONLY the row of a token that falls through to the sequential axpy loop.
        // BIT-IDENTITY: unchanged — every row is either fully overwritten (gdec) or
        // zeroed-then-accumulated exactly as before (fallback).
        // `!swiglu_clamped_at`: the grouped-decode kernels' fused epilogue is plain
        // silu(gate)*up (see the m3 note at the call sites below) — a clamped layer must fall
        // through to the sequential loop's ffn_act_lim. Hoisted into the fire predicate so the
        // uninit/memset invariant at :2418 stays consistent with what actually dispatches.
        let gdec_may_fire = uniform_experts
            && use_cache
            && n_used <= 8
            && gdec_enabled()
            && !cfg.swiglu_clamped_at(il as u32);
        // SLAB-LOCAL RESIDENT ARM (lane/pp-leverb 2026-08-08): the fits-VRAM resident slabs
        // (`dev_exps`, sized per PP device by cx-503b) were consumed ONLY by the pairs/dev
        // arms — every one of which DENIES sigmoid-router archs (step35/M3/Hy3). So on those
        // archs the slabs were uploaded but never read, and every expert went through the
        // SLRU (37 GB H2D staging per pp4096 prime on the Step SKU; and post-cx-503b the
        // dead slabs additionally STARVE the SLRU, which sizes itself on free-VRAM-after-
        // residents). This arm reads the slabs through the SAME kernels the SLRU arm runs —
        // gdec's fused pair when eligible, the per-expert qmatvec twins otherwise — with the
        // slab base + ex*stride as the pointer provenance (the exact bit-identity class
        // `moe_ffn_dev` documents for its resident-vs-SLRU arms; bytes are the same HostExps
        // bytes either way). LOCALITY GATE: `d.dev == e.ctx().ordinal()` — a slab on another
        // device must NOT be dereferenced (m=1 peer reads are the measured 34-150x class,
        // strictly worse than staging); under PP-2 without the prime walker this admits
        // stage-0 layers on dev0 and leaves stage-1 layers on the SLRU, which is the honest
        // interim shape. MEMRA_MOE_SLAB=0 = rollback/A-B seam (read per call).
        // `slab_local` / `slab_bases` are bound ABOVE the router: door D
        // (`MEMRA_MOE_VROWS_DEV_TABLES`) has to pre-decide the routing arm, and this is the
        // predicate it needs. Pure immutable pointer reads, so the hoist is behaviour-neutral.
        // Fused-pair eligibility mirrors the gdec call sites exactly (plain-SiLU epilogue,
        // no macros, m3 clamp excluded, q8-supported qtypes, <=8 experts) — INCLUDING
        // `gdec_enabled()`: the pair IS the gdec kernel pair, so MEMRA_MOE_GDEC=0 must
        // disable it here too. That seam is also the exactness localizer: with GDEC=0 both
        // provenances run the SAME per-expert qmatvec kernels (slab base+stride vs SLRU
        // slot) and must be BIT-IDENTICAL — the true provenance-only pair — while GDEC=1
        // compares the fused-pair class against the SLRU's hit/miss MIX (gdec for
        // all-resident tokens, staged loop for misses), which is a dispatch-class
        // comparison, not a provenance one.
        let slab_fused_may_fire = slab_bases.is_some()
            && n_used <= 8
            && gdec_enabled()
            && !cfg.swiglu_clamped_at(il as u32)
            && cfg.m3.is_none()
            && no_exp_macros
            && moe_q8;
        // FUSED MoE EPILOGUE (lane/glm53-epilogue 2026-08-28, MEMRA_MOE_FUSED_EPI default OFF).
        // The arm glm5_next is denied by every other predicate in this function. It runs the SAME
        // launch pair shape as gdec — one gate/up kernel, one down/FMA kernel per token — but
        // with the three things this arch actually needs, none of which any existing fused
        // epilogue has:
        //   * the SIGMOID noaux_tc router's sel/w (host-routed above; the fused softmax router
        //     `moe_router_topk` that pairs/dev use would pick different experts — the M3
        //     gate-MISMATCH 74602-vs-92 lesson);
        //   * the PRE-clamped SwiGLU epilogue `silu(min(g,l)) * clamp(u,±l)` — step35's POST form
        //     is a different, plausible-but-wrong program (`fused_post_limit`);
        //   * the per-expert NVFP4 `weight_scale_2` macro fold, gate/up through the kernel's
        //     gs/us and down through the routing weight, exactly as `ffn_act_lim` + `axpy_into`
        //     do it in the sequential loop.
        // UNLIKE gdec it does NOT require the layer to be already-resident: it ADMITS the
        // 3*n_used selected blocks through the same `dispatch_source` the sequential loop uses
        // (hit = no copy, miss = the identical H2D into a slot) and only then collects the fixed
        // slot addresses. The staged bytes are unchanged — the §B.3 provenance property — so the
        // arm engages at any miss rate instead of gdec's P(all resident). It needs the cache to
        // hold 3*n_used blocks at once; `moe_fused_epi_token_q8` returns false when it cannot and
        // the token falls through to the sequential loop below.
        // TWO PROVENANCES, ONE LAUNCH PATH (slab arm added 2026-08-28). The SLRU arm below keys
        // on `slab_local.is_none()`; the SLAB arm keys on the slab existing. They differ ONLY in
        // where the eight expert pointers come from and both call `moe_fused_epi_launch`, so the
        // macro fold, the clamp and the kernel pair cannot drift apart between them.
        //
        // The slab arm is not an optimization, it is the arm that matters. Full two-card expert
        // residency makes `dev_exps` present on every stage engine, which makes `slab_local`
        // `Some`, which under the original predicate DENIED the fused epilogue outright — the
        // measured A/B would have read 0 dispatches and looked like "no effect". The residency
        // config is the serving config now, so the slab provenance is the one the product runs.
        //
        // It is also the SIMPLER arm: a slab holds every expert by construction, so there is no
        // admission, no eviction, no pass-2 re-verification and no fall-through. The SLRU arm's
        // capacity floor and re-check exist only because admission can move a slot.
        let fused_epi_common = n_used <= 8
            && moe_q8
            && cfg.m3.is_none()
            && cfg.sigmoid_router().is_some()
            && matches!(lim_exp, Some(SwigluClamp::Pre(l)) if l > 1e-6)
            && moe_fused_epi_enabled();
        let fused_epi_may_fire = fused_epi_common
            && uniform_experts
            && use_cache
            && cache_dispatch
            && slab_local.is_none();
        let fused_epi_slab_may_fire = fused_epi_common && slab_bases.is_some();
        // VERIFY-ROWS BATCHED ROUTED-EXPERT ARM (lane/glm5-vrest, 2026-08-31; rides
        // `MEMRA_GLM5_VERIFY_BATCH` — only the verify walk's batched arm passes `vrows`).
        // ONE launch pair covers ALL t x n_used routed pairs (the fused-epilogue kernels'
        // verify-rows twins) instead of the per-(token,expert) loop's ~49 launches per
        // token-layer — the flip-reprice cell-2 vrest wall (9.46 ms/row marginal at K=3).
        // Bit identity per row vs the sequential chain is the bar and it is structural:
        // routing is the SAME host invocation above; per-pair dots are qmatvec_expert_q8's
        // g-strided order; the epilogue is swiglu_preclamped_mul_scaled_f32's expression
        // with the per-expert macro fold exactly where ffn_act_lim/axpy_into fold it; the
        // down accumulation is the slot-ordered __fmaf_rn chain (the gdec-gated class).
        // Gated by glm5_verify_batch_gpu (kernel pair vs sequential chain + swapped-pair
        // and dropped-macro reds) and the glm5_tparallel_verify_gpu walk battery on the
        // NVFP4+macro serving expert class. Fail-closed: any unqualified shape falls
        // through to the unchanged loop below. Same slab-only scope as the fused epilogue
        // (the serving config's provenance); n_used<=8 mirrors its cap.
        let vrows_fires = vrows
            && t >= 2
            && slab_bases.is_some()
            && moe_q8
            && uniform_experts
            && n_used <= 8
            && cfg.sigmoid_router().is_some()
            && matches!(lim_exp, Some(SwigluClamp::Pre(l)) if l > 1e-6)
            && !cpu_hybrid;
        // moe_out memset elision: EVERY full-row-overwrite arm (gdec, slab fused, fused epilogue,
        // verify-rows) allocates uninit; a token that falls through to any accumulating loop
        // zeroes its own row. The fused epilogue's `moe_down8_fma_q8` fully overwrites `dst[o]`,
        // same as gdec's; `moe_down8_fma_q8_rows` fully overwrites every row.
        let mut moe_out = if gdec_may_fire
            || slab_fused_may_fire
            || fused_epi_may_fire
            || fused_epi_slab_may_fire
            || vrows_fires
        {
            e.uninit(t * n_embd)?
        } else {
            e.zeros(t * n_embd)?
        };
        // The router readback above already established a host boundary. Copy each small-t hidden
        // row once so CPU miss experts can start while the owner thread queues resident GPU work.
        let cpu_input = if cpu_hybrid {
            Some(routed_cpu_input.ok_or("CPU expert routing did not return the MoE input")?)
        } else {
            None
        };

        // Door D's predicate was evaluated at the router, BEFORE `vrows_fires` existed. If the
        // two ever disagreed, the layer would have skipped its readback and then dispatched a
        // per-row arm holding an EMPTY host selection — a silent wrong-answer class. The two
        // predicates share every conjunct by construction; assert it rather than hope.
        if vrows_dev && !vrows_fires {
            return Err(
                "MEMRA_MOE_VROWS_DEV_TABLES routed device-only but the verify-rows arm did not \
                 fire: the door-D and vrows_fires predicates disagree"
                    .into(),
            );
        }
        if vrows_fires {
            let Some(SwigluClamp::Pre(limit)) = lim_exp else {
                return Err(
                    "verify-rows MoE arm fired without a live PRE clamp: the predicate and \
                     the dispatch disagree"
                        .into(),
                );
            };
            let bases = slab_bases.expect("vrows_fires carries slab_bases.is_some()");
            let sel = match sel_dev.as_ref() {
                Some((si, sw)) => VrowsSel::Dev(si, sw),
                None => VrowsSel::Host(&sel_all, &w_all),
            };
            Self::moe_vrows_pairs_q8(
                e,
                m,
                z,
                sel,
                il,
                bases,
                t,
                n_embd,
                n_ff_exp,
                n_used,
                limit,
                &mut moe_out,
            )?;
            if memra_reference::hidden_trace::enabled() {
                memra_reference::hidden_trace::emit_last_row(
                    "routed",
                    il as i64,
                    t,
                    n_embd,
                    &e.dtoh(&moe_out)?,
                );
            }
            Self::moe_shexp_add(e, m, z, zq8, t, cfg, lim_shexp, &mut moe_out)?;
            return Ok(moe_out);
        }

        // GPU scratch: one slot per proj, big enough for ONE expert (default stage-every-token path).
        // STAGE 2: LAZY — allocated only if the no-cache staging path actually runs (under
        // MEMRA_MOE_CACHE they were 3 dead ~1MB alloc_zeros + memset + free per layer per token,
        // measured ~123 memsets/token of the decode wall).
        let g_len = m.gate_exps.max_expert_bytes(); // 860160 for the uniform 35B gate
        let u_len = m.up_exps.max_expert_bytes(); // 860160 for the uniform 35B up
        let d_len = m.down_exps.max_expert_bytes(); // 1114112 for the uniform 35B down
        let mut scratch_g: Option<CudaSlice<u8>> = None;
        let mut scratch_u: Option<CudaSlice<u8>> = None;
        let mut scratch_d: Option<CudaSlice<u8>> = None;
        // `max_block` (the GLOBAL max expert stride across all layers) is passed in — the cache slots
        // are FIXED-ADDRESS and must fit any layer's block (UD/dynamic GGUFs vary quant per layer).

        // EDGE-1 §C.2/C.3: the optional pipeline queues the next selected expert's MISS blocks on
        // the copy stream before launching the current expert's compute. Pending slots stay invisible
        // to cache hits until dispatch inserts the completion-event wait; current gate/up/down ids are
        // protected from eviction. This changes scheduling only, never the GGUF bytes or GEMM path.
        let page_window = moe_page_prefetch_window();

        // 2. PER TOKEN: routed-expert loop. The ONE dispatch change vs Stage-1: a resident slot
        //    (cache HIT, no H2D) OR a staged slot (MISS) feeds the SAME unchanged qmatvec_view.
        for tok in 0..t {
            let sel = &sel_all[tok * n_used..(tok + 1) * n_used];
            let w = &w_all[tok * n_used..(tok + 1) * n_used];
            let zt = z.slice(tok * n_embd..(tok + 1) * n_embd); // CudaView<f32>
            let mut tok_q8: Option<(CudaSlice<i8>, CudaSlice<f32>)> = None;

            // STAGE-2 GROUPED DECODE (2026-07-04, MEMRA_MOE_GDEC default ON, =0 rollback): fold
            // this token's whole routed-expert FFN (8x gate/up/silu + 8x down/axpy = 40 launches)
            // into TWO launches via expert-pointer indirection over the fixed-address cache slots.
            // Fires only when ALL 3*n_used blocks are ALREADY cache-resident (pure-HIT: zero
            // memcpy, zero admission, so no slot can move under the collected pointers) — any
            // miss falls through to the sequential loop below, which admits as before. In steady
            // state on a fully-resident rig every token-layer takes the grouped path.
            // BIT-IDENTITY: each in-kernel dot reproduces qmatvec_f32's exact reduction; SiLU is
            // silu_mul_f32's exact expression; the down accumulation is a slot-ordered
            // __fmaf_rn chain == the sequential axpy_f32 chain (MEMRA_MOE_GDEC_GATE compares).
            // cfg.m3: the grouped kernels' fused epilogues are plain SiLU — M3's swigluoai must
            // NOT take them until the kernels grow the clamped variant. NVFP4 experts carry
            // per-expert macro-scales the fused kernels don't fold — those fall through too.
            let no_macros = m.gate_exps.macros.is_none()
                && m.up_exps.macros.is_none()
                && m.down_exps.macros.is_none();
            // SLAB-LOCAL FUSED PAIR (lane/pp-leverb 2026-08-08): the gdec launch pair
            // (moe_gate_up_silu8_q8 + moe_down8_fma_q8 — the EXACT kernels, same FP chains)
            // with pointers computed from the resident slab base + ex*stride instead of
            // collected SLRU slot addresses. No cache lock, no residency predicate — the
            // slab holds every expert by construction, so this arm never falls through
            // (gdec's P(all 24 resident) ≈ 0.37 coin-flip and the miss path's ~49-launch
            // staging both die). Bit-identity class: pointer provenance only, the same
            // slab-vs-SLRU equivalence moe_ffn_dev documents. Ordered BEFORE gdec: when a
            // slab exists it is strictly better (no lock, no miss).
            if slab_fused_may_fire {
                let (pg, pu, pd) = slab_bases.unwrap();
                let mut gp = [0u64; 8];
                let mut up = [0u64; 8];
                let mut dp = [0u64; 8];
                for (j, &ex) in sel.iter().enumerate() {
                    let ex = ex as usize;
                    gp[j] = pg + (ex * m.gate_exps.expert_stride) as u64;
                    up[j] = pu + (ex * m.up_exps.expert_stride) as u64;
                    dp[j] = pd + (ex * m.down_exps.expert_stride) as u64;
                }
                let mut wv = [0f32; 8];
                wv[..n_used].copy_from_slice(w);
                if tok_q8.is_none() {
                    tok_q8 = Some(e.quantize_q8_1_view(&zt, 1, n_embd)?);
                }
                let (zq, zd) = tok_q8.as_ref().unwrap();
                let act = e.moe_gate_up_silu8_q8(
                    crate::WPtr8(gp),
                    crate::WPtr8(up),
                    zq,
                    zd,
                    n_embd,
                    n_ff_exp,
                    n_used,
                    m.gate_exps.qtype,
                    m.up_exps.qtype,
                    m.gate_exps.row_bytes,
                    m.up_exps.row_bytes,
                )?;
                let (aq2, ad2) = e.quantize_q8_1(&act, n_used, n_ff_exp)?;
                let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                e.moe_down8_fma_q8(
                    crate::WPtr8(dp),
                    crate::F32x8(wv),
                    &aq2,
                    &ad2,
                    &mut dst,
                    n_ff_exp,
                    n_embd,
                    n_used,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?;
                continue;
            }
            // FUSED MoE EPILOGUE, SLAB PROVENANCE. Ordered first: when a local slab exists it is
            // strictly better than anything the SLRU can offer — every expert is present by
            // construction, so there is no admission, no eviction and no fall-through. This is
            // the arm the two-card residency serving config actually runs.
            if fused_epi_slab_may_fire {
                let Some(SwigluClamp::Pre(limit)) = lim_exp else {
                    return Err(
                        "fused MoE epilogue (slab) fired without a live PRE clamp: the \
                                predicate and the dispatch disagree"
                            .into(),
                    );
                };
                let (pg, pu, pd) = slab_bases.unwrap();
                let mut g = [0u64; 8];
                let mut u = [0u64; 8];
                let mut d = [0u64; 8];
                for (j, &ex) in sel.iter().enumerate() {
                    let ex = ex as usize;
                    g[j] = pg + (ex * m.gate_exps.expert_stride) as u64;
                    u[j] = pu + (ex * m.up_exps.expert_stride) as u64;
                    d[j] = pd + (ex * m.down_exps.expert_stride) as u64;
                }
                if tok_q8.is_none() {
                    tok_q8 = Some(e.quantize_q8_1_view(&zt, 1, n_embd)?);
                }
                let (zq, zd) = tok_q8.as_ref().unwrap();
                Self::moe_fused_epi_launch(
                    e,
                    m,
                    zq,
                    zd,
                    sel,
                    w,
                    g,
                    u,
                    d,
                    &mut moe_out,
                    tok,
                    n_embd,
                    n_ff_exp,
                    n_used,
                    limit,
                )?;
                continue;
            }
            // FUSED MoE EPILOGUE, SLRU PROVENANCE. Ordered before gdec (which this arch never
            // reaches anyway: `gdec_may_fire` carries `!swiglu_clamped_at`). A `false` return
            // means the cache could not hold 3*n_used blocks at once — the token falls through to
            // the sequential loop, which zeroes its own row below.
            if fused_epi_may_fire {
                let Some(SwigluClamp::Pre(limit)) = lim_exp else {
                    return Err(
                        "fused MoE epilogue fired without a live PRE clamp: the predicate and \
                         the dispatch disagree"
                            .into(),
                    );
                };
                if tok_q8.is_none() {
                    tok_q8 = Some(e.quantize_q8_1_view(&zt, 1, n_embd)?);
                }
                let (zq, zd) = tok_q8.as_ref().unwrap();
                if Self::moe_fused_epi_token_q8(
                    e,
                    m,
                    il,
                    max_block,
                    zq,
                    zd,
                    sel,
                    w,
                    &mut moe_out,
                    tok,
                    n_embd,
                    n_ff_exp,
                    n_used,
                    limit,
                )? {
                    continue;
                }
            }
            if gdec_may_fire && moe_q8 && cfg.m3.is_none() && no_macros {
                if tok_q8.is_none() {
                    tok_q8 = Some(e.quantize_q8_1_view(&zt, 1, n_embd)?);
                }
                let (zq, zd) = tok_q8.as_ref().unwrap();
                if Self::moe_gdec_token_q8(
                    e,
                    m,
                    il,
                    max_block,
                    zq,
                    zd,
                    sel,
                    w,
                    &mut moe_out,
                    tok,
                    n_embd,
                    n_ff_exp,
                    n_used,
                )? {
                    continue;
                }
            } else if gdec_may_fire
                && cfg.m3.is_none()
                && no_macros
                && Self::moe_gdec_token(
                    e,
                    m,
                    il,
                    max_block,
                    &zt,
                    sel,
                    w,
                    &mut moe_out,
                    tok,
                    n_embd,
                    n_ff_exp,
                    n_used,
                )?
            {
                continue;
            }

            // STAGE 2 memset-elision invariant: moe_out was allocated UNINIT when gdec or the
            // slab pair could fire. This token fell through to a sequential axpy loop, which
            // ACCUMULATES — zero its row first (row-sized memset; other rows are owned by the
            // full-overwrite arms). slab_fused_may_fire never actually reaches here (its arm
            // has no fallible predicate), included for the allocation invariant's symmetry.
            if gdec_may_fire || slab_fused_may_fire || fused_epi_may_fire || fused_epi_slab_may_fire
            {
                let mut row = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                e.memset_zeros_view(&mut row)?;
            }

            // Fiddler-style split at whole-expert granularity. Partial residency is deliberately a
            // CPU assignment: transferring the missing projection would reintroduce the PCIe/NVMe
            // stall this path exists to remove, while mixing projections would require another
            // activation round-trip. Weight addresses remain valid until this worker is joined at
            // the bottom of the token scope.
            let mut cpu_mask = vec![false; sel.len()];
            let cpu_worker = if let Some(host_input) = cpu_input.as_ref() {
                let gpu_resident = if use_cache {
                    e.with_moe_cache(max_block, |cache, _| {
                        Ok(sel
                            .iter()
                            .map(|&expert| {
                                let expert = expert as u16;
                                [PROJ_GATE, PROJ_UP, PROJ_DOWN]
                                    .into_iter()
                                    .filter(|&projection| {
                                        cache
                                            .resident(BlockId::new(il, projection, expert))
                                            .is_some()
                                    })
                                    .count()
                            })
                            .collect::<Vec<_>>())
                    })?
                } else {
                    vec![0; sel.len()]
                };
                let mut cpu_selected = Vec::new();
                for (index, (&expert, &route_weight)) in sel.iter().zip(w).enumerate() {
                    if gpu_resident[index] != 3 {
                        cpu_mask[index] = true;
                        crate::cpu_experts::record_incomplete_gpu_residency(gpu_resident[index]);
                        let expert = expert as usize;
                        cpu_selected.push((expert, route_weight));
                    }
                }
                if crate::cpu_experts::predictor_enabled() {
                    // Fire-and-forget lookahead: the predictor worker scores layers il+1..
                    // from this layer's MoE input and prefetches predicted-and-missing
                    // experts into the companion RAM cache. Never blocks this thread.
                    let row = &host_input[tok * n_embd..(tok + 1) * n_embd];
                    crate::cpu_experts::predictor_submit(il, row);
                }
                if cpu_selected.is_empty() {
                    None
                } else {
                    let row = &host_input[tok * n_embd..(tok + 1) * n_embd];
                    let job = crate::cpu_experts::prepare_job(m, il, &cpu_selected, row)
                        .map_err(std::io::Error::other)?;
                    Some(crate::cpu_experts::submit(job).map_err(std::io::Error::other)?)
                }
            } else {
                None
            };

            let worker_window = worker_disk_prefetch
                .then(worker_prefetch_window)
                .unwrap_or(0);
            for (j, &ex) in sel.iter().enumerate() {
                if cpu_mask[j] {
                    continue;
                }
                let ex = ex as usize;
                // PER-EXPERT SLAB READ (lane/pp-leverb 2026-08-08): the layers the fused
                // pair above excludes — step35's CLAMPED layers 43/44 (ffn_act_lim has no
                // fused form) and macro-carrying artifacts — still have their bytes in the
                // local resident slab. Same kernels as the SLRU arms (`qmatvec_expert_q8` /
                // `qmatvec_view`), same ffn_act_lim/macro folds; provenance = slab base +
                // ex*stride. No dispatch lock, no admission, no prefetch — nothing to miss.
                if let Some(d) = slab_local {
                    let gl = m.gate_exps.expert_layout(ex);
                    let ul = m.up_exps.expert_layout(ex);
                    let dl = m.down_exps.expert_layout(ex);
                    let (g0, u0, d0) = (
                        ex * m.gate_exps.expert_stride,
                        ex * m.up_exps.expert_stride,
                        ex * m.down_exps.expert_stride,
                    );
                    let (gate, up) = if moe_q8 {
                        if tok_q8.is_none() {
                            tok_q8 = Some(e.quantize_q8_1_view(&zt, 1, n_embd)?);
                        }
                        let (zq, zd) = tok_q8.as_ref().unwrap();
                        (
                            e.qmatvec_expert_q8(
                                &d.gate,
                                g0..g0 + gl.len,
                                zq,
                                zd,
                                1,
                                m.gate_exps.in_f,
                                m.gate_exps.out_f,
                                gl.qtype,
                                gl.row_bytes,
                            )?,
                            e.qmatvec_expert_q8(
                                &d.up,
                                u0..u0 + ul.len,
                                zq,
                                zd,
                                1,
                                m.up_exps.in_f,
                                m.up_exps.out_f,
                                ul.qtype,
                                ul.row_bytes,
                            )?,
                        )
                    } else {
                        (
                            m.qmatvec_view(
                                e,
                                &d.gate,
                                g0..g0 + gl.len,
                                &zt,
                                1,
                                m.gate_exps.in_f,
                                m.gate_exps.out_f,
                                gl.qtype,
                                gl.row_bytes,
                            )?,
                            m.qmatvec_view(
                                e,
                                &d.up,
                                u0..u0 + ul.len,
                                &zt,
                                1,
                                m.up_exps.in_f,
                                m.up_exps.out_f,
                                ul.qtype,
                                ul.row_bytes,
                            )?,
                        )
                    };
                    let mut act = e.uninit(n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        n_ff_exp,
                    )?;
                    let y = if moe_q8 {
                        let (aq2, ad2) = e.quantize_q8_1(&act, 1, n_ff_exp)?;
                        e.qmatvec_expert_q8(
                            &d.down,
                            d0..d0 + dl.len,
                            &aq2,
                            &ad2,
                            1,
                            m.down_exps.in_f,
                            m.down_exps.out_f,
                            dl.qtype,
                            dl.row_bytes,
                        )?
                    } else {
                        let actv = act.slice(0..n_ff_exp);
                        m.qmatvec_view(
                            e,
                            &d.down,
                            d0..d0 + dl.len,
                            &actv,
                            1,
                            m.down_exps.in_f,
                            m.down_exps.out_f,
                            dl.qtype,
                            dl.row_bytes,
                        )?
                    };
                    let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                    e.axpy_into(&y, w[j] * m.down_exps.macro_scale(ex), &mut dst, n_embd)?;
                    continue;
                }
                for next in page_prefetch_positions(j, sel.len(), page_window) {
                    Self::moe_prefetch_host_expert(sel[next] as usize, m);
                }
                let keep = [
                    crate::moe_cache::BlockId::new(il, crate::moe_cache::PROJ_GATE, ex as u16),
                    crate::moe_cache::BlockId::new(il, crate::moe_cache::PROJ_UP, ex as u16),
                    crate::moe_cache::BlockId::new(il, crate::moe_cache::PROJ_DOWN, ex as u16),
                ];
                if worker_disk_prefetch && worker_window > 0 {
                    for next in worker_prefetch_positions(j, sel.len(), worker_window) {
                        Self::moe_prefetch_disk_expert(
                            e,
                            il,
                            sel[next] as usize,
                            m,
                            max_block,
                            &keep,
                        )?;
                    }
                } else if cache_dispatch
                    && !cpu_hybrid
                    && moe_prefetch_enabled()
                    && j + 1 < sel.len()
                {
                    let next = sel[j + 1] as usize;
                    Self::moe_prefetch_expert(e, il, next, m, max_block, &keep)?;
                }
                let [gate_q8, up_q8, down_q8] = [moe_q8; 3];
                if cache_dispatch && (gate_q8 || up_q8 || down_q8) {
                    // dp4a EXPERT PATH (MEMRA_MOE_Q8): quantize z-row once per token. Mixed expert
                    // layouts stay on the metadata-aware f32 path.
                    if (gate_q8 || up_q8) && tok_q8.is_none() {
                        tok_q8 = Some(e.quantize_q8_1_view(&zt, 1, n_embd)?);
                    }
                    let gate = if gate_q8 {
                        let (zq, zd) = tok_q8.as_ref().unwrap();
                        Self::moe_cached_gemm_q8(e, il, PROJ_GATE, ex, m, max_block, zq, zd)?
                    } else {
                        Self::moe_cached_gemm(e, il, PROJ_GATE, ex, m, max_block, &zt)?
                    };
                    let up = if up_q8 {
                        let (zq, zd) = tok_q8.as_ref().unwrap();
                        Self::moe_cached_gemm_q8(e, il, PROJ_UP, ex, m, max_block, zq, zd)?
                    } else {
                        Self::moe_cached_gemm(e, il, PROJ_UP, ex, m, max_block, &zt)?
                    };
                    let mut act = e.uninit(n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        n_ff_exp,
                    )?;
                    let y = if down_q8 {
                        let (aq2, ad2) = e.quantize_q8_1(&act, 1, n_ff_exp)?;
                        Self::moe_cached_gemm_q8(e, il, PROJ_DOWN, ex, m, max_block, &aq2, &ad2)?
                    } else {
                        let actv = act.slice(0..n_ff_exp);
                        Self::moe_cached_gemm(e, il, PROJ_DOWN, ex, m, max_block, &actv)?
                    };
                    let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                    // down-proj macro folds into the accumulate weight (1.0 for non-macro archs).
                    e.axpy_into(&y, w[j] * m.down_exps.macro_scale(ex), &mut dst, n_embd)?;
                } else if cache_dispatch {
                    // SLRU residency cache: per-projection, dispatch the block (HIT => resident slot,
                    // MISS => staged slot) then run the SAME unchanged qmatvec_view from that slot.
                    // The bytes the kernel reads are byte-for-byte the same GGUF block (§B.3); the
                    // only difference between HIT and MISS is whether the memcpy_htod ran.
                    let gate = Self::moe_cached_gemm(e, il, PROJ_GATE, ex, m, max_block, &zt)?;
                    let up = Self::moe_cached_gemm(e, il, PROJ_UP, ex, m, max_block, &zt)?;
                    let mut act = e.uninit(n_ff_exp)?; // activation fully overwrites
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        n_ff_exp,
                    )?;
                    let actv = act.slice(0..n_ff_exp);
                    let y = Self::moe_cached_gemm(e, il, PROJ_DOWN, ex, m, max_block, &actv)?;
                    let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                    // down-proj macro folds into the accumulate weight (post-matmul linear scale).
                    e.axpy_into(&y, w[j] * m.down_exps.macro_scale(ex), &mut dst, n_embd)?;
                } else if cache_frozen {
                    // A later prompt prime must not change the CPU/GPU assignment frozen after the
                    // first prime. Reuse every fixed resident projection directly and stage only a
                    // true miss through the ordinary scratch slot. This preserves the established
                    // f32-dequant numeric path while avoiding a full-bank reread on every prime.
                    let gate = Self::moe_frozen_gemm(
                        e,
                        il,
                        PROJ_GATE,
                        ex,
                        m,
                        max_block,
                        &zt,
                        &mut scratch_g,
                        g_len,
                    )?;
                    let up = Self::moe_frozen_gemm(
                        e,
                        il,
                        PROJ_UP,
                        ex,
                        m,
                        max_block,
                        &zt,
                        &mut scratch_u,
                        u_len,
                    )?;
                    let mut act = e.uninit(n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        n_ff_exp,
                    )?;
                    let actv = act.slice(0..n_ff_exp);
                    let y = Self::moe_frozen_gemm(
                        e,
                        il,
                        PROJ_DOWN,
                        ex,
                        m,
                        max_block,
                        &actv,
                        &mut scratch_d,
                        d_len,
                    )?;
                    let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                    e.axpy_into(&y, w[j] * m.down_exps.macro_scale(ex), &mut dst, n_embd)?;
                } else {
                    // Stage-1: stage gate/up/down for expert `ex` into the scratch slots, then GEMM.
                    // Lazy scratch: first no-cache expert allocates the 3 slots (uninit — stage_expert
                    // fully overwrites the byte range the GEMM reads).
                    if scratch_g.is_none() {
                        scratch_g = Some(e.alloc_u8_uninit(g_len)?);
                        scratch_u = Some(e.alloc_u8_uninit(u_len)?);
                        scratch_d = Some(e.alloc_u8_uninit(d_len)?);
                    }
                    let (sg, su, sd) = (
                        scratch_g.as_mut().unwrap(),
                        scratch_u.as_mut().unwrap(),
                        scratch_d.as_mut().unwrap(),
                    );
                    let gl = m.gate_exps.expert_layout(ex);
                    let ul = m.up_exps.expert_layout(ex);
                    let dl = m.down_exps.expert_layout(ex);
                    e.stage_expert(m.gate_exps.expert_bytes(ex), sg, 0)?;
                    let gate = m.qmatvec_view(
                        e,
                        sg,
                        0..gl.len,
                        &zt,
                        1,
                        m.gate_exps.in_f,
                        m.gate_exps.out_f,
                        gl.qtype,
                        gl.row_bytes,
                    )?;

                    e.stage_expert(m.up_exps.expert_bytes(ex), su, 0)?;
                    let up = m.qmatvec_view(
                        e,
                        su,
                        0..ul.len,
                        &zt,
                        1,
                        m.up_exps.in_f,
                        m.up_exps.out_f,
                        ul.qtype,
                        ul.row_bytes,
                    )?;

                    let mut act = e.uninit(n_ff_exp)?; // activation fully overwrites
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        n_ff_exp,
                    )?;

                    e.stage_expert(m.down_exps.expert_bytes(ex), sd, 0)?;
                    let actv = act.slice(0..n_ff_exp);
                    let y = m.qmatvec_view(
                        e,
                        sd,
                        0..dl.len,
                        &actv,
                        1,
                        m.down_exps.in_f,
                        m.down_exps.out_f,
                        dl.qtype,
                        dl.row_bytes,
                    )?;

                    let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                    e.axpy_into(&y, w[j] * m.down_exps.macro_scale(ex), &mut dst, n_embd)?;
                }
            }
            if let Some(worker) = cpu_worker {
                let cpu_output = worker.wait().map_err(std::io::Error::other)?;
                let cpu_output = e.htod(&cpu_output)?;
                let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                e.axpy_into(&cpu_output, 1.0, &mut dst, n_embd)?;
            }
            if cpu_hybrid && !cache_frozen && cpu_expert_profile_admit_enabled() {
                for (j, &ex) in sel.iter().enumerate() {
                    if cpu_mask[j] {
                        Self::moe_profile_admit_expert(e, il, ex as usize, m, max_block)?;
                    }
                }
            }
        }

        if memra_reference::hidden_trace::enabled() {
            memra_reference::hidden_trace::emit_last_row(
                "routed",
                il as i64,
                t,
                n_embd,
                &e.dtoh(&moe_out)?,
            );
        }

        Self::moe_shexp_add(e, m, z, zq8, t, cfg, lim_shexp, &mut moe_out)?;

        Ok(moe_out)
    }

    /// The glm5 TP-2 EP walk (`MEMRA_GLM5_TP`): one MoE layer's routed-expert FFN over
    /// whole-expert contiguous halves. The ROUTER is the unchanged root-side program
    /// (`moe_router_logits` + `moe_route_sigmoid_cfg` — bit-identical selection by
    /// construction); each rank computes its owned slots' UNWEIGHTED expert rows through
    /// the sequential per-expert program (gate/up qmatvec + `ffn_act_lim` + down qmatvec —
    /// per-expert-independent dots), the peer's rows return host-canonically, and root
    /// applies the slot-ordered `axpy` accumulation chain — the same rounded-operation
    /// sequence the plain sequential walk applies. The ROOT-owned shared expert then adds
    /// through the extracted `moe_shexp_add`, verbatim.
    ///
    /// Three transport arms behind ONE routing (lane/glm5-ep-diet; sel/w are shared so a
    /// dispatch change can never change selection):
    ///   * `MEMRA_GLM5_EP_GROUPED_PRIME` (prefill shapes only): per-rank grouped-GEMM prime
    ///     over the rank slabs — the plain grouped-prefill program split by ownership.
    ///     Falls closed to the arms below whenever the plain arm's conjuncts do not hold.
    ///   * `MEMRA_GLM5_EP_DIET`: the v1 walk's kernels and combine chain with dieted data
    ///     movement — one bulk fan-out, zero per-slot host round-trips, one combine launch.
    ///     Decode-byte-identical to v1 by construction.
    ///   * default: the v1 per-slot host-canonical walk, byte-for-byte.
    #[allow(clippy::too_many_arguments)]
    fn moe_ffn_glm5_ep(
        e: &Engine,
        m: &MoeWeights,
        ep: &crate::glm5_tp::Glm5EpExps,
        z: &CudaSlice<f32>,
        zq8: Option<&(CudaSlice<i8>, CudaSlice<f32>)>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        prefill: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let moe = cfg
            .moe
            .as_ref()
            .ok_or("glm5 EP execution requires MoE model metadata")?;
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        let sig = cfg
            .sigmoid_router()
            .ok_or("glm5 EP execution requires the sigmoid router")?;
        let lim_exp = cfg.clamp_exp_at(il as u32);
        let lim_shexp = cfg.clamp_shexp_at(il as u32);
        if ep.slabs.iter().map(|s| s.n_experts).sum::<usize>() != n_expert {
            return Err(format!(
                "glm5 EP slabs cover {:?} experts, model declares {n_expert}",
                ep.slabs.iter().map(|s| s.n_experts).collect::<Vec<_>>()
            )
            .into());
        }
        let rt = &ep.rt;
        let ranks = ep.ranks();

        // Root router, unchanged program (selection bit-identical to the sequential arm).
        let logits = Self::moe_router_logits(e, m, z, t, cfg)?;
        let (sel_all, w_all) =
            Self::moe_route_sigmoid_cfg(e, &logits, t, n_expert, n_used, m, sig)?;
        crate::moesd::record_host_routes(il, n_expert, n_used, &sel_all)?;
        // The route trace taps ride this walk too (sel/w are already host-side here);
        // the EP walk must never be a blind spot for the co-activation measurement.
        Self::trace_moe_routes(il, t, &sel_all, &w_all)?;

        // EP grouped prime (`MEMRA_GLM5_EP_GROUPED_PRIME`, default OFF): keyed exactly like
        // the plain grouped-prefill arm (`prefill` + t above the per-token tier) and honoring
        // the family rollback (`MEMRA_MOE_GROUPED_PREFILL=0` kills it too). The announce
        // prints once per process PER FLAG VALUE, in both arms, so an A/B grep distinguishes
        // engagement without the line being an arm-local cost.
        if prefill && t > MOE_DEV_MAX_T {
            static EPGP_ANNOUNCED: std::sync::atomic::AtomicU8 =
                std::sync::atomic::AtomicU8::new(0);
            let enabled = crate::glm5_ep_grouped_prime_on() && moe_grouped_prefill_enabled();
            let bit = 1u8 << u8::from(enabled);
            if EPGP_ANNOUNCED.fetch_or(bit, std::sync::atomic::Ordering::Relaxed) & bit == 0 {
                eprintln!(
                    "[glm5-ep-grouped-prime] flag={} t={t} il={il} (announce printed in both \
                     arms; engagement is the dispatch counter + per-layer execute line)",
                    if enabled { "on" } else { "off" },
                );
            }
            if enabled
                && let Some(mut out) =
                    Self::moe_ffn_glm5_ep_grouped_prime(e, m, ep, z, &sel_all, &w_all, t, cfg, il)?
            {
                Self::moe_shexp_add(e, m, z, zq8, t, cfg, lim_shexp, &mut out)?;
                return Ok(out);
            }
        }

        // EP dispatch diet (`MEMRA_GLM5_EP_DIET`, default OFF): same kernels, same combine
        // chain, restructured movement. Read per call — `=0`/unset restores the v1 walk.
        if crate::glm5_ep_diet_on() {
            let mut out = Self::moe_ffn_glm5_ep_diet(e, m, ep, z, &sel_all, &w_all, t, cfg, il)?;
            Self::moe_shexp_add(e, m, z, zq8, t, cfg, lim_shexp, &mut out)?;
            return Ok(out);
        }

        let mut moe_out = e.zeros(t * n_embd)?;
        use crate::glm5_tp_transport::Glm5TpTransport as TpXport;
        let hop = ep.rt.hop(e);
        // Peer replicas of `z`, one arm each (lane/glm5-tp-transport):
        //   host-canonical — v1's EXACT pattern: one draining `dtoh` of the whole block here,
        //     then one row `htod` per token PER PEER RANK inside the loop (at two ranks that
        //     is byte- and hop-identical to v1). Preserved hop-for-hop so
        //     `MEMRA_GLM5_TP_TRANSPORT=0` reproduces the banked v1 walk, not a faster cousin.
        //   peer-pull — ONE device copy of the whole `[t, n_embd]` block per peer rank; rows
        //     are sliced out of it. Same bytes on the peers, the host uploads and drains
        //     removed.
        let z_host = match hop.transport {
            TpXport::HostCanonical => Some(crate::glm5_tp_transport::host_stage_block(
                &hop,
                0,
                z,
                t * n_embd,
            )?),
            TpXport::PeerPull => None,
        };
        let z_peer_bulks = match hop.transport {
            TpXport::PeerPull => Some(crate::glm5_tp_transport::fanout_f32(&hop, z, t * n_embd)?),
            TpXport::HostCanonical => None,
        };
        for tok in 0..t {
            let sel = &sel_all[tok * n_used..(tok + 1) * n_used];
            let w = &w_all[tok * n_used..(tok + 1) * n_used];
            let zt = z.slice(tok * n_embd..(tok + 1) * n_embd);
            // The host-canonical arm materializes this token's peer rows here (v1's
            // per-token `htod`, one per peer rank); the peer-pull arm slices them out of the
            // bulk blocks. The holders must outlive the views, hence the two-step.
            let z_peer_row_holders: Option<Vec<CudaSlice<f32>>> = match &z_host {
                Some(h) => {
                    let mut rows = Vec::with_capacity(ranks - 1);
                    for r in 1..ranks {
                        rows.push(crate::glm5_tp_transport::host_row_to(
                            &hop,
                            r,
                            &h[tok * n_embd..(tok + 1) * n_embd],
                        )?);
                    }
                    Some(rows)
                }
                None => None,
            };
            // Per slot, in ROUTER SLOT ORDER: compute the UNWEIGHTED expert row on its
            // owner, then fmaf-accumulate on root — the plain walk's exact chain.
            for (j, &ex) in sel.iter().enumerate() {
                let ex = ex as usize;
                let owner = ep.owner(ex);
                if owner != 0 {
                    // Engagement counter FIRST (a red skip still counts as ROUTED).
                    crate::glm5_tp::GLM5_EP_PEER_SLOT_DISPATCHES
                        .fetch_add(1, std::sync::atomic::Ordering::Relaxed);
                    // Gate red arm: dropping the peers' slots MUST diverge — the
                    // non-vacuity proof that the peer ranks contribute real expert work.
                    if matches!(
                        crate::glm5_tp::gate_red(),
                        Ok(Some(crate::glm5_tp::GateRed::SkipPeerCombine))
                    ) {
                        continue;
                    }
                }
                let zin_holder;
                let (dev, slab, zin) = if owner == 0 {
                    (e, &ep.slabs[0], &zt)
                } else {
                    zin_holder = match (&z_peer_row_holders, &z_peer_bulks) {
                        (Some(rows), _) => rows[owner - 1].slice(0..n_embd),
                        (None, Some(bulks)) => {
                            bulks[owner - 1].slice(tok * n_embd..(tok + 1) * n_embd)
                        }
                        (None, None) => {
                            return Err(
                                "glm5 EP: neither transport arm staged the peer activation".into(),
                            );
                        }
                    };
                    (
                        crate::glm5_tp::rank_engine(e, rt, owner),
                        &ep.slabs[owner],
                        &zin_holder,
                    )
                };
                // Placement indirection: the owner's slab packs its experts in
                // ascending-id order; `local_of` is the slot (identical to
                // `ex - first_expert` under the even split).
                let local = ep.local_of[ex] as usize;
                let gl = m.gate_exps.expert_stride;
                let ul = m.up_exps.expert_stride;
                let dl = m.down_exps.expert_stride;
                let gate = dev.qmatvec_view(
                    &slab.gate,
                    local * gl..(local + 1) * gl,
                    zin,
                    1,
                    m.gate_exps.in_f,
                    m.gate_exps.out_f,
                    m.gate_exps.qtype,
                    m.gate_exps.row_bytes,
                )?;
                let up = dev.qmatvec_view(
                    &slab.up,
                    local * ul..(local + 1) * ul,
                    zin,
                    1,
                    m.up_exps.in_f,
                    m.up_exps.out_f,
                    m.up_exps.qtype,
                    m.up_exps.row_bytes,
                )?;
                let mut act = dev.uninit(n_ff_exp)?; // activation fully overwrites
                Self::ffn_act_lim(
                    dev,
                    cfg,
                    &gate,
                    &up,
                    m.gate_exps.macro_scale(ex),
                    m.up_exps.macro_scale(ex),
                    lim_exp,
                    &mut act,
                    n_ff_exp,
                )?;
                let actv = act.slice(0..n_ff_exp);
                let y = dev.qmatvec_view(
                    &slab.down,
                    local * dl..(local + 1) * dl,
                    &actv,
                    1,
                    m.down_exps.in_f,
                    m.down_exps.out_f,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?;
                // The owner's row returns through the armed transport; the root row stays
                // put. The slot-ordered axpy below is the ONE cross-rank arithmetic site and
                // it reproduces the sequential walk's accumulate chain operation for
                // operation — unchanged by which transport delivered the row.
                let y_root = if owner == 0 {
                    y
                } else {
                    crate::glm5_tp_transport::return_row_to_root(&hop, owner, &y, n_embd)?
                };
                let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                e.axpy_into(
                    &y_root,
                    w[j] * m.down_exps.macro_scale(ex),
                    &mut dst,
                    n_embd,
                )?;
            }
        }

        Self::moe_shexp_add(e, m, z, zq8, t, cfg, lim_shexp, &mut moe_out)?;
        Ok(moe_out)
    }

    /// The DIETED glm5 EP walk (`MEMRA_GLM5_EP_DIET`, lane/glm5-ep-diet): the v1 walk's
    /// per-slot expert kernels and its exact slot-ordered combine chain, with the data
    /// movement restructured in whole groups (the tp2-battery's measured 13-18 ms/token v1
    /// join+dispatch tax, attributed to per-token host fan-out x42 layers + ~4-5 sync
    /// peer-slot round-trips/layer + interleaved host-blocked issue):
    ///
    ///   1. ONE bulk peer z fan-out per layer-call ([t, n_embd] in one upload; SKIPPED
    ///      entirely when the call routed no peer-owned expert — the placement-map
    ///      multiplier: a single-rank layer-call moves zero activation bytes off root).
    ///   2. Peer-owned rows compute back-to-back on the peer stream into a compact block
    ///      (issue order cannot change bytes: every row is an independent per-expert
    ///      program; the combine order below is fixed by the id table, not by issue).
    ///   3. Root-owned rows compute on the root stream, un-blocked by peer returns.
    ///   4. ONE bulk peer return (peer DtoH + root HtoD of the compact block) replaces the
    ///      per-slot round-trip dribble.
    ///   5. ONE `moe_pairs_scatter` launch applies the per-token slot-ordered fmaf chain —
    ///      the kernel header carries the byte-identity contract vs the zeros +
    ///      sequential-`axpy_f32` chain this replaces, and the weights are the SAME host
    ///      fold (`w * macro_scale(ex)`) v1 passed per launch.
    ///
    /// BYTE-IDENTICAL to the v1 walk (and to plain, wherever v1 is) by construction: same
    /// kernels over the same bytes; copies (dtod / bulk DtoH+HtoD) preserve bits; the one
    /// arithmetic site keeps its exact chain. Transport stays HOST-CANONICAL: the two bulk
    /// hops of steps 1 and 4 are the named native-P2P swap points for the box arc (the
    /// `MEMRA_STEP_TP_BULK_P2P` precedent: peer copies 61,452 -> ~21/layer on step; the
    /// glm5 seam inherits `configure_native_p2p` but does NOT wire it on the rig — the
    /// same-device dual-context emulation has no real peer transport to qualify).
    #[allow(clippy::too_many_arguments)]
    fn moe_ffn_glm5_ep_diet(
        e: &Engine,
        m: &MoeWeights,
        ep: &crate::glm5_tp::Glm5EpExps,
        z: &CudaSlice<f32>,
        sel_all: &[u32],
        w_all: &[f32],
        t: usize,
        cfg: &ModelConfig,
        il: u16,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        use std::sync::atomic::Ordering;
        let moe = cfg
            .moe
            .as_ref()
            .ok_or("glm5 EP execution requires MoE model metadata")?;
        let n_embd = cfg.n_embd as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        let lim_exp = cfg.clamp_exp_at(il as u32);
        let rt = &ep.rt;
        let n_pairs = t * n_used;
        if sel_all.len() < n_pairs || w_all.len() < n_pairs || z.len() < t * n_embd {
            return Err("glm5 EP diet geometry".into());
        }
        let red_skip_peer = matches!(
            crate::glm5_tp::gate_red(),
            Ok(Some(crate::glm5_tp::GateRed::SkipPeerCombine))
        );

        // Slab-position table: root-owned pairs pack the slab head in pair order; each peer
        // rank's pairs pack a contiguous tail segment (so every rank's bulk return is ONE
        // contiguous upload). `ids[p]` is pair p's slab row; the scatter walks ids in slot
        // order per token, which is what pins the combine chain to v1's regardless of
        // packing. At two ranks this is byte-for-byte the original head/tail split.
        let ranks = ep.ranks();
        let mut per_rank = vec![0usize; ranks];
        for &s in sel_all.iter().take(n_pairs) {
            let ex = s as usize;
            if ex >= ep.owner_of.len() {
                return Err(format!("glm5 EP diet: selection {ex} outside the bank").into());
            }
            per_rank[ep.owner(ex)] += 1;
        }
        let mut base = vec![0usize; ranks];
        for r in 1..ranks {
            base[r] = base[r - 1] + per_rank[r - 1];
        }
        let mut ids = vec![0i32; n_pairs];
        {
            let mut k = vec![0usize; ranks];
            for (p, id) in ids.iter_mut().enumerate() {
                let r = ep.owner(sel_all[p] as usize);
                *id = (base[r] + k[r]) as i32;
                k[r] += 1;
            }
        }

        crate::glm5_tp::GLM5_EP_DIET_DISPATCHES.fetch_add(1, Ordering::Relaxed);
        for r in 1..ranks {
            crate::glm5_tp::GLM5_EP_DIET_FANOUT_UPLOADS_AVOIDED.fetch_add(
                if per_rank[r] > 0 {
                    (t - 1) as u64
                } else {
                    t as u64
                },
                Ordering::Relaxed,
            );
        }
        static EP_DIET_MARKED: std::sync::atomic::AtomicBool =
            std::sync::atomic::AtomicBool::new(false);
        if !EP_DIET_MARKED.swap(true, Ordering::Relaxed) {
            eprintln!(
                "[glm5-ep-diet] engaged: bulk fan-out + compact peer staging + single \
                 slot-ordered scatter combine; per-slot host round-trips removed \
                 transport={} performance_claim=false",
                ep.rt.transport.name(),
            );
        }

        // Per-slot expert program, shared verbatim with the v1 walk (same kernels, same
        // argument order): gate/up qmatvec + ffn_act_lim + down qmatvec on the OWNING rank.
        let expert_row = |dev: &Engine,
                          slab: &crate::glm5_tp::EpRankSlab,
                          zin: &cudarc::driver::CudaView<f32>,
                          ex: usize|
         -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
            let local = ep.local_of[ex] as usize;
            let gl = m.gate_exps.expert_stride;
            let ul = m.up_exps.expert_stride;
            let dl = m.down_exps.expert_stride;
            let gate = dev.qmatvec_view(
                &slab.gate,
                local * gl..(local + 1) * gl,
                zin,
                1,
                m.gate_exps.in_f,
                m.gate_exps.out_f,
                m.gate_exps.qtype,
                m.gate_exps.row_bytes,
            )?;
            let up = dev.qmatvec_view(
                &slab.up,
                local * ul..(local + 1) * ul,
                zin,
                1,
                m.up_exps.in_f,
                m.up_exps.out_f,
                m.up_exps.qtype,
                m.up_exps.row_bytes,
            )?;
            let mut act = dev.uninit(n_ff_exp)?; // activation fully overwrites
            Self::ffn_act_lim(
                dev,
                cfg,
                &gate,
                &up,
                m.gate_exps.macro_scale(ex),
                m.up_exps.macro_scale(ex),
                lim_exp,
                &mut act,
                n_ff_exp,
            )?;
            let actv = act.slice(0..n_ff_exp);
            dev.qmatvec_view(
                &slab.down,
                local * dl..(local + 1) * dl,
                &actv,
                1,
                m.down_exps.in_f,
                m.down_exps.out_f,
                m.down_exps.qtype,
                m.down_exps.row_bytes,
            )
        };

        // Pass 1 — PEERS: one bulk fan-out per pair-owning rank, then every owned row
        // back-to-back on that rank's stream into its compact block. No host boundary until
        // the bulk returns.
        let hop = ep.rt.hop(e);
        let mut y_peer_blks: Vec<Option<CudaSlice<f32>>> = (0..ranks).map(|_| None).collect();
        for r in 1..ranks {
            if per_rank[r] == 0 {
                continue;
            }
            let dev = crate::glm5_tp::rank_engine(e, rt, r);
            // SWAP POINT 1 (bulk fan-out) — the named transport shape, to this rank only
            // (a rank with zero owned pairs moves zero activation bytes off root).
            let z_r = crate::glm5_tp_transport::fanout_f32_to(&hop, r, z, t * n_embd)?;
            // Under the skip-peer-combine red the block stays ZERO for skipped rows (a red
            // must drop the peer contribution loudly, never multiply garbage into the chain).
            let mut blk = if red_skip_peer {
                dev.zeros(per_rank[r] * n_embd)?
            } else {
                dev.uninit(per_rank[r] * n_embd)?
            };
            let mut k = 0usize;
            for tok in 0..t {
                let sel = &sel_all[tok * n_used..(tok + 1) * n_used];
                for &ex in sel.iter() {
                    let ex = ex as usize;
                    if ep.owner(ex) != r {
                        continue;
                    }
                    // Engagement counters FIRST (a red skip still counts as ROUTED).
                    crate::glm5_tp::GLM5_EP_PEER_SLOT_DISPATCHES.fetch_add(1, Ordering::Relaxed);
                    crate::glm5_tp::GLM5_EP_DIET_PEER_ROUNDTRIPS_AVOIDED
                        .fetch_add(1, Ordering::Relaxed);
                    if red_skip_peer {
                        k += 1;
                        continue;
                    }
                    let zt_r = z_r.slice(tok * n_embd..(tok + 1) * n_embd);
                    let y = expert_row(dev, &ep.slabs[r], &zt_r, ex)?;
                    dev.copy_into(&mut blk, k * n_embd, &y, n_embd)?;
                    k += 1;
                }
            }
            y_peer_blks[r] = Some(blk);
        }

        // Pass 2 — ROOT: every root-owned row into the slab head, never blocked on a peer
        // return (the v1 walk interleaved root issue behind per-slot peer syncs).
        let mut y_all = e.uninit(n_pairs * n_embd)?;
        {
            let mut k = 0usize;
            for tok in 0..t {
                let sel = &sel_all[tok * n_used..(tok + 1) * n_used];
                let zt = z.slice(tok * n_embd..(tok + 1) * n_embd);
                for &ex in sel.iter() {
                    let ex = ex as usize;
                    if ep.owner(ex) != 0 {
                        continue;
                    }
                    let y = expert_row(e, &ep.slabs[0], &zt, ex)?;
                    e.copy_into(&mut y_all, k * n_embd, &y, n_embd)?;
                    k += 1;
                }
            }
        }

        // SWAP POINT 2 (bulk returns) — Pass 3: ONE rank->root block move into each rank's
        // tail segment. On host-canonical each is the ONE draining peer sync of that rank's
        // layer-call share, exactly as before; on peer-pull each is one event-ordered device
        // copy and no host boundary at all.
        for r in 1..ranks {
            if let Some(blk) = &y_peer_blks[r] {
                crate::glm5_tp_transport::return_block_to_root(
                    &hop,
                    r,
                    blk,
                    &mut y_all,
                    base[r] * n_embd,
                    per_rank[r] * n_embd,
                )?;
                crate::glm5_tp::GLM5_EP_DIET_BULK_RETURNS.fetch_add(1, Ordering::Relaxed);
            }
        }

        // Pass 4 — ONE combine launch. Weights are v1's exact host fold, placed at slab
        // positions; the scatter walks each token's pairs in SLOT order (ids[p], p pair-major),
        // reproducing zeros + n_used sequential axpy_f32 per the kernel's bit contract.
        let mut wd = vec![0f32; n_pairs];
        for ((&id, &w), &s) in ids.iter().zip(w_all.iter()).zip(sel_all.iter()) {
            wd[id as usize] = w * m.down_exps.macro_scale(s as usize);
        }
        let pw = e.htod(&wd)?;
        let toff: Vec<i32> = (0..=t).map(|tok| (tok * n_used) as i32).collect();
        let toff_d = e.htod_i32(&toff)?;
        let ids_d = e.htod_i32(&ids)?;
        let mut moe_out = e.uninit(t * n_embd)?; // the scatter fully overwrites
        e.moe_pairs_scatter(&y_all, &pw, &toff_d, &ids_d, &mut moe_out, t, n_embd)?;
        Ok(moe_out)
    }

    /// The EP GROUPED PRIME (`MEMRA_GLM5_EP_GROUPED_PRIME`, lane/glm5-ep-diet): the plain
    /// walk's grouped-prefill program (`moe_ffn_grouped_prefill_sigmoid`, default ON on the
    /// serving artifact — 85 -> 616-639 tok/s prefill in its box A/B) split by expert
    /// ownership. Per rank: expert-major CSR over the rank's OWNED (token, expert) pairs,
    /// one grouped f16 GEMM per projection over the rank's resident EP slab (pointer tables
    /// minted at arm time), the PRE-clamped SwiGLU epilogue, the per-expert macro folds,
    /// and the slot-ordered per-token scatter — all composed from the SAME Engine calls the
    /// plain arm makes, so per-expert GEMM bytes match the plain grouped arm's (grouping is
    /// per expert, and an expert's token rows all live on its owner). The ONE new
    /// reassociation is the per-token partial add (root chain + peer chain instead of one
    /// 8-term chain) — band-gated on minted NVFP4 slabs (`glm5_ep_diet_doors_gpu`), never
    /// claimed byte.
    ///
    /// Returns `Ok(None)` — fall closed to the sequential EP walk — whenever the plain
    /// grouped arm's own admission would (f16g-ineligible qtypes, bank/top-k shape, no
    /// sigmoid clamp form). The rig fixture's Q8_0 bank therefore ALWAYS falls closed;
    /// `glm5-tp-gate`'s grouped arm proves exactly that (dispatch counter pinned 0, walk
    /// bytes unchanged).
    #[allow(clippy::too_many_arguments)]
    fn moe_ffn_glm5_ep_grouped_prime(
        e: &Engine,
        m: &MoeWeights,
        ep: &crate::glm5_tp::Glm5EpExps,
        z: &CudaSlice<f32>,
        sel_all: &[u32],
        w_all: &[f32],
        t: usize,
        cfg: &ModelConfig,
        il: u16,
    ) -> Result<Option<CudaSlice<f32>>, Box<dyn std::error::Error>> {
        use std::sync::atomic::Ordering;
        let moe = cfg
            .moe
            .as_ref()
            .ok_or("glm5 EP grouped prime requires MoE model metadata")?;
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        // The plain grouped arm's admission, mirrored term for term (fall closed, never a
        // new admission class). MEMRA_MOE_GATE is the sequential byte-identity oracle; this
        // arm is a band class and must not shadow that comparison.
        if crate::moe_f16g_mode() == 0 || std::env::var("MEMRA_MOE_GATE").is_ok() {
            return Ok(None);
        }
        if !(f16g_proj_ok(m.gate_exps.qtype, n_embd)
            && f16g_proj_ok(m.up_exps.qtype, n_embd)
            && f16g_proj_ok(m.down_exps.qtype, n_ff_exp))
        {
            return Ok(None);
        }
        if n_expert > 512 || n_used == 0 || n_used > 8 {
            return Ok(None);
        }
        let lim_exp = cfg.clamp_exp_at(il as u32);
        if matches!(lim_exp, Some(SwigluClamp::Post(_))) {
            return Err(
                "EP grouped prime is qualified for the PRE-clamped SwiGLU form only; \
                 a POST-clamp layer must ride the sequential arm"
                    .into(),
            );
        }
        let n_pairs = t * n_used;
        if sel_all.len() < n_pairs || w_all.len() < n_pairs || z.len() < t * n_embd {
            return Err("EP grouped prime geometry".into());
        }
        let rt = &ep.rt;
        let red_skip_peer = matches!(
            crate::glm5_tp::gate_red(),
            Ok(Some(crate::glm5_tp::GateRed::SkipPeerCombine))
        );

        // One rank's whole grouped program: CSR over OWNED pairs -> grouped gate/up GEMMs ->
        // macro folds -> PRE-clamped epilogue -> grouped down GEMM -> CSR->local permute ->
        // slot-ordered scatter into the rank partial [t, n_embd] (empty token windows write
        // 0.0 — the scatter fully overwrites, so partials add cleanly on root).
        let rank_pass = |dev: &Engine,
                         rank: u8,
                         ptr_row: &CudaSlice<u64>,
                         z_dev: &CudaSlice<f32>|
         -> Result<Option<CudaSlice<f32>>, Box<dyn std::error::Error>> {
            // Expert-major CSR restricted to this rank, local pair index l in ascending
            // global-pair order (so per-token slot order == ascending l).
            let mut buckets_l: Vec<Vec<i32>> = vec![Vec::new(); n_expert];
            let mut local_tok = Vec::new(); // token of local pair l
            let mut local_ex = Vec::new(); // expert of local pair l (macro folds)
            let mut local_wd = Vec::new(); // v1's exact weight fold, at local positions
            let mut local_count_per_tok = vec![0i32; t];
            for p in 0..n_pairs {
                let ex = sel_all[p] as usize;
                if ex >= n_expert {
                    return Err(format!("EP grouped prime selection {ex} >= {n_expert}").into());
                }
                if ep.owner(ex) != rank as usize {
                    continue;
                }
                let l = local_tok.len() as i32;
                buckets_l[ex].push(l);
                let tok = p / n_used;
                local_tok.push(tok as i32);
                local_ex.push(ex);
                local_wd.push(w_all[p] * m.down_exps.macro_scale(ex));
                local_count_per_tok[tok] += 1;
            }
            let n_owned = local_tok.len();
            if n_owned == 0 {
                return Ok(None);
            }
            let mut ex_ids: Vec<i32> = Vec::new();
            let mut ex_off: Vec<i32> = vec![0];
            let mut ex_pairs: Vec<i32> = Vec::with_capacity(n_owned); // local l, CSR order
            let mut csr_tok: Vec<i32> = Vec::with_capacity(n_owned);
            for (e_id, b) in buckets_l.iter().enumerate() {
                if !b.is_empty() {
                    ex_ids.push(e_id as i32);
                    for &l in b {
                        ex_pairs.push(l);
                        csr_tok.push(local_tok[l as usize]);
                    }
                    ex_off.push(ex_pairs.len() as i32);
                }
            }
            let n_active = ex_ids.len();
            if n_active == 0 || n_active > 512 {
                return Err(format!("EP grouped prime n_active {n_active} outside 1..=512").into());
            }

            let exi = dev.htod_i32(&ex_ids)?;
            let exo = dev.htod_i32(&ex_off)?;
            let exp_d = dev.htod_i32(&ex_pairs)?;
            let csr_tok_d = dev.htod_i32(&csr_tok)?;

            // GATE/UP grouped GEMMs over the rank slab, CSR order end to end.
            let (z16, zs) = dev.moe_f16g_act(z_dev, Some(&csr_tok_d), n_embd, n_owned)?;
            let mut g = dev.moe_f16_grouped(
                ptr_row,
                0,
                n_expert,
                &exi,
                &ex_off,
                &exo,
                &z16,
                &zs,
                n_embd,
                n_ff_exp,
                n_active,
                n_owned,
                m.gate_exps.qtype,
                m.gate_exps.row_bytes,
            )?;
            if m.gate_exps.macros.is_some() {
                let mg: Vec<f32> = ex_pairs
                    .iter()
                    .map(|&l| m.gate_exps.macro_scale(local_ex[l as usize]))
                    .collect();
                let mg_d = dev.htod(&mg)?;
                dev.scale_rows(&mut g, &mg_d, n_ff_exp, n_owned)?;
            }
            let mut u = dev.moe_f16_grouped(
                ptr_row,
                1,
                n_expert,
                &exi,
                &ex_off,
                &exo,
                &z16,
                &zs,
                n_embd,
                n_ff_exp,
                n_active,
                n_owned,
                m.up_exps.qtype,
                m.up_exps.row_bytes,
            )?;
            if m.up_exps.macros.is_some() {
                let mu: Vec<f32> = ex_pairs
                    .iter()
                    .map(|&l| m.up_exps.macro_scale(local_ex[l as usize]))
                    .collect();
                let mu_d = dev.htod(&mu)?;
                dev.scale_rows(&mut u, &mu_d, n_ff_exp, n_owned)?;
            }

            // Epilogue: PRE-clamped SwiGLU (POST refused above), plain-silu pair otherwise.
            let act = match lim_exp {
                Some(SwigluClamp::Pre(limit)) => {
                    let mut a = dev.uninit(n_owned * n_ff_exp)?;
                    dev.swiglu_preclamped_mul_scaled(
                        &g,
                        &u,
                        1.0,
                        1.0,
                        limit,
                        &mut a,
                        n_owned * n_ff_exp,
                    )?;
                    a
                }
                None => dev.moe_pairs_silu_mul(&g, &u, n_owned * n_ff_exp)?,
                Some(SwigluClamp::Post(_)) => unreachable!("refused before any launch"),
            };

            // DOWN grouped GEMM, permute CSR -> local pair order, slot-ordered scatter.
            let (a16, a_s) = dev.moe_f16g_act(&act, None, n_ff_exp, n_owned)?;
            let d_csr = dev.moe_f16_grouped(
                ptr_row,
                2,
                n_expert,
                &exi,
                &ex_off,
                &exo,
                &a16,
                &a_s,
                n_ff_exp,
                n_embd,
                n_active,
                n_owned,
                m.down_exps.qtype,
                m.down_exps.row_bytes,
            )?;
            let y_local = dev.rows_permute(&d_csr, &exp_d, n_owned, n_embd)?;
            let mut toff: Vec<i32> = Vec::with_capacity(t + 1);
            let mut acc = 0i32;
            toff.push(0);
            for &c in &local_count_per_tok {
                acc += c;
                toff.push(acc);
            }
            let tids: Vec<i32> = (0..n_owned as i32).collect();
            let pw = dev.htod(&local_wd)?;
            let toff_d = dev.htod_i32(&toff)?;
            let tids_d = dev.htod_i32(&tids)?;
            let mut partial = dev.uninit(t * n_embd)?; // scatter fully overwrites
            dev.moe_pairs_scatter(&y_local, &pw, &toff_d, &tids_d, &mut partial, t, n_embd)?;
            Ok(Some(partial))
        };

        // Peer passes first (their GEMMs overlap root's), each on its own runtime binding —
        // the grouped-MoE FFI follows the RUNTIME device, not cudarc's pushed context
        // (`bind_runtime_device`'s contract). Engagement counters count ROUTED peer pairs
        // before any red skip, exactly like the sequential walk.
        let ranks = ep.ranks();
        let n_peer_pairs = sel_all
            .iter()
            .take(n_pairs)
            .filter(|&&ex| ep.owner(ex as usize) != 0)
            .count() as u64;
        crate::glm5_tp::GLM5_EP_PEER_SLOT_DISPATCHES.fetch_add(n_peer_pairs, Ordering::Relaxed);
        let hop = ep.rt.hop(e);
        let mut peer_partials: Vec<Option<CudaSlice<f32>>> = (0..ranks).map(|_| None).collect();
        for r in 1..ranks {
            let rank_owns_pairs = sel_all
                .iter()
                .take(n_pairs)
                .any(|&ex| ep.owner(ex as usize) == r);
            if !rank_owns_pairs {
                continue;
            }
            let dev = crate::glm5_tp::rank_engine(e, rt, r);
            // SWAP POINT 1 (bulk fan-out) — the named transport shape, to this rank only.
            let z_r = crate::glm5_tp_transport::fanout_f32_to(&hop, r, z, t * n_embd)?;
            dev.bind_runtime_device(dev.ctx().ordinal() as i32)?;
            let res = rank_pass(dev, r as u8, &ep.ptr_rows[r], &z_r);
            e.bind_runtime_device(e.ctx().ordinal() as i32)?;
            peer_partials[r] = res?;
        }
        let root_partial = rank_pass(e, 0, &ep.ptr_rows[0], z)?;

        // Root combine: root partial + bulk-returned peer partials (SWAP POINT 2, the named
        // transport shape). One partial add per contributing rank — the same reassociation
        // class the two-rank arm band-gated (root chain + per-rank chains instead of one
        // 8-term chain), never claimed byte. The skip-peer-combine red drops every peer
        // partial AFTER counting — the loud non-vacuity arm.
        let mut out = match root_partial {
            Some(p) => p,
            None => e.zeros(t * n_embd)?,
        };
        for r in 1..ranks {
            if let Some(pp) = &peer_partials[r]
                && !red_skip_peer
            {
                let pp_root =
                    crate::glm5_tp_transport::return_row_to_root(&hop, r, pp, t * n_embd)?;
                let mut dst = out.slice_mut(0..t * n_embd);
                e.axpy_into(&pp_root, 1.0, &mut dst, t * n_embd)?;
                crate::glm5_tp::GLM5_EP_DIET_BULK_RETURNS.fetch_add(1, Ordering::Relaxed);
            }
        }
        crate::glm5_tp::GLM5_EP_GROUPED_PRIME_DISPATCHES.fetch_add(1, Ordering::Relaxed);
        static EPGP_LOGGED: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        let layer_bit = 1u64 << (il as u64 % 64);
        if EPGP_LOGGED.fetch_or(layer_bit, Ordering::Relaxed) & layer_bit == 0 {
            eprintln!(
                "[glm5-ep-grouped-prime] execute layer={il} tokens={t} \
                 provenance=ep-rank-slabs router=sigmoid-host-oracle epilogue=pre-clamped \
                 combine=rank-partial-add transport={} performance_claim=false \
                 (logged once per layer)",
                hop.transport.name(),
            );
        }
        Ok(Some(out))
    }

    /// Step 3 of the MoE body — SHARED EXPERT (ALWAYS-ON, no routing) on the SAME z —
    /// qwen35moe only. OLMoE and most vanilla MoE have NO shared expert (the shexp tensors
    /// are absent / `None`); skip it then. gate_inp_shexp is OPTIONAL: qwen35moe gates the
    /// shared expert (sigmoid(gate_inp) x sh); MiniMax-M3 (DeepSeek-V3 class) has NO shexp
    /// gate — the shared expert adds directly. (Extracted verbatim from the sequential body
    /// so the glm5 EP-2 walk adds the ROOT-owned shared expert through the identical
    /// program.)
    #[allow(clippy::too_many_arguments)]
    fn moe_shexp_add(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        zq8: Option<&(CudaSlice<i8>, CudaSlice<f32>)>,
        t: usize,
        cfg: &ModelConfig,
        lim_shexp: Option<memra_gguf::config::SwigluClamp>,
        moe_out: &mut CudaSlice<f32>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        if let (Some(gate_shexp), Some(up_shexp), Some(down_shexp)) =
            (&m.gate_shexp, &m.up_shexp, &m.down_shexp)
        {
            let n_embd = cfg.n_embd as usize;
            let n_ff_sh = gate_shexp.out_features(); // 512
            // Q8 TRUNK-FUSION (decode t=1): gate_shexp+up_shexp are Q8_0 same-shape on the 35B —
            // ONE fused2 launch (also folds the two per-matmul re-quantizes of z into one).
            // Bit-identical per (tensor,row); falls back to the two matmul calls when ineligible.
            // Small-t (spec verify 2..15) rides matmul_decode_exact so shexp FP chains match the
            // t==1 decode chain per column (cuBLASLt n-dependence + dp4a-vs-mmvq class); real
            // prefill keeps the batched matmul. Activation routes through ffn_act (SiLU for
            // softmax archs, clamped swigluoai for M3 — identical to silu_mul when cfg.m3 is None).
            let verify_t = t > 1 && t < PRIME_MIN_T;
            let (sg_gate, sg_up) = if t == 1 {
                shexp_gate_up_t1(e, gate_shexp, up_shexp, z, zq8)?
            } else if verify_t {
                (
                    e.matmul_decode_exact(gate_shexp, z, t)?,
                    e.matmul_decode_exact(up_shexp, z, t)?,
                )
            } else {
                (e.matmul(gate_shexp, z, t)?, e.matmul(up_shexp, z, t)?) // [T, 512] each
            };
            let mut sa = e.uninit(t * n_ff_sh)?; // activation fully overwrites
            Self::ffn_act_lim(
                e,
                cfg,
                &sg_gate,
                &sg_up,
                1.0,
                1.0,
                lim_shexp,
                &mut sa,
                t * n_ff_sh,
            )?;
            let sh = if verify_t {
                e.matmul_decode_exact(down_shexp, &sa, t)?
            } else {
                e.matmul(down_shexp, &sa, t)?
            }; // [T, n_embd]

            // shexp gate: qwen35moe sigmoid-gates via ffn_gate_inp_shexp (1-D ne=[n_embd] ->
            // out_f=1); M3 has no gate tensor -> weight 1.0. Decode + verify ride the fused
            // sigmoid-dot kernel (one fold order for both chains; kills the per-layer
            // cuBLASLt m=1 splitK GEMM — 40x/step, ~10% of the H100 q35 decode step).
            // SERVE ISOLATION (lane/concat-prime-exact, 2026-08-02): PREFILL rides it too.
            // This out_f=1 cuBLASLt GEMV is the SECOND m-dependent op in the trunk (probed:
            // rows [0,19) move by 1.07e-4 between m=74 and m=75 while sigmoid_dot_rows is
            // BIT-IDENTICAL — allw-shexpgate-o35b.log). The gate multiplies the shared
            // expert's contribution into every token's residual, so under cross-request
            // concat prefill a session's hidden state depended on its co-arrivals' token
            // count. MEMRA_ROUTER_PREFILL_EXACT=0 reverts to the batched cuBLASLt linear.
            // DOOR H (`MEMRA_GLM5_HTOD_DIET`): glm5 has no `ffn_gate_inp_shexp`, so this is the
            // LIVE arm on the serving artifact and it re-uploaded a constant `vec![1.0f32; t]`
            // on every MoE layer-call — 42 pageable HtoD per ship round (26.9% of the round's
            // 156). The resident ones buffer feeds the SAME `add_scaled_rows_f32` kernel the
            // same 1.0 values, so the arms are bit-identical.
            if m.gate_inp_shexp.is_none() && crate::glm5_htod_diet_on() {
                crate::GLM5_HTOD_DIET_AVOIDED.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
                e.add_scaled_rows_ones(&sh, moe_out, n_embd, t)?;
                return Ok(());
            }
            let g = match &m.gate_inp_shexp {
                Some(gate_inp_shexp) => {
                    if t < PRIME_MIN_T || crate::router_prefill_exact_on() {
                        e.sigmoid_dot_rows(z, gate_inp_shexp.float_data(), n_embd, t)?
                    } else {
                        let gs = e.linear(z, gate_inp_shexp.float_data(), t, n_embd, 1)?;
                        let mut g = e.uninit(t)?; // sigmoid fully overwrites
                        e.sigmoid(&gs, &mut g, t)?;
                        g
                    }
                }
                None => e.htod(&vec![1.0f32; t])?,
            };
            // moe_out[r, :] += sh[r, :] * g[r]   (per-token scalar gate; g=1 ungated)
            e.add_scaled_rows(&sh, &g, moe_out, n_embd, t)?;
        }
        Ok(())
    }

    /// Stage-1 (no-cache) per-DECODE-TOKEN H2D bytes: every routed block re-staged every layer every
    /// token = sum over MoE layers of n_used * (gate+up+down expert_stride). The §D.4 PCIe baseline.
    pub fn stage1_h2d_per_token(&self) -> u64 {
        use crate::hybrid::Ffn;
        let n_used = self
            .cfg
            .moe
            .as_ref()
            .map(|m| m.expert_used_count as u64)
            .unwrap_or(0);
        let mut bytes = 0u64;
        for l in self.layers.iter() {
            if let Ffn::Moe(m) = &l.ffn {
                bytes += n_used
                    * (m.gate_exps.max_expert_bytes()
                        + m.up_exps.max_expert_bytes()
                        + m.down_exps.max_expert_bytes()) as u64;
            }
        }
        bytes
    }

    /// Largest expert block (bytes) across ALL MoE layers + the MTP head — the fixed cache slot size.
    /// UD/dynamic GGUFs quant different layers differently, so `expert_stride` varies per layer; the
    /// residency cache slots are fixed-address and must fit any block, so size to this global max.
    pub(crate) fn max_moe_block(&self) -> usize {
        use crate::hybrid::Ffn;
        let mut mx = 0usize;
        let mut scan = |ffn: &Ffn| {
            if let Ffn::Moe(m) = ffn {
                mx = mx
                    .max(m.gate_exps.max_expert_bytes())
                    .max(m.up_exps.max_expert_bytes())
                    .max(m.down_exps.max_expert_bytes());
            }
        };
        for l in self.layers.iter() {
            scan(&l.ffn);
        }
        if let Some(mtp) = self.mtp.as_ref() {
            scan(&mtp.ffn);
        }
        mx
    }

    /// Exact retained projection lengths for cache class sizing. Pruned ids keep router positions
    /// but have no bytes and therefore consume no residency slot.
    pub(crate) fn moe_cache_block_sizes(&self) -> Vec<usize> {
        use crate::hybrid::Ffn;
        let mut sizes = Vec::new();
        let mut scan = |ffn: &Ffn| {
            let Ffn::Moe(m) = ffn else { return };
            for ex in 0..m.gate_exps.n_expert {
                if m.active_experts.as_ref().is_some_and(|active| !active[ex]) {
                    continue;
                }
                for exps in [&m.gate_exps, &m.up_exps, &m.down_exps] {
                    let len = exps.expert_layout(ex).len;
                    if len > 0 {
                        sizes.push(len);
                    }
                }
            }
        };
        for layer in &self.layers {
            scan(&layer.ffn);
        }
        if let Some(mtp) = &self.mtp {
            scan(&mtp.ffn);
        }
        sizes
    }

    /// Persist the frozen residency set so a later process can restage it directly and skip
    /// the profiling warmup. Plain text: a versioned header binding slot geometry, then one
    /// `layer proj ex` triple per line. A mismatched or stale profile is rejected at load
    /// (header check) or degrades to fewer restaged blocks (per-id checks); either way the
    /// post-freeze argmax gate still validates the serving assignment.
    pub fn save_cpu_expert_residency_profile(
        &self,
        e: &Engine,
        path: &std::path::Path,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let Some(ids) = e.export_moe_residency() else {
            return Err("no MoE residency cache to persist".into());
        };
        let mut body = format!(
            "memra-freeze-profile v1 max_block={} blocks={}\n",
            self.max_moe_block(),
            ids.len()
        );
        for (layer, proj, ex) in &ids {
            body.push_str(&format!("{layer} {proj} {ex}\n"));
        }
        let tmp = path.with_extension("tmp");
        std::fs::write(&tmp, body)?;
        std::fs::rename(&tmp, path)?;
        println!(
            "[moe-cache] freeze profile saved: {} blocks -> {}",
            ids.len(),
            path.display()
        );
        Ok(())
    }

    /// Restage a saved freeze profile and freeze immediately, skipping the profiling warmup.
    /// Returns false (leaving the cache untouched for a normal warmup) when the profile is
    /// missing or its header does not match this model's slot geometry.
    pub fn restore_cpu_expert_residency_profile(
        &self,
        e: &Engine,
        path: &std::path::Path,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        use crate::hybrid::Ffn;
        use crate::moe_cache::BlockId;
        let Ok(content) = std::fs::read_to_string(path) else {
            return Ok(false);
        };
        let mut lines = content.lines();
        let Some(header) = lines.next() else {
            return Ok(false);
        };
        let expected = format!("memra-freeze-profile v1 max_block={}", self.max_moe_block());
        if !header.starts_with(&expected) {
            println!(
                "[moe-cache] freeze profile ignored (geometry mismatch): {}",
                path.display()
            );
            return Ok(false);
        }
        let mut by_layer: std::collections::HashMap<u16, Vec<BlockId>> =
            std::collections::HashMap::new();
        for line in lines {
            let mut fields = line.split_whitespace();
            let (Some(layer), Some(proj), Some(ex)) = (fields.next(), fields.next(), fields.next())
            else {
                continue;
            };
            let (Ok(layer), Ok(proj), Ok(ex)) =
                (layer.parse::<u16>(), proj.parse::<u8>(), ex.parse::<u16>())
            else {
                continue;
            };
            by_layer
                .entry(layer)
                .or_default()
                .push(BlockId::new(layer, proj, ex));
        }
        let requested: usize = by_layer.values().map(Vec::len).sum();
        if requested == 0 {
            return Ok(false);
        }
        let max_block = self.max_moe_block();
        let mut restaged = 0usize;
        let mut stage_layer =
            |layer_index: u16, ffn: &Ffn| -> Result<(), Box<dyn std::error::Error>> {
                let Ffn::Moe(m) = ffn else { return Ok(()) };
                let Some(ids) = by_layer.get(&layer_index) else {
                    return Ok(());
                };
                e.with_moe_cache(max_block, |cache, eng| {
                    for id in ids {
                        if cache.restage_block(*id, m, eng)? {
                            restaged += 1;
                        }
                    }
                    Ok(())
                })
            };
        for (index, layer) in self.layers.iter().enumerate() {
            stage_layer(index as u16, &layer.ffn)?;
        }
        if let Some(mtp) = self.mtp.as_ref() {
            stage_layer(u16::MAX, &mtp.ffn)?;
        }
        e.freeze_moe_cache();
        println!(
            "[moe-cache] freeze profile restored: {restaged}/{requested} blocks restaged from {}",
            path.display()
        );
        Ok(true)
    }

    /// Freeze the heterogeneous CPU/GPU split after the caller's discarded profile warmup.
    pub fn freeze_cpu_expert_residency(
        &self,
        e: &Engine,
    ) -> Result<(), Box<dyn std::error::Error>> {
        e.freeze_moe_cache();
        Ok(())
    }

    /// FFN activation dispatch: swigluoai (clamped, alpha/limit) when cfg.m3 says so, else the
    /// standard SiLU*up. One seam so every FFN site (dense, routed expert, shared expert) follows
    /// the model's activation exactly.
    ///
    /// NO-`il` FORM: cannot apply step35's PER-LAYER SwiGLU clamp. Only call it from a site whose
    /// layer provably has no live limit (dense-FFN layers, MTP blocks) — `ffn_act_lim` is the
    /// form for anything that can land on a clamped layer.
    pub fn ffn_act(
        e: &Engine,
        cfg: &ModelConfig,
        gate: &CudaSlice<f32>,
        up: &CudaSlice<f32>,
        act: &mut CudaSlice<f32>,
        n: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        Self::ffn_act_scaled(e, cfg, gate, up, 1.0, 1.0, act, n)
    }

    /// ffn_act with per-tensor post-matmul macro-scales folded in (gs/us == 1.0 -> identical
    /// float ops to ffn_act; used by the ModelOpt NVFP4 expert path where each expert tensor
    /// carries a `weight_scale_2`). Same no-`il` contract as `ffn_act`.
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn ffn_act_scaled(
        e: &Engine,
        cfg: &ModelConfig,
        gate: &CudaSlice<f32>,
        up: &CudaSlice<f32>,
        gs: f32,
        us: f32,
        act: &mut CudaSlice<f32>,
        n: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        Self::ffn_act_lim(e, cfg, gate, up, gs, us, None, act, n)
    }

    /// ffn_act_scaled + a PER-LAYER clamped SwiGLU. `limit`:
    ///   * `None`          -> the unclamped dispatch (every arch with no live clamp).
    ///   * `Some(Post(l))` -> step35: `min(silu(gate*gs), l) * clamp(up*us, +-l)`
    ///     (llama-graph.cpp:2146/1751, non-DEEPSEEK4 branch).
    ///   * `Some(Pre(l))`  -> glm5_next: `silu(min(gate*gs, l)) * clamp(up*us, +-l)`.
    ///     Callers source it from `cfg.clamp_exp_at(il)` (routed experts) or `cfg.clamp_shexp_at(il)`
    ///     (shared expert / dense MLP) — on step35 the two arrays are SEPARATE and a layer can have
    ///     one without the other. The `> 1e-6` eps gate lives in the accessors, so a `Some` here is
    ///     already known live. The match is exhaustive so a new clamp form cannot default to either
    ///     existing one.
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn ffn_act_lim(
        e: &Engine,
        cfg: &ModelConfig,
        gate: &CudaSlice<f32>,
        up: &CudaSlice<f32>,
        gs: f32,
        us: f32,
        limit: Option<SwigluClamp>,
        act: &mut CudaSlice<f32>,
        n: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        if let Some(m3) = cfg.m3.as_ref() {
            debug_assert!(
                limit.is_none(),
                "m3 swigluoai and the step35/glm5_next clamps are different archs"
            );
            return e.swigluoai_mul_scaled(
                gate,
                up,
                gs,
                us,
                m3.swiglu_alpha,
                m3.swiglu_limit,
                act,
                n,
            );
        }
        match limit {
            Some(SwigluClamp::Post(l)) => {
                return e.swiglu_clamped_mul_scaled(gate, up, gs, us, l, act, n);
            }
            Some(SwigluClamp::Pre(l)) => {
                return e.swiglu_preclamped_mul_scaled(gate, up, gs, us, l, act, n);
            }
            None => {}
        }
        if gs == 1.0 && us == 1.0 {
            return e.silu_mul(gate, up, act, n);
        }
        e.silu_mul_scaled(gate, up, gs, us, act, n)
    }

    /// The bare POST limit for the fused kernels whose epilogue HARDCODES step35's form
    /// (`matvec_bf16_dual_silu` / `_rows`, qmatvec.cu:10676). `Ok` = the kernel may run;
    /// `Err(())` = glm5_next's PRE form, which has no fused twin, and the caller MUST return its
    /// not-handled value so the layer falls through to the unfused `ffn_act_lim` seam. Feeding a
    /// PRE limit to a POST epilogue compiles, runs, and returns plausible-but-wrong logits.
    fn fused_post_limit(lim: Option<SwigluClamp>) -> Result<Option<f32>, ()> {
        match lim {
            None => Ok(None),
            Some(SwigluClamp::Post(l)) => Ok(Some(l)),
            Some(SwigluClamp::Pre(_)) => Err(()),
        }
    }

    /// Routing for the whole batch: returns (sel [T*n_used] expert ids, w [T*n_used] renorm weights),
    /// token-major. Default = the Stage-1 host path (dtoh logits, softmax-256, stable DESC top-k,
    /// renorm). MEMRA_FUSED_ROUTER = the device kernel (§A) which reproduces the same numerics; we
    /// still dtoh the tiny [T,n_used] sel/w buffers (64 B/token vs 1 KB/token) — the host loop
    /// indexes HostExps.bytes on the CPU to choose the DMA source (§A.2 output staging).
    fn moe_route(
        e: &Engine,
        logits: &CudaSlice<f32>,
        t: usize,
        n_expert: usize,
        n_used: usize,
    ) -> Result<(Vec<u32>, Vec<f32>), Box<dyn std::error::Error>> {
        Self::moe_route_cfg(e, logits, t, n_expert, n_used, None)
    }

    /// DeepSeek-V3-class sigmoid routing (Step-3.7, MiniMax-M3, Hy3, GLM-DSA). Reference:
    /// M3/Hy3 modeling code — scores = sigmoid(logits); selection over scores + expert bias
    /// (M3 `e_score_correction_bias` / Hy3 `expert_bias`, both surfaced as `exp_probs_b`);
    /// weights = un-biased scores of the selected experts, sum-normalized when `route_norm`,
    /// x scaling factor (M3 routed_scaling_factor 2.0 / Hy3 router_scaling_factor 2.826).
    /// Default uses one device kernel plus the same pinned sel/w readback contract as the fused
    /// softmax router. `MEMRA_SIG_ROUTER=0` restores the full-logit DtoH host oracle.
    #[allow(clippy::too_many_arguments)]
    fn moe_route_sigmoid_cfg(
        e: &Engine,
        logits: &CudaSlice<f32>,
        t: usize,
        n_expert: usize,
        n_used: usize,
        m: &MoeWeights,
        (sf, route_norm): (f32, bool),
    ) -> Result<(Vec<u32>, Vec<f32>), Box<dyn std::error::Error>> {
        if sigmoid_router_enabled() {
            return e.moe_router_sigmoid_topk_host(
                logits,
                t,
                n_expert,
                n_used,
                m.active_count(),
                &m.exp_probs_b_dev,
                &m.active_experts_dev,
                sf,
                route_norm,
            );
        }
        let lg = e.dtoh(logits)?;
        Self::moe_route_sigmoid_host(
            &lg,
            t,
            n_expert,
            n_used,
            m.exp_probs_b.as_deref(),
            sf,
            route_norm,
            m.active_experts.as_deref(),
        )
    }

    /// Softmax router for qwen35moe/OLMoE. An active-mask overlay retains the host oracle because
    /// the existing softmax device kernel has no mask input.
    #[allow(clippy::excessive_precision)] // allow: literal kept verbatim from the reference/measured value
    fn moe_route_cfg(
        e: &Engine,
        logits: &CudaSlice<f32>,
        t: usize,
        n_expert: usize,
        n_used: usize,
        active: Option<&[bool]>,
    ) -> Result<(Vec<u32>, Vec<f32>), Box<dyn std::error::Error>> {
        // LAUNCH-STRUCTURE STAGE 1 (2026-07-05): fused router DEFAULT ON (MEMRA_FUSED_ROUTER=0
        // rollback) via the single-sync pinned readback — softmax arch only.
        if active.is_none() && !matches!(std::env::var("MEMRA_FUSED_ROUTER").as_deref(), Ok("0")) {
            return e.moe_router_topk_host(logits, t, n_expert, n_used);
        }
        // Host oracle (the §D bit-identity reference).
        let lg = e.dtoh(logits)?; // [T*n_expert] host
        let mut sel = vec![0u32; t * n_used];
        let mut w_out = vec![0f32; t * n_used];
        for tok in 0..t {
            let row = &lg[tok * n_expert..(tok + 1) * n_expert];
            // softmax over ALL n_expert (stable: subtract max)
            let maxl = row
                .iter()
                .enumerate()
                .filter(|(i, _)| active.is_none_or(|mask| mask[*i]))
                .map(|(_, &x)| x)
                .fold(f32::NEG_INFINITY, f32::max);
            let mut probs = vec![0f32; n_expert];
            let mut den = 0f32;
            for i in 0..n_expert {
                if active.is_some_and(|mask| !mask[i]) {
                    continue;
                }
                let x = (row[i] - maxl).exp();
                probs[i] = x;
                den += x;
            }
            for p in probs.iter_mut() {
                *p /= den;
            }
            // stable DESC sort: prob DESC, ascending-index tiebreak.
            let mut idx: Vec<usize> = (0..n_expert)
                .filter(|&i| active.is_none_or(|mask| mask[i]))
                .collect();
            idx.sort_by(|&a, &b| probs[b].total_cmp(&probs[a]).then(a.cmp(&b)));
            let sl = &idx[..n_used];
            let mut wv: Vec<f32> = sl.iter().map(|&i| probs[i]).collect();
            let mut ws: f32 = wv.iter().sum();
            ws = ws.max(6.103515625e-5_f32); // F16 smallest normal, clamp BEFORE divide
            for x in wv.iter_mut() {
                *x /= ws;
            }
            for j in 0..n_used {
                sel[tok * n_used + j] = sl[j] as u32;
                w_out[tok * n_used + j] = wv[j];
            }
        }
        Ok((sel, w_out))
    }

    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn moe_route_sigmoid_with_input(
        e: &Engine,
        logits: &CudaSlice<f32>,
        input: &CudaSlice<f32>,
        t: usize,
        in_features: usize,
        n_expert: usize,
        n_used: usize,
        bias: Option<&[f32]>,
        (sf, route_norm): (f32, bool),
        active: Option<&[bool]>,
    ) -> Result<(Vec<u32>, Vec<f32>, Vec<f32>), Box<dyn std::error::Error>> {
        let logit_values =
            active_matrix_values(logits.len(), t, n_expert, "sigmoid router logits")?;
        let input_values =
            active_matrix_values(input.len(), t, in_features, "sigmoid router input")?;
        let (lg, input) = e.dtoh_pair_views(
            &logits.slice(0..logit_values),
            &input.slice(0..input_values),
        )?;
        let (sel, w) =
            Self::moe_route_sigmoid_host(&lg, t, n_expert, n_used, bias, sf, route_norm, active)?;
        Ok((sel, w, input))
    }

    /// Start the prediction-guided prefetch worker (MEMRA_MOE_PREFETCH=depth). Call after
    /// residency freeze: the worker filters against a static snapshot of the frozen HBM set.
    /// Builds a fully-owned per-layer table (host router copies via one-time DtoH, bias,
    /// active mask, prebuilt projection descriptors) so no model reference escapes.
    pub fn start_moe_prefetch_predictor(
        &self,
        e: &Engine,
        cfg: &ModelConfig,
    ) -> Result<(), Box<dyn std::error::Error>> {
        use crate::hybrid::Ffn;
        let Some(sig) = cfg.sigmoid_router() else {
            return Err("prefetch predictor requires a sigmoid-router arch".into());
        };
        let resident: std::collections::HashSet<(u16, u8, u16)> = e
            .export_moe_residency()
            .ok_or("prefetch predictor needs the frozen MoE residency cache")?
            .into_iter()
            .collect();
        let mut layers = Vec::new();
        for (index, layer) in self.layers.iter().enumerate() {
            let Ffn::Moe(m) = &layer.ffn else { continue };
            let crate::model::GpuTensor::Float { data, .. } = &m.gate_inp else {
                continue;
            };
            let router = e.dtoh(data)?;
            let n_expert = m.gate_exps.n_expert;
            let n_embd = m.gate_exps.in_f;
            if router.len() != n_embd * n_expert {
                continue;
            }
            let build = |exps: &crate::model::HostExps| {
                (0..n_expert)
                    .map(|expert| crate::cpu_experts::predictor_projection(exps, expert))
                    .collect::<Vec<_>>()
            };
            layers.push((
                index as u16,
                crate::cpu_experts::PredictLayerInit {
                    router,
                    bias: m.exp_probs_b.clone(),
                    active: m.active_experts.clone(),
                    n_embd,
                    n_used: cfg
                        .moe
                        .as_ref()
                        .map(|moe| moe.expert_used_count as usize)
                        .ok_or("prefetch predictor requires MoE config")?,
                    sig,
                    weights_n_expert: n_expert,
                    gate: build(&m.gate_exps),
                    up: build(&m.up_exps),
                    down: build(&m.down_exps),
                },
            ));
        }
        crate::cpu_experts::start_prefetch_predictor(layers, resident).map_err(|error| error.into())
    }

    /// Sigmoid-routing oracle shared by the prefetch predictor and `kernel-check`: identical
    /// selection math to the rollback runtime, applied to host-computed logits.
    #[allow(clippy::too_many_arguments)]
    pub fn moe_route_sigmoid_host_public(
        logits: &[f32],
        t: usize,
        n_expert: usize,
        n_used: usize,
        bias: Option<&[f32]>,
        sf: f32,
        route_norm: bool,
        active: Option<&[bool]>,
    ) -> Result<(Vec<u32>, Vec<f32>), Box<dyn std::error::Error>> {
        Self::moe_route_sigmoid_host(logits, t, n_expert, n_used, bias, sf, route_norm, active)
    }

    #[allow(clippy::too_many_arguments)]
    fn moe_route_sigmoid_host(
        lg: &[f32],
        t: usize,
        n_expert: usize,
        n_used: usize,
        bias: Option<&[f32]>,
        sf: f32,
        route_norm: bool,
        active: Option<&[bool]>,
    ) -> Result<(Vec<u32>, Vec<f32>), Box<dyn std::error::Error>> {
        let active_count = active
            .map(|mask| mask.iter().filter(|&&enabled| enabled).count())
            .unwrap_or(n_expert);
        crate::sigrouter_contract::validate_active_count(n_used, active_count)?;
        if lg.len() != t * n_expert {
            return Err(format!(
                "sigmoid router logits length mismatch: got {}, expected {}",
                lg.len(),
                t * n_expert,
            )
            .into());
        }
        let mut sel = vec![0u32; t * n_used];
        let mut w_out = vec![0f32; t * n_used];
        for tok in 0..t {
            let row = &lg[tok * n_expert..(tok + 1) * n_expert];
            let scores: Vec<f32> = row.iter().map(|&x| 1.0 / (1.0 + (-x).exp())).collect();
            // selection score = sigmoid + bias; weight = plain sigmoid.
            let selsc: Vec<f32> = match bias {
                Some(b) => scores.iter().zip(b).map(|(s, bb)| s + bb).collect(),
                None => scores.clone(),
            };
            let mut idx: Vec<usize> = (0..n_expert)
                .filter(|&i| active.is_none_or(|mask| mask[i]))
                .collect();
            idx.sort_by(|&a, &b| selsc[b].total_cmp(&selsc[a]).then(a.cmp(&b)));
            let sl = &idx[..n_used];
            let mut wv: Vec<f32> = sl.iter().map(|&i| scores[i]).collect();
            if route_norm {
                let ws: f32 = wv.iter().sum::<f32>().max(1e-20);
                for x in wv.iter_mut() {
                    *x = *x / ws * sf;
                }
            } else {
                for x in wv.iter_mut() {
                    *x *= sf;
                }
            }
            for j in 0..n_used {
                sel[tok * n_used + j] = sl[j] as u32;
                w_out[tok * n_used + j] = wv[j];
            }
        }
        Ok((sel, w_out))
    }

    /// Step-3.7 resident-slab dispatch: sigmoid top-k outputs remain on device and feed the
    /// existing slot-ordered q8 expert kernels directly. Mixed layouts, spill, remote slabs,
    /// macro-scaled experts, and observation modes are denied by the caller.
    #[allow(clippy::too_many_arguments)]
    fn moe_ffn_sigmoid_dev(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        zq8: Option<&(CudaSlice<i8>, CudaSlice<f32>)>,
        logits: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        (scaling_factor, route_norm): (f32, bool),
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let moe = cfg.moe.as_ref().unwrap();
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        let dev = m.dev_exps.as_ref().unwrap();
        debug_assert_eq!(dev.dev, e.ctx().ordinal());
        debug_assert!(m.has_uniform_expert_layout());
        debug_assert!(!m.has_macros);

        let (sel_d, w_d) = e.moe_router_sigmoid_topk(
            logits,
            t,
            n_expert,
            n_used,
            m.active_count(),
            &m.exp_probs_b_dev,
            &m.active_experts_dev,
            scaling_factor,
            route_norm,
        )?;
        crate::moesd::record_device_routes(e, il, n_expert, n_used, &sel_d)?;
        if let Some(fp8) = dev.fp8_blk.as_ref() {
            debug_assert_eq!(m.gate_exps.qtype, crate::QT_F8_E4M3_BLK);
            debug_assert_eq!(m.up_exps.qtype, crate::QT_F8_E4M3_BLK);
            debug_assert_eq!(m.down_exps.qtype, crate::QT_F8_E4M3_BLK);
            debug_assert_eq!(fp8.gate.rows, m.gate_exps.out_f.div_ceil(128));
            debug_assert_eq!(fp8.up.rows, m.up_exps.out_f.div_ceil(128));
            debug_assert_eq!(fp8.down.rows, m.down_exps.out_f.div_ceil(128));

            // Official Step-3.7 FP8 uses dynamic per-token/per-128 E4M3
            // activations with block-128 E4M3 weights. This deliberately
            // simple resident reference is the correctness oracle for later
            // grouped and TP/EP execution. MEMRA_ST_E4M3=0 chooses the
            // load-time Q8 diagnostic representation, so one process never
            // crosses between numerical programs.
            let selected = e.dtoh_i32(&sel_d)?;
            let route_weights = e.dtoh(&w_d)?;
            let mut moe_out = e.zeros(t * n_embd)?;
            for tok in 0..t {
                let zt = z.slice(tok * n_embd..(tok + 1) * n_embd);
                let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                for j in 0..n_used {
                    let pair = tok * n_used + j;
                    let expert = selected[pair] as usize;
                    let gate = Self::moe_resident_fp8_e4m3(
                        e,
                        &m.gate_exps,
                        &dev.gate,
                        &fp8.gate,
                        expert,
                        &zt,
                        1,
                    )?;
                    let up = Self::moe_resident_fp8_e4m3(
                        e, &m.up_exps, &dev.up, &fp8.up, expert, &zt, 1,
                    )?;
                    let mut act = e.uninit(n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        1.0,
                        1.0,
                        cfg.clamp_exp_at(il as u32),
                        &mut act,
                        n_ff_exp,
                    )?;
                    let act = act.slice(0..n_ff_exp);
                    let down = Self::moe_resident_fp8_e4m3(
                        e,
                        &m.down_exps,
                        &dev.down,
                        &fp8.down,
                        expert,
                        &act,
                        1,
                    )?;
                    e.axpy_into(&down, route_weights[pair], &mut dst, n_embd)?;
                }
            }
            if std::env::var("MEMRA_SIG_ROUTER_DISPATCH_TRACE").as_deref() == Ok("1") {
                eprintln!(
                    "[sigrouter-dev] layer={il} tokens={t} experts={n_expert} used={n_used} \
                     native=fp8blk-w8a8-e4m3-reference clamp={}",
                    cfg.clamp_exp_at(il as u32).is_some(),
                );
            }
            Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut moe_out)?;
            return Ok(moe_out);
        }
        let (gate_row_bytes, up_row_bytes) = if dev.gu_il {
            let combined = m.gate_exps.row_bytes + m.up_exps.row_bytes;
            (combined, combined)
        } else {
            (m.gate_exps.row_bytes, m.up_exps.row_bytes)
        };
        let (zq, zd) = match (t, zq8) {
            (1, Some((q, d))) => (q.clone(), d.clone()),
            _ => e.quantize_q8_1(z, t, n_embd)?,
        };
        let n_pairs = t * n_used;
        let mut moe_out = if cfg.clamp_exp_at(il as u32).is_some() {
            // The final Step layers retain the established separate gate/up -> clamp -> down
            // arithmetic. Pair rows are derived from token position; selected expert ids and
            // routing weights remain the device router's buffers throughout.
            let pair_tok: Vec<i32> = (0..n_pairs).map(|pair| (pair / n_used) as i32).collect();
            let pair_tok_d = e.htod_i32(&pair_tok)?;
            let gate = e.moe_pairs_matvec_q8(
                &dev.ptr_row,
                0,
                &pair_tok_d,
                &sel_d,
                &zq,
                &zd,
                n_embd,
                n_ff_exp,
                n_expert,
                n_pairs,
                m.gate_exps.qtype,
                gate_row_bytes,
            )?;
            let up = e.moe_pairs_matvec_q8(
                &dev.ptr_row,
                1,
                &pair_tok_d,
                &sel_d,
                &zq,
                &zd,
                n_embd,
                n_ff_exp,
                n_expert,
                n_pairs,
                m.up_exps.qtype,
                up_row_bytes,
            )?;
            let mut act = e.uninit(n_pairs * n_ff_exp)?;
            Self::ffn_act_lim(
                e,
                cfg,
                &gate,
                &up,
                1.0,
                1.0,
                cfg.clamp_exp_at(il as u32),
                &mut act,
                n_pairs * n_ff_exp,
            )?;
            let (aq2, ad2) = e.quantize_q8_1(&act, n_pairs, n_ff_exp)?;
            let pair_self: Vec<i32> = (0..n_pairs as i32).collect();
            let pair_self_d = e.htod_i32(&pair_self)?;
            let down = e.moe_pairs_matvec_q8(
                &dev.ptr_row,
                2,
                &pair_self_d,
                &sel_d,
                &aq2,
                &ad2,
                n_ff_exp,
                n_embd,
                n_expert,
                n_pairs,
                m.down_exps.qtype,
                m.down_exps.row_bytes,
            )?;
            let tok_off: Vec<i32> = (0..=t).map(|tok| (tok * n_used) as i32).collect();
            let tok_ids: Vec<i32> = (0..n_pairs as i32).collect();
            let tok_off_d = e.htod_i32(&tok_off)?;
            let tok_ids_d = e.htod_i32(&tok_ids)?;
            let mut output = e.uninit(t * n_embd)?;
            e.moe_pairs_scatter(&down, &w_d, &tok_off_d, &tok_ids_d, &mut output, t, n_embd)?;
            output
        } else {
            let act = e.moe_gate_up_silu8_dev_q8_rows(
                &dev.ptr_row,
                &sel_d,
                &zq,
                &zd,
                t,
                n_embd,
                n_ff_exp,
                n_used,
                n_expert,
                m.gate_exps.qtype,
                m.up_exps.qtype,
                gate_row_bytes,
                up_row_bytes,
                &m.dev_macros,
            )?;
            let (aq2, ad2) = e.quantize_q8_1(&act, n_pairs, n_ff_exp)?;
            let mut output = e.uninit(t * n_embd)?;
            e.moe_down8_fma_dev_q8_rows_g(
                &dev.ptr_row,
                &sel_d,
                &w_d,
                &aq2,
                &ad2,
                &mut output,
                t,
                n_ff_exp,
                n_embd,
                n_used,
                n_expert,
                m.down_exps.qtype,
                m.down_exps.row_bytes,
            )?;
            output
        };

        if std::env::var("MEMRA_SIG_ROUTER_DISPATCH_TRACE").as_deref() == Ok("1") {
            eprintln!(
                "[sigrouter-dev] layer={il} tokens={t} experts={n_expert} used={n_used} clamp={} gu_il={}",
                cfg.clamp_exp_at(il as u32).is_some(),
                dev.gu_il,
            );
        }
        Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut moe_out)?;
        Ok(moe_out)
    }

    #[allow(clippy::too_many_arguments)]
    fn moe_resident_fp8_e4m3(
        e: &Engine,
        exps: &crate::model::HostExps,
        bytes: &CudaSlice<u8>,
        scales: &crate::hybrid::DevExpertFp8ProjectionScales,
        expert: usize,
        x: &cudarc::driver::CudaView<f32>,
        m: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let layout = exps.expert_layout(expert);
        debug_assert_eq!(layout.qtype, crate::QT_F8_E4M3_BLK);
        debug_assert_eq!(scales.rows * scales.cols, scales.expert_stride);
        let byte_start = expert * exps.expert_stride;
        let scale_start = expert * scales.expert_stride;
        let weight = bytes.slice(byte_start..byte_start + layout.len);
        let scale = scales
            .scales
            .slice(scale_start..scale_start + scales.expert_stride);
        e.qmatvec_mmq_fp8_blk_view(&weight, &scale, x, m, exps.in_f, exps.out_f)
    }

    /// LAUNCH-STRUCTURE STAGE 3: the ZERO-DtoH fully-resident MoE FFN. Caller guarantees the
    /// layer's device pointer row exists (checked under the cache lock). Router top-k runs on
    /// device; sel/w are consumed by the `_dev` matvec twins directly; NOTHING crosses PCIe.
    /// Same numerics as the fused-router + gdec chain (kernel-level bit-identity, see the
    /// MoE PREFILL PAIR-BATCH: host routing (sel/w like the sequential path), then 5 launches
    /// TOTAL per layer (quantize z, gate-pairs, up-pairs, silu, act-quantize, down-pairs,
    /// scatter) regardless of T or expert count. Bit-identity class: per (pair,row) dot =
    /// qmatvec_expert_q8 order; per-token accumulation slot-ordered (scatter kernel).
    fn moe_ffn_pairs(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        logits: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let moe = cfg.moe.as_ref().unwrap();
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        // This arm has no `il` and every kernel in it fuses PLAIN silu(gate)*up, so a clamped
        // model must never reach it. The caller's gate at `moe_ffn_sequential_zq8` denies per
        // layer via `swiglu_clamped_at(il)`; assert the whole-model form here so a future caller
        // that forgets the gate fails loudly in debug instead of returning wrong logits.
        debug_assert!(
            !cfg.swiglu_clamped_anywhere(),
            "moe_ffn_pairs has no per-layer clamp: fused epilogues are plain SiLU"
        );
        let dev = m.dev_exps.as_ref().unwrap();
        // WALL-GAP ARC: interleaved gate/up slab strides (see moe_ffn_dev).
        let (rbg_d, rbu_d) = if dev.gu_il {
            let sxx = m.gate_exps.row_bytes + m.up_exps.row_bytes;
            (sxx, sxx)
        } else {
            (m.gate_exps.row_bytes, m.up_exps.row_bytes)
        };

        let (sel_all, w_all) = Self::moe_route(e, logits, t, n_expert, n_used)?;
        let n_pairs = t * n_used;
        // pair arrays: pair p = (token p/n_used, slot p%n_used) — ALREADY slot-ordered per token,
        // so the CSR is trivial: tok_pair_off[tok] = tok*n_used, ids identity.
        let pair_tok: Vec<i32> = (0..n_pairs).map(|p| (p / n_used) as i32).collect();
        let pair_ex: Vec<i32> = sel_all.iter().map(|&x| x as i32).collect();
        let pair_w: Vec<f32> = w_all.clone();
        let tok_off: Vec<i32> = (0..=t).map(|tok| (tok * n_used) as i32).collect();
        let tok_ids: Vec<i32> = (0..n_pairs as i32).collect();
        let pt = e.htod_i32(&pair_tok)?;
        let px = e.htod_i32(&pair_ex)?;
        let pw = e.htod(&pair_w)?;
        let toff = e.htod_i32(&tok_off)?;
        let tids = e.htod_i32(&tok_ids)?;

        // z quantized ONCE for all tokens; gate/up pair matvecs; silu; act quantize; down; scatter.
        // EXPERT-MAJOR CSR (rung 2): pairs grouped by expert -> the kernel reuses each weight
        // row across the expert's token group (llama-MMQ's core win). Host grouping is O(pairs).
        let mut by_ex: Vec<Vec<i32>> = vec![Vec::new(); n_expert];
        for p in 0..n_pairs {
            by_ex[pair_ex[p] as usize].push(p as i32);
        }
        let mut ex_ids: Vec<i32> = Vec::new();
        let mut ex_off: Vec<i32> = vec![0];
        let mut ex_pairs: Vec<i32> = Vec::with_capacity(n_pairs);
        for (ex, list) in by_ex.iter().enumerate() {
            if list.is_empty() {
                continue;
            }
            ex_ids.push(ex as i32);
            ex_pairs.extend_from_slice(list);
            ex_off.push(ex_pairs.len() as i32);
        }
        let n_active = ex_ids.len();
        let exi = e.htod_i32(&ex_ids)?;
        let exo = e.htod_i32(&ex_off)?;
        let exp_d = e.htod_i32(&ex_pairs)?;
        let _ = &px; // pair-major twin keeps it; em path uses CSR

        // INT8-MMA EXPERT MMQ (MEMRA_MOE_MMA=1, opt-in): the m16n8k16.s8 tensor-core analog of the
        // _dec dp4a kernel (cu/mmq_iq_experts.cu). Same CSR grouping; per-expert matvec runs as a
        // 128x128-tile int8 MMA GEMM over the expert's token group. Weight IQ nibbles decode to int8
        // at tile-load + per-32 float scale; activation is q8_1_mmq (D4, same quant class as dp4a).
        // FP-ORDER differs from dp4a (MMA reduction) — logits SHIFT, gated on argmax/spec/closeness,
        // NOT byte-identity (like the W4A8 path). Requires IQ3_S/IQ4_XS + in_f % 256 == 0.
        // t >= 16 (GEMM_M-class rule): the MMA tile needs token volume (crossover ~200 tok/expert;
        // microbench: dp4a wins at tiny groups). ALSO an exactness requirement — spec verify
        // batches (t=2..K+2) must ride the dp4a path whose FP order matches the T=1 decode chain,
        // else K=1 self-consistency FAILs (caught 2026-07-06: MMA at T=2 flipped a verify argmax).
        // DEFAULT ON (2026-07-06, third flip — this time with the real culprit fixed): the
        // "MMA prime breaks spec" failure was the ROUTER's cuBLASLt n-dependence (d994271),
        // not MMA's own FP order — both this and the k-quant arms were innocent suspects whose
        // margin shifts surfaced the router bug. With the router decode-exact at verify t, the
        // full battery is green with MMA on (spec p1/p2/p3 PASS, raw K=1..8 PASS, argmax MATCH,
        // pp6257 2862 = 2.1x dec). t>=16 floor still required: verify batches must ride dp4a
        // (dispatch parity with the T=1 decode chain). MEMRA_MOE_MMA=0 rollback;
        // MEMRA_MOE_MMA_T overrides the floor (bisect seam).
        static MMA_T: std::sync::OnceLock<usize> = std::sync::OnceLock::new();
        let mma_t = *MMA_T.get_or_init(|| {
            std::env::var("MEMRA_MOE_MMA_T")
                .ok()
                .and_then(|v| v.parse().ok())
                .unwrap_or(16)
        });
        let use_mma = std::env::var("MEMRA_MOE_MMA")
            .map(|v| v != "0")
            .unwrap_or(true)
            && t >= mma_t
            && q8_expert_dec_supported(m.gate_exps.qtype)
            && q8_expert_dec_supported(m.up_exps.qtype)
            && q8_expert_dec_supported(m.down_exps.qtype)
            && n_embd.is_multiple_of(256)
            && n_ff_exp.is_multiple_of(256);
        // GROUPED f16 LANE admission (MEMRA_MOE_F16G, rounds 46-49): its own door, no longer a
        // subset of the MMQ arm — q35's k-quant stragglers (Q6_K/Q4_K down, one Q3_K gate/up
        // layer) fail q8_expert_dec_supported but dequant fine to f16, so f16g must be able to
        // take a layer the MMQ arm would reject. Same t >= mma_t floor as MMA: decode and
        // spec-verify batches must ride the dp4a path whose FP order matches the T=1 chain.
        // MODE 2 (sm_120a naked default, lane/f16g-default-rearb 2026-08-02): every layer
        // whose three projections pass f16g_proj_ok rides the sk visitor with direct tile
        // loaders — with IQ4_XS/IQ3_S direct coverage the sk arm beats the int8-MMA MMQ
        // tiles on the IQ-bank models too (q35 board-2048 +33.9%, KAT pp512 +46.7%).
        // AUTO-KQUANT (mode 3, MEMRA_MOE_F16G=3, lane/q4k-expert-prefill): admit f16g ONLY
        // where the MMA arm can't take the layer's QTYPES — the k-quant expert class whose
        // baseline is the per-pair _em fallback (Ornith-35B Q4_K: board-2048 3.14x). Its
        // MMA-capable carve-out (IQ3_S/IQ4_XS/Q4_0 banks on MMQ) was priced pre-IQ-direct;
        // it survives as the rollback seam. Keyed on qtype capability, NOT use_mma, so
        // MEMRA_MOE_MMA=0 stays a pure dp4a rollback seam.
        let mma_capable = q8_expert_dec_supported(m.gate_exps.qtype)
            && q8_expert_dec_supported(m.up_exps.qtype)
            && q8_expert_dec_supported(m.down_exps.qtype)
            && n_embd.is_multiple_of(256)
            && n_ff_exp.is_multiple_of(256);
        let f16g_mode = crate::moe_f16g_mode();
        let f16g = f16g_mode != 0
            && t >= mma_t
            && (f16g_mode != 3 || !mma_capable)
            && f16g_proj_ok(m.gate_exps.qtype, n_embd)
            && f16g_proj_ok(m.up_exps.qtype, n_embd)
            && f16g_proj_ok(m.down_exps.qtype, n_ff_exp);
        if use_mma || f16g {
            // GROUPED f16 LANE (MEMRA_MOE_F16G, rounds 46-49, experimental door): dequant
            // the active experts ONCE per projection to f16 and run one grouped f16 GEMM over
            // the CSR groups. f16-mirror numeric class — argmax/spec gated before promotion.
            // NOTE: the pair-gather kernel needs pair p ordered EXPERT-MAJOR (ex_pairs order);
            // the y rows come back in that same CSR order, so gate/up/down all stay pair-major
            // in ex_pairs order — but moe_pairs_silu_mul and the scatter consume PAIR-ID order.
            // We therefore gather activations per ex_pairs and scatter y back through ex_pairs.
            let y_down = if f16g {
                // CSR order end-to-end: gather z rows by the pair's TOKEN (pair p's token is
                // p / n_used — the trivial CSR above), silu in CSR order (elementwise), one
                // permute at the very end back to pair-id order for the scatter.
                let csr_tok: Vec<i32> = ex_pairs.iter().map(|&p| p / n_used as i32).collect();
                let csr_tok_d = e.htod_i32(&csr_tok)?;
                let (z_f16, z_s) = e.moe_f16g_act(z, Some(&csr_tok_d), n_embd, n_pairs)?;
                let g_csr = e.moe_f16_grouped(
                    &dev.ptr_row,
                    0,
                    n_expert,
                    &exi,
                    &ex_off,
                    &exo,
                    &z_f16,
                    &z_s,
                    n_embd,
                    n_ff_exp,
                    n_active,
                    n_pairs,
                    m.gate_exps.qtype,
                    rbg_d,
                )?;
                let u_csr = e.moe_f16_grouped(
                    &dev.ptr_row,
                    1,
                    n_expert,
                    &exi,
                    &ex_off,
                    &exo,
                    &z_f16,
                    &z_s,
                    n_embd,
                    n_ff_exp,
                    n_active,
                    n_pairs,
                    m.up_exps.qtype,
                    rbu_d,
                )?;
                let act_csr = e.moe_pairs_silu_mul(&g_csr, &u_csr, n_pairs * n_ff_exp)?;
                let (a_f16, a_s) = e.moe_f16g_act(&act_csr, None, n_ff_exp, n_pairs)?;
                let d_csr = e.moe_f16_grouped(
                    &dev.ptr_row,
                    2,
                    n_expert,
                    &exi,
                    &ex_off,
                    &exo,
                    &a_f16,
                    &a_s,
                    n_ff_exp,
                    n_embd,
                    n_active,
                    n_pairs,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?;
                e.rows_permute(&d_csr, &exp_d, n_pairs, n_embd)?
            } else {
                // gate/up: activation = z, token-major over t tokens; pair_tok gathers the routed row.
                let z_scr = e.mmq_iq_quantize_act(z, n_embd, t)?;
                let gate = e.mmq_iq_experts(
                    &dev.ptr_row,
                    0,
                    n_expert,
                    &exi,
                    &exo,
                    &exp_d,
                    &pt,
                    &z_scr,
                    n_embd,
                    n_ff_exp,
                    n_active,
                    n_pairs,
                    t,
                    m.gate_exps.qtype,
                    rbg_d,
                )?;
                let up = e.mmq_iq_experts(
                    &dev.ptr_row,
                    1,
                    n_expert,
                    &exi,
                    &exo,
                    &exp_d,
                    &pt,
                    &z_scr,
                    n_embd,
                    n_ff_exp,
                    n_active,
                    n_pairs,
                    t,
                    m.up_exps.qtype,
                    rbu_d,
                )?;
                // down: activation = silu(gate)*up, pair-major [n_pairs, n_ff_exp]; pair_tok =
                // identity. FUSED ACT-EPILOGUE (default on): one launch computes the activation in
                // registers and writes ONLY the quantized scratch — the two-pass chain
                // (moe_pairs_silu_mul writes act f32, mmq_iq_quantize_act re-reads it) is the
                // MEMRA_MOE_FUSE_ACTQ=0 rollback. Scratch bytes are BYTE-IDENTICAL (kernel-check).
                let a_scr = if crate::moe_fuse_actq_on() {
                    e.mmq_iq_fused_act_quant(&gate, &up, n_ff_exp, n_pairs, 0)?
                } else {
                    let act = e.moe_pairs_silu_mul(&gate, &up, n_pairs * n_ff_exp)?;
                    e.mmq_iq_quantize_act(&act, n_ff_exp, n_pairs)?
                };
                let pair_self: Vec<i32> = (0..n_pairs as i32).collect();
                let pself = e.htod_i32(&pair_self)?;
                e.mmq_iq_experts(
                    &dev.ptr_row,
                    2,
                    n_expert,
                    &exi,
                    &exo,
                    &exp_d,
                    &pself,
                    &a_scr,
                    n_ff_exp,
                    n_embd,
                    n_active,
                    n_pairs,
                    n_pairs,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?
            };
            let mut moe_out = e.uninit(t * n_embd)?;
            e.moe_pairs_scatter(&y_down, &pw, &toff, &tids, &mut moe_out, t, n_embd)?;
            if let (Some(gate_shexp), Some(up_shexp), Some(down_shexp)) =
                (&m.gate_shexp, &m.up_shexp, &m.down_shexp)
            {
                let n_ff_sh = gate_shexp.out_features();
                let sg_gate = e.matmul(gate_shexp, z, t)?;
                let sg_up = e.matmul(up_shexp, z, t)?;
                let mut sa = e.uninit(t * n_ff_sh)?;
                Self::ffn_act(e, cfg, &sg_gate, &sg_up, &mut sa, t * n_ff_sh)?;
                let sh = e.matmul(down_shexp, &sa, t)?;
                // shexp gate: qwen35moe sigmoid-gates; M3 has no gate tensor -> weight 1.0.
                // SERVE ISOLATION (lane/concat-prime-exact): the fused sigmoid-dot is the
                // m-INVARIANT form — see the sequential arm's note. This is the PAIRS arm,
                // i.e. the one real prefill actually takes on a resident-expert MoE model,
                // so the concat-prime isolation fix has to land here as well.
                let g = match &m.gate_inp_shexp {
                    Some(gate_inp_shexp) if crate::router_prefill_exact_on() => {
                        e.sigmoid_dot_rows(z, gate_inp_shexp.float_data(), n_embd, t)?
                    }
                    Some(gate_inp_shexp) => {
                        let gs = e.linear(z, gate_inp_shexp.float_data(), t, n_embd, 1)?;
                        let mut g = e.uninit(t)?;
                        e.sigmoid(&gs, &mut g, t)?;
                        g
                    }
                    None => e.htod(&vec![1.0f32; t])?,
                };
                e.add_scaled_rows(&sh, &g, &mut moe_out, n_embd, t)?;
            }
            return Ok(moe_out);
        }

        // DECODE-ONCE MMQ (rung 3, MEMRA_MOE_DEC=1 default-on): dequant each weight group once per
        // (row,group) then dp4a across the expert's tokens. _em re-decoded per token (NEUTRAL).
        let dec = std::env::var("MEMRA_MOE_DEC")
            .map(|v| v != "0")
            .unwrap_or(true);
        let matvec = |proj,
                      exi: &_,
                      exo: &_,
                      exp_d: &_,
                      pt: &_,
                      aq: &_,
                      ad: &_,
                      inf,
                      outf,
                      qtype,
                      rb|
         -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
            // _dec's decode-once extractors are IQ-only; k-quant expert layers take the _em dot path.
            let dec = dec && q8_expert_dec_supported(qtype);
            if dec {
                e.moe_pairs_matvec_q8_dec(
                    &dev.ptr_row,
                    proj,
                    exi,
                    exo,
                    exp_d,
                    pt,
                    aq,
                    ad,
                    inf,
                    outf,
                    n_expert,
                    n_active,
                    n_pairs,
                    qtype,
                    rb,
                )
            } else {
                e.moe_pairs_matvec_q8_em(
                    &dev.ptr_row,
                    proj,
                    exi,
                    exo,
                    exp_d,
                    pt,
                    aq,
                    ad,
                    inf,
                    outf,
                    n_expert,
                    n_active,
                    n_pairs,
                    qtype,
                    rb,
                )
            }
        };
        let (zq, zd) = e.quantize_q8_1(z, t, n_embd)?;
        let gate = matvec(
            0,
            &exi,
            &exo,
            &exp_d,
            &pt,
            &zq,
            &zd,
            n_embd,
            n_ff_exp,
            m.gate_exps.qtype,
            rbg_d,
        )?;
        let up = matvec(
            1,
            &exi,
            &exo,
            &exp_d,
            &pt,
            &zq,
            &zd,
            n_embd,
            n_ff_exp,
            m.up_exps.qtype,
            rbu_d,
        )?;
        let act = e.moe_pairs_silu_mul(&gate, &up, n_pairs * n_ff_exp)?;
        let (aq2, ad2) = e.quantize_q8_1(&act, n_pairs, n_ff_exp)?;
        // down consumes PAIR-major activation rows: pair_tok = identity.
        let pair_self: Vec<i32> = (0..n_pairs as i32).collect();
        let pself = e.htod_i32(&pair_self)?;
        let y_down = matvec(
            2,
            &exi,
            &exo,
            &exp_d,
            &pself,
            &aq2,
            &ad2,
            n_ff_exp,
            n_embd,
            m.down_exps.qtype,
            m.down_exps.row_bytes,
        )?;
        let mut moe_out = e.uninit(t * n_embd)?; // scatter fully overwrites per (token,col)
        e.moe_pairs_scatter(&y_down, &pw, &toff, &tids, &mut moe_out, t, n_embd)?;

        // SHARED EXPERT epilogue — same as the other paths.
        // gate_inp_shexp is OPTIONAL: qwen35moe gates the shared expert (sigmoid(gate_inp) x sh);
        // MiniMax-M3 (DeepSeek-V3 class) has NO shexp gate — the shared expert adds directly.
        if let (Some(gate_shexp), Some(up_shexp), Some(down_shexp)) =
            (&m.gate_shexp, &m.up_shexp, &m.down_shexp)
        {
            let n_ff_sh = gate_shexp.out_features();
            // These decode-exact forms are required by the new Step resident arm. Keep the
            // established grouped shared-expert program for every other architecture: widening
            // this to Gemma changed its speculative acceptance despite green argmax gates.
            let step_exact = true;
            let verify_t = step_exact && t > 1 && t < PRIME_MIN_T;
            let (sg_gate, sg_up) = if step_exact && t == 1 {
                shexp_gate_up_t1(e, gate_shexp, up_shexp, z, None)?
            } else if verify_t {
                let mut fused = None;
                if crate::spec::spec_fused_t()
                    && (2..=4).contains(&t)
                    && e.uses_q8_1_fast(gate_shexp)
                    && e.uses_q8_1_fast(up_shexp)
                {
                    let (zq, zd) = e.quantize_q8_1(z, t, n_embd)?;
                    fused = e.matmul_q8_fused2_t(gate_shexp, up_shexp, &zq, &zd, t)?;
                }
                match fused {
                    Some(pair) => pair,
                    None => (
                        e.matmul_decode_exact(gate_shexp, z, t)?,
                        e.matmul_decode_exact(up_shexp, z, t)?,
                    ),
                }
            } else {
                (e.matmul(gate_shexp, z, t)?, e.matmul(up_shexp, z, t)?)
            };
            let mut sa = e.uninit(t * n_ff_sh)?;
            e.silu_mul(&sg_gate, &sg_up, &mut sa, t * n_ff_sh)?;
            let sh = if verify_t {
                e.matmul_decode_exact(down_shexp, &sa, t)?
            } else {
                e.matmul(down_shexp, &sa, t)?
            };
            // shexp gate: qwen35moe sigmoid-gates; M3 has no gate tensor -> weight 1.0.
            // m-INVARIANT fused sigmoid-dot under router_prefill_exact (serve isolation,
            // lane/concat-prime-exact) — every shexp-gate arm shares the same form so a
            // dispatch choice cannot change bits.
            let g = match &m.gate_inp_shexp {
                Some(gate_inp_shexp) if crate::router_prefill_exact_on() => {
                    e.sigmoid_dot_rows(z, gate_inp_shexp.float_data(), n_embd, t)?
                }
                Some(gate_inp_shexp) => {
                    let gs = e.linear(z, gate_inp_shexp.float_data(), t, n_embd, 1)?;
                    let mut g = e.uninit(t)?;
                    e.sigmoid(&gs, &mut g, t)?;
                    g
                }
                None => e.htod(&vec![1.0f32; t])?,
            };
            e.add_scaled_rows(&sh, &g, &mut moe_out, n_embd, t)?;
        }
        Ok(moe_out)
    }

    /// kernel headers); the shared-expert epilogue is byte-identical to moe_ffn_sequential's.
    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::too_many_arguments)]
    fn moe_ffn_dev(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        zq8: Option<&(CudaSlice<i8>, CudaSlice<f32>)>,
        logits: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        max_block: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let moe = cfg.moe.as_ref().unwrap();
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        // moe_router_topk is SOFTMAX-only (no exp_probs_b bias, no expert_weights_scale) and every
        // gate_up kernel here fuses PLAIN silu(gate)*up. `dev_ok` denies sigmoid-router archs and
        // clamped layers; assert both so a future caller that skips the gate fails loudly.
        debug_assert!(
            cfg.sigmoid_router().is_none(),
            "moe_ffn_dev routes SOFTMAX: a sigmoid-router arch would pick wrong experts"
        );
        debug_assert!(
            !cfg.swiglu_clamped_at(il as u32),
            "moe_ffn_dev's fused epilogue is plain SiLU: no clamped form"
        );

        // device top-k: sel [t, n_used] i32, w [t, n_used] f32 — stays on device.
        let (sel_d, mut w_d) = e.moe_router_topk(logits, t, n_expert, n_used)?;
        // Down-projection macro fold (compressed-tensors NVFP4 artifacts): one tiny launch,
        // skipped entirely for macro-free experts (every k-quant GGUF).
        if m.has_macros {
            e.moe_w_scale_by_expert(&mut w_d, &sel_d, &m.dev_macros, n_expert, t * n_used)?;
        }

        // moe_out rows are FULLY overwritten by moe_down8_fma_dev — uninit (stage-2 rule).
        let mut moe_out = e.uninit(t * n_embd)?;

        // RESIDENT-EXPERTS arm: the pointer row comes from the load-time slab (no cache, no
        // lock). Same kernels/loop as the SLRU arm below — only the row's provenance differs.
        if let Some(dev) = m.dev_exps.as_ref() {
            // WALL-GAP ARC: interleaved gate/up slab (MEMRA_MOE_GU_IL) -> both projections use
            // the combined stride; up's base is offset in the ptr table. Down unchanged.
            let (rbg_d, rbu_d) = if dev.gu_il {
                let sxx = m.gate_exps.row_bytes + m.up_exps.row_bytes;
                (sxx, sxx)
            } else {
                (m.gate_exps.row_bytes, m.up_exps.row_bytes)
            };
            let q8 = moe_q8_enabled_for_model(cfg, m);
            // SMALL-M ROWS ARM (MEMRA_SPEC_M2, lane/spec-m2): batch the verify token loop —
            // ONE batched z-quantize + ONE gate_up rows launch + ONE act quantize + ONE down
            // rows launch (4 launches/layer, was 4t). BIT-IDENTICAL per token to the serial
            // loop below (rows twins = the _v/w8h2v per-token programs on a grid.z token axis;
            // quantize_q8_1 is per-32-block row-independent). Gated to the AUTO kernel modes —
            // a custom MEMRA_MOE_DEVQ8_GU/DOWN diagnostic run keeps the serial loop so the
            // dispatched kernel stays exactly the env-selected one — and to the w8h2v shape
            // (n_ff_exp==512, n_used<=8), the same contract the AUTO down dispatch keys on.
            let rows_arm = q8
                && t > 1
                && crate::spec::spec_m2()
                && n_ff_exp == 512
                && n_used <= 8
                && std::env::var("MEMRA_MOE_DEVQ8_GU")
                    .map(|v| v.is_empty() || v == "v")
                    .unwrap_or(true)
                && std::env::var("MEMRA_MOE_DEVQ8_DOWN")
                    .map(|v| v.is_empty() || v == "w8h2v")
                    .unwrap_or(true);
            // CSR EXPERT-DEDUP gate_up (verify-cost target #1, DEFAULT ON 2026-07-10): the
            // owner-scan kernel serves every (token, slot) pair of an expert from ONE block,
            // deduping the 38-40% duplicated weight-stream+decode the overlap probe measured
            // (gate_up 55.0 -> 39.7us/launch; +1.3-2.1% spec e2e p2, +0.6-1.7% p3, all K).
            // Bit-identical to the _rows twins (explicit-intrinsic accumulate — the ULP/fmad
            // lesson; =2 byte-compare verified zero diffs). down stays on _rows (CSR down
            // measured 23.5 -> 37.5us: 16-group rows can't amortize the serial pair loop).
            // MEMRA_MOE_CSR=0 rollback; =2 runs BOTH paths and byte-compares (debug).
            let csr_mode = std::env::var("MEMRA_MOE_CSR")
                .ok()
                .and_then(|v| v.parse::<i32>().ok())
                .unwrap_or(1);
            // NVFP4 admission REVERTED 2026-08-21 (lane/samplat, decode-batch-gate2 find):
            // the csr_nvfp4 kernel drifts last-ULP vs the rows program (11041/32768 ACT
            // elements at t=8) and the drift is BATCH-COMPOSITION-DEPENDENT — gate2 (B=8 vs
            // isolated) FAILED on the ornith15 artifact, the one-numeric-program law's batch
            // axis. Three chain-pinning attempts did not close it (receipts,
            // research/samplat-20260821/); a source-verbatim per-pair helper form IS
            // bit-identical but loses the dedup win (-3% vs rows). NVFP4 stays on the rows
            // twins until a cached form passes gate2 + the =2 byte-compare at t=8. The
            // increment-1 qualification hole: =2 ran across run-spec (solo verify shapes),
            // never decode-batch-gate at B=8 on the MoE model itself.
            // MEMRA_MOE_CSR_NVFP4=1 (lane/orndecode, DIAGNOSTIC PROBE ONLY): re-admits NVFP4
            // to the CSR arm and widens it to the exact-16 decode widths, so gate2 B=12/16 +
            // the =2 byte-compare can re-adjudicate the cached form at the widths where the
            // serial dev loop hurts most (B=16 tick: 1280 launches/step). The v0.100.1
            // de-admission verdict above stands until those gates are GREEN on the MoE
            // artifact; this door must never default on.
            let csr_nvfp4_probe = std::env::var("MEMRA_MOE_CSR_NVFP4").as_deref() == Ok("1");
            let csr_qt = |qt: i32| {
                qt == crate::QT_IQ4_XS
                    || qt == crate::QT_IQ3_S
                    || (csr_nvfp4_probe && qt == crate::QT_NVFP4)
            };
            let csr_t_max = if csr_nvfp4_probe { MOE_DEV_MAX_T } else { 10 };
            let csr_uniform = m.gate_exps.qtype == m.up_exps.qtype;
            let csr_arm = rows_arm
                && csr_mode > 0
                && t <= csr_t_max
                && csr_uniform
                && csr_qt(m.gate_exps.qtype)
                && csr_qt(m.up_exps.qtype)
                && csr_qt(m.down_exps.qtype);
            if csr_arm {
                if csr_mode == 2 {
                    static ENGAGED: std::sync::Once = std::sync::Once::new();
                    ENGAGED.call_once(|| eprintln!("[memra] moe CSR byte-compare mode ON (t={t})"));
                }
                let n_pairs = t * n_used;
                let (zq, zd) = e.quantize_q8_1(z, t, n_embd)?;
                let act = e.moe_gate_up_silu8_dev_q8_csr(
                    &dev.ptr_row,
                    &sel_d,
                    &zq,
                    &zd,
                    n_pairs,
                    n_embd,
                    n_ff_exp,
                    n_used,
                    n_expert,
                    m.gate_exps.qtype,
                    m.up_exps.qtype,
                    rbg_d,
                    rbu_d,
                )?;
                let (aq2, ad2) = e.quantize_q8_1(&act, n_pairs, n_ff_exp)?;
                // down stays on the _rows twin — BOTH CSR down variants measured negative
                // (v1 serial pairs 23.5->37.5us; v2 warp-parallel+SMEM -14% e2e, K=8 -37%):
                // 16-group rows have too little decode to amortize any dedup structure.
                e.moe_down8_fma_dev_q8_rows(
                    &dev.ptr_row,
                    &sel_d,
                    &w_d,
                    &aq2,
                    &ad2,
                    &mut moe_out,
                    t,
                    n_ff_exp,
                    n_embd,
                    n_used,
                    n_expert,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?;
                if csr_mode == 2 {
                    // DEBUG BYTE-COMPARE: run the _rows twins on the same inputs, diff bits.
                    let act_r = e.moe_gate_up_silu8_dev_q8_rows(
                        &dev.ptr_row,
                        &sel_d,
                        &zq,
                        &zd,
                        t,
                        n_embd,
                        n_ff_exp,
                        n_used,
                        n_expert,
                        m.gate_exps.qtype,
                        m.up_exps.qtype,
                        rbg_d,
                        rbu_d,
                        &m.dev_macros,
                    )?;
                    let mut out_r = e.uninit(t * n_embd)?;
                    let (aq2r, ad2r) = e.quantize_q8_1(&act_r, n_pairs, n_ff_exp)?;
                    e.moe_down8_fma_dev_q8_rows(
                        &dev.ptr_row,
                        &sel_d,
                        &w_d,
                        &aq2r,
                        &ad2r,
                        &mut out_r,
                        t,
                        n_ff_exp,
                        n_embd,
                        n_used,
                        n_expert,
                        m.down_exps.qtype,
                        m.down_exps.row_bytes,
                    )?;
                    let (a1, a2) = (e.dtoh(&act)?, e.dtoh(&act_r)?);
                    let (o1, o2) = (e.dtoh(&moe_out)?, e.dtoh(&out_r)?);
                    let ba = a1
                        .iter()
                        .zip(&a2)
                        .filter(|(x, y)| x.to_bits() != y.to_bits())
                        .count();
                    let bo = o1
                        .iter()
                        .zip(&o2)
                        .filter(|(x, y)| x.to_bits() != y.to_bits())
                        .count();
                    if ba + bo > 0 {
                        eprintln!(
                            "[csr-check] il={il} t={t} ACT diffs={ba}/{} OUT diffs={bo}/{}",
                            a1.len(),
                            o1.len()
                        );
                        // First 4 differing ACT elements: (pair, o, csr, rows) + that pair's expert
                        let sel_h = e.dtoh_i32(&sel_d)?;
                        let mut shown = 0;
                        for (i, (x, y)) in a1.iter().zip(&a2).enumerate() {
                            if x.to_bits() != y.to_bits() && shown < 4 {
                                let (p, o) = (i / n_ff_exp, i % n_ff_exp);
                                let ex = sel_h[p];
                                let npx = sel_h.iter().filter(|&&v| v == ex).count();
                                eprintln!(
                                    "  ACT p={p} ex={ex} np={npx} o={o} csr={x:e} rows={y:e}"
                                );
                                shown += 1;
                            }
                        }
                        std::process::exit(3);
                    }
                }
            } else if rows_arm {
                // TEMP PROBE (MEMRA_MOE_OVERLAP=1): cross-token expert-activation overlap at
                // verify — sizes the CSR dedup win (unique experts vs t*n_used pairs).
                if std::env::var("MEMRA_MOE_OVERLAP").as_deref() == Ok("1") {
                    use std::sync::atomic::{AtomicU64, Ordering};
                    static PAIRS: AtomicU64 = AtomicU64::new(0);
                    static UNIQ: AtomicU64 = AtomicU64::new(0);
                    static CALLS: AtomicU64 = AtomicU64::new(0);
                    let sel_h = e.dtoh_i32(&sel_d)?;
                    let mut u: Vec<i32> = sel_h.clone();
                    u.sort_unstable();
                    u.dedup();
                    PAIRS.fetch_add(sel_h.len() as u64, Ordering::Relaxed);
                    UNIQ.fetch_add(u.len() as u64, Ordering::Relaxed);
                    let c = CALLS.fetch_add(1, Ordering::Relaxed) + 1;
                    if c.is_multiple_of(480) {
                        let p = PAIRS.load(Ordering::Relaxed);
                        let q = UNIQ.load(Ordering::Relaxed);
                        eprintln!(
                            "[overlap] calls={c} pairs={p} unique={q} ratio={:.3} (t={t})",
                            q as f64 / p as f64
                        );
                    }
                }
                let (zq, zd) = e.quantize_q8_1(z, t, n_embd)?;
                let act = e.moe_gate_up_silu8_dev_q8_rows(
                    &dev.ptr_row,
                    &sel_d,
                    &zq,
                    &zd,
                    t,
                    n_embd,
                    n_ff_exp,
                    n_used,
                    n_expert,
                    m.gate_exps.qtype,
                    m.up_exps.qtype,
                    rbg_d,
                    rbu_d,
                    &m.dev_macros,
                )?;
                let (aq2, ad2) = e.quantize_q8_1(&act, t * n_used, n_ff_exp)?;
                e.moe_down8_fma_dev_q8_rows(
                    &dev.ptr_row,
                    &sel_d,
                    &w_d,
                    &aq2,
                    &ad2,
                    &mut moe_out,
                    t,
                    n_ff_exp,
                    n_embd,
                    n_used,
                    n_expert,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?;
            } else {
                for tok in 0..t {
                    let zt = z.slice(tok * n_embd..(tok + 1) * n_embd);
                    let selt = sel_d.slice(tok * n_used..(tok + 1) * n_used);
                    let wt = w_d.slice(tok * n_used..(tok + 1) * n_used);
                    let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                    if q8 {
                        let (zq, zd) = match (t, zq8) {
                            (1, Some((q, d))) => (q.clone(), d.clone()),
                            _ => e.quantize_q8_1_view(&zt, 1, n_embd)?,
                        };
                        let act = e.moe_gate_up_silu8_dev_q8(
                            &dev.ptr_row,
                            &selt,
                            &zq,
                            &zd,
                            n_embd,
                            n_ff_exp,
                            n_used,
                            n_expert,
                            m.gate_exps.qtype,
                            m.up_exps.qtype,
                            rbg_d,
                            rbu_d,
                            &m.dev_macros,
                        )?;
                        let (aq2, ad2) = e.quantize_q8_1(&act, n_used, n_ff_exp)?;
                        e.moe_down8_fma_dev_q8(
                            &dev.ptr_row,
                            &selt,
                            &wt,
                            &aq2,
                            &ad2,
                            &mut dst,
                            n_ff_exp,
                            n_embd,
                            n_used,
                            n_expert,
                            m.down_exps.qtype,
                            m.down_exps.row_bytes,
                        )?;
                    } else {
                        let act = e.moe_gate_up_silu8_dev(
                            &dev.ptr_row,
                            &selt,
                            &zt,
                            n_embd,
                            n_ff_exp,
                            n_used,
                            n_expert,
                            m.gate_exps.qtype,
                            m.up_exps.qtype,
                            rbg_d,
                            rbu_d,
                            &m.dev_macros,
                        )?;
                        e.moe_down8_fma_dev(
                            &dev.ptr_row,
                            &selt,
                            &wt,
                            &act,
                            &mut dst,
                            n_ff_exp,
                            n_embd,
                            n_used,
                            n_expert,
                            m.down_exps.qtype,
                            m.down_exps.row_bytes,
                        )?;
                    }
                }
            }
        } else {
            // Launch under the cache lock: the row borrow lives as long as the closure, and the
            // lock covers only launch ISSUE (µs), same policy as moe_cached_gemm.
            // Q8 ARM PARITY (2026-07-06): the SLRU arm ran the f32-dequant _dev kernels only —
            // 80us/launch vs the q8 twins' 15us on the SAME shapes (fixed-build profile: 228
            // f32 launches = 36ms of the 64-tok window). Same q8 gate + kernels as the resident
            // arm above; MEMRA_MOE_Q8=0 restores the byte-identical f32 path.
            let q8 = moe_q8_enabled_for_model(cfg, m);
            e.with_moe_cache(max_block, |c, eng| {
                let row = c
                    .layer_dev_row(il, n_expert, eng)?
                    .ok_or("moe_ffn_dev: layer row vanished under the lock")?;
                for tok in 0..t {
                    let zt = z.slice(tok * n_embd..(tok + 1) * n_embd);
                    let selt = sel_d.slice(tok * n_used..(tok + 1) * n_used);
                    let wt = w_d.slice(tok * n_used..(tok + 1) * n_used);
                    let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                    if q8 {
                        let (zq, zd) = match (t, zq8) {
                            (1, Some((q, d))) => (q.clone(), d.clone()),
                            _ => eng.quantize_q8_1_view(&zt, 1, n_embd)?,
                        };
                        let act = eng.moe_gate_up_silu8_dev_q8(
                            row,
                            &selt,
                            &zq,
                            &zd,
                            n_embd,
                            n_ff_exp,
                            n_used,
                            n_expert,
                            m.gate_exps.qtype,
                            m.up_exps.qtype,
                            m.gate_exps.row_bytes,
                            m.up_exps.row_bytes,
                            &m.dev_macros,
                        )?;
                        let (aq2, ad2) = eng.quantize_q8_1(&act, n_used, n_ff_exp)?;
                        eng.moe_down8_fma_dev_q8(
                            row,
                            &selt,
                            &wt,
                            &aq2,
                            &ad2,
                            &mut dst,
                            n_ff_exp,
                            n_embd,
                            n_used,
                            n_expert,
                            m.down_exps.qtype,
                            m.down_exps.row_bytes,
                        )?;
                    } else {
                        let act = eng.moe_gate_up_silu8_dev(
                            row,
                            &selt,
                            &zt,
                            n_embd,
                            n_ff_exp,
                            n_used,
                            n_expert,
                            m.gate_exps.qtype,
                            m.up_exps.qtype,
                            m.gate_exps.row_bytes,
                            m.up_exps.row_bytes,
                            &m.dev_macros,
                        )?;
                        eng.moe_down8_fma_dev(
                            row,
                            &selt,
                            &wt,
                            &act,
                            &mut dst,
                            n_ff_exp,
                            n_embd,
                            n_used,
                            n_expert,
                            m.down_exps.qtype,
                            m.down_exps.row_bytes,
                        )?;
                    }
                }
                // instrumentation parity with the host paths (3 blocks/expert-slot, all hits).
                c.hits += (t * 3 * n_used) as u64;
                Ok(())
            })?;
        }

        // SHARED EXPERT epilogue — byte-identical to moe_ffn_sequential step 3 (incl. its Q8
        // TRUNK-FUSION arm: fused2 is bit-identical to the two matmul calls per (tensor,row)).
        // gate_inp_shexp is OPTIONAL: qwen35moe gates the shared expert (sigmoid(gate_inp) x sh);
        // MiniMax-M3 (DeepSeek-V3 class) has NO shexp gate — the shared expert adds directly.
        if let (Some(gate_shexp), Some(up_shexp), Some(down_shexp)) =
            (&m.gate_shexp, &m.up_shexp, &m.down_shexp)
        {
            let n_ff_sh = gate_shexp.out_features();
            // verify-t (2..15) decode-exact arm: this fn now serves the spec verify batches
            // (pairs gate moved to t>=PRIME_MIN_T), so the shexp chain must match t==1 per col.
            let verify_t = t > 1 && t < PRIME_MIN_T;
            let (sg_gate, sg_up) = if t == 1 {
                shexp_gate_up_t1(e, gate_shexp, up_shexp, z, zq8)?
            } else if verify_t {
                // VERIFY-TIER TRUNK FUSION (MEMRA_SPEC_FUSED_T, t=2-4): the shexp gate+up pair
                // rides one shared quantize + one fused2 batched launch instead of two
                // decode-exact calls. Bit-identical per (tensor,token,row) — see spec_fused_t.
                let mut fused = None;
                if crate::spec::spec_fused_t()
                    && (2..=4).contains(&t)
                    && e.uses_q8_1_fast(gate_shexp)
                    && e.uses_q8_1_fast(up_shexp)
                {
                    let (zq, zd) = e.quantize_q8_1(z, t, n_embd)?;
                    fused = e.matmul_q8_fused2_t(gate_shexp, up_shexp, &zq, &zd, t)?;
                }
                match fused {
                    Some(pair) => pair,
                    None => (
                        e.matmul_decode_exact(gate_shexp, z, t)?,
                        e.matmul_decode_exact(up_shexp, z, t)?,
                    ),
                }
            } else {
                (e.matmul(gate_shexp, z, t)?, e.matmul(up_shexp, z, t)?)
            };
            let mut sa = e.uninit(t * n_ff_sh)?; // silu_mul fully overwrites
            e.silu_mul(&sg_gate, &sg_up, &mut sa, t * n_ff_sh)?;
            let sh = if verify_t {
                e.matmul_decode_exact(down_shexp, &sa, t)?
            } else {
                e.matmul(down_shexp, &sa, t)?
            };
            // shexp gate: qwen35moe sigmoid-gates; M3 has no gate tensor -> weight 1.0.
            // Same fused sigmoid-dot as moe_ffn_sequential step 3 (byte-identity contract
            // between the two arms; prefill keeps the batched cuBLASLt linear).
            let g = match &m.gate_inp_shexp {
                Some(gate_inp_shexp) => {
                    // router_prefill_exact_on(): the fused sigmoid-dot is m-INVARIANT and
                    // serves EVERY t (serve isolation, lane/concat-prime-exact).
                    if t < PRIME_MIN_T || crate::router_prefill_exact_on() {
                        e.sigmoid_dot_rows(z, gate_inp_shexp.float_data(), n_embd, t)?
                    } else {
                        let gs = e.linear(z, gate_inp_shexp.float_data(), t, n_embd, 1)?;
                        let mut g = e.uninit(t)?;
                        e.sigmoid(&gs, &mut g, t)?;
                        g
                    }
                }
                None => e.htod(&vec![1.0f32; t])?,
            };
            e.add_scaled_rows(&sh, &g, &mut moe_out, n_embd, t)?;
        }

        Ok(moe_out)
    }

    /// STAGE-2 GROUPED DECODE (2026-07-04): run ONE token's whole routed-expert FFN in TWO
    /// launches when every one of its 3*n_used blocks is ALREADY cache-resident. Returns
    /// Ok(true) if the grouped path ran (caller skips the sequential loop for this token);
    /// Ok(false) on ANY miss (caller falls through — the sequential loop stages/admits as
    /// before, so the NEXT occurrence takes the grouped path). Pointer safety: cache slots are
    /// fixed-address for the engine's lifetime and the pure-HIT path performs no admission, so
    /// the collected raw pointers cannot move between collection and launch (single-threaded
    /// decode; the lock is held only for collection, launches are stream-ordered after any
    /// prior same-stream staging writes).
    #[allow(clippy::too_many_arguments)]
    /// q8 twin of moe_gdec_token (dp4a arc): same residency check + 2-launch shape; the fused
    /// kernels consume the pre-quantized z-row and re-quantize act per slot batch.
    #[allow(clippy::too_many_arguments)]
    fn moe_gdec_token_q8(
        e: &Engine,
        m: &MoeWeights,
        il: u16,
        max_block: usize,
        zq: &CudaSlice<i8>,
        zd: &CudaSlice<f32>,
        sel: &[u32],
        w: &[f32],
        moe_out: &mut CudaSlice<f32>,
        tok: usize,
        n_embd: usize,
        n_ff_exp: usize,
        n_used: usize,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, PROJ_DOWN, PROJ_GATE, PROJ_UP};
        use cudarc::driver::DevicePtr;
        let ptrs = e.with_moe_cache(max_block, |c, eng| {
            let mut g = [0u64; 8];
            let mut u = [0u64; 8];
            let mut d = [0u64; 8];
            for (j, &ex) in sel.iter().enumerate() {
                let ex = ex as u16;
                let (Some(sg), Some(su), Some(sd)) = (
                    c.resident(BlockId::new(il, PROJ_GATE, ex)),
                    c.resident(BlockId::new(il, PROJ_UP, ex)),
                    c.resident(BlockId::new(il, PROJ_DOWN, ex)),
                ) else {
                    return Ok(None);
                };
                let __s = eng.stream();
                let (pg, _e0) = c.slot(sg).device_ptr(&__s);
                let (pu, _e1) = c.slot(su).device_ptr(&__s);
                let (pd, _e2) = c.slot(sd).device_ptr(&__s);
                g[j] = pg;
                u[j] = pu;
                d[j] = pd;
            }
            if cpu_expert_profile_admit_enabled() && !c.is_frozen() {
                for &ex in sel {
                    let ex = ex as u16;
                    for proj in [PROJ_GATE, PROJ_UP, PROJ_DOWN] {
                        c.note_profile_hit(BlockId::new(il, proj, ex));
                    }
                }
            }
            c.hits += (3 * n_used) as u64;
            Ok(Some((g, u, d)))
        })?;
        let Some((g, u, d)) = ptrs else {
            return Ok(false);
        };
        let mut wv = [0f32; 8];
        wv[..n_used].copy_from_slice(w);
        let act = e.moe_gate_up_silu8_q8(
            crate::WPtr8(g),
            crate::WPtr8(u),
            zq,
            zd,
            n_embd,
            n_ff_exp,
            n_used,
            m.gate_exps.qtype,
            m.up_exps.qtype,
            m.gate_exps.row_bytes,
            m.up_exps.row_bytes,
        )?;
        // per-slot act quantize: [n_used, n_ff] rows in one quantize launch.
        let (aq2, ad2) = e.quantize_q8_1(&act, n_used, n_ff_exp)?;
        let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
        e.moe_down8_fma_q8(
            crate::WPtr8(d),
            crate::F32x8(wv),
            &aq2,
            &ad2,
            &mut dst,
            n_ff_exp,
            n_embd,
            n_used,
            m.down_exps.qtype,
            m.down_exps.row_bytes,
        )?;
        Ok(true)
    }

    /// glm5_next's fused MoE epilogue for ONE token-layer: the sigmoid router's already-selected
    /// `(sel, w)`, the PRE-clamped SwiGLU and the per-expert NVFP4 macro fold, in one launch
    /// pair. Returns `false` when the cache cannot hold this token's `3*n_used` blocks at once,
    /// in which case the caller must run the sequential loop (which zeroes its own row).
    ///
    /// WHY IT DOES NOT NEED A RESIDENT LAYER, unlike `moe_gdec_token_q8`. gdec collects pointers
    /// from blocks that are ALREADY resident and bails on the first miss, because a miss would
    /// mean an admission that could move a slot under the pointers it has already taken. This arm
    /// inverts the order: it ADMITS all `3*n_used` blocks first, through the same
    /// `dispatch_source` the sequential loop calls per projection (a hit copies nothing, a miss
    /// runs the identical `memcpy_htod` into a slot), and only then takes the addresses, in a
    /// second pass, with the cache lock still held. Nothing can move between the last admission
    /// and the pointer read, and the kernels are issued on the compute stream immediately after —
    /// the same in-order guarantee the sequential loop already relies on when it dispatches
    /// expert j+1 after launching expert j's kernels.
    ///
    /// The slot-capacity check is the fail-closed seam: `n_slots()` is a whole-cache bound, and
    /// with fewer than `3*n_used` slots an admission is guaranteed to evict one of this token's
    /// own blocks. The second pass re-reads `resident()` for every block rather than trusting the
    /// dispatch's return, so an eviction the capacity check did not predict falls through loudly
    /// to the sequential loop instead of dereferencing a reused slot.
    ///
    /// BIT-IDENTITY CLASS. Against the sequential loop this arm is a DISPATCH-class change, not a
    /// provenance one: the same block bytes and the same macro scales, but the gate/up dots are
    /// the fused kernel's warp reduction rather than `qmatvec_expert_q8`'s per-projection one, and
    /// the down accumulation is `moe_down8_fma_q8`'s slot-ordered `__fmaf_rn` chain rather than 8
    /// separate `axpy_into` calls. Those chains are the ones the gdec family documents as
    /// reproducing the sequential chain exactly; `tests/glm5_moe_epilogue_gpu.rs::the_two_arms_agree`
    /// measures the actual bit disagreement rather than asserting the claim.
    #[allow(clippy::too_many_arguments)]
    fn moe_fused_epi_token_q8(
        e: &Engine,
        m: &MoeWeights,
        il: u16,
        max_block: usize,
        zq: &CudaSlice<i8>,
        zd: &CudaSlice<f32>,
        sel: &[u32],
        w: &[f32],
        moe_out: &mut CudaSlice<f32>,
        tok: usize,
        n_embd: usize,
        n_ff_exp: usize,
        n_used: usize,
        limit: f32,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, DispatchSlot, PROJ_DOWN, PROJ_GATE, PROJ_UP};
        use cudarc::driver::DevicePtr;
        debug_assert!(
            limit > 1e-6,
            "the fused epilogue's kernel collapses every gate to silu(0) at limit 0"
        );
        debug_assert_eq!(sel.len(), n_used);
        debug_assert_eq!(w.len(), n_used);

        let ptrs = e.with_moe_cache(max_block, |c, eng| {
            // Fail closed: below this bound an admission MUST evict one of this token's own
            // blocks, so there is no pointer set that stays valid.
            if c.n_slots() < 3 * n_used {
                return Ok(None);
            }
            // PASS 1 — admit. Identical dispatch to the sequential loop's `moe_cached_gemm_q8`,
            // projection for projection; only the GEMM is deferred.
            for &ex in sel.iter() {
                let ex_usize = ex as usize;
                for (proj, exps) in [
                    (PROJ_GATE, &m.gate_exps),
                    (PROJ_UP, &m.up_exps),
                    (PROJ_DOWN, &m.down_exps),
                ] {
                    let id = BlockId::new(il, proj, ex as u16);
                    let DispatchSlot::Resident(_) =
                        c.dispatch_source(id, exps.expert_source(ex_usize), eng)?;
                }
            }
            // PASS 2 — take the fixed slot addresses, with nothing left to admit.
            let mut g = [0u64; 8];
            let mut u = [0u64; 8];
            let mut d = [0u64; 8];
            for (j, &ex) in sel.iter().enumerate() {
                let ex = ex as u16;
                let (Some(sg), Some(su), Some(sd)) = (
                    c.resident(BlockId::new(il, PROJ_GATE, ex)),
                    c.resident(BlockId::new(il, PROJ_UP, ex)),
                    c.resident(BlockId::new(il, PROJ_DOWN, ex)),
                ) else {
                    return Ok(None);
                };
                let __s = eng.stream();
                let (pg, _e0) = c.slot(sg).device_ptr(&__s);
                let (pu, _e1) = c.slot(su).device_ptr(&__s);
                let (pd, _e2) = c.slot(sd).device_ptr(&__s);
                g[j] = pg;
                u[j] = pu;
                d[j] = pd;
            }
            Ok(Some((g, u, d)))
        })?;
        let Some((g, u, d)) = ptrs else {
            return Ok(false);
        };

        Self::moe_fused_epi_launch(
            e, m, zq, zd, sel, w, g, u, d, moe_out, tok, n_embd, n_ff_exp, n_used, limit,
        )?;
        Ok(true)
    }

    /// The fused epilogue's ONLY launch path, shared by both provenances (SLRU slot addresses and
    /// device-resident slab base+stride). Everything that could differ semantically between them
    /// — the per-expert macro fold, the clamp, the kernel pair, the dispatch counter — lives here
    /// exactly once, so the two arms cannot drift into being different programs. The callers
    /// differ only in how they filled `g`/`u`/`d`.
    ///
    /// Per-expert macro scales in router slot order: gate/up ride the kernel's epilogue exactly
    /// where `ffn_act_lim`'s gs/us ride the unfused loop, and down folds into the routing weight
    /// exactly where `axpy_into`'s `w[j] * macro_scale(ex)` folds it. `macro_scale` answers 1.0
    /// for a macro-free bank, so a k-quant GGUF takes this path with no fold and no branch.
    #[allow(clippy::too_many_arguments)]
    fn moe_fused_epi_launch(
        e: &Engine,
        m: &MoeWeights,
        zq: &CudaSlice<i8>,
        zd: &CudaSlice<f32>,
        sel: &[u32],
        w: &[f32],
        g: [u64; 8],
        u: [u64; 8],
        d: [u64; 8],
        moe_out: &mut CudaSlice<f32>,
        tok: usize,
        n_embd: usize,
        n_ff_exp: usize,
        n_used: usize,
        limit: f32,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let mut gs = [0f32; 8];
        let mut us = [0f32; 8];
        let mut wv = [0f32; 8];
        for (j, &ex) in sel.iter().enumerate() {
            let ex = ex as usize;
            gs[j] = m.gate_exps.macro_scale(ex);
            us[j] = m.up_exps.macro_scale(ex);
            wv[j] = w[j] * m.down_exps.macro_scale(ex);
        }
        let act = e.moe_gate_up_preclamp8_q8(
            crate::WPtr8(g),
            crate::WPtr8(u),
            zq,
            zd,
            crate::F32x8(gs),
            crate::F32x8(us),
            limit,
            n_embd,
            n_ff_exp,
            n_used,
            m.gate_exps.qtype,
            m.up_exps.qtype,
            m.gate_exps.row_bytes,
            m.up_exps.row_bytes,
        )?;
        // per-slot act quantize: [n_used, n_ff] rows in one quantize launch.
        let (aq2, ad2) = e.quantize_q8_1(&act, n_used, n_ff_exp)?;
        let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
        e.moe_down8_fma_q8(
            crate::WPtr8(d),
            crate::F32x8(wv),
            &aq2,
            &ad2,
            &mut dst,
            n_ff_exp,
            n_embd,
            n_used,
            m.down_exps.qtype,
            m.down_exps.row_bytes,
        )?;
        crate::MOE_FUSED_EPI_DISPATCHES.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
        Ok(())
    }

    /// The verify-rows batched routed-expert program (lane/glm5-vrest): one layer-call's
    /// WHOLE t x n_used pair union through the fused-epilogue kernels' rows twins —
    /// one gate/up+preclamp launch, one pair-major activation quantize, one down+FMA
    /// launch — with pointers computed from the resident slab base + ex*stride (the
    /// sequential slab arm's exact arithmetic) and the macro folds landing exactly where
    /// `ffn_act_lim` / `axpy_into` land them. `moe_out` rows are FULLY overwritten.
    #[allow(clippy::too_many_arguments)]
    // allow: the parameter list mirrors its dispatch-arm caller's contract
    fn moe_vrows_pairs_q8(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        sel: VrowsSel<'_>,
        il: u16,
        (pg, pu, pd): (u64, u64, u64),
        t: usize,
        n_embd: usize,
        n_ff_exp: usize,
        n_used: usize,
        limit: f32,
        moe_out: &mut CudaSlice<f32>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let n_pairs = t * n_used;
        // Door E (MEMRA_MOE_VROWS_DEDUP_ORDER, default OFF): the gate/up launch walks the pair
        // union EXPERT-MAJOR, reading the visit order from a FOURTH plane appended to the pointer
        // table (planes: gate | up | down | order). Carrying it in the existing table is what
        // makes the door free on the host arm — the order plane rides the single `htod_u64_into`
        // that was already uploading the pointers, so no new transfer and no new pool appear.
        // Door M (`MEMRA_MOE_VROWS_PACK`) refuses it in the launcher, so do not build the plane
        // when the refuted pack door is armed.
        let order_on = crate::moe_vrows_dedup_order_on() && !crate::moe_vrows_pack_on();
        let n_planes = if order_on { 4 } else { 3 };
        // Door W (MEMRA_GLM5_VERIFY_WS): the whole staging set — tables, token quantize,
        // act, pair quantize — draws from the verify workspace and recycles at the end of
        // the call (vws_* are alloc_uninit/plain-drop with the door off, so the OFF arm is
        // byte-for-byte the shipped program). Every buffer is fully overwritten before any
        // read by the SAME kernels (the sites' standing uninit contract).
        let mut ptrs_d = e.vws_uninit_u64(n_planes * n_pairs)?;
        let mut scl_d = e.vws_uninit(3 * n_pairs)?;
        // ONE launch path, TWO table provenances (the fused-epilogue arm's own discipline):
        // only the plane-major (gate | up | down) pointer/scale tables are built differently,
        // and door D's kernel evaluates the SAME terms as the host loop, so nothing downstream
        // can tell the arms apart. See the `moe_vrows_tables_from_sel` kernel comment for the
        // term-by-term bit-identity argument.
        match sel {
            VrowsSel::Host(sel_all, w_all) => {
                debug_assert_eq!(sel_all.len(), n_pairs);
                debug_assert_eq!(w_all.len(), n_pairs);
                let mut ptrs = vec![0u64; n_planes * n_pairs];
                let mut scl = vec![0f32; 3 * n_pairs];
                for (p, (&ex, &w)) in sel_all.iter().zip(w_all).enumerate() {
                    let ex = ex as usize;
                    ptrs[p] = pg + (ex * m.gate_exps.expert_stride) as u64;
                    ptrs[n_pairs + p] = pu + (ex * m.up_exps.expert_stride) as u64;
                    ptrs[2 * n_pairs + p] = pd + (ex * m.down_exps.expert_stride) as u64;
                    scl[p] = m.gate_exps.macro_scale(ex);
                    scl[n_pairs + p] = m.up_exps.macro_scale(ex);
                    // down-proj macro folds into the accumulate weight (1.0 for non-macro
                    // banks) — the axpy_into fold, verbatim.
                    scl[2 * n_pairs + p] = w * m.down_exps.macro_scale(ex);
                }
                if order_on {
                    // The order plane rides the SAME upload — the door adds no HtoD on this arm.
                    ptrs[3 * n_pairs..].copy_from_slice(&crate::vrows_expert_major_order(sel_all));
                    // The box receipt: the slab reads whose repeat visit this schedule places
                    // inside the reuse window (host arm only — see MOE_VROWS_SLAB_READS_AVOIDED).
                    let (visits, distinct) = crate::vrows_overlap_counts(sel_all);
                    crate::MOE_VROWS_SLAB_READS_AVOIDED
                        .fetch_add(visits - distinct, std::sync::atomic::Ordering::Relaxed);
                }
                e.htod_u64_into(&ptrs, &mut ptrs_d)?;
                e.htod_f32_into(&scl, &mut scl_d)?;
                // MEMRA_MOE_VROWS_DEDUP_STAT: size the ONLY remaining byte lever on this pair
                // (LANE.md §1 — it already runs at ~90% of theoretical DRAM peak, so the sole
                // way to cut it further is reading a shared expert slab once for the rows that
                // share it). `1 - distinct/visits` IS that lever; measuring it costs a bitset.
                if crate::moe_vrows_dedup_stat_on() {
                    let (visits, distinct) = crate::vrows_overlap_counts(sel_all);
                    debug_assert_eq!(visits, n_pairs as u64);
                    crate::MOE_VROWS_PAIR_VISITS
                        .fetch_add(visits, std::sync::atomic::Ordering::Relaxed);
                    crate::MOE_VROWS_PAIR_DISTINCT
                        .fetch_add(distinct, std::sync::atomic::Ordering::Relaxed);
                    crate::moe_vrows_dedup_report();
                }
            }
            VrowsSel::Dev(sel_d, selw_d) => {
                let macros = match (
                    m.gate_exps.macros.as_deref(),
                    m.up_exps.macros.as_deref(),
                    m.down_exps.macros.as_deref(),
                ) {
                    (Some(g), Some(u), Some(d)) => Some((g, u, d)),
                    // A partially-macro bank would need per-plane 1.0 defaults the kernel does
                    // not carry; the serving artifact's three planes are all present or all
                    // absent, so refuse rather than guess.
                    (None, None, None) => None,
                    _ => {
                        return Err("vrows device tables: expert macro planes are not uniform \
                                    across gate/up/down"
                            .into());
                    }
                };
                e.moe_vrows_tables_from_sel(
                    sel_d,
                    selw_d,
                    il,
                    macros,
                    (pg, pu, pd),
                    (
                        m.gate_exps.expert_stride,
                        m.up_exps.expert_stride,
                        m.down_exps.expert_stride,
                    ),
                    n_pairs,
                    &mut ptrs_d,
                    &mut scl_d,
                )?;
                if order_on {
                    // Door E on the device arm: one extra launch (the host arm gets the plane for
                    // free inside its existing upload). Bit-identical to the host's stable sort by
                    // (expert id, pair index) — gated directly against it in glm5_dedup_sched_gpu.
                    e.moe_vrows_order_from_sel(sel_d, n_pairs, &mut ptrs_d)?;
                }
                if crate::MOE_VROWS_DEV_TABLES_DISPATCHES
                    .fetch_add(1, std::sync::atomic::Ordering::Relaxed)
                    == 0
                {
                    eprintln!(
                        "[moe-vrows-dev-tables] engaged: pointer/scale tables built on device \
                         from the router's own sel/w; the per-layer pinned readback and its \
                         cuStreamSynchronize are skipped (MEMRA_MOE_VROWS_DEV_TABLES=1)"
                    );
                }
            }
        }
        // Token rows quantized in one launch; per-row q8_1 bytes are position-independent
        // (the batched-MMVQ class), bit-gated against the per-token quantize_q8_1_view.
        let (mut zq, mut zd) = (
            e.vws_uninit_i8(t * n_embd)?,
            e.vws_uninit(t * (n_embd / 32))?,
        );
        e.quantize_q8_1_into(z, t, n_embd, &mut zq, &mut zd)?;
        let act = e.moe_gate_up_preclamp8_q8_rows(
            &ptrs_d,
            &scl_d,
            &zq,
            &zd,
            limit,
            n_embd,
            n_ff_exp,
            n_used,
            n_pairs,
            m.gate_exps.qtype,
            m.up_exps.qtype,
            m.gate_exps.row_bytes,
            m.up_exps.row_bytes,
        )?;
        // Pair-major activation quantize: [n_pairs, n_ff] rows in one launch.
        let (mut aq2, mut ad2) = (
            e.vws_uninit_i8(n_pairs * n_ff_exp)?,
            e.vws_uninit(n_pairs * (n_ff_exp / 32))?,
        );
        e.quantize_q8_1_into(&act, n_pairs, n_ff_exp, &mut aq2, &mut ad2)?;
        e.moe_down8_fma_q8_rows(
            &ptrs_d,
            &scl_d,
            &aq2,
            &ad2,
            moe_out,
            n_ff_exp,
            n_embd,
            n_used,
            n_pairs,
            m.down_exps.qtype,
            m.down_exps.row_bytes,
        )?;
        // Everything above is dead after the down launch (stream-ordered reuse is safe on
        // this engine's stream, the same guarantee the async free relies on).
        e.vws_recycle_u64(ptrs_d);
        e.vws_recycle(scl_d);
        e.vws_recycle_i8(zq);
        e.vws_recycle(zd);
        e.vws_recycle(act);
        e.vws_recycle_i8(aq2);
        e.vws_recycle(ad2);
        if crate::MOE_VROWS_DISPATCHES.fetch_add(1, std::sync::atomic::Ordering::Relaxed) == 0 {
            eprintln!(
                "[glm5-vrows] verify MoE batched across rows: pairs={n_pairs} (t={t} x \
                 {n_used}), one gate/up+preclamp launch + one down/FMA launch per layer-call \
                 (rides MEMRA_GLM5_VERIFY_BATCH)"
            );
        }
        Ok(())
    }

    /// GROUPED MoE PREFILL for the sigmoid-router glm5_next class (`MEMRA_MOE_GROUPED_PREFILL`,
    /// default ON since 2026-08-29, `=0` rollback). One call covers a whole prefill chunk's
    /// routed-expert FFN for one layer:
    ///
    ///   1. ROUTER: the SAME m-invariant `moe_router_logits` + `moe_route_sigmoid_cfg` host
    ///      oracle invocation the sequential arm makes, so selected experts and routing weights
    ///      are BIT-identical to the sequential arm by construction. Only the GEMM accumulation
    ///      order may move (the grouped GEMM is measured non-bit-stable,
    ///      `run_tensor_parallel_routes_nvfp4_prime_grouped`'s MEMRA_MOE_DETERM note), which is
    ///      why the acceptance gate is reference-band + routing-exactness, not byte identity.
    ///   2. TOKEN-SORT BY EXPERT: host counting sort of the (token, expert) pairs into an
    ///      expert-major CSR (vLLM's `moe_align_block_size` shape; same O(pairs) build the
    ///      softmax `moe_ffn_pairs` arm and the step37 grouped prime use).
    ///   3. ONE GROUPED TENSOR-CORE GEMM PER PROJECTION over the resident NVFP4 slab
    ///      (`moe_f16_grouped`, the sk single-kernel visitor with the NVFP4 direct tile
    ///      loaders; the step37 grouped-prime kernel class, 170-270 TFLOP/s on its lane's
    ///      sizing rows, generalized off the TP runtime to the single-device `dev_exps`
    ///      pointer-table provenance). Each expert's weights stream through tensor cores ONCE
    ///      per layer per chunk instead of once per (token, expert): at t=4096 that replaces
    ///      the sequential loop's 49 launches x 4096 tokens (~200k launches and ~113 MB x 4096
    ///      of expert VRAM re-reads per layer) with a ~15-launch chunk-wide program.
    ///   4. EPILOGUE: glm5_next's PRE-clamped SwiGLU `silu(min(g,l)) * clamp(u,±l)` with the
    ///      per-expert `weight_scale_2` macro fold: gate/up macros land BEFORE the nonlinearity
    ///      (`scale_rows` per CSR row; silu is nonlinear, so the fold cannot commute past it),
    ///      down macros fold into the scatter weight, exactly where the fused epilogue and the
    ///      sequential loop's `ffn_act_lim`/`axpy_into` put them.
    ///   5. SCATTER: permute CSR rows back to pair order, then the slot-ordered weighted
    ///      per-token accumulation (`moe_pairs_scatter`, the sequential-axpy accumulation
    ///      class). Shared expert rides the canonical clamp-aware grouped add.
    ///
    /// Returns `Ok(None)` (fail closed to the sequential arm) for every unqualified shape:
    /// no local resident slab, f16g door off, a projection the grouped kernel cannot walk, or
    /// an expert count past the sk visitor's group cap. Numeric class: f16-mirror activations
    /// (`moe_f16g_act` row-normalized f16), the class the softmax pairs f16g arm and the
    /// step37 grouped prime already serve prefill with; gated by
    /// `tests/glm5_moe_grouped_prefill_gpu.rs` against `memra_reference` at the fused-epilogue
    /// gate's tolerance class, plus the run-gen first-token argmax gate on real prompts.
    fn moe_ffn_grouped_prefill_sigmoid(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
    ) -> Result<Option<CudaSlice<f32>>, Box<dyn std::error::Error>> {
        // Placement: the LOCAL resident slab only. The SLRU cannot serve a 4096-token chunk's
        // expert working set (a glm5 layer is 288 x 3 blocks against ~285 slots/layer on the
        // serving recipe), and a remote slab must never be dereferenced (m=1 peer reads are the
        // measured 34-150x class). Fail closed: the sequential arm stages as before.
        let Some(dev) = m
            .dev_exps
            .as_ref()
            .filter(|d| moe_slab_enabled() && d.dev == e.ctx().ordinal())
        else {
            return Ok(None);
        };
        if crate::moe_f16g_mode() == 0 {
            return Ok(None);
        }
        // MEMRA_MOE_GATE is the BYTE-identity oracle between sequential-class dispatches; this
        // arm is a different numeric class with its own reference-band gate, so it must not
        // shadow that comparison.
        if std::env::var("MEMRA_MOE_GATE").is_ok() {
            return Ok(None);
        }
        let moe = cfg
            .moe
            .as_ref()
            .ok_or("grouped sigmoid prefill requires MoE model metadata")?;
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        if !(f16g_proj_ok(m.gate_exps.qtype, n_embd)
            && f16g_proj_ok(m.up_exps.qtype, n_embd)
            && f16g_proj_ok(m.down_exps.qtype, n_ff_exp))
        {
            return Ok(None);
        }
        // The sk visitor's direct-lane group cap (mirrors the grouped prime's guard). glm5's
        // 288 experts fit; a wider bank falls closed rather than erring mid-forward.
        if n_expert > 512 || n_used == 0 || n_used > 8 {
            return Ok(None);
        }
        let sigmoid = cfg
            .sigmoid_router()
            .ok_or("grouped sigmoid prefill requires the sigmoid router")?;
        // glm5_next carries the PRE form on every clamped layer (`clamp_exp_at`); a POST-form
        // arch reaching this arm is an unqualified semantic program, and the clamp-form law
        // says no dispatch site may pick a form by default. Err, not assert.
        let lim_exp = cfg.clamp_exp_at(il as u32);
        if matches!(lim_exp, Some(SwigluClamp::Post(_))) {
            return Err(
                "grouped sigmoid prefill is qualified for the PRE-clamped SwiGLU form only; \
                 a POST-clamp layer must ride the sequential arm"
                    .into(),
            );
        }

        // 1. ROUTER. One selector shared with the sequential arm so changing dispatch cannot
        // change logits, selected expert ids, or routing weights (the routing-exactness half of
        // the acceptance gate holds by construction). The host readback here is the same one
        // the sequential arm performs; killing it is the L4 host-sync diet, not this arm.
        let logits = Self::moe_router_logits(e, m, z, t, cfg)?;
        Self::trace_sigmoid_router_logits(e, il, t, n_expert, n_used, &logits, m, sigmoid)?;
        let (sel_all, w_all) =
            Self::moe_route_sigmoid_cfg(e, &logits, t, n_expert, n_used, m, sigmoid)?;
        crate::moesd::record_host_routes(il, n_expert, n_used, &sel_all)?;
        Self::trace_moe_routes(il, t, &sel_all, &w_all)?;
        Self::trace_moe_input(e, il, t, n_embd, z)?;

        let mprof = std::env::var("MEMRA_PRIME_PROF").as_deref() == Ok("1");
        let mut mt = std::time::Instant::now();
        let mut phase = |on: bool| -> f64 {
            if on {
                let _ = e.stream().synchronize();
                let v = mt.elapsed().as_secs_f64() * 1e3;
                mt = std::time::Instant::now();
                v
            } else {
                0.0
            }
        };
        let d_router = phase(mprof);

        // 2. TOKEN-SORT BY EXPERT: expert-major CSR over the (token, expert) pairs.
        let n_pairs = t * n_used;
        if sel_all.len() < n_pairs || w_all.len() < n_pairs || z.len() < t * n_embd {
            return Err("grouped sigmoid prefill geometry".into());
        }
        let mut buckets: Vec<Vec<i32>> = vec![Vec::new(); n_expert];
        for (p, &s_id) in sel_all.iter().take(n_pairs).enumerate() {
            let s_id = s_id as usize;
            if s_id >= n_expert {
                return Err(format!("grouped prefill selection {s_id} >= {n_expert}").into());
            }
            buckets[s_id].push(p as i32);
        }
        let mut ex_ids: Vec<i32> = Vec::new();
        let mut ex_off: Vec<i32> = vec![0];
        let mut ex_pairs: Vec<i32> = Vec::with_capacity(n_pairs);
        for (e_id, b) in buckets.iter().enumerate() {
            if !b.is_empty() {
                ex_ids.push(e_id as i32);
                ex_pairs.extend_from_slice(b);
                ex_off.push(ex_pairs.len() as i32);
            }
        }
        let n_active = ex_ids.len();
        if n_active == 0 || n_active > 512 {
            return Err(format!("grouped prefill n_active {n_active} outside 1..=512").into());
        }
        let csr_tok: Vec<i32> = ex_pairs.iter().map(|&p| p / n_used as i32).collect();

        // 3. MACRO PLANES. Gate/up per-CSR-row scales (before silu); down folds into the
        // scatter weight; `macro_scale` answers 1.0 for a macro-free bank, so the fold is
        // skipped rather than launched as a no-op.
        let wd: Vec<f32> = (0..n_pairs)
            .map(|p| w_all[p] * m.down_exps.macro_scale(sel_all[p] as usize))
            .collect();

        // Interleaved gate/up slab strides (see moe_ffn_pairs / moe_ffn_dev).
        let (rbg_d, rbu_d) = if dev.gu_il {
            let sxx = m.gate_exps.row_bytes + m.up_exps.row_bytes;
            (sxx, sxx)
        } else {
            (m.gate_exps.row_bytes, m.up_exps.row_bytes)
        };

        let exi = e.htod_i32(&ex_ids)?;
        let exo = e.htod_i32(&ex_off)?;
        let exp_d = e.htod_i32(&ex_pairs)?;
        let csr_tok_d = e.htod_i32(&csr_tok)?;
        let pw = e.htod(&wd)?;

        // 4. GATE/UP grouped GEMMs over the bank, CSR order end-to-end.
        let (z16, zs) = e.moe_f16g_act(z, Some(&csr_tok_d), n_embd, n_pairs)?;
        let mut g = e.moe_f16_grouped(
            &dev.ptr_row,
            0,
            n_expert,
            &exi,
            &ex_off,
            &exo,
            &z16,
            &zs,
            n_embd,
            n_ff_exp,
            n_active,
            n_pairs,
            m.gate_exps.qtype,
            rbg_d,
        )?;
        if m.gate_exps.macros.is_some() {
            let mg: Vec<f32> = ex_pairs
                .iter()
                .map(|&p| m.gate_exps.macro_scale(sel_all[p as usize] as usize))
                .collect();
            let mg_d = e.htod(&mg)?;
            e.scale_rows(&mut g, &mg_d, n_ff_exp, n_pairs)?;
        }
        let mut u = e.moe_f16_grouped(
            &dev.ptr_row,
            1,
            n_expert,
            &exi,
            &ex_off,
            &exo,
            &z16,
            &zs,
            n_embd,
            n_ff_exp,
            n_active,
            n_pairs,
            m.up_exps.qtype,
            rbu_d,
        )?;
        if m.up_exps.macros.is_some() {
            let mu: Vec<f32> = ex_pairs
                .iter()
                .map(|&p| m.up_exps.macro_scale(sel_all[p as usize] as usize))
                .collect();
            let mu_d = e.htod(&mu)?;
            e.scale_rows(&mut u, &mu_d, n_ff_exp, n_pairs)?;
        }

        // 5. EPILOGUE: glm5_next's PRE-clamped SwiGLU (the POST form was refused above); a
        // config with no live limit takes the plain-silu pair kernel.
        let act = match lim_exp {
            Some(SwigluClamp::Pre(limit)) => {
                let mut a = e.uninit(n_pairs * n_ff_exp)?;
                // Scales are 1.0: the per-expert macros already landed via scale_rows (an
                // exact *1.0 inside the kernel keeps the value chain unchanged).
                e.swiglu_preclamped_mul_scaled(
                    &g,
                    &u,
                    1.0,
                    1.0,
                    limit,
                    &mut a,
                    n_pairs * n_ff_exp,
                )?;
                a
            }
            None => e.moe_pairs_silu_mul(&g, &u, n_pairs * n_ff_exp)?,
            Some(SwigluClamp::Post(_)) => unreachable!("refused before any launch"),
        };
        let d_gemm_gu = phase(mprof);

        // 6. DOWN grouped GEMM (CSR order), permute back to pair order, weighted scatter.
        let (a16, a_s) = e.moe_f16g_act(&act, None, n_ff_exp, n_pairs)?;
        let d_csr = e.moe_f16_grouped(
            &dev.ptr_row,
            2,
            n_expert,
            &exi,
            &ex_off,
            &exo,
            &a16,
            &a_s,
            n_ff_exp,
            n_embd,
            n_active,
            n_pairs,
            m.down_exps.qtype,
            m.down_exps.row_bytes,
        )?;
        let y_pair = e.rows_permute(&d_csr, &exp_d, n_pairs, n_embd)?;
        let toff: Vec<i32> = (0..=t).map(|tok| (tok * n_used) as i32).collect();
        let tids: Vec<i32> = (0..n_pairs as i32).collect();
        let toff_d = e.htod_i32(&toff)?;
        let tids_d = e.htod_i32(&tids)?;
        // The scatter fully overwrites every (token, col): slot-ordered accumulation over the
        // token's n_used pairs, the sequential-axpy class.
        let mut moe_out = e.uninit(t * n_embd)?;
        e.moe_pairs_scatter(&y_pair, &pw, &toff_d, &tids_d, &mut moe_out, t, n_embd)?;
        let d_down = phase(mprof);

        // 7. SHARED EXPERT: the canonical clamp-aware grouped add (reads clamp_shexp_at and
        // the optional shexp gate; glm5_next has a live shared expert on every MoE layer).
        Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut moe_out)?;
        if mprof {
            let d_shared = phase(true);
            eprintln!(
                "[moe-grouped-prefill-prof] il={il} t={t} router={d_router:.1}ms \
                 gemm_gu={d_gemm_gu:.1}ms down_scatter={d_down:.1}ms shared={d_shared:.1}ms"
            );
        }

        crate::MOE_GROUPED_PREFILL_DISPATCHES.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
        // Once per layer per process: the engagement receipt line (the A/B greps for it; the
        // both-arms flag announce lives at the dispatch site).
        static GPF_LOGGED: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        let layer_bit = 1u64 << (il as u64 % 64);
        if GPF_LOGGED.fetch_or(layer_bit, std::sync::atomic::Ordering::Relaxed) & layer_bit == 0 {
            eprintln!(
                "[moe-grouped-prefill] execute layer={il} tokens={t} n_active={n_active} \
                 provenance=resident-slab router=sigmoid-host-oracle epilogue=pre-clamped \
                 macro_fold=gate-up-rows+down-weight performance_claim=false \
                 (logged once per layer)"
            );
        }
        Ok(Some(moe_out))
    }

    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn moe_gdec_token(
        e: &Engine,
        m: &MoeWeights,
        il: u16,
        max_block: usize,
        zt: &cudarc::driver::CudaView<f32>,
        sel: &[u32],
        w: &[f32],
        moe_out: &mut CudaSlice<f32>,
        tok: usize,
        n_embd: usize,
        n_ff_exp: usize,
        n_used: usize,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, PROJ_DOWN, PROJ_GATE, PROJ_UP};
        use cudarc::driver::DevicePtr;
        // One lock hold: residency-check all 3*n_used blocks, collect raw slot pointers.
        let ptrs = e.with_moe_cache(max_block, |c, eng| {
            let mut g = [0u64; 8];
            let mut u = [0u64; 8];
            let mut d = [0u64; 8];
            for (j, &ex) in sel.iter().enumerate() {
                let ex = ex as u16;
                let (Some(sg), Some(su), Some(sd)) = (
                    c.resident(BlockId::new(il, PROJ_GATE, ex)),
                    c.resident(BlockId::new(il, PROJ_UP, ex)),
                    c.resident(BlockId::new(il, PROJ_DOWN, ex)),
                ) else {
                    return Ok(None);
                };
                let __s = eng.stream();
                let (pg, _e0) = c.slot(sg).device_ptr(&__s);
                let (pu, _e1) = c.slot(su).device_ptr(&__s);
                let (pd, _e2) = c.slot(sd).device_ptr(&__s);
                g[j] = pg;
                u[j] = pu;
                d[j] = pd;
            }
            if cpu_expert_profile_admit_enabled() && !c.is_frozen() {
                for &ex in sel {
                    let ex = ex as u16;
                    for proj in [PROJ_GATE, PROJ_UP, PROJ_DOWN] {
                        c.note_profile_hit(BlockId::new(il, proj, ex));
                    }
                }
            }
            c.hits += (3 * n_used) as u64; // instrumentation parity with dispatch()
            Ok(Some((g, u, d)))
        })?;
        let Some((g, u, d)) = ptrs else {
            return Ok(false);
        };
        let mut wv = [0f32; 8];
        wv[..n_used].copy_from_slice(w);
        // 2 launches: (gate+up+silu) x8, then (down + slot-ordered FMA accumulate) x8.
        let act = e.moe_gate_up_silu8(
            crate::WPtr8(g),
            crate::WPtr8(u),
            zt,
            n_embd,
            n_ff_exp,
            n_used,
            m.gate_exps.qtype,
            m.up_exps.qtype,
            m.gate_exps.row_bytes,
            m.up_exps.row_bytes,
        )?;
        let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
        e.moe_down8_fma_into(
            crate::WPtr8(d),
            crate::F32x8(wv),
            &act,
            &mut dst,
            n_ff_exp,
            n_embd,
            n_used,
            m.down_exps.qtype,
            m.down_exps.row_bytes,
        )?;
        Ok(true)
    }

    /// EDGE-1 §B.3: dispatch one expert projection through the SLRU cache, then run the SAME
    /// `qmatvec_view` from whichever slot it landed in (resident HIT or staged MISS). `x` is the
    /// sliced activation row. `proj` selects the gate/up/down HostExps tensor. Returns y = W_expert @ x.
    /// q8 twin of moe_cached_gemm: same dispatch/slot mechanics, dp4a expert kernel.
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn moe_cached_gemm_q8(
        e: &Engine,
        il: u16,
        proj: u8,
        ex: usize,
        m: &MoeWeights,
        max_block: usize,
        aq: &CudaSlice<i8>,
        ad: &CudaSlice<f32>,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, DispatchSlot, PROJ_GATE, PROJ_UP};
        let exps = match proj {
            PROJ_GATE => &m.gate_exps,
            PROJ_UP => &m.up_exps,
            _ => &m.down_exps,
        };
        let layout = exps.expert_layout(ex);
        let id = BlockId::new(il, proj, ex as u16);
        let source = exps.expert_source(ex);
        e.with_moe_cache(max_block, |c, eng| {
            let slot = c.dispatch_source(id, source, eng)?;
            let DispatchSlot::Resident(sl) = slot;
            let buf = c.slot(sl);
            eng.qmatvec_expert_q8(
                buf,
                0..layout.len,
                aq,
                ad,
                1,
                exps.in_f,
                exps.out_f,
                layout.qtype,
                layout.row_bytes,
            )
        })
    }

    fn moe_cached_gemm(
        e: &Engine,
        il: u16,
        proj: u8,
        ex: usize,
        m: &MoeWeights,
        max_block: usize,
        x: &cudarc::driver::CudaView<f32>,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, DispatchSlot, PROJ_GATE, PROJ_UP};
        let exps = match proj {
            PROJ_GATE => &m.gate_exps,
            PROJ_UP => &m.up_exps,
            _ => &m.down_exps,
        };
        let layout = exps.expert_layout(ex);
        let id = BlockId::new(il, proj, ex as u16);
        let source = exps.expert_source(ex);
        // dispatch under the lock (lookup/admit/memcpy-issue), then resolve the slot and GEMM.
        e.with_moe_cache(max_block, |c, eng| {
            let slot = c.dispatch_source(id, source, eng)?;
            // resolve the device buffer for this slot; the GEMM is enqueued on the compute stream
            // (the same stream the memcpy was issued on, so ordering holds without extra sync).
            let DispatchSlot::Resident(sl) = slot;
            let buf = c.slot(sl);
            m.qmatvec_view(
                eng,
                buf,
                0..layout.len,
                x,
                1,
                exps.in_f,
                exps.out_f,
                layout.qtype,
                layout.row_bytes,
            )
        })
    }

    /// Populate the warmup cache for an expert whose current-token output intentionally ran on
    /// CPU. No GEMM is launched and callers invoke this only after the CPU result has completed,
    /// so the current forward's backend assignment and output remain unchanged.
    fn moe_profile_admit_expert(
        e: &Engine,
        il: u16,
        ex: usize,
        m: &MoeWeights,
        max_block: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, PROJ_DOWN, PROJ_GATE, PROJ_UP};
        e.with_moe_cache(max_block, |cache, eng| {
            for (proj, exps) in [
                (PROJ_GATE, &m.gate_exps),
                (PROJ_UP, &m.up_exps),
                (PROJ_DOWN, &m.down_exps),
            ] {
                let id = BlockId::new(il, proj, ex as u16);
                let _ = cache.dispatch_source(id, exps.expert_source(ex), eng)?;
            }
            Ok(())
        })
    }

    /// Read a projection from the immutable residency set when present; otherwise use one
    /// transient slot without admitting or evicting anything. Used only by post-freeze prefill.
    #[allow(clippy::too_many_arguments)]
    fn moe_frozen_gemm(
        e: &Engine,
        il: u16,
        proj: u8,
        ex: usize,
        m: &MoeWeights,
        max_block: usize,
        x: &cudarc::driver::CudaView<f32>,
        scratch: &mut Option<CudaSlice<u8>>,
        scratch_len: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, PROJ_GATE, PROJ_UP};
        let exps = match proj {
            PROJ_GATE => &m.gate_exps,
            PROJ_UP => &m.up_exps,
            _ => &m.down_exps,
        };
        let layout = exps.expert_layout(ex);
        let id = BlockId::new(il, proj, ex as u16);
        if let Some(output) = e.with_moe_cache(max_block, |cache, eng| {
            let Some(slot) = cache.resident(id) else {
                return Ok(None);
            };
            let buf = cache.slot(slot);
            Ok(Some(m.qmatvec_view(
                eng,
                buf,
                0..layout.len,
                x,
                1,
                exps.in_f,
                exps.out_f,
                layout.qtype,
                layout.row_bytes,
            )?))
        })? {
            return Ok(output);
        }
        if scratch.is_none() {
            *scratch = Some(e.alloc_u8_uninit(scratch_len)?);
        }
        let scratch = scratch.as_mut().unwrap();
        e.stage_expert(exps.expert_bytes(ex), scratch, 0)?;
        m.qmatvec_view(
            e,
            scratch,
            0..layout.len,
            x,
            1,
            exps.in_f,
            exps.out_f,
            layout.qtype,
            layout.row_bytes,
        )
    }

    fn moe_prefetch_expert(
        e: &Engine,
        il: u16,
        ex: usize,
        m: &MoeWeights,
        max_block: usize,
        keep: &[crate::moe_cache::BlockId],
    ) -> Result<(), Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, PROJ_DOWN, PROJ_GATE, PROJ_UP};
        e.with_moe_cache(max_block, |c, eng| {
            for (proj, exps) in [
                (PROJ_GATE, &m.gate_exps),
                (PROJ_UP, &m.up_exps),
                (PROJ_DOWN, &m.down_exps),
            ] {
                let id = BlockId::new(il, proj, ex as u16);
                let _ = c.prefetch_source(id, exps.expert_source(ex), keep, eng)?;
            }
            Ok(())
        })
    }

    /// Worker-mode disk lookahead for grouped prefill. Memory sources are deliberately skipped so
    /// selecting `worker` changes only storage scheduling here; all CUDA work stays in dispatch.
    fn moe_prefetch_disk_expert(
        e: &Engine,
        il: u16,
        ex: usize,
        m: &MoeWeights,
        max_block: usize,
        keep: &[crate::moe_cache::BlockId],
    ) -> Result<(), Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, PROJ_DOWN, PROJ_GATE, PROJ_UP};
        e.with_moe_cache(max_block, |c, eng| {
            for (proj, exps) in [
                (PROJ_GATE, &m.gate_exps),
                (PROJ_UP, &m.up_exps),
                (PROJ_DOWN, &m.down_exps),
            ] {
                let source = exps.expert_source(ex);
                if let crate::model::ExpertSource::Disk { .. } = &source {
                    let id = BlockId::new(il, proj, ex as u16);
                    let _ = c.prefetch_source(id, source, keep, eng)?;
                }
            }
            Ok(())
        })
    }

    #[inline]
    fn moe_prefetch_host_expert(ex: usize, m: &MoeWeights) {
        let _ = m.gate_exps.prefetch_expert_pages(ex);
        let _ = m.up_exps.prefetch_expert_pages(ex);
        let _ = m.down_exps.prefetch_expert_pages(ex);
    }
}

// ================================================================================================
// A2: EXPERT-GROUPED MoE PREFILL (MEMRA_MOE_GROUPED=1). Resident-case prototype.
//
// Instead of the per-token loop (T * 8 experts * 3 projections = 12024 individual m=1 matvecs),
// this groups tokens by expert and runs ONE matmul per active expert per projection at m=m_e.
// On a 501-token prefill with ~170 active experts, that's ~510 matmuls (vs 12024).
//
// EXACTNESS: per-token accumulation across its 8 experts is reordered (grouped processes experts
// in expert-id order, not the router's top-k order). To preserve bit-identity with the sequential
// loop, we use an 8-SLOT scheme: expert outputs are scattered into slots keyed by the token's
// top-k position (0..7), then reduced in that fixed order. This makes the f32 addition order
// identical to the per-token loop regardless of expert processing order.
//
// Memory: T * 8 * n_embd * 4 = 501 * 8 * 2048 * 4 = ~32 MB (slot buffer). Fine on 96GB.
// ================================================================================================

impl HybridModel {
    /// Resident-slab fast path for host-routed grouped prefill. Unclamped layers batch the
    /// sequential fused q8 program over the token axis; clamped layers use the separate
    /// expert-major q8 chain so `ffn_act_lim` remains authoritative.
    #[allow(clippy::too_many_arguments)]
    fn moe_ffn_grouped_resident_q8(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        sel_all: &[u32],
        w_all: &[f32],
        table: &CudaSlice<u64>,
        gu_il: bool,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let moe = cfg.moe.as_ref().unwrap();
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        let n_pairs = t * n_used;
        debug_assert_eq!(sel_all.len(), n_pairs);
        debug_assert_eq!(w_all.len(), n_pairs);
        debug_assert!(
            m.gate_exps.macros.is_none()
                && m.up_exps.macros.is_none()
                && m.down_exps.macros.is_none(),
            "resident grouped q8 does not fold per-expert macro scales",
        );

        // The rows twins run the resident sequential program verbatim on grid.z = token:
        // fused gate/up/SiLU per slot, batched activation quantization, then the original
        // slot-ordered down/FMA chain. Routing remains the sigmoid selector above (device by
        // default; MEMRA_SIG_ROUTER=0 is the host oracle); these kernels consume sel/w only and
        // never enter the softmax router.
        if !cfg.swiglu_clamped_at(il as u32) {
            let sel: Vec<i32> = sel_all.iter().map(|&expert| expert as i32).collect();
            let sel_d = e.htod_i32(&sel)?;
            let w_d = e.htod(w_all)?;
            let (gate_row_bytes, up_row_bytes) = if gu_il {
                let combined = m.gate_exps.row_bytes + m.up_exps.row_bytes;
                (combined, combined)
            } else {
                (m.gate_exps.row_bytes, m.up_exps.row_bytes)
            };
            let (zq, zd) = e.quantize_q8_1(z, t, n_embd)?;
            let act = e.moe_gate_up_silu8_dev_q8_rows(
                table,
                &sel_d,
                &zq,
                &zd,
                t,
                n_embd,
                n_ff_exp,
                n_used,
                n_expert,
                m.gate_exps.qtype,
                m.up_exps.qtype,
                gate_row_bytes,
                up_row_bytes,
                &m.dev_macros,
            )?;
            let (aq2, ad2) = e.quantize_q8_1(&act, n_pairs, n_ff_exp)?;
            let mut moe_out = e.uninit(t * n_embd)?;
            e.moe_down8_fma_dev_q8_rows_g(
                table,
                &sel_d,
                &w_d,
                &aq2,
                &ad2,
                &mut moe_out,
                t,
                n_ff_exp,
                n_embd,
                n_used,
                n_expert,
                m.down_exps.qtype,
                m.down_exps.row_bytes,
            )?;

            if std::env::var("MEMRA_MOE_STATS").is_ok() {
                let mut counts = vec![0usize; n_expert];
                for &expert in sel_all {
                    counts[expert as usize] += 1;
                }
                let mut sizes: Vec<usize> =
                    counts.into_iter().filter(|&count| count != 0).collect();
                sizes.sort_unstable();
                let mean = sizes.iter().sum::<usize>() as f64 / sizes.len().max(1) as f64;
                println!(
                    "moe-grouped il={il} t={t} dispatch=resident-q8-rows active={}/{} \
                     m_e: min={} median={} mean={mean:.1} max={}",
                    sizes.len(),
                    n_expert,
                    sizes.first().copied().unwrap_or(0),
                    sizes.get(sizes.len() / 2).copied().unwrap_or(0),
                    sizes.last().copied().unwrap_or(0),
                );
            }
            return Ok(moe_out);
        }

        // Clamped layers keep gate/up, activation, and down as separate stages. The pair-major
        // matvec body is qmatvec_expert_q8 verbatim, while one launch covers every routed pair.
        // Pair ids stay in router slot order so scatter preserves the sequential FMA chain.
        let pair_tok: Vec<i32> = (0..n_pairs).map(|pair| (pair / n_used) as i32).collect();
        let pair_ex: Vec<i32> = sel_all.iter().map(|&expert| expert as i32).collect();
        let tok_off: Vec<i32> = (0..=t).map(|tok| (tok * n_used) as i32).collect();
        let tok_ids: Vec<i32> = (0..n_pairs as i32).collect();

        let mut by_expert: Vec<Vec<i32>> = vec![Vec::new(); n_expert];
        for (pair, &expert) in pair_ex.iter().enumerate() {
            by_expert[expert as usize].push(pair as i32);
        }

        let pair_tok_d = e.htod_i32(&pair_tok)?;
        let pair_ex_d = e.htod_i32(&pair_ex)?;
        let pair_w_d = e.htod(w_all)?;
        let tok_off_d = e.htod_i32(&tok_off)?;
        let tok_ids_d = e.htod_i32(&tok_ids)?;

        let matvec = |proj: i32,
                      pair_rows: &CudaSlice<i32>,
                      aq: &CudaSlice<i8>,
                      ad: &CudaSlice<f32>,
                      in_f: usize,
                      out_f: usize,
                      qtype: i32,
                      row_bytes: usize|
         -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
            e.moe_pairs_matvec_q8(
                table, proj, pair_rows, &pair_ex_d, aq, ad, in_f, out_f, n_expert, n_pairs, qtype,
                row_bytes,
            )
        };

        let (gate_row_bytes, up_row_bytes) = if gu_il {
            let combined = m.gate_exps.row_bytes + m.up_exps.row_bytes;
            (combined, combined)
        } else {
            (m.gate_exps.row_bytes, m.up_exps.row_bytes)
        };
        let (zq, zd) = e.quantize_q8_1(z, t, n_embd)?;
        let gate = matvec(
            0,
            &pair_tok_d,
            &zq,
            &zd,
            n_embd,
            n_ff_exp,
            m.gate_exps.qtype,
            gate_row_bytes,
        )?;
        let up = matvec(
            1,
            &pair_tok_d,
            &zq,
            &zd,
            n_embd,
            n_ff_exp,
            m.up_exps.qtype,
            up_row_bytes,
        )?;
        let mut act = e.uninit(n_pairs * n_ff_exp)?;
        Self::ffn_act_lim(
            e,
            cfg,
            &gate,
            &up,
            1.0,
            1.0,
            cfg.clamp_exp_at(il as u32),
            &mut act,
            n_pairs * n_ff_exp,
        )?;
        let (aq2, ad2) = e.quantize_q8_1(&act, n_pairs, n_ff_exp)?;
        let pair_self: Vec<i32> = (0..n_pairs as i32).collect();
        let pair_self_d = e.htod_i32(&pair_self)?;
        let down = matvec(
            2,
            &pair_self_d,
            &aq2,
            &ad2,
            n_ff_exp,
            n_embd,
            m.down_exps.qtype,
            m.down_exps.row_bytes,
        )?;
        let mut moe_out = e.uninit(t * n_embd)?;
        e.moe_pairs_scatter(
            &down,
            &pair_w_d,
            &tok_off_d,
            &tok_ids_d,
            &mut moe_out,
            t,
            n_embd,
        )?;

        if std::env::var("MEMRA_MOE_STATS").is_ok() {
            let mut sizes: Vec<usize> = by_expert
                .iter()
                .filter_map(|pairs| (!pairs.is_empty()).then_some(pairs.len()))
                .collect();
            sizes.sort_unstable();
            let mean = sizes.iter().sum::<usize>() as f64 / sizes.len().max(1) as f64;
            println!(
                "moe-grouped il={il} t={t} dispatch=resident-q8-clamped-pairs active={}/{} \
                 m_e: min={} median={} mean={mean:.1} max={}",
                sizes.len(),
                n_expert,
                sizes.first().copied().unwrap_or(0),
                sizes.get(sizes.len() / 2).copied().unwrap_or(0),
                sizes.last().copied().unwrap_or(0),
            );
        }
        Ok(moe_out)
    }

    /// MEMRA_SHEXP_SPLIT worker: the shared expert's gate/up/down rows split across both
    /// devices (dev1 idles during E3), act halves exchanged both ways, downs row-split —
    /// per-element/per-row programs identical, so `sh` is BIT-IDENTICAL to the single-device
    /// arm. Process-static workspace pinned by the gate tensor pointer; rank1 holds one-time
    /// row-half replicas (~10MB/layer x 42). Returns None when ineligible.
    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::map_entry)] // allow: the init body is fallible (`?`); Entry::or_insert_with cannot propagate errors
    fn shexp_split_matvec(
        e: &Engine,
        rank1: &Engine,
        wg: &CudaSlice<u8>,
        wu: &CudaSlice<u8>,
        wd: &CudaSlice<u8>,
        z: &CudaSlice<f32>,
        lim: Option<SwigluClamp>,
        cfg: &ModelConfig,
        il: u16,
        n_embd: usize,
        n_ff_sh: usize,
    ) -> Result<Option<CudaSlice<f32>>, Box<dyn std::error::Error>> {
        use cudarc::driver::DevicePtr;
        if !n_ff_sh.is_multiple_of(2) || !n_embd.is_multiple_of(2) {
            return Ok(None);
        }
        let hf = n_ff_sh / 2;
        let nd = n_embd / 2;
        struct Rep {
            wg1: CudaSlice<u8>,
            wu1: CudaSlice<u8>,
            wd1: CudaSlice<u8>,
        }
        struct SplitWs {
            pin_dev: usize,
            // e side
            gate0: CudaSlice<f32>,
            up0: CudaSlice<f32>,
            act: CudaSlice<f32>,
            sh_buf: CudaSlice<f32>,
            ev_z: cudarc::driver::CudaEvent,
            ev_act0: cudarc::driver::CudaEvent,
            // rank1 side
            z1: CudaSlice<f32>,
            g1: CudaSlice<f32>,
            u1: CudaSlice<f32>,
            a1h: CudaSlice<f32>,
            act1: CudaSlice<f32>,
            y1: CudaSlice<f32>,
            ev_act1: cudarc::driver::CudaEvent,
            ev_y1: cudarc::driver::CudaEvent,
            raw_act_e: u64,
            raw_sh_e: u64,
            raw_z1: u64,
            raw_a1h: u64,
            raw_act1: u64,
            raw_y1: u64,
        }
        static WS: std::sync::Mutex<Option<SplitWs>> = std::sync::Mutex::new(None);
        static REPS: std::sync::Mutex<Option<std::collections::HashMap<u64, Rep>>> =
            std::sync::Mutex::new(None);
        let mut guard = WS.lock().map_err(|_| "shexp split lock is poisoned")?;
        let mut reps_guard = REPS.lock().map_err(|_| "shexp reps lock is poisoned")?;
        let reps = reps_guard.get_or_insert_with(std::collections::HashMap::new);
        let pins = e.ctx().ordinal();
        if guard.as_ref().is_none_or(|w| w.pin_dev != pins) {
            let (gate0, up0, act, sh_buf, ev_z, ev_act0) = {
                let _m = e.gpu.enter_main()?;
                (
                    e.htod(&vec![0.0f32; hf])?,
                    e.htod(&vec![0.0f32; hf])?,
                    e.htod(&vec![0.0f32; n_ff_sh])?,
                    e.htod(&vec![0.0f32; n_embd])?,
                    e.ctx().new_event(None)?,
                    e.ctx().new_event(None)?,
                )
            };
            let (z1, g1, u1, a1h, act1, y1, ev_act1, ev_y1) = {
                let _r = rank1.gpu.enter_main()?;
                (
                    rank1.htod(&vec![0.0f32; n_embd])?,
                    rank1.htod(&vec![0.0f32; hf])?,
                    rank1.htod(&vec![0.0f32; hf])?,
                    rank1.htod(&vec![0.0f32; hf])?,
                    rank1.htod(&vec![0.0f32; n_ff_sh])?,
                    rank1.htod(&vec![0.0f32; nd])?,
                    rank1.ctx().new_event(None)?,
                    rank1.ctx().new_event(None)?,
                )
            };
            let (raw_act_e, raw_sh_e) = {
                let _m = e.gpu.enter_main()?;
                let stream = e.stream();
                let (a, _g0) = act.device_ptr(&stream);
                let (b, _g1) = sh_buf.device_ptr(&stream);
                (a, b)
            };
            let (raw_z1, raw_a1h, raw_act1, raw_y1) = {
                let _r = rank1.gpu.enter_main()?;
                let rs = rank1.stream();
                let (a, _g0) = z1.device_ptr(&rs);
                let (b, _g1) = a1h.device_ptr(&rs);
                let (c, _g2) = act1.device_ptr(&rs);
                let (d, _g3) = y1.device_ptr(&rs);
                (a, b, c, d)
            };
            *guard = Some(SplitWs {
                pin_dev: pins,
                gate0,
                up0,
                act,
                sh_buf,
                ev_z,
                ev_act0,
                z1,
                g1,
                u1,
                a1h,
                act1,
                y1,
                ev_act1,
                ev_y1,
                raw_act_e,
                raw_sh_e,
                raw_z1,
                raw_a1h,
                raw_act1,
                raw_y1,
            });
        }
        let ws = guard.as_mut().expect("armed above");
        let wg_pin = {
            let _m = e.gpu.enter_main()?;
            let stream = e.stream();
            let (p, _g) = wg.device_ptr(&stream);
            p
        };
        if !reps.contains_key(&wg_pin) {
            // One-time per layer: upload rank1's row halves (gate/up rows [hf..], down rows [nd..]).
            let up = |src: &CudaSlice<u8>,
                      off_bytes: usize,
                      len: usize|
             -> Result<CudaSlice<u8>, Box<dyn std::error::Error>> {
                use cudarc::driver::sys;
                let sptr = {
                    let _m = e.gpu.enter_main()?;
                    let stream = e.stream();
                    let (p, _g) = src.device_ptr(&stream);
                    p + off_bytes as u64
                };
                let dst = {
                    let _r = rank1.gpu.enter_main()?;
                    rank1.alloc_u8_uninit(len)?
                };
                let dptr = {
                    let _r = rank1.gpu.enter_main()?;
                    let rs = rank1.stream();
                    let (p, _g) = dst.device_ptr(&rs);
                    p
                };
                let _r = rank1.gpu.enter_main()?;
                let r = unsafe {
                    sys::cuMemcpyAsync(
                        dptr as sys::CUdeviceptr,
                        sptr as sys::CUdeviceptr,
                        len,
                        rank1.stream().cu_stream() as sys::CUstream,
                    )
                };
                if r != sys::CUresult::CUDA_SUCCESS {
                    return Err(format!("shexp split replica upload: {r:?}").into());
                }
                rank1.stream().synchronize()?;
                Ok(dst)
            };
            let wg1 = up(wg, hf * n_embd * 2, hf * n_embd * 2)?;
            let wu1 = up(wu, hf * n_embd * 2, hf * n_embd * 2)?;
            let wd1 = up(wd, nd * n_ff_sh * 2, nd * n_ff_sh * 2)?;
            reps.insert(wg_pin, Rep { wg1, wu1, wd1 });
        }
        let _ = il;
        // Per token, evented split flow.
        let raw_z = {
            let _m = e.gpu.enter_main()?;
            let stream = e.stream();
            let (p, _g) = z.device_ptr(&stream);
            ws.ev_z.record(&stream)?;
            p
        };
        // rank1: pull z, its dual half, its act half; push act half to e; pull e's act half.
        {
            let rep = reps.get(&wg_pin).expect("uploaded above");
            let _r = rank1.gpu.enter_main()?;
            rank1.stream().wait(&ws.ev_z)?;
            crate::tp::raw_copy_bytes(ws.raw_z1, raw_z, n_embd * 4, rank1)?;
            let SplitWs {
                z1, g1, u1, a1h, ..
            } = &mut *ws;
            rank1.matvec_bf16_dual_into(&rep.wg1, &rep.wu1, z1, g1, u1, n_embd, hf)?;
            Self::ffn_act_lim(rank1, cfg, g1, u1, 1.0, 1.0, lim, a1h, hf)?;
            // local place into act1[hf..] + P2P push into e's act[hf..]
            crate::tp::raw_copy_bytes(ws.raw_act1 + (hf * 4) as u64, ws.raw_a1h, hf * 4, rank1)?;
            crate::tp::raw_copy_bytes(ws.raw_act_e + (hf * 4) as u64, ws.raw_a1h, hf * 4, rank1)?;
            ws.ev_act1.record(&rank1.stream())?;
        }
        // e: its dual half + act half; publish it; then wait rank1's half and run down lo.
        {
            let _m = e.gpu.enter_main()?;
            let SplitWs {
                gate0, up0, act, ..
            } = &mut *ws;
            let wg_lo = wg.slice(0..hf * n_embd * 2);
            let wu_lo = wu.slice(0..hf * n_embd * 2);
            e.matvec_bf16_dual_view_into(&wg_lo, &wu_lo, z, gate0, up0, n_embd, hf)?;
            Self::ffn_act_lim(e, cfg, gate0, up0, 1.0, 1.0, lim, act, hf)?;
            ws.ev_act0.record(&e.stream())?;
        }
        // rank1: pull e's act half into act1[0..hf], run down hi, push y half.
        {
            let rep = reps.get(&wg_pin).expect("uploaded above");
            let _r = rank1.gpu.enter_main()?;
            rank1.stream().wait(&ws.ev_act0)?;
            crate::tp::raw_copy_bytes(ws.raw_act1, ws.raw_act_e, hf * 4, rank1)?;
            let SplitWs { act1, y1, .. } = &mut *ws;
            rank1.matvec_bf16_into(&rep.wd1, act1, y1, n_ff_sh, nd)?;
            crate::tp::raw_copy_bytes(ws.raw_sh_e + (nd * 4) as u64, ws.raw_y1, nd * 4, rank1)?;
            ws.ev_y1.record(&rank1.stream())?;
        }
        // e: down lo into sh_buf[0..nd]; join rank1's half; hand back an owned sh.
        {
            let _m = e.gpu.enter_main()?;
            e.stream().wait(&ws.ev_act1)?;
            let SplitWs { act, sh_buf, .. } = &mut *ws;
            let wd_lo = wd.slice(0..nd * n_ff_sh * 2);
            e.matvec_bf16_view_into(&wd_lo, act, sh_buf, n_ff_sh, nd)?;
            e.stream().wait(&ws.ev_y1)?;
            let mut sh = e.uninit(n_embd)?;
            {
                let mut dst = sh.slice_mut(0..n_embd);
                e.stream()
                    .memcpy_dtod(&ws.sh_buf.slice(0..n_embd), &mut dst)?;
            }
            Ok(Some(sh))
        }
    }

    /// SHEXP OVERLAP issue (MEMRA_SHEXP_OVERLAP=1): the shared expert reads only `z`, so
    /// its kernels (dual matvec+SwiGLU, down) are issued on e's stream from the routes
    /// PREJOIN hook — they execute while the peer rank drains its sweep, filling dev0's
    /// join wait. The down lands in ITS OWN row (the pre-#2e split program — receipted
    /// bit-identical to the fused down+addscale) and `shexp_overlap_apply` adds it after
    /// the join with the exact add_scaled_rows expression: values unchanged.
    fn shexp_overlap_issue(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        cfg: &ModelConfig,
        il: u16,
        n_embd: usize,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        if cfg.m3.is_some() || m.gate_inp_shexp.is_some() {
            return Ok(false);
        }
        let (
            Some(crate::model::GpuTensor::FloatBf16 { data: wg, .. }),
            Some(crate::model::GpuTensor::FloatBf16 { data: wu, .. }),
            Some(crate::model::GpuTensor::FloatBf16 { data: wd, .. }),
        ) = (&m.gate_shexp, &m.up_shexp, &m.down_shexp)
        else {
            return Ok(false);
        };
        let n_ff_sh = m
            .gate_shexp
            .as_ref()
            .expect("matched Some above")
            .out_features();
        // The dual-silu epilogue is step35's POST form only; a PRE-clamped layer declines here
        // and takes the unfused seam.
        let Ok(lim) = Self::fused_post_limit(cfg.clamp_shexp_at(il as u32)) else {
            return Ok(false);
        };
        let mut guard = SHEXP_OV_WS
            .lock()
            .map_err(|_| "shexp overlap workspace lock is poisoned")?;
        let pins = (e.ctx().ordinal(), n_embd, n_ff_sh);
        if guard
            .as_ref()
            .is_none_or(|(d, ne, nf, ..)| (*d, *ne, *nf) != pins)
        {
            *guard = Some((
                pins.0,
                pins.1,
                pins.2,
                e.uninit(n_ff_sh)?,
                e.uninit(n_embd)?,
            ));
        }
        let (_, _, _, act, sh) = guard.as_mut().expect("armed above");
        e.matvec_bf16_dual_silu_into(wg, wu, z, act, n_embd, n_ff_sh, lim)?;
        e.matvec_bf16_into(wd, act, sh, n_ff_sh, n_embd)?;
        drop(guard);
        Ok(true)
    }

    /// SHEXP ON DEV1 issue (MEMRA_SHEXP_DEV1=1): the shared expert runs on rank1 — the
    /// idle device — with replica weights (one-time P2P upload), the SAME kernels and the
    /// SAME split program as the dev0 overlap (dual matvec+SwiGLU, f32acc down), so the
    /// values are bit-identical. z rides one 16KB P2P pull behind an e-stream event; the
    /// down row lands root-resident (single P2P store pass); apply waits ev_done on e.
    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::map_entry)] // allow: the init body is fallible (`?`); Entry::or_insert_with cannot propagate errors
    fn shexp_dev1_issue(
        e: &Engine,
        rank1: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        cfg: &ModelConfig,
        il: u16,
        n_embd: usize,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        use cudarc::driver::DevicePtr;
        if cfg.m3.is_some() || m.gate_inp_shexp.is_some() {
            return Ok(false);
        }
        let (
            Some(crate::model::GpuTensor::FloatBf16 { data: wg, .. }),
            Some(crate::model::GpuTensor::FloatBf16 { data: wu, .. }),
            Some(crate::model::GpuTensor::FloatBf16 { data: wd, .. }),
        ) = (&m.gate_shexp, &m.up_shexp, &m.down_shexp)
        else {
            return Ok(false);
        };
        let n_ff_sh = m
            .gate_shexp
            .as_ref()
            .expect("matched Some above")
            .out_features();
        // POST-form epilogue only (see `fused_post_limit`): a PRE-clamped layer declines
        // (Ok(false) = nothing issued, caller falls back per column).
        let Ok(lim) = Self::fused_post_limit(cfg.clamp_shexp_at(il as u32)) else {
            return Ok(false);
        };
        // Shared scratch, geometry-keyed.
        let mut ws_guard = SHEXP_D1_WS
            .lock()
            .map_err(|_| "shexp dev1 workspace lock is poisoned")?;
        if ws_guard
            .as_ref()
            .is_none_or(|(k, ..)| *k != (n_embd, n_ff_sh))
        {
            let (act1, z1, ev_done) = {
                let _r1 = rank1.gpu.enter_main()?;
                (
                    rank1.htod(&vec![0.0f32; n_ff_sh])?,
                    rank1.htod(&vec![0.0f32; n_embd])?,
                    rank1.ctx().new_event(None)?,
                )
            };
            let (sh_root, ev_z) = {
                let _main = e.gpu.enter_main()?;
                (e.htod(&vec![0.0f32; n_embd])?, e.ctx().new_event(None)?)
            };
            *ws_guard = Some(((n_embd, n_ff_sh), act1, z1, sh_root, ev_z, ev_done));
        }
        // Per-LAYER weight replicas (gate/up/down differ per layer): one-time P2P upload.
        let mut reps_guard = SHEXP_D1_REPS
            .lock()
            .map_err(|_| "shexp dev1 replica lock is poisoned")?;
        let reps = reps_guard.get_or_insert_with(Default::default);
        if !reps.contains_key(&il) {
            let (wg1, wu1, wd1) = {
                let _r1 = rank1.gpu.enter_main()?;
                (
                    rank1.alloc_u8_uninit(n_ff_sh * n_embd * 2)?,
                    rank1.alloc_u8_uninit(n_ff_sh * n_embd * 2)?,
                    rank1.alloc_u8_uninit(n_embd * n_ff_sh * 2)?,
                )
            };
            for (src, dst) in [(wg, &wg1), (wu, &wu1), (wd, &wd1)] {
                let s_ptr = {
                    let _main = e.gpu.enter_main()?;
                    let stream = e.stream();
                    let (p, _g) = src.device_ptr(&stream);
                    p
                };
                let d_ptr = {
                    let _r1 = rank1.gpu.enter_main()?;
                    let stream = rank1.stream();
                    let (p, _g) = dst.device_ptr(&stream);
                    p
                };
                let _r1 = rank1.gpu.enter_main()?;
                crate::tp::raw_copy_bytes(d_ptr, s_ptr, src.len(), rank1)?;
            }
            {
                let _r1 = rank1.gpu.enter_main()?;
                rank1.stream().synchronize()?;
            }
            reps.insert(il, (wg1, wu1, wd1));
        }
        let (wg1, wu1, wd1) = reps.get(&il).expect("armed above");
        let (_, act1, z1, sh_root, ev_z, ev_done) = ws_guard.as_mut().expect("armed above");
        // z ready on e's stream -> rank1 pulls it, runs the split shexp, pushes the down
        // row root-side (single store pass), rings ev_done.
        let (raw_z, raw_sh) = {
            let _main = e.gpu.enter_main()?;
            let stream = e.stream();
            let (a, _g0) = z.device_ptr(&stream);
            let (b, _g1) = sh_root.device_ptr(&stream);
            ev_z.record(&stream)?;
            (a, b)
        };
        {
            let _r1 = rank1.gpu.enter_main()?;
            rank1.stream().wait(ev_z)?;
            let raw_z1 = {
                let stream = rank1.stream();
                let (p, _g) = z1.device_ptr(&stream);
                p
            };
            crate::tp::raw_copy_bytes(raw_z1, raw_z, n_embd * 4, rank1)?;
            rank1.matvec_bf16_dual_silu_into(wg1, wu1, z1, act1, n_embd, n_ff_sh, lim)?;
            // down writes the ROOT-resident row over P2P via the raw-output twin of
            // matvec_bf16_into: reuse the view launcher with a slice view is not possible
            // cross-device, so launch on the raw pointer.
            rank1.matvec_bf16_raw_out(wd1, act1, raw_sh, n_ff_sh, n_embd)?;
            ev_done.record(&rank1.stream())?;
        }
        Ok(true)
    }

    /// Apply the dev1 shared expert: wait ev_done on e, then the exact add_scaled_rows.
    fn shexp_dev1_apply(
        e: &Engine,
        output: &mut CudaSlice<f32>,
        n_embd: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let guard = SHEXP_D1_WS
            .lock()
            .map_err(|_| "shexp dev1 workspace lock is poisoned")?;
        let (pin, _, _, sh_root, _, ev_done) =
            guard.as_ref().ok_or("shexp dev1 apply without issue")?;
        if pin.0 != n_embd {
            return Err("shexp dev1 width drifted".into());
        }
        let _main = e.gpu.enter_main()?;
        e.stream().wait(ev_done)?;
        static ONES_D1: std::sync::Mutex<Option<(usize, CudaSlice<f32>)>> =
            std::sync::Mutex::new(None);
        let mut og = ONES_D1.lock().map_err(|_| "ones lock is poisoned")?;
        if og.as_ref().is_none_or(|(d, _)| *d != e.ctx().ordinal()) {
            *og = Some((e.ctx().ordinal(), e.htod(&[1.0f32])?));
        }
        let ones = &og.as_ref().expect("armed above").1;
        e.add_scaled_rows(sh_root, ones, output, n_embd, 1)?;
        Ok(())
    }

    /// MOE TAIL FUSION M1 helper: pre-arm the overlap ws + persistent ones row and
    /// return their RAW pointers (None when the overlap is ineligible — the caller then
    /// takes the split path). Mirrors shexp_overlap_issue's eligibility exactly.
    fn shexp_overlap_tail_ptrs(
        e: &Engine,
        m: &MoeWeights,
        cfg: &ModelConfig,
        n_embd: usize,
    ) -> Result<Option<(u64, u64)>, Box<dyn std::error::Error>> {
        use cudarc::driver::DevicePtr;
        if cfg.m3.is_some() || m.gate_inp_shexp.is_some() {
            return Ok(None);
        }
        let (
            Some(crate::model::GpuTensor::FloatBf16 { .. }),
            Some(crate::model::GpuTensor::FloatBf16 { .. }),
            Some(crate::model::GpuTensor::FloatBf16 { .. }),
        ) = (&m.gate_shexp, &m.up_shexp, &m.down_shexp)
        else {
            return Ok(None);
        };
        let n_ff_sh = m
            .gate_shexp
            .as_ref()
            .expect("matched Some above")
            .out_features();
        let mut guard = SHEXP_OV_WS
            .lock()
            .map_err(|_| "shexp overlap workspace lock is poisoned")?;
        let pins = (e.ctx().ordinal(), n_embd, n_ff_sh);
        if guard
            .as_ref()
            .is_none_or(|(d, ne, nf, ..)| (*d, *ne, *nf) != pins)
        {
            *guard = Some((
                pins.0,
                pins.1,
                pins.2,
                e.uninit(n_ff_sh)?,
                e.uninit(n_embd)?,
            ));
        }
        let sh_raw = {
            let (_, _, _, _, sh) = guard.as_ref().expect("armed above");
            let stream = e.stream();
            let (p, _g) = sh.device_ptr(&stream);
            p
        };
        static ONES_T3: std::sync::Mutex<Option<(usize, CudaSlice<f32>)>> =
            std::sync::Mutex::new(None);
        let mut og = ONES_T3.lock().map_err(|_| "ones lock is poisoned")?;
        if og.as_ref().is_none_or(|(d, _)| *d != e.ctx().ordinal()) {
            *og = Some((e.ctx().ordinal(), e.htod(&[1.0f32])?));
        }
        let ones_raw = {
            let stream = e.stream();
            let (p, _g) = og.as_ref().expect("armed above").1.device_ptr(&stream);
            p
        };
        Ok(Some((sh_raw, ones_raw)))
    }

    /// Apply the overlapped shared expert: output[r] += sh[r] * 1.0 — the exact
    /// add_scaled_rows program the split path used (persistent ones row, no htod).
    fn shexp_overlap_apply(
        e: &Engine,
        output: &mut CudaSlice<f32>,
        n_embd: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let guard = SHEXP_OV_WS
            .lock()
            .map_err(|_| "shexp overlap workspace lock is poisoned")?;
        let (_, ne, _, _, sh) = guard.as_ref().ok_or("shexp overlap apply without issue")?;
        if *ne != n_embd {
            return Err("shexp overlap width drifted".into());
        }
        static ONES_OV: std::sync::Mutex<Option<(usize, CudaSlice<f32>)>> =
            std::sync::Mutex::new(None);
        let mut og = ONES_OV.lock().map_err(|_| "ones lock is poisoned")?;
        if og.as_ref().is_none_or(|(d, _)| *d != e.ctx().ordinal()) {
            *og = Some((e.ctx().ordinal(), e.htod(&[1.0f32])?));
        }
        let ones = &og.as_ref().expect("armed above").1;
        e.add_scaled_rows(sh, ones, output, n_embd, 1)?;
        Ok(())
    }

    fn moe_ffn_grouped_add_shared(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        moe_out: &mut CudaSlice<f32>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        // MEMRA_STEP_TP_TIMING=1: shared-expert wall (syncs e's stream at exit to bill the
        // queued matmuls here rather than at the next host readback).
        static SHEXP_NS: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static SHEXP_CALLS: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        let shexp_timing = std::env::var("MEMRA_STEP_TP_TIMING").as_deref() == Ok("1");
        let shexp_started = shexp_timing.then(std::time::Instant::now);
        let result = Self::moe_ffn_grouped_add_shared_inner(e, m, z, t, cfg, il, moe_out);
        if let Some(started) = shexp_started {
            use std::sync::atomic::Ordering;
            e.stream().synchronize()?;
            let ns = SHEXP_NS.fetch_add(started.elapsed().as_nanos() as u64, Ordering::Relaxed)
                + started.elapsed().as_nanos() as u64;
            let calls = SHEXP_CALLS.fetch_add(1, Ordering::Relaxed) + 1;
            if calls.is_multiple_of(430) {
                eprintln!(
                    "[moe-shexp-timing] calls={calls} total_ms={:.1} avg_us={:.1}",
                    ns as f64 / 1.0e6,
                    ns as f64 / calls as f64 / 1.0e3,
                );
            }
        }
        result
    }

    #[allow(clippy::too_many_arguments)]
    fn moe_ffn_grouped_add_shared_inner(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        moe_out: &mut CudaSlice<f32>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let n_embd = cfg.n_embd as usize;
        if let (Some(gate_shexp), Some(up_shexp), Some(down_shexp)) =
            (&m.gate_shexp, &m.up_shexp, &m.down_shexp)
        {
            let n_ff_sh = gate_shexp.out_features();
            let lim = cfg.clamp_shexp_at(il as u32);
            // T=1 DECODE FUSION (2026-08-20): the ffn_swiglu_decode fast-path program, ported
            // — one shared quantize feeds gate+up (matmul at m=1 quantizes internally per
            // call with identical bytes, so sharing it is bit-identical), the dual NVFP4/Q8
            // launch covers both when available, and silu_mul_scaled_q8_1 emits down's
            // operand pre-quantized (kernel_check-proven identities). This path measured
            // 167us/layer as separate matmuls + 5 allocs at decode.
            let fused = t == 1
                && lim.is_none()
                && cfg.m3.is_none()
                && e.uses_q8_1_fast(gate_shexp)
                && e.uses_q8_1_fast(up_shexp);
            let canonical_w4a16_rows =
                t <= 32 && m.step_ep.as_ref().is_some_and(|ep| ep.nvfp4_device_routes);
            // MEMRA_BF16_MMV class: both projections in ONE launch (bit-identical per row to
            // the two matvec_bf16 launches matmul would issue). W4A16 distributed execution
            // uses the same row program for decode and verify; a t=1-only fusion accumulated
            // sub-ULP residual drift from the first MoE layer onward.
            let bf16_dual = if (t == 1 || canonical_w4a16_rows)
                && crate::Engine::bf16_mmv_on()
                && n_embd.is_multiple_of(8)
            {
                match (gate_shexp, up_shexp) {
                    (
                        crate::model::GpuTensor::FloatBf16 { data: wg, .. },
                        crate::model::GpuTensor::FloatBf16 { data: wu, .. },
                    ) => Some((wg, wu)),
                    _ => None,
                }
            } else {
                None
            };
            let sh = if let Some((wg, wu)) = bf16_dual {
                // Persistent shared-expert workspace: sizes are constant across every MoE
                // layer, so one process-level set pinned by (device, n_embd, n_ff_sh) removes
                // the four per-layer allocations. Buffers are fully overwritten each call.
                type SharedExpertWorkspace = (
                    usize,
                    usize,
                    usize,
                    CudaSlice<f32>,
                    CudaSlice<f32>,
                    CudaSlice<f32>,
                    CudaSlice<f32>,
                );
                static SHEXP_WS: std::sync::Mutex<
                    Option<std::collections::HashMap<usize, SharedExpertWorkspace>>,
                > = std::sync::Mutex::new(None);
                let down_bf16 = match down_shexp {
                    crate::model::GpuTensor::FloatBf16 { data, .. } => Some(data),
                    _ => None,
                };
                let mut guard = SHEXP_WS
                    .lock()
                    .map_err(|_| "shexp workspace lock is poisoned")?;
                let capacity = if canonical_w4a16_rows { 32 } else { 1 };
                let device = e.ctx().ordinal();
                let workspaces = guard.get_or_insert_with(Default::default);
                if workspaces
                    .get(&device)
                    .is_none_or(|(ne, nf, cap, ..)| (*ne, *nf, *cap) != (n_embd, n_ff_sh, capacity))
                {
                    workspaces.insert(
                        device,
                        (
                            n_embd,
                            n_ff_sh,
                            capacity,
                            e.uninit(capacity * n_ff_sh)?,
                            e.uninit(capacity * n_ff_sh)?,
                            e.uninit(capacity * n_ff_sh)?,
                            e.uninit(capacity * n_embd)?,
                        ),
                    );
                }
                // MEMRA_SHEXP_SPLIT=1: bit-identical row split across both devices; falls
                // through to the single-device arm when ineligible.
                {
                    static ON: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
                    let split_on = *ON
                        .get_or_init(|| std::env::var("MEMRA_SHEXP_SPLIT").as_deref() == Ok("1"));
                    if split_on
                        && t == 1
                        && let (Some(wd), Some(rank1)) = (
                            match down_shexp {
                                crate::model::GpuTensor::FloatBf16 { data, .. } => Some(data),
                                _ => None,
                            },
                            m.step_tp.as_ref().and_then(|st| st.runtime.rank_engine(1)),
                        )
                        && let Some(sh) = Self::shexp_split_matvec(
                            e, rank1, wg, wu, wd, z, lim, cfg, il, n_embd, n_ff_sh,
                        )?
                    {
                        drop(guard);
                        let gate = match &m.gate_inp_shexp {
                            Some(gate_inp_shexp) => {
                                e.sigmoid_dot_rows(z, gate_inp_shexp.float_data(), n_embd, t)?
                            }
                            None => e.htod(&vec![1.0f32; t])?,
                        };
                        e.add_scaled_rows(&sh, &gate, moe_out, n_embd, t)?;
                        return Ok(());
                    }
                }
                let (_, _, _, gate, up, act, sh_buf) = workspaces
                    .get_mut(&device)
                    .expect("shexp workspace initialized above");
                // FUSION #2b needs the POST form its epilogue hardcodes; m3's swigluoai and
                // glm5_next's PRE clamp both take the unfused dual-matmul + ffn_act_lim arm.
                if let (true, Ok(lim_post)) = (cfg.m3.is_none(), Self::fused_post_limit(lim)) {
                    // dual matvec + SwiGLU act in one launch — exact dual per-row program +
                    // exact silu/clamped expression, bit-identical.
                    if canonical_w4a16_rows {
                        e.matvec_bf16_dual_silu_rows_into(
                            wg, wu, z, act, n_embd, n_ff_sh, lim_post, t,
                        )?;
                    } else {
                        e.matvec_bf16_dual_silu_into(wg, wu, z, act, n_embd, n_ff_sh, lim_post)?;
                    }
                    let _ = (&gate, &up);
                } else {
                    e.matvec_bf16_dual_into(wg, wu, z, gate, up, n_embd, n_ff_sh)?;
                    Self::ffn_act_lim(e, cfg, gate, up, 1.0, 1.0, lim, act, n_ff_sh)?;
                }
                if let Some(down) = down_bf16 {
                    if canonical_w4a16_rows {
                        e.matvec_bf16_rows_into(down, act, sh_buf, n_ff_sh, n_embd, t)?;
                        let mut sh = e.uninit(t * n_embd)?;
                        {
                            let mut dst = sh.slice_mut(0..t * n_embd);
                            e.stream()
                                .memcpy_dtod(&sh_buf.slice(0..t * n_embd), &mut dst)?;
                        }
                        sh
                    } else {
                        // FUSION #2e (gate-less shexp only, MEMRA_FUSE_DOWN_ADDSCALE=0 reverts):
                        // down matvec + scaled accumulate straight into moe_out in ONE launch —
                        // exact f32acc per-row program + the exact add_scaled_rows expression
                        // (dst[r] += y_r * 1.0). Replaces down + ownership alloc + 16KB copy +
                        // add_scaled (3 launches + alloc -> 1 launch); bit-identical because the
                        // accumulate consumes the same f32 the split path stored and reloaded.
                        static FUSE_DA: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
                        let fuse_da = *FUSE_DA.get_or_init(|| {
                            std::env::var("MEMRA_FUSE_DOWN_ADDSCALE").as_deref() != Ok("0")
                        });
                        if fuse_da && m.gate_inp_shexp.is_none() {
                            static ONES1: std::sync::Mutex<Option<(usize, CudaSlice<f32>)>> =
                                std::sync::Mutex::new(None);
                            let mut og = ONES1.lock().map_err(|_| "shexp ones lock is poisoned")?;
                            if og.as_ref().is_none_or(|(d, _)| *d != e.ctx().ordinal()) {
                                *og = Some((e.ctx().ordinal(), e.htod(&[1.0f32])?));
                            }
                            let ones = &og.as_ref().expect("armed above").1;
                            e.matvec_bf16_down_addscale_into(
                                down, act, ones, moe_out, n_ff_sh, n_embd,
                            )?;
                            return Ok(());
                        }
                        e.matvec_bf16_into(down, act, sh_buf, n_ff_sh, n_embd)?;
                        let sh = e.uninit(n_embd)?;
                        // One alloc keeps the ownership contract; the copy is 16KB on-stream.
                        let mut sh = sh;
                        {
                            let mut dst = sh.slice_mut(0..n_embd);
                            e.stream().memcpy_dtod(&sh_buf.slice(0..n_embd), &mut dst)?;
                        }
                        sh
                    }
                } else {
                    e.matmul(down_shexp, act, t)?
                }
            } else if fused {
                let (zq, zd) = e.quantize_q8_1(z, 1, n_embd)?;
                let pair = match e.matmul_pre_dual_noscale(gate_shexp, up_shexp, &zq, &zd, 1)? {
                    Some((gate, up)) => Some((gate, up)),
                    None => {
                        match (
                            e.matmul_pre_noscale(gate_shexp, &zq, &zd, 1)?,
                            e.matmul_pre_noscale(up_shexp, &zq, &zd, 1)?,
                        ) {
                            (Some(gate), Some(up)) => Some((gate, up)),
                            _ => None,
                        }
                    }
                };
                match pair {
                    Some(((gate, gs), (up, us))) => {
                        if e.uses_q8_1_fast(down_shexp) {
                            let (aq, ad) = e.silu_mul_scaled_q8_1(&gate, &up, gs, us, n_ff_sh)?;
                            e.matmul_pre(down_shexp, &aq, &ad, &gate, 1)?
                        } else {
                            let mut act = e.uninit(n_ff_sh)?;
                            e.silu_mul_scaled(&gate, &up, gs, us, &mut act, n_ff_sh)?;
                            e.matmul(down_shexp, &act, 1)?
                        }
                    }
                    None => {
                        let gate = e.matmul_pre(gate_shexp, &zq, &zd, z, 1)?;
                        let up = e.matmul_pre(up_shexp, &zq, &zd, z, 1)?;
                        let mut act = e.uninit(n_ff_sh)?;
                        Self::ffn_act(e, cfg, &gate, &up, &mut act, n_ff_sh)?;
                        e.matmul(down_shexp, &act, 1)?
                    }
                }
            } else {
                let sg_gate = e.matmul(gate_shexp, z, t)?;
                let sg_up = e.matmul(up_shexp, z, t)?;
                let mut sa = e.uninit(t * n_ff_sh)?;
                Self::ffn_act_lim(
                    e,
                    cfg,
                    &sg_gate,
                    &sg_up,
                    1.0,
                    1.0,
                    lim,
                    &mut sa,
                    t * n_ff_sh,
                )?;
                e.matmul(down_shexp, &sa, t)?
            };
            let gate = match &m.gate_inp_shexp {
                Some(gate_inp_shexp) => {
                    if t < PRIME_MIN_T || crate::router_prefill_exact_on() {
                        e.sigmoid_dot_rows(z, gate_inp_shexp.float_data(), n_embd, t)?
                    } else {
                        let raw = e.linear(z, gate_inp_shexp.float_data(), t, n_embd, 1)?;
                        let mut gate = e.uninit(t)?;
                        e.sigmoid(&raw, &mut gate, t)?;
                        gate
                    }
                }
                // t=1 hot path: the per-layer htod of a ones row is a PAGEABLE H2D that
                // synchronizes the stream — measured as the biggest per-layer host gap
                // (44.6us x 42, eager gap table 2026-08-21). One persistent ones row per
                // device serves every layer; larger t (prefill) keeps the plain htod.
                None if t == 1 => {
                    static ONES: std::sync::Mutex<
                        Option<std::collections::HashMap<usize, CudaSlice<f32>>>,
                    > = std::sync::Mutex::new(None);
                    let mut guard = ONES.lock().map_err(|_| "shexp ones lock is poisoned")?;
                    let device = e.ctx().ordinal();
                    let rows = guard.get_or_insert_with(Default::default);
                    // One entry lookup, not three (contains_key + insert + get). The vacant arm
                    // stays fallible, which is why this is `match` and not `or_insert_with`.
                    use std::collections::hash_map::Entry;
                    let ones = match rows.entry(device) {
                        Entry::Occupied(occupied) => occupied.into_mut(),
                        Entry::Vacant(vacant) => vacant.insert(e.htod(&[1.0f32])?),
                    };
                    e.add_scaled_rows(&sh, ones, moe_out, n_embd, t)?;
                    return Ok(());
                }
                None => e.htod(&vec![1.0f32; t])?,
            };
            e.add_scaled_rows(&sh, &gate, moe_out, n_embd, t)?;
        }
        Ok(())
    }

    /// A2 expert-grouped MoE FFN (prefill path, MEMRA_MOE_GROUPED=1). Same semantics as moe_ffn:
    /// z [T, n_embd] -> moe_out [T, n_embd]. BIT-IDENTICAL to moe_ffn when using the slot scheme.
    pub(crate) fn moe_ffn_grouped(
        e: &Engine,
        m: &MoeWeights,
        z: &CudaSlice<f32>,
        t: usize,
        cfg: &ModelConfig,
        il: u16,
        max_block: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let moe = cfg.moe.as_ref().unwrap();
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        // step35 per-layer SwiGLU clamp; None on every other arch / unclamped layer.
        let lim_exp = cfg.clamp_exp_at(il as u32);

        // 1. ROUTER: exactly the same m-invariant sigmoid selector as the sequential path
        // (device by default; MEMRA_SIG_ROUTER=0 is the host oracle). The grouped dispatch never
        // enters the softmax-only pairs/dev router.
        let logits = Self::moe_router_logits(e, m, z, t, cfg)?;
        if let Some(sig) = cfg.sigmoid_router() {
            Self::trace_sigmoid_router_logits(e, il, t, n_expert, n_used, &logits, m, sig)?;
        }
        let (sel_all, w_all) = if let Some(sig) = cfg.sigmoid_router() {
            Self::moe_route_sigmoid_cfg(e, &logits, t, n_expert, n_used, m, sig)?
        } else {
            Self::moe_route_cfg(e, &logits, t, n_expert, n_used, m.active_experts.as_deref())?
        };
        crate::moesd::record_host_routes(il, n_expert, n_used, &sel_all)?;
        Self::trace_moe_routes(il, t, &sel_all, &w_all)?;
        Self::trace_moe_input(e, il, t, n_embd, z)?;

        // Fits-VRAM Step35 under the PP stage split: use the expert-major q8 arithmetic directly
        // from the owning device's uniform resident slab. This is not `moe_ffn_pairs`: routing
        // already happened above and the activation is clamp-aware. Mixed layouts, remote slabs,
        // macro-scaled experts, q8-disabled configs, and spill fall through to metadata-aware A2.
        let no_exp_macros = m.gate_exps.macros.is_none()
            && m.up_exps.macros.is_none()
            && m.down_exps.macros.is_none();
        let resident_q8 = m.dev_exps.as_ref().filter(|dev| {
            m.has_uniform_expert_layout()
                && no_exp_macros
                && moe_q8_enabled_for_model(cfg, m)
                && moe_slab_enabled()
                && dev.dev == e.ctx().ordinal()
        });
        if let Some(dev) = resident_q8 {
            let mut moe_out = Self::moe_ffn_grouped_resident_q8(
                e,
                m,
                z,
                t,
                cfg,
                il,
                &sel_all,
                &w_all,
                &dev.ptr_row,
                dev.gu_il,
            )?;
            Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut moe_out)?;
            return Ok(moe_out);
        }

        // 2. BUILD PER-EXPERT TOKEN LISTS (host-side grouping).
        // For each expert e, we need: which tokens use it, their positions in z, their top-k
        // slot index (for bit-identical accumulation), and their weights.
        struct ExpertGroup {
            tok_indices: Vec<i32>,  // indices into z rows (0..T-1)
            slot_indices: Vec<i32>, // top-k slot (0..n_used-1) for that token-expert pair
            weights: Vec<f32>,      // renormalized weight for that token-expert pair
        }
        let mut groups: Vec<ExpertGroup> = (0..n_expert)
            .map(|_| ExpertGroup {
                tok_indices: Vec::new(),
                slot_indices: Vec::new(),
                weights: Vec::new(),
            })
            .collect();

        for tok in 0..t {
            for j in 0..n_used {
                let ex = sel_all[tok * n_used + j] as usize;
                let w = w_all[tok * n_used + j];
                groups[ex].tok_indices.push(tok as i32);
                groups[ex].slot_indices.push(j as i32);
                groups[ex].weights.push(w);
            }
        }

        // 3. ALLOCATE SLOT BUFFER: [T, n_used, n_embd] f32, zero-initialized.
        // Each token's 8 expert contributions land in their respective slots.
        let mut slot_buf = e.zeros(t * n_used * n_embd)?;
        let mut wbuf = e.zeros(t * n_used)?; // [T, n_used] weight buffer for FMA reduce

        // Expert weight dimensions (used in both cache and staging paths).
        let g_len = m.gate_exps.max_expert_bytes();
        let u_len = m.up_exps.max_expert_bytes();
        let d_len = m.down_exps.max_expert_bytes();
        let moe_q8 = moe_q8_enabled_for_model(cfg, m);
        // The per-expert slab view is legal only for the ordinary contiguous uniform layout.
        // Interleaved GU slabs require the pointer-table fast path above.
        let slab_local = m
            .dev_exps
            .as_ref()
            .filter(|dev| !dev.gu_il && moe_slab_enabled() && dev.dev == e.ctx().ordinal());
        let use_cache =
            slab_local.is_none() && Engine::moe_cache_enabled() && !e.moe_cache_frozen();
        // The sequential no-cache/frozen staging oracle is f32. Use q8 only where sequential
        // also does: a local resident slab or a live SLRU dispatch.
        let grouped_q8 = moe_q8 && (slab_local.is_some() || use_cache);

        // GPU scratch for staging (only allocated without a local slab or cache).
        let (mut scratch_g, mut scratch_u, mut scratch_d) = if slab_local.is_none() && !use_cache {
            (
                Some(e.alloc_u8(g_len)?),
                Some(e.alloc_u8(u_len)?),
                Some(e.alloc_u8(d_len)?),
            )
        } else {
            (None, None, None)
        };

        // 4. PER ACTIVE EXPERT: gather, compute, scatter.
        // Processing ORDER: DESCENDING m_e (biggest token batches first) — the concluded winner
        // (rig5090 2026-07-04, the ascending-id arm and its MEMRA_MOE_ORDER seam removed): desc is
        // a first-forward win at partial cache capacity — the hot (big-m_e) experts are admitted
        // to the SLRU before the small-m_e tail can pollute it, so residency converges in ONE
        // forward instead of several: auto-cache T=501 126.9 -> 169.9 tok/s (1.34x), cap512
        // 119.6 -> 160.8 (and kills the rep-to-rep bimodal); wash (<2%) at cap64 pure-spill and
        // at long prompts where every expert stages regardless. Order is FREE to change without
        // breaking the byte-identity gate: the slot scheme pins each token's accumulation order
        // regardless of expert processing order (the whole point of the slots).
        let mut order: Vec<usize> = (0..n_expert)
            .filter(|&ex| !groups[ex].tok_indices.is_empty())
            .collect();
        order.sort_by(|&a, &b| {
            groups[b]
                .tok_indices
                .len()
                .cmp(&groups[a].tok_indices.len())
                .then(a.cmp(&b))
        });
        let mut m_dist: Vec<usize> = Vec::new(); // for stats
        let page_window = moe_page_prefetch_window();
        let worker_disk_prefetch = use_cache && crate::spill_pread::worker_enabled();
        if worker_disk_prefetch
            && let Some(first) = grouped_worker_prefetch_position(order.len(), None)
        {
            Self::moe_prefetch_disk_expert(e, il, order[first], m, max_block, &[])?;
        }
        for (order_pos, &ex) in order.iter().enumerate() {
            for next in page_prefetch_positions(order_pos, order.len(), page_window) {
                Self::moe_prefetch_host_expert(order[next], m);
            }
            if worker_disk_prefetch
                && let Some(next) = grouped_worker_prefetch_position(order.len(), Some(order_pos))
            {
                use crate::moe_cache::{BlockId, PROJ_DOWN, PROJ_GATE, PROJ_UP};
                let keep = [
                    BlockId::new(il, PROJ_GATE, ex as u16),
                    BlockId::new(il, PROJ_UP, ex as u16),
                    BlockId::new(il, PROJ_DOWN, ex as u16),
                ];
                Self::moe_prefetch_disk_expert(e, il, order[next], m, max_block, &keep)?;
            }
            let grp = &groups[ex];
            let m_e = grp.tok_indices.len();
            m_dist.push(m_e);
            let gl = m.gate_exps.expert_layout(ex);
            let ul = m.up_exps.expert_layout(ex);
            let dl = m.down_exps.expert_layout(ex);

            // Upload index/weight arrays to device. The down-proj per-expert macro-scale
            // (ModelOpt weight_scale_2) folds into the scatter weights — post-matmul linear,
            // same fold as the sequential loop's `w[j] * macro_scale(ex)`. 1.0 for GGUF experts.
            let tok_idx_d = e.htod_i32(&grp.tok_indices)?;
            let slot_idx_d = e.htod_i32(&grp.slot_indices)?;
            let dmac = m.down_exps.macro_scale(ex);
            let weight_d = if dmac == 1.0 {
                e.htod(&grp.weights)?
            } else {
                let scaled: Vec<f32> = grp.weights.iter().map(|&w| w * dmac).collect();
                e.htod(&scaled)?
            };

            // GATHER: collect m_e activation rows from z into a contiguous buffer.
            let mut gathered = e.zeros(m_e * n_embd)?;
            e.gather_rows(z, &tok_idx_d, &mut gathered, n_embd, m_e)?;
            let gv = gathered.slice(0..m_e * n_embd);

            // Compute gate/up/down from the local slab, metadata-aware cache, or staging. The q8
            // form keeps each gathered row in the sequential arithmetic class at `m=m_e`.
            let y = if let Some(dev) = slab_local {
                let gate_start = ex * m.gate_exps.expert_stride;
                let up_start = ex * m.up_exps.expert_stride;
                let down_start = ex * m.down_exps.expert_stride;
                if grouped_q8 {
                    let (zq, zd) = e.quantize_q8_1(&gathered, m_e, n_embd)?;
                    let gate = e.qmatvec_expert_q8(
                        &dev.gate,
                        gate_start..gate_start + gl.len,
                        &zq,
                        &zd,
                        m_e,
                        m.gate_exps.in_f,
                        m.gate_exps.out_f,
                        gl.qtype,
                        gl.row_bytes,
                    )?;
                    let up = e.qmatvec_expert_q8(
                        &dev.up,
                        up_start..up_start + ul.len,
                        &zq,
                        &zd,
                        m_e,
                        m.up_exps.in_f,
                        m.up_exps.out_f,
                        ul.qtype,
                        ul.row_bytes,
                    )?;
                    let mut act = e.uninit(m_e * n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        m_e * n_ff_exp,
                    )?;
                    let (aq2, ad2) = e.quantize_q8_1(&act, m_e, n_ff_exp)?;
                    e.qmatvec_expert_q8(
                        &dev.down,
                        down_start..down_start + dl.len,
                        &aq2,
                        &ad2,
                        m_e,
                        m.down_exps.in_f,
                        m.down_exps.out_f,
                        dl.qtype,
                        dl.row_bytes,
                    )?
                } else {
                    let gate = m.qmatvec_view(
                        e,
                        &dev.gate,
                        gate_start..gate_start + gl.len,
                        &gv,
                        m_e,
                        m.gate_exps.in_f,
                        m.gate_exps.out_f,
                        gl.qtype,
                        gl.row_bytes,
                    )?;
                    let up = m.qmatvec_view(
                        e,
                        &dev.up,
                        up_start..up_start + ul.len,
                        &gv,
                        m_e,
                        m.up_exps.in_f,
                        m.up_exps.out_f,
                        ul.qtype,
                        ul.row_bytes,
                    )?;
                    let mut act = e.uninit(m_e * n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        m_e * n_ff_exp,
                    )?;
                    let actv = act.slice(0..m_e * n_ff_exp);
                    m.qmatvec_view(
                        e,
                        &dev.down,
                        down_start..down_start + dl.len,
                        &actv,
                        m_e,
                        m.down_exps.in_f,
                        m.down_exps.out_f,
                        dl.qtype,
                        dl.row_bytes,
                    )?
                }
            } else if use_cache {
                use crate::moe_cache::{BlockId, PROJ_DOWN, PROJ_GATE, PROJ_UP};
                if grouped_q8 {
                    let (zq, zd) = e.quantize_q8_1(&gathered, m_e, n_embd)?;
                    let gate = e.with_moe_cache(max_block, |cache, eng| {
                        let id = BlockId::new(il, PROJ_GATE, ex as u16);
                        let slot = cache.dispatch_source(id, m.gate_exps.expert_source(ex), eng)?;
                        eng.qmatvec_expert_q8(
                            cache.buf(slot),
                            0..gl.len,
                            &zq,
                            &zd,
                            m_e,
                            m.gate_exps.in_f,
                            m.gate_exps.out_f,
                            gl.qtype,
                            gl.row_bytes,
                        )
                    })?;
                    let up = e.with_moe_cache(max_block, |cache, eng| {
                        let id = BlockId::new(il, PROJ_UP, ex as u16);
                        let slot = cache.dispatch_source(id, m.up_exps.expert_source(ex), eng)?;
                        eng.qmatvec_expert_q8(
                            cache.buf(slot),
                            0..ul.len,
                            &zq,
                            &zd,
                            m_e,
                            m.up_exps.in_f,
                            m.up_exps.out_f,
                            ul.qtype,
                            ul.row_bytes,
                        )
                    })?;
                    let mut act = e.uninit(m_e * n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        m_e * n_ff_exp,
                    )?;
                    let (aq2, ad2) = e.quantize_q8_1(&act, m_e, n_ff_exp)?;
                    e.with_moe_cache(max_block, |cache, eng| {
                        let id = BlockId::new(il, PROJ_DOWN, ex as u16);
                        let slot = cache.dispatch_source(id, m.down_exps.expert_source(ex), eng)?;
                        eng.qmatvec_expert_q8(
                            cache.buf(slot),
                            0..dl.len,
                            &aq2,
                            &ad2,
                            m_e,
                            m.down_exps.in_f,
                            m.down_exps.out_f,
                            dl.qtype,
                            dl.row_bytes,
                        )
                    })?
                } else {
                    let gate = e.with_moe_cache(max_block, |cache, eng| {
                        let id = BlockId::new(il, PROJ_GATE, ex as u16);
                        let slot = cache.dispatch_source(id, m.gate_exps.expert_source(ex), eng)?;
                        m.qmatvec_view(
                            eng,
                            cache.buf(slot),
                            0..gl.len,
                            &gv,
                            m_e,
                            m.gate_exps.in_f,
                            m.gate_exps.out_f,
                            gl.qtype,
                            gl.row_bytes,
                        )
                    })?;
                    let up = e.with_moe_cache(max_block, |cache, eng| {
                        let id = BlockId::new(il, PROJ_UP, ex as u16);
                        let slot = cache.dispatch_source(id, m.up_exps.expert_source(ex), eng)?;
                        m.qmatvec_view(
                            eng,
                            cache.buf(slot),
                            0..ul.len,
                            &gv,
                            m_e,
                            m.up_exps.in_f,
                            m.up_exps.out_f,
                            ul.qtype,
                            ul.row_bytes,
                        )
                    })?;
                    let mut act = e.uninit(m_e * n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        m_e * n_ff_exp,
                    )?;
                    let actv = act.slice(0..m_e * n_ff_exp);
                    e.with_moe_cache(max_block, |cache, eng| {
                        let id = BlockId::new(il, PROJ_DOWN, ex as u16);
                        let slot = cache.dispatch_source(id, m.down_exps.expert_source(ex), eng)?;
                        m.qmatvec_view(
                            eng,
                            cache.buf(slot),
                            0..dl.len,
                            &actv,
                            m_e,
                            m.down_exps.in_f,
                            m.down_exps.out_f,
                            dl.qtype,
                            dl.row_bytes,
                        )
                    })?
                }
            } else {
                let sg = scratch_g.as_mut().unwrap();
                let su = scratch_u.as_mut().unwrap();
                let sd = scratch_d.as_mut().unwrap();
                e.stage_expert(m.gate_exps.expert_bytes(ex), sg, 0)?;
                e.stage_expert(m.up_exps.expert_bytes(ex), su, 0)?;
                e.stage_expert(m.down_exps.expert_bytes(ex), sd, 0)?;
                if grouped_q8 {
                    let (zq, zd) = e.quantize_q8_1(&gathered, m_e, n_embd)?;
                    let gate = e.qmatvec_expert_q8(
                        sg,
                        0..gl.len,
                        &zq,
                        &zd,
                        m_e,
                        m.gate_exps.in_f,
                        m.gate_exps.out_f,
                        gl.qtype,
                        gl.row_bytes,
                    )?;
                    let up = e.qmatvec_expert_q8(
                        su,
                        0..ul.len,
                        &zq,
                        &zd,
                        m_e,
                        m.up_exps.in_f,
                        m.up_exps.out_f,
                        ul.qtype,
                        ul.row_bytes,
                    )?;
                    let mut act = e.uninit(m_e * n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        m_e * n_ff_exp,
                    )?;
                    let (aq2, ad2) = e.quantize_q8_1(&act, m_e, n_ff_exp)?;
                    e.qmatvec_expert_q8(
                        sd,
                        0..dl.len,
                        &aq2,
                        &ad2,
                        m_e,
                        m.down_exps.in_f,
                        m.down_exps.out_f,
                        dl.qtype,
                        dl.row_bytes,
                    )?
                } else {
                    let gate = m.qmatvec_view(
                        e,
                        sg,
                        0..gl.len,
                        &gv,
                        m_e,
                        m.gate_exps.in_f,
                        m.gate_exps.out_f,
                        gl.qtype,
                        gl.row_bytes,
                    )?;
                    let up = m.qmatvec_view(
                        e,
                        su,
                        0..ul.len,
                        &gv,
                        m_e,
                        m.up_exps.in_f,
                        m.up_exps.out_f,
                        ul.qtype,
                        ul.row_bytes,
                    )?;
                    let mut act = e.uninit(m_e * n_ff_exp)?;
                    Self::ffn_act_lim(
                        e,
                        cfg,
                        &gate,
                        &up,
                        m.gate_exps.macro_scale(ex),
                        m.up_exps.macro_scale(ex),
                        lim_exp,
                        &mut act,
                        m_e * n_ff_exp,
                    )?;
                    let actv = act.slice(0..m_e * n_ff_exp);
                    m.qmatvec_view(
                        e,
                        sd,
                        0..dl.len,
                        &actv,
                        m_e,
                        m.down_exps.in_f,
                        m.down_exps.out_f,
                        dl.qtype,
                        dl.row_bytes,
                    )?
                }
            };

            // SCATTER into slot buffer: each row goes to slot_buf[tok, slot, :].
            e.scatter_slot(
                &y,
                &tok_idx_d,
                &slot_idx_d,
                &weight_d,
                &mut slot_buf,
                &mut wbuf,
                n_embd,
                n_used,
                m_e,
            )?;
        }

        // 5. REDUCE SLOTS: sum the 8 slots per token into the final moe_out.
        let mut moe_out = e.zeros(t * n_embd)?;
        e.reduce_slots(&slot_buf, &wbuf, &mut moe_out, n_embd, n_used, t)?;

        // STATS: print m-distribution when MEMRA_MOE_STATS is set.
        if std::env::var("MEMRA_MOE_STATS").is_ok() && !m_dist.is_empty() {
            m_dist.sort_unstable();
            let active = m_dist.len();
            let mean = m_dist.iter().sum::<usize>() as f64 / active as f64;
            let median = m_dist[active / 2];
            let max_m = *m_dist.last().unwrap();
            let min_m = m_dist[0];
            let above16 = m_dist.iter().filter(|&&x| x >= 16).count();
            println!(
                "moe-grouped il={il} t={t} active={active}/{n_expert} \
                      m_e: min={min_m} median={median} mean={mean:.1} max={max_m} \
                      above_gemm_threshold(>=16)={above16}/{active}"
            );
        }

        Self::moe_ffn_grouped_add_shared(e, m, z, t, cfg, il, &mut moe_out)?;
        Ok(moe_out)
    }

    /// Lane-3 M2: cross-stream MoE for lockstep decode. Routes all m stream rows in one
    /// batch, executes fully-HBM-resident experts through the grouped gather/GEMM/scatter
    /// machinery at m_e>1 (weight reads amortized across streams), and assigns any expert
    /// with a missing projection to that row's CPU companion call (whole-expert granularity,
    /// same rule as the sequential frozen path). Slot-pinned accumulation keeps each row's
    /// expert-sum order identical to the sequential path.
    pub(crate) fn moe_ffn_lockstep(
        &self,
        e: &Engine,
        m: &MoeWeights,
        zbatch: &CudaSlice<f32>,
        mrows: usize,
        il: u16,
        max_block: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        use crate::moe_cache::{BlockId, PROJ_DOWN, PROJ_GATE, PROJ_UP};
        let cfg = &self.cfg;
        let moe = cfg.moe.as_ref().unwrap();
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        // step35 per-layer SwiGLU clamp; None on every other arch / unclamped layer.
        let lim_exp = cfg.clamp_exp_at(il as u32);
        let lim_shexp = cfg.clamp_shexp_at(il as u32);

        let logits = e.matmul(&m.gate_inp, zbatch, mrows)?;
        if let Some(sig) = cfg.sigmoid_router() {
            Self::trace_sigmoid_router_logits(e, il, mrows, n_expert, n_used, &logits, m, sig)?;
        }
        let (sel_all, w_all) = if let Some(sig) = cfg.sigmoid_router() {
            Self::moe_route_sigmoid_cfg(e, &logits, mrows, n_expert, n_used, m, sig)?
        } else {
            Self::moe_route_cfg(
                e,
                &logits,
                mrows,
                n_expert,
                n_used,
                m.active_experts.as_deref(),
            )?
        };
        Self::trace_moe_routes(il, mrows, &sel_all, &w_all)?;

        // Residency split at whole-expert granularity against the (frozen) cache.
        let resident_expert: Vec<bool> = e.with_moe_cache(max_block, |c, _| {
            Ok((0..n_expert)
                .map(|ex| {
                    [PROJ_GATE, PROJ_UP, PROJ_DOWN]
                        .into_iter()
                        .all(|p| c.resident(BlockId::new(il, p, ex as u16)).is_some())
                })
                .collect())
        })?;

        struct Group {
            rows: Vec<i32>,
            slots: Vec<i32>,
            weights: Vec<f32>,
        }
        let mut groups: std::collections::HashMap<usize, Group> = Default::default();
        let mut cpu_rows: Vec<Vec<(usize, f32)>> = vec![Vec::new(); mrows];
        let mut cpu_by_expert: std::collections::HashMap<usize, Vec<(usize, f32)>> =
            Default::default();
        for row in 0..mrows {
            for j in 0..n_used {
                let ex = sel_all[row * n_used + j] as usize;
                let w = w_all[row * n_used + j];
                if resident_expert[ex] {
                    let group = groups.entry(ex).or_insert_with(|| Group {
                        rows: Vec::new(),
                        slots: Vec::new(),
                        weights: Vec::new(),
                    });
                    group.rows.push(row as i32);
                    group.slots.push(j as i32);
                    group.weights.push(w);
                } else {
                    crate::cpu_experts::record_incomplete_gpu_residency(0);
                    cpu_rows[row].push((ex, w));
                    cpu_by_expert.entry(ex).or_default().push((row, w));
                }
            }
        }

        // CPU tickets first: reads/compute overlap the GPU grouped work below. Experts routed
        // by >=2 streams go through the multi-row ABI (weight decode amortized across rows);
        // each row's remaining experts stay one ordinary per-row call. Contribution FP-sum
        // order per row differs from the sequential single-call chunk — part of the
        // documented lockstep numeric class.
        let host_rows = e.dtoh(zbatch)?;
        let rows_ok = crate::cpu_experts::rows_supported();
        enum CpuPart {
            Single { row: usize },
            Rows { rows: Vec<usize> },
        }
        let mut tickets: Vec<(CpuPart, crate::cpu_experts::CpuExpertTicket)> = Vec::new();
        let mut rows_served: std::collections::HashSet<(usize, usize)> = Default::default();
        if rows_ok {
            let mut shared: Vec<(usize, Vec<(usize, f32)>)> = cpu_by_expert
                .into_iter()
                .filter(|(_, rows)| rows.len() >= 2)
                .collect();
            shared.sort_by_key(|(ex, _)| *ex);
            for (ex, mut row_weights) in shared {
                row_weights.sort_by_key(|(row, _)| *row);
                let inputs: Vec<(&[f32], f32)> = row_weights
                    .iter()
                    .map(|&(row, w)| (&host_rows[row * n_embd..(row + 1) * n_embd], w))
                    .collect();
                let job = crate::cpu_experts::prepare_rows_job(m, ex, &inputs)
                    .map_err(std::io::Error::other)?;
                for &(row, _) in &row_weights {
                    rows_served.insert((row, ex));
                }
                tickets.push((
                    CpuPart::Rows {
                        rows: row_weights.iter().map(|&(row, _)| row).collect(),
                    },
                    crate::cpu_experts::submit_rows(job).map_err(std::io::Error::other)?,
                ));
            }
        }
        for (row, selected) in cpu_rows.iter().enumerate() {
            let leftover: Vec<(usize, f32)> = selected
                .iter()
                .copied()
                .filter(|&(ex, _)| !rows_served.contains(&(row, ex)))
                .collect();
            if leftover.is_empty() {
                continue;
            }
            let host_row = &host_rows[row * n_embd..(row + 1) * n_embd];
            let job = crate::cpu_experts::prepare_job(m, il, &leftover, host_row)
                .map_err(std::io::Error::other)?;
            tickets.push((
                CpuPart::Single { row },
                crate::cpu_experts::submit(job).map_err(std::io::Error::other)?,
            ));
        }

        let mut slot_buf = e.zeros(mrows * n_used * n_embd)?;
        let mut wbuf = e.zeros(mrows * n_used)?;
        let mut order: Vec<usize> = groups.keys().copied().collect();
        order.sort_by(|&a, &b| {
            groups[&b]
                .rows
                .len()
                .cmp(&groups[&a].rows.len())
                .then(a.cmp(&b))
        });
        for &ex in &order {
            let group = &groups[&ex];
            let m_e = group.rows.len();
            let gl = m.gate_exps.expert_layout(ex);
            let ul = m.up_exps.expert_layout(ex);
            let dl = m.down_exps.expert_layout(ex);
            let row_idx_d = e.htod_i32(&group.rows)?;
            let slot_idx_d = e.htod_i32(&group.slots)?;
            let dmac = m.down_exps.macro_scale(ex);
            let weight_d = if dmac == 1.0 {
                e.htod(&group.weights)?
            } else {
                let scaled: Vec<f32> = group.weights.iter().map(|&w| w * dmac).collect();
                e.htod(&scaled)?
            };
            let mut gathered = e.zeros(m_e * n_embd)?;
            e.gather_rows(zbatch, &row_idx_d, &mut gathered, n_embd, m_e)?;
            let gv = gathered.slice(0..m_e * n_embd);
            let gate = e.with_moe_cache(max_block, |c, eng| {
                let slot = c
                    .resident(BlockId::new(il, PROJ_GATE, ex as u16))
                    .ok_or("lockstep resident expert vanished (cache not frozen?)")?;
                m.qmatvec_view(
                    eng,
                    c.buf(crate::moe_cache::DispatchSlot::Resident(slot)),
                    0..gl.len,
                    &gv,
                    m_e,
                    m.gate_exps.in_f,
                    m.gate_exps.out_f,
                    gl.qtype,
                    gl.row_bytes,
                )
            })?;
            let up = e.with_moe_cache(max_block, |c, eng| {
                let slot = c
                    .resident(BlockId::new(il, PROJ_UP, ex as u16))
                    .ok_or("lockstep resident expert vanished (cache not frozen?)")?;
                m.qmatvec_view(
                    eng,
                    c.buf(crate::moe_cache::DispatchSlot::Resident(slot)),
                    0..ul.len,
                    &gv,
                    m_e,
                    m.up_exps.in_f,
                    m.up_exps.out_f,
                    ul.qtype,
                    ul.row_bytes,
                )
            })?;
            let mut act = e.zeros(m_e * n_ff_exp)?;
            Self::ffn_act_lim(
                e,
                cfg,
                &gate,
                &up,
                m.gate_exps.macro_scale(ex),
                m.up_exps.macro_scale(ex),
                lim_exp,
                &mut act,
                m_e * n_ff_exp,
            )?;
            let actv = act.slice(0..m_e * n_ff_exp);
            let y = e.with_moe_cache(max_block, |c, eng| {
                let slot = c
                    .resident(BlockId::new(il, PROJ_DOWN, ex as u16))
                    .ok_or("lockstep resident expert vanished (cache not frozen?)")?;
                m.qmatvec_view(
                    eng,
                    c.buf(crate::moe_cache::DispatchSlot::Resident(slot)),
                    0..dl.len,
                    &actv,
                    m_e,
                    m.down_exps.in_f,
                    m.down_exps.out_f,
                    dl.qtype,
                    dl.row_bytes,
                )
            })?;
            e.scatter_slot(
                &y,
                &row_idx_d,
                &slot_idx_d,
                &weight_d,
                &mut slot_buf,
                &mut wbuf,
                n_embd,
                n_used,
                m_e,
            )?;
        }
        let mut moe_out = e.zeros(mrows * n_embd)?;
        e.reduce_slots(&slot_buf, &wbuf, &mut moe_out, n_embd, n_used, mrows)?;

        // CPU contributions join BEFORE the shared expert (the sequential path's placement).
        let mut row_sums: Vec<Option<Vec<f32>>> = vec![None; mrows];
        for (part, ticket) in tickets {
            let cpu_output = ticket.wait().map_err(std::io::Error::other)?;
            let mut add_row = |row: usize, chunk: &[f32]| {
                let sum = row_sums[row].get_or_insert_with(|| vec![0.0f32; n_embd]);
                for (accumulator, value) in sum.iter_mut().zip(chunk) {
                    *accumulator += value;
                }
            };
            match part {
                CpuPart::Single { row } => add_row(row, &cpu_output),
                CpuPart::Rows { rows } => {
                    for (slot, row) in rows.into_iter().enumerate() {
                        add_row(row, &cpu_output[slot * n_embd..(slot + 1) * n_embd]);
                    }
                }
            }
        }
        for (row, sum) in row_sums.into_iter().enumerate() {
            let Some(sum) = sum else { continue };
            let cpu_output = e.htod(&sum)?;
            let mut dst = moe_out.slice_mut(row * n_embd..(row + 1) * n_embd);
            e.axpy_into(&cpu_output, 1.0, &mut dst, n_embd)?;
        }

        if let (Some(gate_shexp), Some(up_shexp), Some(down_shexp)) =
            (&m.gate_shexp, &m.up_shexp, &m.down_shexp)
        {
            let n_ff_sh = gate_shexp.out_features();
            let sg_gate = e.matmul(gate_shexp, zbatch, mrows)?;
            let sg_up = e.matmul(up_shexp, zbatch, mrows)?;
            let mut sa = e.zeros(mrows * n_ff_sh)?;
            Self::ffn_act_lim(
                e,
                cfg,
                &sg_gate,
                &sg_up,
                1.0,
                1.0,
                lim_shexp,
                &mut sa,
                mrows * n_ff_sh,
            )?;
            let sh = e.matmul(down_shexp, &sa, mrows)?;
            // lockstep rows ARE decode tokens: fused sigmoid-dot per row so batched serving
            // decode matches the single-sequence decode chain bit-for-bit.
            let g = match &m.gate_inp_shexp {
                Some(gate_inp_shexp) => {
                    e.sigmoid_dot_rows(zbatch, gate_inp_shexp.float_data(), n_embd, mrows)?
                }
                None => e.htod(&vec![1.0f32; mrows])?,
            };
            e.add_scaled_rows(&sh, &g, &mut moe_out, n_embd, mrows)?;
        }

        Ok(moe_out)
    }
}

// ============================ gemma4 (R8 verified wiring) ==================================
// Node-for-node vs llama.cpp src/models/gemma4.cpp:180-405 (HANDOVER "R8 VERIFIED WIRING").
// v0 bring-up: full attention everywhere (exact for prompts < sliding_window 1024 — R6 masking
// later), sdpa_naive (hd 512 has no FA stamp), sequential host-staged MoE (the perf arms grow
// gemma variants after the correctness gate).
impl HybridModel {
    /// Per-layer attention geometry (R5): (head_dim, n_kv, n_head, rope_base, scale, is_swa).
    /// Rotary width the gemma-4 config DECLARES for layer `il`, per SWA/global class — the
    /// `n_rot` argument of the fused `rms_norm_qkv_rope*` kernels
    /// (`Engine::full_width_rope_only`, lane/graph-s-key-exactness-20260819).
    ///
    /// It exists so the fusion's full-width assumption is checked against the CONFIG instead of
    /// against itself: passing `hd` twice would guard nothing. On every gemma-4 artifact both
    /// routes set `rope_dims_{global,swa} == key_length_{global,swa}` (GGUF reads
    /// `rope.dimension_count(_swa)`, the safetensors route derives them from
    /// `global_head_dim`/`head_dim`), which is exactly WHY the fusion is legal here — gemma's
    /// "partial" rotary is per-dimension frequency scaling through the `rope_freqs` factors
    /// tensor, not an n_rot truncation. An arch that truncates instead (qwen3.5: 64 of 256;
    /// step35 full-attn: 64 of 128) must not reach these kernels, and now cannot silently.
    pub(crate) fn gemma4_rope_dims(&self, il: usize) -> usize {
        let g = self
            .cfg
            .gemma4
            .as_ref()
            .expect("gemma4_rope_dims on a non-gemma4 config");
        if g.swa_pattern[il] {
            g.rope_dims_swa as usize
        } else {
            g.rope_dims_global as usize
        }
    }

    pub(crate) fn gemma4_geom(&self, il: usize) -> (usize, usize, usize, f32, f32, bool) {
        let g = self.cfg.gemma4.as_ref().unwrap();
        let swa = g.swa_pattern[il];
        let hd = if swa {
            g.key_length_swa
        } else {
            g.key_length_global
        } as usize;
        // attention scale = 1.0 (llama gemma4.cpp:11 "Gemma4 uses self.scaling = 1.0" — q/k are
        // per-head rms-normed; NOT the 1/sqrt(hd) default. Bring-up bug: 1/sqrt(hd) left token-0
        // rows exact (softmax over one element) while every later position drifted).
        (
            hd,
            g.head_count_kv[il] as usize,
            self.cfg.n_head as usize,
            if swa {
                g.rope_base_swa
            } else {
                g.rope_base_global
            },
            1.0,
            swa,
        )
    }

    /// Suppress-token mask over t logits rows (tokenizer.ggml.suppress_tokens; no-op when the
    /// model ships none). NOT monotonic like softcap — must run before every argmax/sample, so
    /// every gemma4 logits tail (forward/prime/decode/dc/verify/e4b) calls this on device ld.
    pub(crate) fn gemma4_suppress(
        &self,
        e: &Engine,
        ld: &mut CudaSlice<f32>,
        t: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        if let Some((ids, n)) = self.gemma4_aux.as_ref().and_then(|a| a.suppress_d.as_ref()) {
            // suppress_d is primary-owned by the audited invariant (serving picks the last/head
            // stage as primary, and this tail runs only after the last stage). The assert turns
            // that argued invariant into a checked one: any topology violating primary==head
            // trips here in debug instead of silently peer-reading a device-0 buffer.
            #[cfg(debug_assertions)]
            crate::debug_assert_tensor_stream_device(
                ids,
                &e.stream(),
                "gemma4_suppress.suppress_d",
            );
            e.mask_ids_rows(ld, ids, *n, self.output.out_features(), t)?;
        }
        Ok(())
    }

    /// gemma4 attention (R5 geometry, R7 weightless V-norm on the RAW K projection, R9 dual rope).
    /// `cache`: Some => PRIME mode — append the T post-rope K / normed V rows into the quantized
    /// KV cache (same per-row quantize math as the decode append) and advance len. Fresh-prompt
    /// only (v0): attends within `tokens` via the f32 sdpa.
    #[allow(clippy::too_many_arguments)]
    /// ONE-PROGRAM seam (lane/gemma-restore-exactness-20260819, DEFAULT OFF): route dense
    /// gemma SWA prefill attention through the WINDOWED arm at every prompt length instead of
    /// switching program at `t > sliding_window`. The door is the measured cause of the
    /// prefix-restore exactness violation: below it the SWA layers attend unwindowed on f32
    /// operands, above it windowed on bf16 operands, so rows[0..932) of a 932-row prime and of
    /// a 1048-row prime differ in 59 of 60 layers. `=1` collapses it to one program so the
    /// published prefix KV stops depending on the total prompt length. Off by default because
    /// collapsing changes the bytes short prompts get today; the flip is an owner call.
    fn gemma_fa_one_program() -> bool {
        static ON: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
        *ON.get_or_init(|| std::env::var("MEMRA_GEMMA_FA_ONE_PROGRAM").as_deref() == Ok("1"))
    }

    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn gemma4_attn_prime(
        &self,
        e: &Engine,
        fa: &crate::hybrid::FullAttnLayer,
        il: usize,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: Option<&mut Cache>,
        island: Option<&CudaSlice<i32>>,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let (hd, nkv, nh, base, scale, swa) = self.gemma4_geom(il);
        let eps = self.cfg.rms_eps;
        let aux = self.gemma4_aux.as_ref().unwrap();
        let ones = aux.ones(e);
        #[cfg(debug_assertions)]
        crate::debug_assert_tensor_stream_device(ones, &e.stream(), "gemma4_attn_prime.ones");

        // quantize-once window: q/k/v share `h` — the MMQ D4 activation quantizes once
        // (h stays borrowed across the triple, so the cache key can't go stale).
        e.mmq_act_begin();
        let q0 = e.matmul(&fa.wq, h, t)?; // [t, nh*hd]
        if il == 0 && std::env::var("MEMRA_G4_PRIME_TRACE").as_deref() == Ok("1") {
            let v = e.dtoh(&q0)?;
            let nan = v.iter().filter(|x| x.is_nan()).count();
            let amax = v.iter().fold(0f32, |a, x| a.max(x.abs()));
            eprintln!(
                "[g4-prime-trace] L0 q0: nan={nan}/{} amax={amax:.3}",
                v.len()
            );
        }
        let k0 = e.matmul(&fa.wk, h, t)?; // [t, nkv*hd]
        // globals ship no v_proj (wv := wk at load) — V input is the SAME projection output;
        // reuse k0 instead of re-running the identical matmul (K=V dedup, 5 layers).
        let v0 = if swa {
            e.matmul(&fa.wv, h, t)?
        } else {
            e.clone_dtod(&k0)?
        };
        if il == 0 && std::env::var("MEMRA_G4_PRIME_TRACE").as_deref() == Ok("1") {
            for (tag, buf) in [("k0", &k0), ("v0", &v0)] {
                let v = e.dtoh(buf)?;
                let nan = v.iter().filter(|x| x.is_nan()).count();
                let amax = v.iter().fold(0f32, |a, x| a.max(x.abs()));
                eprintln!(
                    "[g4-prime-trace] L0 {tag}: nan={nan}/{} amax={amax:.3}",
                    v.len()
                );
            }
        }

        let mut q = e.uninit(t * nh * hd)?;
        let mut k = e.uninit(t * nkv * hd)?;
        // R7: V = weightless rms_norm of the raw projection; NEVER roped.
        let mut v = e.uninit(t * nkv * hd)?;
        // 31B glue lane: producers emit the bf16 FA operands (norm emits vb; rope emits qb/kb
        // post-rope) — kills 3 f32->bf16 converts + re-reads per layer. Bit-identical operands
        // (same __float2bfloat16); MEMRA_FA_EMIT=0 reverts to the convert-in-FA path.
        static EMIT: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
        // Island primes take the mask-capable naive kernel below; keep the operands f32
        // (the bf16 FA emit path has no island consumer). Text-only keeps emit unchanged.
        let emit = island.is_none()
            && t >= 16
            && crate::Engine::qkvnorm_w_on_prefill(nh * t + 2 * nkv * t, hd)
            && *EMIT.get_or_init(|| {
                std::env::var("MEMRA_FA_EMIT")
                    .map(|s| s != "0")
                    .unwrap_or(true)
            });
        let mut qb = e.alloc_uninit::<u8>(if emit { t * nh * hd * 2 } else { 1 })?;
        let mut kb = e.alloc_uninit::<u8>(if emit { t * nkv * hd * 2 } else { 1 })?;
        let mut vb = e.alloc_uninit::<u8>(if emit { t * nkv * hd * 2 } else { 1 })?;
        // f16-P/V door: emit V as f16 straight from the norm when this layer's FA consumer
        // reads f16 — kills the per-layer bf16->f16 re-encode (1.4GB/pass on 31B SWA).
        let v_f16 = emit
            && crate::fa_f16pv_on()
            && match hd {
                512 => true,
                256 => swa && crate::faw_hp_on() && nh % 2 == 0 && (nh / nkv) % 2 == 0,
                _ => false,
            };
        if emit {
            e.rms_norm_qkv_w4b(
                &q0,
                &k0,
                &v0,
                fa.q_norm.float_data(),
                fa.k_norm.float_data(),
                ones,
                &mut q,
                &mut k,
                &mut v,
                &mut vb,
                hd,
                nh * t,
                nkv * t,
                eps,
                v_f16,
            )?;
        } else {
            e.rms_norm_qkv(
                &q0,
                &k0,
                &v0,
                fa.q_norm.float_data(),
                fa.k_norm.float_data(),
                ones,
                &mut q,
                &mut k,
                &mut v,
                hd,
                nh * t,
                nkv * t,
                eps,
            )?;
        }

        let ff = if swa {
            None
        } else {
            Some(
                aux.rope_freqs(e)
                    .expect("gemma4 global rope needs rope_freqs.weight"),
            )
        };
        #[cfg(debug_assertions)]
        if let Some(ff) = ff {
            crate::debug_assert_tensor_stream_device(
                ff,
                &e.stream(),
                "gemma4_attn_prime.rope_freqs",
            );
        }
        if emit {
            e.rope_neox2_bf16e(
                &mut q, &mut k, &mut qb, &mut kb, pos_d, hd, hd, nh, nkv, t, base, 1.0, ff,
            )?;
        } else {
            e.rope_neox2(&mut q, &mut k, pos_d, hd, hd, nh, nkv, t, base, 1.0, ff)?;
        }

        if let Some(cache) = cache {
            let kvl = cache.kv[il].as_mut().unwrap();
            assert_eq!(kvl.len, 0, "gemma4 prime is fresh-prompt only (v0)");
            e.append_kv_quantized_rows(
                &k,
                &v,
                &mut kvl.k,
                &mut kvl.v,
                kvl.len,
                t,
                kvl.kv_dim_k,
                kvl.kv_dim_v,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on()),
            )?;
            kvl.len += t;
        }
        let mut attn = e.zeros(t * nh * hd)?;
        // R6: SWA layers mask keys older than sliding_window once the prompt exceeds it
        // (windowed naive twin; fa windowed stamps later). Under the window, full attention
        // is exact — SWA rides fa_prefill (hd-256 stamp), the hd-512 globals stay naive.
        let win = self.cfg.gemma4.as_ref().unwrap().sliding_window as usize;
        if let Some(span) = island {
            // Masked-prefill arm: every layer routes through the island-aware naive
            // kernel (correctness-first, same posture as the vision tower v1). The
            // window argument keeps the R6 shortcut: 0 while the prompt fits the
            // window, the real window beyond it.
            let w = if swa && t > win { win } else { 0 };
            e.sdpa_naive_island(&q, &k, &v, &mut attn, span, hd, nh, nkv, t, t, scale, w)?;
        } else if swa && (t > win || Self::gemma_fa_one_program()) {
            if hd == 256 && std::env::var("MEMRA_NOFA").is_err() {
                if emit {
                    e.fa_prefill_w_pre(
                        &qb, &kb, &vb, &mut attn, hd, nh, nkv, t, t, scale, true, win, v_f16,
                    )?;
                } else {
                    e.fa_prefill_w(&q, &k, &v, &mut attn, hd, nh, nkv, t, t, scale, true, win)?;
                }
            } else {
                e.sdpa_naive_w(&q, &k, &v, &mut attn, hd, nh, nkv, t, t, scale, true, win)?;
            }
        } else if hd == 256 && std::env::var("MEMRA_NOFA").is_err() {
            e.fa_prefill(&q, &k, &v, &mut attn, hd, nh, nkv, t, t, scale, true)?;
        } else if hd == 512 && std::env::var("MEMRA_NOFA").is_err() {
            if emit {
                e.fa_prefill_hd512_pre(
                    &qb, &kb, &vb, &mut attn, hd, nh, nkv, t, t, scale, true, v_f16,
                )?;
            } else {
                e.fa_prefill_hd512(&q, &k, &v, &mut attn, hd, nh, nkv, t, t, scale, true)?;
            }
        } else {
            e.sdpa_naive(&q, &k, &v, &mut attn, hd, nh, nkv, t, t, scale, true)?;
        }
        e.matmul(&fa.wo, &attn, t)
    }

    /// Back-compat wrapper (pure prefill, no cache).
    fn gemma4_attn(
        &self,
        e: &Engine,
        fa: &crate::hybrid::FullAttnLayer,
        il: usize,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        self.gemma4_attn_prime(e, fa, il, h, pos_d, t, None, None)
    }

    /// gemma4 MoE with the expert input PRE-QUANTIZED (the q8z tail fusion). Caller guarantees
    /// the fast-arm conditions (resident dev slabs + dp4a qtypes) — decode t=1 and verify rows
    /// arms only, per-token kernel chains identical to the f32-input path (same quantize bytes:
    /// the q8z epilogue is quantize_q8_1 verbatim).
    fn gemma4_moe_q8(
        &self,
        e: &Engine,
        m: &crate::hybrid::MoeWeights,
        bits: &crate::hybrid::Gemma4MoeBits,
        mq: &(CudaSlice<i8>, CudaSlice<f32>),
        router_in: &CudaSlice<f32>,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let cfg = &self.cfg;
        let moe = cfg.moe.as_ref().unwrap();
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;
        // router stays the two-launch pair: THREE fuse variants measured worse (serial-dot
        // -50%, 1024-thread warp-parallel -12% on topk sync overhead — jsonl 2026-07-11);
        // the pair's 12us is kernel time, not launch gaps.
        let logits = if crate::router_kernel_on() {
            e.router_gemv(m.gate_inp.float_data(), router_in, n_embd, n_expert, t)?
        } else {
            e.matmul(&m.gate_inp, router_in, t)?
        };
        let dev = m.dev_exps.as_ref().unwrap();
        let (sel_d, w_d) =
            e.moe_router_topk_scaled(&logits, t, n_expert, n_used, &bits.per_expert_scale_d)?;
        let (zq, zd) = mq;
        if t == 1 {
            let selv = sel_d.slice(0..n_used);
            let wv = w_d.slice(0..n_used);
            let act = e.moe_gate_up_gelu8_dev_q8(
                &dev.ptr_row,
                &selv,
                zq,
                zd,
                n_embd,
                n_ff_exp,
                n_used,
                n_expert,
                m.gate_exps.qtype,
                m.up_exps.qtype,
                m.gate_exps.row_bytes,
                m.up_exps.row_bytes,
            )?;
            let (aq2, ad2) = e.quantize_q8_1(&act, n_used, n_ff_exp)?;
            let mut moe_out = e.uninit(n_embd)?;
            e.moe_down8_fma_dev_q8(
                &dev.ptr_row,
                &selv,
                &wv,
                &aq2,
                &ad2,
                &mut moe_out.slice_mut(0..n_embd),
                n_ff_exp,
                n_embd,
                n_used,
                n_expert,
                m.down_exps.qtype,
                m.down_exps.row_bytes,
            )?;
            return Ok(moe_out);
        }
        let csr = t <= 10 && std::env::var("MEMRA_GEMMA_CSR").as_deref() != Ok("0");
        let act = if csr {
            e.moe_gate_up_gelu8_dev_q8_csr(
                &dev.ptr_row,
                &sel_d,
                zq,
                zd,
                t * n_used,
                n_embd,
                n_ff_exp,
                n_used,
                n_expert,
                m.gate_exps.qtype,
                m.up_exps.qtype,
                m.gate_exps.row_bytes,
                m.up_exps.row_bytes,
            )?
        } else {
            e.moe_gate_up_gelu8_dev_q8_rows(
                &dev.ptr_row,
                &sel_d,
                zq,
                zd,
                t,
                n_embd,
                n_ff_exp,
                n_used,
                n_expert,
                m.gate_exps.qtype,
                m.up_exps.qtype,
                m.gate_exps.row_bytes,
                m.up_exps.row_bytes,
            )?
        };
        let (aq2, ad2) = e.quantize_q8_1(&act, t * n_used, n_ff_exp)?;
        let mut moe_out = e.uninit(t * n_embd)?;
        // down stays rows_g: the CSR dedup twin measured NEGATIVE at nsb=22 too (189.4 vs
        // 207.0 depth spec, bitwise-exact — jsonl 2026-07-10; qwen nsb=16 same verdict).
        e.moe_down8_fma_dev_q8_rows_g(
            &dev.ptr_row,
            &sel_d,
            &w_d,
            &aq2,
            &ad2,
            &mut moe_out,
            t,
            n_ff_exp,
            n_embd,
            n_used,
            n_expert,
            m.down_exps.qtype,
            m.down_exps.row_bytes,
        )?;
        Ok(moe_out)
    }

    /// gemma4 MoE (R2 router prologue input supplied by caller, R3 per-expert output scale).
    /// Sequential host-staged v0 — softmax gating + renorm (moe_route, the qwen recipe), GELU
    /// experts, scale folded into the accumulate weight (post-matmul linear scale, exact fold).
    fn gemma4_moe(
        &self,
        e: &Engine,
        m: &crate::hybrid::MoeWeights,
        bits: &crate::hybrid::Gemma4MoeBits,
        moe_in: &CudaSlice<f32>,
        router_in: &CudaSlice<f32>,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let cfg = &self.cfg;
        let moe = cfg.moe.as_ref().unwrap();
        let n_embd = cfg.n_embd as usize;
        let n_expert = moe.expert_count as usize;
        let n_used = moe.expert_used_count as usize;
        let n_ff_exp = moe.expert_ff_length as usize;

        // Router: the in-house GEMV for ALL small t (decode AND verify ride the same per-column
        // kernel — the cuBLASLt n-dependence flipped top-k at verify t on the 27B, d994271);
        // batched matmul only at real prefill.
        let logits = if t < PRIME_MIN_T && crate::router_kernel_on() {
            e.router_gemv(m.gate_inp.float_data(), router_in, n_embd, n_expert, t)?
        } else {
            e.matmul(&m.gate_inp, router_in, t)?
        };

        // FAST SMALL-T ARM (decode t=1 AND spec verify t=2..15): device softmax-topk router,
        // then PER TOKEN the same fused gate_up GELU + down8 FMA launch pair over the resident
        // dev slabs — verify rides the EXACT decode kernel chain per token (dispatch-parity
        // law; the qwen "verify must be kernel-dispatch-identical to decode" lesson).
        if t < PRIME_MIN_T
            && m.dev_exps.as_ref().is_some_and(|d| !d.gu_il)
            && expert_dp4a_supported(m.gate_exps.qtype)
            && expert_dp4a_supported(m.up_exps.qtype)
            && expert_dp4a_supported(m.down_exps.qtype)
            && std::env::var("MEMRA_GEMMA_MOE_FAST").as_deref() != Ok("0")
        {
            let dev = m.dev_exps.as_ref().unwrap();
            let (sel_d, w_d) =
                e.moe_router_topk_scaled(&logits, t, n_expert, n_used, &bits.per_expert_scale_d)?;
            if t == 1 {
                let (zq, zd) = e.quantize_q8_1(moe_in, 1, n_embd)?;
                let selv = sel_d.slice(0..n_used);
                let wv = w_d.slice(0..n_used);
                let act = e.moe_gate_up_gelu8_dev_q8(
                    &dev.ptr_row,
                    &selv,
                    &zq,
                    &zd,
                    n_embd,
                    n_ff_exp,
                    n_used,
                    n_expert,
                    m.gate_exps.qtype,
                    m.up_exps.qtype,
                    m.gate_exps.row_bytes,
                    m.up_exps.row_bytes,
                )?;
                let (aq2, ad2) = e.quantize_q8_1(&act, n_used, n_ff_exp)?;
                let mut moe_out = e.uninit(n_embd)?;
                e.moe_down8_fma_dev_q8(
                    &dev.ptr_row,
                    &selv,
                    &wv,
                    &aq2,
                    &ad2,
                    &mut moe_out.slice_mut(0..n_embd),
                    n_ff_exp,
                    n_embd,
                    n_used,
                    n_expert,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?;
                return Ok(moe_out);
            }
            // VERIFY ROWS TWINS (t=2..15): ONE launch pair for all tokens; per (token,row,slot)
            // bodies are the t=1 kernels VERBATIM (bit-identical to the per-token loop).
            // gate_up rides the CSR owner-scan dedup when t <= 10 (each duplicated expert's
            // weight stream decoded once; the qwen CSR class). MEMRA_GEMMA_CSR=0 -> rows.
            let (zq, zd) = e.quantize_q8_1(moe_in, t, n_embd)?;
            let csr = t <= 10 && std::env::var("MEMRA_GEMMA_CSR").as_deref() != Ok("0");
            let act = if csr {
                e.moe_gate_up_gelu8_dev_q8_csr(
                    &dev.ptr_row,
                    &sel_d,
                    &zq,
                    &zd,
                    t * n_used,
                    n_embd,
                    n_ff_exp,
                    n_used,
                    n_expert,
                    m.gate_exps.qtype,
                    m.up_exps.qtype,
                    m.gate_exps.row_bytes,
                    m.up_exps.row_bytes,
                )?
            } else {
                e.moe_gate_up_gelu8_dev_q8_rows(
                    &dev.ptr_row,
                    &sel_d,
                    &zq,
                    &zd,
                    t,
                    n_embd,
                    n_ff_exp,
                    n_used,
                    n_expert,
                    m.gate_exps.qtype,
                    m.up_exps.qtype,
                    m.gate_exps.row_bytes,
                    m.up_exps.row_bytes,
                )?
            };
            let (aq2, ad2) = e.quantize_q8_1(&act, t * n_used, n_ff_exp)?;
            let mut moe_out = e.uninit(t * n_embd)?;
            e.moe_down8_fma_dev_q8_rows_g(
                &dev.ptr_row,
                &sel_d,
                &w_d,
                &aq2,
                &ad2,
                &mut moe_out,
                t,
                n_ff_exp,
                n_embd,
                n_used,
                n_expert,
                m.down_exps.qtype,
                m.down_exps.row_bytes,
            )?;
            return Ok(moe_out);
        }

        let (sel_all, mut w_all) = Self::moe_route(e, &logits, t, n_expert, n_used)?;
        for (i, &sx) in sel_all.iter().enumerate() {
            w_all[i] *= bits.per_expert_scale[sx as usize];
        }

        // PREFILL PAIRS ARM (t >= 16): expert-major CSR over the resident slabs — ONE launch per
        // projection covers ALL (token,expert) pairs (the qwen pairs recipe, _em dot = expert_dot_g
        // which has the Q4_0 body; GELU pairs epilogue; R3 scale folded into pair_w).
        if t >= PRIME_MIN_T
            && m.dev_exps.as_ref().is_some_and(|d| !d.gu_il)
            && expert_dp4a_supported(m.gate_exps.qtype)
            && expert_dp4a_supported(m.up_exps.qtype)
            && expert_dp4a_supported(m.down_exps.qtype)
            && std::env::var("MEMRA_GEMMA_MOE_PAIRS").as_deref() != Ok("0")
        {
            let dev = m.dev_exps.as_ref().unwrap();
            let n_pairs = t * n_used;
            let pair_ex: Vec<i32> = sel_all.iter().map(|&x| x as i32).collect();
            let pair_tok: Vec<i32> = (0..n_pairs).map(|p| (p / n_used) as i32).collect();
            let tok_off: Vec<i32> = (0..=t).map(|tok| (tok * n_used) as i32).collect();
            let tok_ids: Vec<i32> = (0..n_pairs as i32).collect();
            let pt = e.htod_i32(&pair_tok)?;
            let pw = e.htod(&w_all)?;
            let toff = e.htod_i32(&tok_off)?;
            let tids = e.htod_i32(&tok_ids)?;
            let mut by_ex: Vec<Vec<i32>> = vec![Vec::new(); n_expert];
            for p in 0..n_pairs {
                by_ex[pair_ex[p] as usize].push(p as i32);
            }
            let mut ex_ids: Vec<i32> = Vec::new();
            let mut ex_off: Vec<i32> = vec![0];
            let mut ex_pairs: Vec<i32> = Vec::with_capacity(n_pairs);
            for (ex, list) in by_ex.iter().enumerate() {
                if list.is_empty() {
                    continue;
                }
                ex_ids.push(ex as i32);
                ex_pairs.extend_from_slice(list);
                ex_off.push(ex_pairs.len() as i32);
            }
            let n_active = ex_ids.len();
            let exi = e.htod_i32(&ex_ids)?;
            let exo = e.htod_i32(&ex_off)?;
            let exp_d = e.htod_i32(&ex_pairs)?;
            // GROUPED f16 LANE (MEMRA_MOE_F16G=1, round 46 arc 2): dequant active experts to
            // f16 once per projection + one grouped f16 GEMM over the CSR groups; CSR order
            // end-to-end (gelu is elementwise), one row permute before the scatter. The
            // ragged down k (704) needs no padding here — cublas takes any k.
            // f16-mirror numeric class, argmax/spec gated. PER-MODEL default OFF (round 50):
            // the gelu class regressed g26 board-2048 prefill -8.3% under the round-49
            // Hopper default — see moe_f16g_gemma_on.
            if crate::moe_f16g_gemma_on()
                && f16g_proj_ok(m.gate_exps.qtype, n_embd)
                && f16g_proj_ok(m.up_exps.qtype, n_embd)
                && f16g_proj_ok(m.down_exps.qtype, n_ff_exp)
            {
                let csr_tok: Vec<i32> = ex_pairs.iter().map(|&p| p / n_used as i32).collect();
                let csr_tok_d = e.htod_i32(&csr_tok)?;
                let (z_f16, z_s) = e.moe_f16g_act(moe_in, Some(&csr_tok_d), n_embd, n_pairs)?;
                let g_csr = e.moe_f16_grouped(
                    &dev.ptr_row,
                    0,
                    n_expert,
                    &exi,
                    &ex_off,
                    &exo,
                    &z_f16,
                    &z_s,
                    n_embd,
                    n_ff_exp,
                    n_active,
                    n_pairs,
                    m.gate_exps.qtype,
                    m.gate_exps.row_bytes,
                )?;
                let u_csr = e.moe_f16_grouped(
                    &dev.ptr_row,
                    1,
                    n_expert,
                    &exi,
                    &ex_off,
                    &exo,
                    &z_f16,
                    &z_s,
                    n_embd,
                    n_ff_exp,
                    n_active,
                    n_pairs,
                    m.up_exps.qtype,
                    m.up_exps.row_bytes,
                )?;
                let act_csr = e.moe_pairs_gelu_mul(&g_csr, &u_csr, n_pairs * n_ff_exp)?;
                let (a_f16, a_s) = e.moe_f16g_act(&act_csr, None, n_ff_exp, n_pairs)?;
                let d_csr = e.moe_f16_grouped(
                    &dev.ptr_row,
                    2,
                    n_expert,
                    &exi,
                    &ex_off,
                    &exo,
                    &a_f16,
                    &a_s,
                    n_ff_exp,
                    n_embd,
                    n_active,
                    n_pairs,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?;
                let y_down = e.rows_permute(&d_csr, &exp_d, n_pairs, n_embd)?;
                let mut moe_out = e.uninit(t * n_embd)?;
                e.moe_pairs_scatter(&y_down, &pw, &toff, &tids, &mut moe_out, t, n_embd)?;
                if std::env::var("MEMRA_F16G_DEBUG").is_ok() {
                    let scan = |v: &[f32]| v.iter().filter(|x| !x.is_finite()).count();
                    let (yd, mo) = (e.dtoh(&y_down)?, e.dtoh(&moe_out)?);
                    eprintln!(
                        "[f16g-debug] post-permute bad={} post-scatter bad={}",
                        scan(&yd),
                        scan(&mo)
                    );
                }
                return Ok(moe_out);
            }
            // gate/up: int8-MMA expert GEMM (in_f = n_embd 2816 = 11x256 tiles ok); down keeps
            // the decode-once dp4a (in_f 704 fails the 256-superblock tiling).
            let mma = n_embd.is_multiple_of(256)
                && std::env::var("MEMRA_GEMMA_MOE_MMA").as_deref() != Ok("0");
            let (gate, up) = if mma {
                let z_scr = e.mmq_iq_quantize_act(moe_in, n_embd, t)?;
                (
                    e.mmq_iq_experts(
                        &dev.ptr_row,
                        0,
                        n_expert,
                        &exi,
                        &exo,
                        &exp_d,
                        &pt,
                        &z_scr,
                        n_embd,
                        n_ff_exp,
                        n_active,
                        n_pairs,
                        t,
                        m.gate_exps.qtype,
                        m.gate_exps.row_bytes,
                    )?,
                    e.mmq_iq_experts(
                        &dev.ptr_row,
                        1,
                        n_expert,
                        &exi,
                        &exo,
                        &exp_d,
                        &pt,
                        &z_scr,
                        n_embd,
                        n_ff_exp,
                        n_active,
                        n_pairs,
                        t,
                        m.up_exps.qtype,
                        m.up_exps.row_bytes,
                    )?,
                )
            } else {
                let (zq, zd) = e.quantize_q8_1(moe_in, t, n_embd)?;
                (
                    e.moe_pairs_matvec_q8_dec(
                        &dev.ptr_row,
                        0,
                        &exi,
                        &exo,
                        &exp_d,
                        &pt,
                        &zq,
                        &zd,
                        n_embd,
                        n_ff_exp,
                        n_expert,
                        n_active,
                        n_pairs,
                        m.gate_exps.qtype,
                        m.gate_exps.row_bytes,
                    )?,
                    e.moe_pairs_matvec_q8_dec(
                        &dev.ptr_row,
                        1,
                        &exi,
                        &exo,
                        &exp_d,
                        &pt,
                        &zq,
                        &zd,
                        n_embd,
                        n_ff_exp,
                        n_expert,
                        n_active,
                        n_pairs,
                        m.up_exps.qtype,
                        m.up_exps.row_bytes,
                    )?,
                )
            };
            let pair_self: Vec<i32> = (0..n_pairs as i32).collect();
            let pself = e.htod_i32(&pair_self)?;
            // DOWN through the int8-MMA expert GEMM (2026-07-31, g26 prefill lever): the
            // ragged k (n_ff_exp=704 on the 26B) rides a PADDED k-walk — in_f rounds up
            // to the 256-val superblock (768) while the act quantizer's zero padding
            // makes every padded-k product exactly zero (weight overread bytes multiply
            // zero int8 act values; the dev slab carries 144B tail slack for the OOB).
            // The old dp4a matvec was 11.3ms/call at m=T (the 0.07x prefill wall).
            // MEMRA_GEMMA_MOE_MMA=0 reverts down together with gate/up.
            // FUSED ACT-EPILOGUE (default on): gelu_tanh(gate)*up + D4 quantize in one
            // launch — no f32 act buffer (the fused kernel zero-pads the ragged tail
            // exactly like the two-pass quantizer). MEMRA_MOE_FUSE_ACTQ=0 rollback.
            // Scratch bytes are BYTE-IDENTICAL (kernel-check gated).
            let y_down = if mma {
                let in_pad = n_ff_exp.div_ceil(256) * 256;
                let a_scr = if crate::moe_fuse_actq_on() {
                    e.mmq_iq_fused_act_quant(&gate, &up, n_ff_exp, n_pairs, 1)?
                } else {
                    let act = e.moe_pairs_gelu_mul(&gate, &up, n_pairs * n_ff_exp)?;
                    e.mmq_iq_quantize_act(&act, n_ff_exp, n_pairs)?
                };
                e.mmq_iq_experts(
                    &dev.ptr_row,
                    2,
                    n_expert,
                    &exi,
                    &exo,
                    &exp_d,
                    &pself,
                    &a_scr,
                    in_pad,
                    n_embd,
                    n_active,
                    n_pairs,
                    n_pairs,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?
            } else {
                let act = e.moe_pairs_gelu_mul(&gate, &up, n_pairs * n_ff_exp)?;
                let (aq2, ad2) = e.quantize_q8_1(&act, n_pairs, n_ff_exp)?;
                e.moe_pairs_matvec_q8_dec(
                    &dev.ptr_row,
                    2,
                    &exi,
                    &exo,
                    &exp_d,
                    &pself,
                    &aq2,
                    &ad2,
                    n_ff_exp,
                    n_embd,
                    n_expert,
                    n_active,
                    n_pairs,
                    m.down_exps.qtype,
                    m.down_exps.row_bytes,
                )?
            };
            let mut moe_out = e.uninit(t * n_embd)?;
            e.moe_pairs_scatter(&y_down, &pw, &toff, &tids, &mut moe_out, t, n_embd)?;
            return Ok(moe_out);
        }

        let g_len = m.gate_exps.expert_stride;
        let u_len = m.up_exps.expert_stride;
        let d_len = m.down_exps.expert_stride;
        // Resident dev slabs (fits-VRAM regime): read each expert straight from the device slab
        // at ex*stride — zero H2D, SAME qmatvec_view kernel/bytes as the staged path. Staging is
        // the spill fallback.
        let dev = m.dev_exps.as_ref().filter(|d| !d.gu_il);
        let (mut sg, mut su, mut sd) = if dev.is_some() {
            (None, None, None)
        } else {
            (
                Some(e.alloc_u8_uninit(g_len)?),
                Some(e.alloc_u8_uninit(u_len)?),
                Some(e.alloc_u8_uninit(d_len)?),
            )
        };
        let mut moe_out = e.zeros(t * n_embd)?;
        for tok in 0..t {
            let sel = &sel_all[tok * n_used..(tok + 1) * n_used];
            let w = &w_all[tok * n_used..(tok + 1) * n_used];
            let zt = moe_in.slice(tok * n_embd..(tok + 1) * n_embd);
            for (j, &ex) in sel.iter().enumerate() {
                let ex = ex as usize;
                let gate = match dev {
                    Some(d) => m.qmatvec_view(
                        e,
                        &d.gate,
                        ex * g_len..(ex + 1) * g_len,
                        &zt,
                        1,
                        m.gate_exps.in_f,
                        m.gate_exps.out_f,
                        m.gate_exps.qtype,
                        m.gate_exps.row_bytes,
                    )?,
                    None => {
                        let sg = sg.as_mut().unwrap();
                        e.stage_expert(m.gate_exps.expert_bytes(ex), sg, 0)?;
                        m.qmatvec_view(
                            e,
                            sg,
                            0..g_len,
                            &zt,
                            1,
                            m.gate_exps.in_f,
                            m.gate_exps.out_f,
                            m.gate_exps.qtype,
                            m.gate_exps.row_bytes,
                        )?
                    }
                };
                let up = match dev {
                    Some(d) => m.qmatvec_view(
                        e,
                        &d.up,
                        ex * u_len..(ex + 1) * u_len,
                        &zt,
                        1,
                        m.up_exps.in_f,
                        m.up_exps.out_f,
                        m.up_exps.qtype,
                        m.up_exps.row_bytes,
                    )?,
                    None => {
                        let su = su.as_mut().unwrap();
                        e.stage_expert(m.up_exps.expert_bytes(ex), su, 0)?;
                        m.qmatvec_view(
                            e,
                            su,
                            0..u_len,
                            &zt,
                            1,
                            m.up_exps.in_f,
                            m.up_exps.out_f,
                            m.up_exps.qtype,
                            m.up_exps.row_bytes,
                        )?
                    }
                };
                let mut act = e.uninit(n_ff_exp)?;
                e.gelu_tanh_mul(&gate, &up, &mut act, n_ff_exp)?;
                let actv = act.slice(0..n_ff_exp);
                let y = match dev {
                    Some(d) => m.qmatvec_view(
                        e,
                        &d.down,
                        ex * d_len..(ex + 1) * d_len,
                        &actv,
                        1,
                        m.down_exps.in_f,
                        m.down_exps.out_f,
                        m.down_exps.qtype,
                        m.down_exps.row_bytes,
                    )?,
                    None => {
                        let sd = sd.as_mut().unwrap();
                        e.stage_expert(m.down_exps.expert_bytes(ex), sd, 0)?;
                        m.qmatvec_view(
                            e,
                            sd,
                            0..d_len,
                            &actv,
                            1,
                            m.down_exps.in_f,
                            m.down_exps.out_f,
                            m.down_exps.qtype,
                            m.down_exps.row_bytes,
                        )?
                    }
                };
                let mut dst = moe_out.slice_mut(tok * n_embd..(tok + 1) * n_embd);
                e.axpy_into(&y, w[j], &mut dst, n_embd)?;
            }
        }
        Ok(moe_out)
    }

    /// One gemma4 trunk layer (R8): x -> x_next.
    fn gemma4_layer(
        &self,
        e: &Engine,
        il: usize,
        layer: &crate::hybrid::HybridLayer,
        x: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;

        let mut h = e.zeros(t * n_embd)?;
        e.rms_norm(x, layer.attn_norm.float_data(), &mut h, n_embd, t, eps)?;
        let Mixer::Full(fa) = &layer.mixer else {
            panic!("gemma4 layer {il} not full-attn")
        };
        let o = self.gemma4_attn(e, fa, il, &h, pos_d, t)?;
        // gemma order: post_attention_norm applies to the ATTENTION OUTPUT, then the residual.
        let mut cur = e.zeros(t * n_embd)?;
        e.rms_norm(
            &o,
            layer.post_attn_norm.float_data(),
            &mut cur,
            n_embd,
            t,
            eps,
        )?;
        self.gemma4_layer_tail_add(e, layer, &cur, x, t)
    }

    /// Everything after the attention output in a gemma4 layer: the residual add (cur + x ->
    /// attn_out) FUSED with the three attn_out norms, then shared FFN + router + MoE + combine +
    /// layer scale — shared verbatim by the prefill, decode and verify paths.
    fn gemma4_layer_tail_add(
        &self,
        e: &Engine,
        layer: &crate::hybrid::HybridLayer,
        cur: &CudaSlice<f32>,
        x: &CudaSlice<f32>,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        Ok(self.gemma4_layer_tail_add_n(e, layer, cur, x, t, None)?.0)
    }

    /// tail_add with the NEXT layer's attn_norm fused into the closing add+scale (one launch
    /// produces both x_next and h_next — the cross-layer fusion). `next_norm` None = last layer.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    fn gemma4_layer_tail_add_n(
        &self,
        e: &Engine,
        layer: &crate::hybrid::HybridLayer,
        cur: &CudaSlice<f32>,
        x: &CudaSlice<f32>,
        t: usize,
        next_norm: Option<&CudaSlice<f32>>,
    ) -> Result<(CudaSlice<f32>, Option<CudaSlice<f32>>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let bits = layer.gemma4.as_ref().unwrap();
        let (sn, attn_out) = self.gemma4_layer_tail_core(e, layer, cur, x, t)?;
        let mut xn = e.uninit(t * n_embd)?;
        match next_norm {
            Some(w) => {
                let mut hn = e.uninit(t * n_embd)?;
                e.add_scale_rms_norm(
                    &sn,
                    &attn_out,
                    bits.layer_scale,
                    w,
                    &mut xn,
                    &mut hn,
                    n_embd,
                    t,
                    self.cfg.rms_eps,
                )?;
                Ok((xn, Some(hn)))
            }
            None => {
                e.add_scale(&sn, &attn_out, bits.layer_scale, &mut xn, t * n_embd)?;
                Ok((xn, None))
            }
        }
    }

    /// Tail core: attn_out = cur+x (fused with the 3 norms), both FFN branches, the combine
    /// norm — returns (sn, attn_out) for the closing add+scale variants.
    fn gemma4_layer_tail_core(
        &self,
        e: &Engine,
        layer: &crate::hybrid::HybridLayer,
        cur: &CudaSlice<f32>,
        x: &CudaSlice<f32>,
        t: usize,
    ) -> Result<(CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        self.gemma4_layer_tail_core_pn(e, layer, cur, x, t, None, false)
    }

    /// tail_core with an optional PRE-NORM fold (glue-fusion lane): `pre_norm = Some(wa)`
    /// means `cur` is the RAW attention output and the dense entry runs
    /// rms(cur, wa) + residual-add + ffn_norm as ONE launch (E4B's post-attn norm fold).
    /// `defer_post_norm`: dense-arm exit returns RAW f0 (ffn_down output) instead of
    /// sn = rms(f0, post_ffw) — the caller fuses the post-norm into its residual emit
    /// (rms_pre_add_q8_1, E4B glue wave 5). MoE arm ignores it.
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn gemma4_layer_tail_core_pn(
        &self,
        e: &Engine,
        layer: &crate::hybrid::HybridLayer,
        cur: &CudaSlice<f32>,
        x: &CudaSlice<f32>,
        t: usize,
        pre_norm: Option<&CudaSlice<f32>>,
        defer_post_norm: bool,
    ) -> Result<(CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let bits = layer.gemma4.as_ref().unwrap();

        // DENSE gemma4 variants (31B/E4B — no MoE, no parallel branch): attn_out = cur + x
        // fused with the single ffn_norm; GELU_PAR ffn; post_ffw_norm.
        let Some(mbits) = bits.moe_bits.as_ref() else {
            let crate::hybrid::Ffn::Dense {
                ffn_gate,
                ffn_up,
                ffn_down,
            } = &layer.ffn
            else {
                panic!("gemma4 dense layer without Dense ffn")
            };
            let mut attn_out = e.uninit(t * n_embd)?;
            let mut zsh = e.uninit(t * n_embd)?;
            // wave-2: with the pre-norm fold active the entry ALSO emits zsh q8_1 — the
            // t=1 fused2 gate/up consume the pair with no standalone quantize launch.
            let mut zpair: Option<(CudaSlice<i8>, CudaSlice<f32>)> = None;
            match pre_norm {
                Some(wa) if t == 1 => {
                    zpair = Some(e.rms_pre_add_rms_norm_q8z(
                        cur,
                        wa,
                        x,
                        bits.ffn_norm.float_data(),
                        &mut attn_out,
                        &mut zsh,
                        n_embd,
                        t,
                        eps,
                    )?);
                }
                Some(wa) => e.rms_pre_add_rms_norm(
                    cur,
                    wa,
                    x,
                    bits.ffn_norm.float_data(),
                    &mut attn_out,
                    &mut zsh,
                    n_embd,
                    t,
                    eps,
                )?,
                None => e.add_rms_norm(
                    cur,
                    x,
                    bits.ffn_norm.float_data(),
                    &mut attn_out,
                    &mut zsh,
                    n_embd,
                    t,
                    eps,
                )?,
            }
            let n_ff = ffn_gate.out_features();
            // FFN persistent slab (counter-barrier form) FALSIFIED here 2026-07-14
            // (falsification #7, jsonl row): PDL glue already hides the launch boundaries
            // it fused; barrier + worst-segment occupancy net −0.3% (31B depth) / −2.3%
            // (E4B spec). Down's act dependency is all-to-all, so sentinel sync cannot
            // rescue segment C — the megakernel front is closed for the dense tail.
            let (gate, up) = if t == 1 {
                let (zq, zd) = match zpair {
                    Some(p) => p,
                    None => e.quantize_q8_1(&zsh, 1, n_embd)?,
                };
                match e.matmul_q4_fused2(ffn_gate, ffn_up, &zq, &zd)? {
                    Some(p) => p,
                    // NVFP4mix dense trunk: gate/up are both NVFP4 (down stays Q8_0).
                    None => match e.matmul_nvfp4_fused2(ffn_gate, ffn_up, &zq, &zd, 1)? {
                        Some(p) => p,
                        None => (
                            e.matmul_pre(ffn_gate, &zq, &zd, &zsh, 1)?,
                            e.matmul_pre(ffn_up, &zq, &zd, &zsh, 1)?,
                        ),
                    },
                }
            } else {
                // BATCHED FUSED2 (DEFAULT ON 2026-07-13, MEMRA_F2B=0 seam): one segmented
                // launch for the verify's gate+up — the up segment's blocks fill SMs as
                // the gate segment drains (the launch-tail mechanism behind the b-tier
                // plateau; first positive after six falsified in-kernel variants).
                static F2B: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
                let f2b = *F2B.get_or_init(|| std::env::var("MEMRA_F2B").as_deref() != Ok("0"));
                let fused = if f2b {
                    let (zq, zd) = e.quantize_q8_1(&zsh, t, n_embd)?;
                    e.matmul_q4_fused2_batched(ffn_gate, ffn_up, &zq, &zd, t)?
                } else {
                    None
                };
                match fused {
                    Some(p) => p,
                    None => {
                        // quantize-once window: gate/up share `zsh` (borrowed across the pair).
                        e.mmq_act_begin();
                        (e.matmul(ffn_gate, &zsh, t)?, e.matmul(ffn_up, &zsh, t)?)
                    }
                }
            };
            let mut act = e.uninit(t * n_ff)?;
            // act quantize folds into the GELU epilogue (bit-identical q8_1 rounding);
            // ffn_down rides matmul_pre — one quantize launch fewer per layer.
            let f0 = if e.uses_q8_1_fast(ffn_down) {
                let upv = e.view(&up, t * n_ff);
                let up_all = upv.slice(0..t * n_ff);
                let (aq, ad) = e.gelu_tanh_mul_q8_1(&gate, &up_all, &mut act, n_ff, t)?;
                e.matmul_pre(ffn_down, &aq, &ad, &act, t)?
            } else {
                e.gelu_tanh_mul(&gate, &up, &mut act, t * n_ff)?;
                e.matmul(ffn_down, &act, t)?
            };
            if defer_post_norm {
                return Ok((f0, attn_out));
            }
            let mut sn = e.uninit(t * n_embd)?;
            e.rms_norm(
                &f0,
                bits.post_ffw_norm.float_data(),
                &mut sn,
                n_embd,
                t,
                eps,
            )?;
            return Ok((sn, attn_out));
        };

        assert!(pre_norm.is_none(), "pre-norm fold is dense-entry only");
        // MoE variant (26B): attn_out = cur + x fused with the three attn_out norms
        // (ffn_norm + router-scale + pre_ffw_norm_2): ONE launch, chains verbatim. At small t
        // (decode + verify) the zsh and moe_in outputs are EMITTED q8_1 (both consumers are
        // quantized matmuls — two quantize launches + two f32 round-trips fold away).
        let mut attn_out = e.uninit(t * n_embd)?;
        let mut router_in = e.uninit(t * n_embd)?;
        let fast_moe = match &layer.ffn {
            crate::hybrid::Ffn::Moe(m) => {
                m.dev_exps.as_ref().is_some_and(|d| !d.gu_il)
                    && expert_dp4a_supported(m.gate_exps.qtype)
                    && expert_dp4a_supported(m.up_exps.qtype)
                    && expert_dp4a_supported(m.down_exps.qtype)
                    && std::env::var("MEMRA_GEMMA_MOE_FAST").as_deref() != Ok("0")
            }
            _ => false,
        };
        let q8z = t < PRIME_MIN_T && fast_moe;
        let (zsh_f32, zsh_q8, moe_q8) = if q8z {
            let (z0, m2) = e.add_rms_norm3_q8z(
                cur,
                x,
                bits.ffn_norm.float_data(),
                &mbits.router_scale_pre,
                mbits.pre_ffw_norm_2.float_data(),
                &mut attn_out,
                &mut router_in,
                n_embd,
                t,
                eps,
            )?;
            (None, Some(z0), Some(m2))
        } else {
            let mut zsh = e.uninit(t * n_embd)?;
            let mut moe_in = e.uninit(t * n_embd)?;
            e.add_rms_norm3(
                cur,
                x,
                bits.ffn_norm.float_data(),
                &mbits.router_scale_pre,
                mbits.pre_ffw_norm_2.float_data(),
                &mut attn_out,
                &mut zsh,
                &mut router_in,
                &mut moe_in,
                n_embd,
                t,
                eps,
            )?;
            (Some((zsh, moe_in)), None, None)
        };
        let attn_out2 = attn_out;
        #[allow(unused_variables)]
        let attn_out = &attn_out2;
        let n_ff = mbits.shared_gate.out_features();
        let (gate, up) = if let Some((zq, zd)) = zsh_q8.as_ref() {
            if t == 1 {
                match e.matmul_q4_fused2(&mbits.shared_gate, &mbits.shared_up, zq, zd)? {
                    Some(p) => p,
                    None => match e.matmul_nvfp4_fused2(
                        &mbits.shared_gate,
                        &mbits.shared_up,
                        zq,
                        zd,
                        1,
                    )? {
                        Some(p) => p,
                        None => {
                            let h0 = e.zeros(0)?;
                            (
                                e.matmul_pre(&mbits.shared_gate, zq, zd, &h0, 1)?,
                                e.matmul_pre(&mbits.shared_up, zq, zd, &h0, 1)?,
                            )
                        }
                    },
                }
            } else {
                // verify t 2..15: the batched mmvq twins consume the pre-quantized pair.
                let h0 = e.zeros(0)?;
                (
                    e.matmul_pre(&mbits.shared_gate, zq, zd, &h0, t)?,
                    e.matmul_pre(&mbits.shared_up, zq, zd, &h0, t)?,
                )
            }
        } else {
            let (zsh, _) = zsh_f32.as_ref().unwrap();
            (
                e.matmul(&mbits.shared_gate, zsh, t)?,
                e.matmul(&mbits.shared_up, zsh, t)?,
            )
        };
        let mut act = e.uninit(t * n_ff)?;
        e.gelu_tanh_mul(&gate, &up, &mut act, t * n_ff)?;
        let mlp0 = e.matmul(&mbits.shared_down, &act, t)?;
        let crate::hybrid::Ffn::Moe(m) = &layer.ffn else {
            panic!("gemma4 layer not MoE")
        };
        let moe0 = match (&moe_q8, &zsh_f32) {
            (Some(mq), _) => self.gemma4_moe_q8(e, m, mbits, mq, &router_in, t)?,
            (None, Some((_, moe_in))) => self.gemma4_moe(e, m, mbits, moe_in, &router_in, t)?,
            _ => unreachable!(),
        };
        // post_ffw_norm_1(mlp0) + post_ffw_norm_2(moe0): one fused launch, per-row verbatim.
        let mut mlp = e.uninit(t * n_embd)?;
        let mut moe = e.uninit(t * n_embd)?;
        e.rms_norm2x(
            &mlp0,
            &moe0,
            mbits.post_ffw_norm_1.float_data(),
            mbits.post_ffw_norm_2.float_data(),
            &mut mlp,
            &mut moe,
            n_embd,
            t,
            eps,
        )?;

        // combine: rms_norm(mlp + moe, post_ffw_norm) + attn_out, then the layer output scalar.
        // add+norm fused (add_rms_norm == add then rms_norm, kernel-check-pinned identity).
        let mut sum = e.uninit(t * n_embd)?;
        let mut sn = e.uninit(t * n_embd)?;
        e.add_rms_norm(
            &mlp,
            &moe,
            bits.post_ffw_norm.float_data(),
            &mut sum,
            &mut sn,
            n_embd,
            t,
            eps,
        )?;
        Ok((sn, attn_out2))
    }

    /// tail_add with the next attn_norm emitted PRE-QUANTIZED q8_1 (decode/verify loops).
    /// pn-fold front of `gemma4_layer_tail_add_nq` (MEMRA_G4_PNFOLD, GAP-DIAGNOSIS
    /// verdict 7): takes the RAW attention output `o` and, on the dense trunk, folds
    /// post_attn_norm into the tail entry and post_ffw_norm into the residual exit —
    /// the E4B glue chain (13594) backported to the decode/verify trio. Kills the
    /// standalone rms_norm(o) + rms_norm(f0) (+ffn quantize) launches per layer.
    /// BITS-CHANGING vs the two-launch chain; every caller rides this ONE front so
    /// decode == verify == graph parity holds by construction at either seam value.
    /// Non-dense (26B MoE) and seam-off fall to the unfused chain unchanged.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    pub(crate) fn gemma4_layer_tail_add_nq_pn(
        &self,
        e: &Engine,
        layer: &crate::hybrid::HybridLayer,
        o: &CudaSlice<f32>,
        x: &CudaSlice<f32>,
        t: usize,
        next_norm: Option<&CudaSlice<f32>>,
    ) -> Result<(CudaSlice<f32>, Option<(CudaSlice<i8>, CudaSlice<f32>)>), Box<dyn std::error::Error>>
    {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let bits = layer.gemma4.as_ref().unwrap();
        if Engine::g4_pnfold_on() && matches!(layer.ffn, crate::hybrid::Ffn::Dense { .. }) {
            let (f0, attn_out) = self.gemma4_layer_tail_core_pn(
                e,
                layer,
                o,
                x,
                t,
                Some(layer.post_attn_norm.float_data()),
                true,
            )?;
            let mut xn = e.uninit(t * n_embd)?;
            return match next_norm {
                Some(w) => {
                    let pair = e.rms_pre_add_scale_rms_norm_q8_1(
                        &f0,
                        bits.post_ffw_norm.float_data(),
                        &attn_out,
                        bits.layer_scale,
                        w,
                        &mut xn,
                        n_embd,
                        t,
                        eps,
                    )?;
                    Ok((xn, Some(pair)))
                }
                None => {
                    let mut sn = e.uninit(t * n_embd)?;
                    e.rms_norm(
                        &f0,
                        bits.post_ffw_norm.float_data(),
                        &mut sn,
                        n_embd,
                        t,
                        eps,
                    )?;
                    e.add_scale(&sn, &attn_out, bits.layer_scale, &mut xn, t * n_embd)?;
                    Ok((xn, None))
                }
            };
        }
        let mut cur = e.uninit(t * n_embd)?;
        e.rms_norm(
            o,
            layer.post_attn_norm.float_data(),
            &mut cur,
            n_embd,
            t,
            eps,
        )?;
        self.gemma4_layer_tail_add_nq(e, layer, &cur, x, t, next_norm)
    }

    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    pub(crate) fn gemma4_layer_tail_add_nq(
        &self,
        e: &Engine,
        layer: &crate::hybrid::HybridLayer,
        cur: &CudaSlice<f32>,
        x: &CudaSlice<f32>,
        t: usize,
        next_norm: Option<&CudaSlice<f32>>,
    ) -> Result<(CudaSlice<f32>, Option<(CudaSlice<i8>, CudaSlice<f32>)>), Box<dyn std::error::Error>>
    {
        let n_embd = self.cfg.n_embd as usize;
        let bits = layer.gemma4.as_ref().unwrap();
        let (sn, attn_out) = self.gemma4_layer_tail_core(e, layer, cur, x, t)?;
        let mut xn = e.uninit(t * n_embd)?;
        match next_norm {
            Some(w) => {
                let pair = e.add_scale_rms_norm_q8_1(
                    &sn,
                    &attn_out,
                    bits.layer_scale,
                    w,
                    &mut xn,
                    n_embd,
                    t,
                    self.cfg.rms_eps,
                )?;
                Ok((xn, Some(pair)))
            }
            None => {
                e.add_scale(&sn, &attn_out, bits.layer_scale, &mut xn, t * n_embd)?;
                Ok((xn, None))
            }
        }
    }

    /// gemma4 prefill: `last_only` = forward_last semantics (lm_head on the final row only).
    /// R4: final logits softcapped 30*tanh(l/30) on host (monotonic — argmax unaffected).
    fn gemma4_forward(
        &self,
        e: &Engine,
        tokens: &[u32],
        last_only: bool,
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        // E4B routes to its own forward regardless of the caller's entry point (forward /
        // forward_last / prime paths all funnel here for gemma4).
        if self.is_gemma4_e4b() {
            return self.gemma4_e4b_forward(e, tokens, last_only);
        }
        let n_embd = self.cfg.n_embd as usize;
        let t = tokens.len();
        let pos: Vec<i32> = (0..t as i32).collect();
        let pos_d = e.htod_i32(&pos)?;

        let mut x = self.embed(e, tokens)?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), t * n_embd)?;
        // MEMRA_GEMMA_PROBE=1: per-layer trunk stats (host rms + max of the LAST token row) —
        // the bring-up bisect vs llama-eval-callback node stats.
        let probe = std::env::var("MEMRA_GEMMA_PROBE").is_ok();
        let stat =
            |e: &Engine, x: &CudaSlice<f32>, tag: &str| -> Result<(), Box<dyn std::error::Error>> {
                let h = e.dtoh(x)?;
                let bad = h.iter().filter(|v| !v.is_finite()).count();
                let mx = h
                    .iter()
                    .filter(|v| v.is_finite())
                    .fold(0.0f32, |m, v| m.max(v.abs()));
                eprintln!(
                    "[gemma-probe] {tag}: tok0_first3={:?} bad={bad} max={mx:.3e}",
                    &h[..3]
                );
                Ok(())
            };
        if probe {
            stat(e, &x, "embed")?;
        }
        for (il, layer) in self.layers.iter().enumerate() {
            x = self.gemma4_layer(e, il, layer, &x, &pos_d, t)?;
            if probe {
                stat(e, &x, &format!("L{il}"))?;
            }
        }
        let mut hn = e.zeros(t * n_embd)?;
        e.rms_norm(
            &x,
            self.output_norm.float_data(),
            &mut hn,
            n_embd,
            t,
            self.cfg.rms_eps,
        )?;
        let cap = self.cfg.gemma4.as_ref().unwrap().final_logit_softcapping;
        let n_vocab = self.output.out_features();
        let logits = if last_only {
            let hv = e.view(&hn, t * n_embd);
            let last_row = hv.slice((t - 1) * n_embd..t * n_embd);
            let mut hlast = e.zeros(n_embd)?;
            e.copy_view_into(&mut hlast, 0, &last_row, n_embd)?;
            let mut ld = e.matmul(&self.output, &hlast, 1)?;
            e.softcap(&mut ld, cap, n_vocab)?;
            self.gemma4_suppress(e, &mut ld, 1)?;
            e.dtoh(&ld)?
        } else {
            let mut ld = e.matmul(&self.output, &hn, t)?;
            e.softcap(&mut ld, cap, t * n_vocab)?;
            self.gemma4_suppress(e, &mut ld, t)?;
            e.dtoh(&ld)?
        };
        Ok(logits)
    }

    /// gemma4 BATCHED PROMPT PRIME v0 (fresh cache only): the prefill graph over the whole
    /// prompt with each layer's post-rope K / weightless-normed V appended into the quantized
    /// KV cache (decode-append row math). Returns (last-row softcapped logits, h_seed = last
    /// pre-output_norm hidden, hiddens = full pre-output_norm stack [T, n_embd]).
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    pub(crate) fn gemma4_prime(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
        overlay: Option<&crate::vision::EmbedOverlay>,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        // Err, not assert (2026-08-07, lane/gemma4-serve-gaps): a served gemma4 prompt longer
        // than the worker's prefill tick used to chunk here, and chunk 2 (pos > 0) killed the
        // whole worker process on this line. The worker now primes gemma4 monolithically and
        // routes continuation suffixes tokenwise; this is the per-request backstop.
        if cache.pos != 0 {
            return Err(
                "gemma4 prime v0 is fresh-prompt only (no continuation/chunked prime) \
                        — prime the full prompt in one call or decode tokenwise"
                    .into(),
            );
        }
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let t = tokens.len();
        let pos: Vec<i32> = (0..t as i32).collect();
        let pos_d = e.htod_i32(&pos)?;
        let mut x = self.embed(e, tokens)?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), t * n_embd)?;
        // Masked-prefill arm (lane/gemma-vision): splice the tower rows AFTER the
        // sqrt(n_embd) text scale — the reference scales token batches only
        // (llama.cpp gemma4.cpp:182 `ubatch.token ? sqrtf(n_embd) : 1.0f`), so image
        // embeddings enter the trunk raw. Each span becomes an attention ISLAND:
        // bidirectional within itself, causal+SWA everywhere else, matching the
        // reference's llama_set_causal_attn(false) image batch exactly.
        let island: Option<CudaSlice<i32>> = match overlay {
            Some(ov) => {
                let mut span_id = vec![-1i32; t];
                for (i, &(pos, row_off, n_rows)) in ov.spans.iter().enumerate() {
                    if pos + n_rows > t {
                        return Err(format!(
                            "gemma4 overlay span {i} [{pos}, {}) exceeds the prompt ({t})",
                            pos + n_rows
                        )
                        .into());
                    }
                    let view = ov.rows.slice(row_off * n_embd..(row_off + n_rows) * n_embd);
                    e.copy_view_into(&mut x, pos * n_embd, &view, n_rows * n_embd)?;
                    for s in span_id.iter_mut().skip(pos).take(n_rows) {
                        *s = i as i32;
                    }
                }
                // MEMRA_GV_FORCE_CAUSAL=1: WRONG-ARM probe seam — splice the rows but
                // keep the plain causal mask. Exists only so the decisive probe can show
                // the island mask itself changes the answer; never on in serving.
                if std::env::var("MEMRA_GV_FORCE_CAUSAL").as_deref() == Ok("1") {
                    None
                } else {
                    Some(e.htod_i32(&span_id)?)
                }
            }
            None => None,
        };
        for (il, layer) in self.layers.iter().enumerate() {
            let mut h = e.zeros(t * n_embd)?;
            e.rms_norm(&x, layer.attn_norm.float_data(), &mut h, n_embd, t, eps)?;
            let Mixer::Full(fa) = &layer.mixer else {
                panic!("gemma4 layer not full-attn")
            };
            let trace = il == 0 && std::env::var("MEMRA_G4_PRIME_TRACE").as_deref() == Ok("1");
            if trace {
                let v = e.dtoh(&h)?;
                let nan = v.iter().filter(|x| x.is_nan()).count();
                eprintln!("[g4-prime-trace] L0 post-attn_norm: nan={nan}/{}", v.len());
            }
            let o =
                self.gemma4_attn_prime(e, fa, il, &h, &pos_d, t, Some(cache), island.as_ref())?;
            if trace {
                let v = e.dtoh(&o)?;
                let nan = v.iter().filter(|x| x.is_nan()).count();
                eprintln!("[g4-prime-trace] L0 post-attn: nan={nan}/{}", v.len());
            }
            let mut cur = e.zeros(t * n_embd)?;
            e.rms_norm(
                &o,
                layer.post_attn_norm.float_data(),
                &mut cur,
                n_embd,
                t,
                eps,
            )?;
            x = self.gemma4_layer_tail_add(e, layer, &cur, &x, t)?;
            self.dflash_tap(e, cache, il, &x, t)?;
            // MEMRA_G4_PRIME_TRACE=1: per-layer NaN hunt (recipe-probe diagnostic; off = zero cost)
            if std::env::var("MEMRA_G4_PRIME_TRACE").as_deref() == Ok("1") {
                let h = e.dtoh(&x)?;
                let nan = h.iter().filter(|v| v.is_nan()).count();
                let amax = h.iter().fold(0f32, |a, v| a.max(v.abs()));
                eprintln!(
                    "[g4-prime-trace] layer {il}: nan={nan}/{} amax={amax:.3}",
                    h.len()
                );
                if nan > 0 {
                    return Err(format!("g4-prime-trace: first NaN at layer {il}").into());
                }
            }
        }
        cache.pos += t;
        let hiddens = e.clone_dtod(&x)?;
        let xv = e.view(&x, t * n_embd);
        let last_row = xv.slice((t - 1) * n_embd..t * n_embd);
        let mut h_seed = e.zeros(n_embd)?;
        e.copy_view_into(&mut h_seed, 0, &last_row, n_embd)?;
        let mut hn = e.uninit(n_embd)?;
        e.rms_norm(
            &h_seed,
            self.output_norm.float_data(),
            &mut hn,
            n_embd,
            1,
            eps,
        )?;
        let mut ld = e.matmul(&self.output, &hn, 1)?;
        let cap = self.cfg.gemma4.as_ref().unwrap().final_logit_softcapping;
        e.softcap(&mut ld, cap, self.output.out_features())?;
        self.gemma4_suppress(e, &mut ld, 1)?;
        let logits = e.dtoh(&ld)?;
        Ok((logits, h_seed, hiddens))
    }

    /// gemma4 T=1 decode attention: per-layer geometry, quantized-KV append + fa_decode
    /// (vec kernels at hd 256, generic scalar at the globals' hd 512), weightless V-norm,
    /// dual rope, scale 1.0. Takes the attn-normed input PRE-QUANTIZED (the cross-layer
    /// fused norm emits q8 directly — the f32 h never materializes).
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn gemma4_decode_attn(
        &self,
        e: &Engine,
        fa: &crate::hybrid::FullAttnLayer,
        il: usize,
        hq: &CudaSlice<i8>,
        hdq: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let (hd, nkv, nh, base, scale, swa) = self.gemma4_geom(il);
        let eps = self.cfg.rms_eps;
        let aux = self.gemma4_aux.as_ref().unwrap();
        let ones = aux.ones(e);
        #[cfg(debug_assertions)]
        crate::debug_assert_tensor_stream_device(ones, &e.stream(), "gemma4_decode_attn.ones");
        let (hq, hdq) = (hq, hdq);
        let h0 = e.zeros(0)?;
        let h = &h0;
        let (q0, k0, v0) = if swa {
            match e.matmul_q4_fused3(&fa.wq, &fa.wk, &fa.wv, hq, hdq)? {
                Some(t3) => t3,
                // NVFP4mix trio is MIXED-type (wv stays Q8_0), so fused3 can never
                // match — fuse the uniform (q,k) pair and take v as its own single.
                None => match e.matmul_nvfp4_fused2(&fa.wq, &fa.wk, hq, hdq, 1)? {
                    Some((q0, k0)) => {
                        let v0 = e.matmul_pre(&fa.wv, hq, hdq, h, 1)?;
                        (q0, k0, v0)
                    }
                    None => (
                        e.matmul_pre(&fa.wq, hq, hdq, h, 1)?,
                        e.matmul_pre(&fa.wk, hq, hdq, h, 1)?,
                        e.matmul_pre(&fa.wv, hq, hdq, h, 1)?,
                    ),
                },
            }
        } else {
            let (q0, k0) = match e.matmul_q4_fused2(&fa.wq, &fa.wk, hq, hdq)? {
                Some(p) => p,
                None => match e.matmul_nvfp4_fused2(&fa.wq, &fa.wk, hq, hdq, 1)? {
                    Some(p) => p,
                    None => (
                        e.matmul_pre(&fa.wq, hq, hdq, h, 1)?,
                        e.matmul_pre(&fa.wk, hq, hdq, h, 1)?,
                    ),
                },
            };
            let v0 = e.clone_dtod(&k0)?;
            (q0, k0, v0)
        };
        let mut q = e.uninit(nh * hd)?;
        let mut k = e.uninit(nkv * hd)?;
        let mut v = e.uninit(nkv * hd)?;
        // E4B wave-3 fold, m=1 completion (2026-07-23): the rows arms took this fold at
        // 550fcfa5; the decode-step trio kept 2 launches/layer it doesn't need.
        let ff = if swa {
            None
        } else {
            Some(
                aux.rope_freqs(e)
                    .expect("gemma4 global rope needs rope_freqs.weight"),
            )
        };
        #[cfg(debug_assertions)]
        if let Some(ff) = ff {
            crate::debug_assert_tensor_stream_device(
                ff,
                &e.stream(),
                "gemma4_decode_attn.rope_freqs",
            );
        }
        let kvl = cache.kv[il].as_mut().unwrap();
        let kv_fp8 = (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on());
        if crate::Engine::qkv_append_on() {
            // append fold, eager completion (zoo-fusion arc): the dc arms took this fold
            // 2026-07-23; the eager step kept the 2-launch pair it doesn't need. Host-len
            // twin of the dc fold — bit-identical bodies, one launch per layer.
            e.rms_norm_qkv_rope_append(
                &q0,
                &k0,
                &v0,
                fa.q_norm.float_data(),
                fa.k_norm.float_data(),
                ones,
                &mut q,
                &mut k,
                &mut v,
                hd,
                self.gemma4_rope_dims(il),
                nh,
                nkv,
                pos_d,
                nh,
                nkv,
                base,
                1.0,
                ff,
                eps,
                &mut kvl.k,
                &mut kvl.v,
                kvl.len,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                kv_fp8,
            )?;
        } else {
            e.rms_norm_qkv_rope(
                &q0,
                &k0,
                &v0,
                fa.q_norm.float_data(),
                fa.k_norm.float_data(),
                ones,
                &mut q,
                &mut k,
                &mut v,
                hd,
                self.gemma4_rope_dims(il),
                nh,
                nkv,
                pos_d,
                nh,
                nkv,
                base,
                1.0,
                ff,
                eps,
            )?;
            e.append_kv_quantized(
                &k,
                &v,
                &mut kvl.k,
                &mut kvl.v,
                kvl.len,
                kvl.kv_dim_k,
                kvl.kv_dim_v,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                kv_fp8,
            )?;
        }
        kvl.len += 1;
        // R6 decode: SWA layers attend only the last `sliding_window` keys — a token-aligned
        // VIEW OFFSET into the quantized cache (keys carry absolute rope; the mask is purely
        // positional). Globals attend the full history.
        let win = self.cfg.gemma4.as_ref().unwrap().sliding_window as usize;
        let mut attn = e.uninit(nh * hd)?;
        // global layers: SAME hd512 rows twin as verify with t=1 (parity law).
        if !swa
            && hd == 512
            && kvl.len >= crate::fa512_min_tkv()
            && std::env::var("MEMRA_GEMMA_ROWS_W").as_deref() != Ok("0")
        {
            let kp = e.view_u8(&kvl.k, kvl.len * kvl.k_tok_bytes);
            let vp = e.view_u8(&kvl.v, kvl.len * kvl.v_tok_bytes);
            // device-len: eager syncs the counter (async arg-store), dc keeps it live.
            let base = kvl.len as i32;
            e.i32_set_k(&mut kvl.len_d, base)?;
            e.fa_decode_rows(
                &q,
                &kp,
                &vp,
                &mut attn,
                hd,
                nh,
                nkv,
                kvl.len - 1,
                1,
                scale,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                Some((&kvl.len_d, -1)),
                false,
                false,
                None,
            )?;
            return e.matmul(&fa.wo, &attn, 1);
        }
        // windowed regime: SAME rows_w kernel as verify with t=1 (parity law — see verify_attn).
        if swa
            && kvl.len > win
            && hd == 256
            && std::env::var("MEMRA_GEMMA_ROWS_W").as_deref() != Ok("0")
        {
            let kp = e.view_u8(&kvl.k, kvl.len * kvl.k_tok_bytes);
            let vp = e.view_u8(&kvl.v, kvl.len * kvl.v_tok_bytes);
            let base = kvl.len as i32;
            e.i32_set_k(&mut kvl.len_d, base)?;
            e.fa_decode_rows_w(
                &q,
                &kp,
                &vp,
                &mut attn,
                hd,
                nh,
                nkv,
                &kvl.len_d,
                -1,
                1,
                scale,
                win,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                None,
            )?;
            return e.matmul(&fa.wo, &attn, 1);
        }
        let (off_tok, t_kv) = if swa && kvl.len > win {
            (kvl.len - win, win)
        } else {
            (0, kvl.len)
        };
        let k_view = e.view_u8_range(
            &kvl.k,
            off_tok * kvl.k_tok_bytes,
            (off_tok + t_kv) * kvl.k_tok_bytes,
        );
        let v_view = e.view_u8_range(
            &kvl.v,
            off_tok * kvl.v_tok_bytes,
            (off_tok + t_kv) * kvl.v_tok_bytes,
        );
        e.fa_decode_kvmod(
            &q,
            &k_view,
            &v_view,
            &mut attn,
            hd,
            nh,
            nkv,
            t_kv,
            scale,
            kvl.k_tok_bytes,
            kvl.v_tok_bytes,
            swa && crate::Engine::wkv_on(),
        )?;
        e.matmul(&fa.wo, &attn, 1)
    }

    /// gemma4 DEVICE-COUNTER decode step (graph arc): token id + rope pos + KV lengths live in
    /// device counters; ZERO varying host kernel args. `cap_bucket_max` = Some(bucket) for graph
    /// capture (host mirrors untouched, n_splits from bucket, full-buffer KV views) / None for
    /// the eager-dc gate path (host mirrors advanced, live geometry — bit-identical target =
    /// gemma4_decode_step_h's token stream). V1 scope: t_kv <= sliding_window (no window views
    /// in-graph; the driver gates).
    #[allow(clippy::too_many_arguments)]
    pub fn gemma4_decode_step_dc(
        &self,
        e: &Engine,
        token_d: &CudaSlice<u32>,
        pos_d: &mut CudaSlice<i32>,
        embd_gpu: &CudaSlice<u8>,
        embd_qt: i32,
        embd_rb: usize,
        cache: &mut Cache,
        n_vocab: usize,
        cap_bucket_max: Option<(usize, usize)>,
    ) -> Result<CudaSlice<u32>, Box<dyn std::error::Error>> {
        let mut tok_out = e.stream().alloc_zeros::<u32>(1)?;
        self.gemma4_decode_step_dc_into(
            e,
            token_d,
            pos_d,
            embd_gpu,
            embd_qt,
            embd_rb,
            cache,
            n_vocab,
            cap_bucket_max,
            &mut tok_out,
        )?;
        Ok(tok_out)
    }

    /// CAPTURE body: argmax lands in the PERSISTENT `tok_out` (same buffer = same address on
    /// every replay; pass `token_d` itself for the self-feeding graph loop).
    #[allow(clippy::too_many_arguments)]
    pub fn gemma4_decode_step_dc_into(
        &self,
        e: &Engine,
        token_d: &CudaSlice<u32>,
        pos_d: &mut CudaSlice<i32>,
        embd_gpu: &CudaSlice<u8>,
        embd_qt: i32,
        embd_rb: usize,
        cache: &mut Cache,
        n_vocab: usize,
        cap_bucket_max: Option<(usize, usize)>,
        tok_out: &mut CudaSlice<u32>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let mut x = e.embed_gather_device(embd_gpu, token_d, n_embd, embd_qt, embd_rb)?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), n_embd)?;
        let mut h_carry: Option<(CudaSlice<i8>, CudaSlice<f32>)> = None;
        let n_layers = self.layers.len();
        for (il, layer) in self.layers.iter().enumerate() {
            let (hq, hdq) = match h_carry.take() {
                Some(p) => p,
                None => {
                    e.rms_norm_q8_1(&x, self.layers[0].attn_norm.float_data(), n_embd, 1, eps)?
                }
            };
            let Mixer::Full(fa) = &layer.mixer else {
                panic!("gemma4 layer {il} not full-attn")
            };
            let o =
                self.gemma4_decode_attn_dc(e, fa, il, &hq, &hdq, pos_d, cache, cap_bucket_max)?;
            let next_norm = if il + 1 < n_layers {
                Some(self.layers[il + 1].attn_norm.float_data())
            } else {
                None
            };
            let (xn, hn) = self.gemma4_layer_tail_add_nq_pn(e, layer, &o, &x, 1, next_norm)?;
            x = xn;
            h_carry = hn;
        }
        let mut hn = e.uninit(n_embd)?;
        e.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, 1, eps)?;
        let mut logits = e.matmul(&self.output, &hn, 1)?;
        self.gemma4_suppress(e, &mut logits, 1)?; // cap skipped (monotonic); the mask is not
        e.argmax_token_device_into(&logits, tok_out, n_vocab)?;
        e.inc_seqlen(pos_d)?;
        if cap_bucket_max.is_none() {
            cache.pos += 1;
        }
        Ok(())
    }

    // Persistent transient slots for the ALLOC-FREE captured dc step (the graph door):
    // every buffer the step produces per token lives here, allocated ONCE pre-capture, so
    // the captured graph carries zero cuMemAllocAsync/Free nodes (the 226us/launch tax,
    // osrt 2026-07-23). Sized for the model's max per-layer shapes.

    /// Build the slot set (call OUTSIDE any capture).
    pub fn g4_dc_slots(&self, e: &Engine) -> Result<G4DcSlots, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let n_vocab = self.output.out_features();
        let n_layers = self.layers.len();
        let (mut qmax, mut kvmax, mut ffmax) = (0usize, 0usize, 0usize);
        for il in 0..n_layers {
            let (hd, nkv, nh, _b, _s, _w) = self.gemma4_geom(il);
            qmax = qmax.max(nh * hd);
            kvmax = kvmax.max(nkv * hd);
            if let crate::hybrid::Ffn::Dense { ffn_gate, .. } = &self.layers[il].ffn {
                ffmax = ffmax.max(ffn_gate.out_features());
            }
        }
        Ok(G4DcSlots {
            x: e.uninit(n_embd)?,
            xn: e.uninit(n_embd)?,
            cur: e.uninit(n_embd)?,
            hq: e.alloc_i8_uninit(n_embd)?,
            hd_: e.uninit(n_embd / 32)?,
            q0: e.uninit(qmax)?,
            k0: e.uninit(kvmax)?,
            v0: e.uninit(kvmax)?,
            q: e.uninit(qmax)?,
            k: e.uninit(kvmax)?,
            v: e.uninit(kvmax)?,
            attn: e.uninit(qmax)?,
            o: e.uninit(n_embd)?,
            attn_out: e.uninit(n_embd)?,
            zsh: e.uninit(n_embd)?,
            // zq/zd feed the wo matvec (nh*hd rows — 4096 on hd512 globals > n_embd),
            // the ffn entry (n_embd) and the lm_head (n_embd): size for the max.
            zq: e.alloc_i8_uninit(n_embd.max(qmax))?,
            zd: e.uninit(n_embd.max(qmax) / 32)?,
            gate: e.uninit(ffmax)?,
            up: e.uninit(ffmax)?,
            act: e.uninit(ffmax)?,
            actq: e.alloc_i8_uninit(ffmax)?,
            actd: e.uninit(ffmax / 32)?,
            f0: e.uninit(n_embd)?,
            sn: e.uninit(n_embd)?,
            hn: e.uninit(n_embd)?,
            logits: e.uninit(n_vocab)?,
        })
    }

    /// m=1 pre-quantized matvec into a slot — mirrors matmul_pre's m=1 mmvq route exactly
    /// (rp4-mirror bytes; mmvq_supports guaranteed for gemma4 q4_0/q6_K).
    fn g4_matvec_m1_into(
        &self,
        e: &Engine,
        w: &crate::model::GpuTensor,
        aq: &CudaSlice<i8>,
        ad: &CudaSlice<f32>,
        y: &mut CudaSlice<f32>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        use crate::model::GpuTensor;
        let (bytes, qtype, row_bytes, scale, rp) = match w {
            GpuTensor::Quant {
                bytes,
                qtype,
                row_bytes,
                scale,
                rp,
                ..
            } => (bytes, *qtype, *row_bytes, *scale, *rp),
            _ => return Err("g4_matvec_m1_into: non-quant tensor".into()),
        };
        let (mbytes, mrp) = match w {
            GpuTensor::Quant { rp4: Some(m4), .. } => (m4, true),
            _ => (bytes, rp),
        };
        e.qmatvec_mmvq_into(
            mbytes,
            aq,
            ad,
            1,
            w.in_features(),
            w.out_features(),
            qtype,
            row_bytes,
            scale,
            mrp,
            y,
        )
    }

    /// ALLOC-FREE dc step (capture body): kernel-for-kernel mirror of
    /// `gemma4_decode_step_dc_into` at t=1 with every transient slot-fed. Dense gemma4 only
    /// (12B/31B; uniform q4_0 trunk guarantees the fused2/3 arms).
    #[allow(clippy::too_many_arguments)]
    pub fn gemma4_decode_step_dc_slotted(
        &self,
        e: &Engine,
        token_d: &CudaSlice<u32>,
        pos_d: &mut CudaSlice<i32>,
        embd_gpu: &CudaSlice<u8>,
        embd_qt: i32,
        embd_rb: usize,
        cache: &mut Cache,
        n_vocab: usize,
        cap_bucket_max: Option<(usize, usize)>,
        sl: &mut G4DcSlots,
        tok_out: &mut CudaSlice<u32>,
        ring: Option<(&mut CudaSlice<u32>, usize)>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        e.embed_gather_device_into(embd_gpu, token_d, &mut sl.x, n_embd, embd_qt, embd_rb)?;
        e.scale_inplace(&mut sl.x, (n_embd as f32).sqrt(), n_embd)?;
        let n_layers = self.layers.len();
        let mut has_carry = false;
        for il in 0..n_layers {
            if !has_carry {
                e.rms_norm_q8_1_into(
                    &sl.x,
                    self.layers[il].attn_norm.float_data(),
                    n_embd,
                    1,
                    eps,
                    &mut sl.hq,
                    &mut sl.hd_,
                )?;
            }
            has_carry = true;
            let layer = &self.layers[il];
            let Mixer::Full(fa) = &layer.mixer else {
                panic!("gemma4 layer {il} not full-attn")
            };
            self.gemma4_decode_attn_dc_slotted(e, fa, il, pos_d, cache, cap_bucket_max, sl)?;
            // pn-fold: the tail's entry launch consumes RAW sl.o (post_attn folded there);
            // the standalone norm only survives on the unfused seam arm.
            if !Engine::g4_pnfold_on() {
                e.rms_norm(
                    &sl.o,
                    layer.post_attn_norm.float_data(),
                    &mut sl.cur,
                    n_embd,
                    1,
                    eps,
                )?;
            }
            let next_norm = if il + 1 < n_layers {
                Some(self.layers[il + 1].attn_norm.float_data())
            } else {
                None
            };
            self.gemma4_layer_tail_slotted(e, layer, next_norm, sl)?;
            std::mem::swap(&mut sl.x, &mut sl.xn);
        }
        e.rms_norm(
            &sl.x,
            self.output_norm.float_data(),
            &mut sl.hn,
            n_embd,
            1,
            eps,
        )?;
        e.quantize_q8_1_into(&sl.hn, 1, n_embd, &mut sl.zq, &mut sl.zd)?;
        // lm_head via the same m=1 mmvq route as matmul (q6_K on the gemma4 family).
        {
            let (zq, zd) = (&sl.zq, &sl.zd);
            let zq = unsafe { &*(zq as *const CudaSlice<i8>) };
            let zd = unsafe { &*(zd as *const CudaSlice<f32>) };
            self.g4_matvec_m1_into(e, &self.output, zq, zd, &mut sl.logits)?;
        }
        self.gemma4_suppress(e, &mut sl.logits, 1)?;
        e.argmax_token_device_into(&sl.logits, tok_out, n_vocab)?;
        if let Some((ring, base)) = ring {
            // in-graph token parking: slot = (pos - base) % ring.len(); the door drains
            // with ONE sync per chunk instead of a per-token dtoh (the launch-serialization
            // fix — llama's 885us/launch overlaps GPU work because nothing waits per token).
            e.plain_tok_ring(tok_out, pos_d, base, ring)?;
        }
        e.inc_seqlen(pos_d)?;
        if cap_bucket_max.is_none() {
            cache.pos += 1;
        }
        Ok(())
    }

    /// Slot-fed dc attention: mirrors gemma4_decode_attn_dc's CAPTURE arm kernel-for-kernel.
    #[allow(clippy::too_many_arguments)]
    fn gemma4_decode_attn_dc_slotted(
        &self,
        e: &Engine,
        fa: &crate::hybrid::FullAttnLayer,
        il: usize,
        pos_d: &CudaSlice<i32>,
        cache: &mut Cache,
        cap_bucket_max: Option<(usize, usize)>,
        sl: &mut G4DcSlots,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let (hd, nkv, nh, base, scale, swa) = self.gemma4_geom(il);
        let eps = self.cfg.rms_eps;
        let aux = self.gemma4_aux.as_ref().unwrap();
        let ones = aux.ones(e);
        #[cfg(debug_assertions)]
        crate::debug_assert_tensor_stream_device(
            ones,
            &e.stream(),
            "gemma4_decode_attn_dc_slotted.ones",
        );
        {
            let hq = unsafe { &*(&sl.hq as *const CudaSlice<i8>) };
            let hdq = unsafe { &*(&sl.hd_ as *const CudaSlice<f32>) };
            if swa {
                if !e.matmul_q4_fused3_into(
                    &fa.wq, &fa.wk, &fa.wv, hq, hdq, &mut sl.q0, &mut sl.k0, &mut sl.v0,
                )? {
                    // NVFP4mix trio is MIXED-type (wv stays Q8_0): fuse the uniform
                    // (q,k) pair, v through the generic m1 slot matvec — the same two
                    // bodies the eager arm runs, slot-fed for the alloc-free capture.
                    if e.matmul_nvfp4_fused2_into(&fa.wq, &fa.wk, hq, hdq, &mut sl.q0, &mut sl.k0)?
                    {
                        self.g4_matvec_m1_into(e, &fa.wv, hq, hdq, &mut sl.v0)?;
                    } else {
                        return Err("slotted step: fused3 unavailable (non-uniform trunk)".into());
                    }
                }
            } else {
                if !e.matmul_q4_fused2_into(&fa.wq, &fa.wk, hq, hdq, &mut sl.q0, &mut sl.k0)?
                    && !e
                        .matmul_nvfp4_fused2_into(&fa.wq, &fa.wk, hq, hdq, &mut sl.q0, &mut sl.k0)?
                {
                    return Err("slotted step: fused2 unavailable".into());
                }
                let k0r = unsafe { &*(&sl.k0 as *const CudaSlice<f32>) };
                e.copy_into(&mut sl.v0, 0, k0r, nkv * hd)?;
            }
        }
        // E4B wave-3 fold, m=1 completion (2026-07-23) — MUST mirror the dc_into arm
        // kernel-for-kernel (graph stream-identity gate).
        let ff = if swa {
            None
        } else {
            Some(
                aux.rope_freqs(e)
                    .expect("gemma4 global rope needs rope_freqs.weight"),
            )
        };
        #[cfg(debug_assertions)]
        if let Some(ff) = ff {
            crate::debug_assert_tensor_stream_device(
                ff,
                &e.stream(),
                "gemma4_decode_attn_dc_slotted.rope_freqs",
            );
        }
        let kvl = cache.kv[il].as_mut().unwrap();
        let kv_fp8 = (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on());
        if crate::Engine::qkv_append_on() {
            // append fold (2026-07-23): mirrors dc_into.
            e.rms_norm_qkv_rope_append_dc(
                &sl.q0,
                &sl.k0,
                &sl.v0,
                fa.q_norm.float_data(),
                fa.k_norm.float_data(),
                ones,
                &mut sl.q,
                &mut sl.k,
                &mut sl.v,
                hd,
                self.gemma4_rope_dims(il),
                nh,
                nkv,
                pos_d,
                nh,
                nkv,
                base,
                1.0,
                ff,
                eps,
                &mut kvl.k,
                &mut kvl.v,
                &kvl.len_d,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                kv_fp8,
            )?;
        } else {
            e.rms_norm_qkv_rope(
                &sl.q0,
                &sl.k0,
                &sl.v0,
                fa.q_norm.float_data(),
                fa.k_norm.float_data(),
                ones,
                &mut sl.q,
                &mut sl.k,
                &mut sl.v,
                hd,
                self.gemma4_rope_dims(il),
                nh,
                nkv,
                pos_d,
                nh,
                nkv,
                base,
                1.0,
                ff,
                eps,
            )?;
            e.append_kv_quantized_dc(
                &sl.k,
                &sl.v,
                &mut kvl.k,
                &mut kvl.v,
                &kvl.len_d,
                kvl.kv_dim_k,
                kvl.kv_dim_v,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                kv_fp8,
            )?;
        }
        e.inc_seqlen(&mut kvl.len_d)?;
        let (b_swa, b_glob) = cap_bucket_max.expect("slotted step is capture-only");
        let k_view = e.view_u8(&kvl.k, kvl.k.len());
        let v_view = e.view_u8(&kvl.v, kvl.v.len());
        let rows_on = std::env::var("MEMRA_GEMMA_ROWS_W").as_deref() != Ok("0");
        let win = self.cfg.gemma4.as_ref().unwrap().sliding_window as usize;
        // combine-q8 emit (wave-5b m=1 port): the rows arms quantize inside the combine —
        // the standalone quantize launch runs only on the non-rows fallback. MUST mirror
        // the dc_into arm branch-for-branch (stream gate).
        let mut fa_q8 = false;
        if !swa && hd == 512 && b_glob >= crate::fa512_min_tkv() && rows_on {
            e.fa_decode_rows(
                &sl.q,
                &k_view,
                &v_view,
                &mut sl.attn,
                hd,
                nh,
                nkv,
                b_glob - 1,
                1,
                scale,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                Some((&kvl.len_d, -1)),
                false,
                false,
                Some((&mut sl.zq, &mut sl.zd)),
            )?;
            fa_q8 = true;
        } else if swa && b_swa > win && hd == 256 && rows_on {
            e.fa_decode_rows_w(
                &sl.q,
                &k_view,
                &v_view,
                &mut sl.attn,
                hd,
                nh,
                nkv,
                &kvl.len_d,
                -1,
                1,
                scale,
                win,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                Some((&mut sl.zq, &mut sl.zd)),
            )?;
            fa_q8 = true;
        } else {
            let b = if swa { b_swa } else { b_glob };
            e.fa_decode_dc(
                &sl.q,
                &k_view,
                &v_view,
                &mut sl.attn,
                hd,
                nh,
                nkv,
                &kvl.len_d,
                b,
                scale,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                swa && crate::Engine::wkv_on(),
            )?;
        }
        if !fa_q8 {
            let aq = unsafe { &*(&sl.attn as *const CudaSlice<f32>) };
            e.quantize_q8_1_into(aq, 1, nh * hd, &mut sl.zq, &mut sl.zd)?;
        }
        {
            let zq = unsafe { &*(&sl.zq as *const CudaSlice<i8>) };
            let zd = unsafe { &*(&sl.zd as *const CudaSlice<f32>) };
            self.g4_matvec_m1_into(e, &fa.wo, zq, zd, &mut sl.o)?;
        }
        Ok(())
    }

    /// Slot-fed dense layer tail: mirrors gemma4_layer_tail_core (t=1, no pre-norm fold) +
    /// tail_add_nq kernel-for-kernel; the next layer's (hq, hd_) carry lands in the slots.
    fn gemma4_layer_tail_slotted(
        &self,
        e: &Engine,
        layer: &crate::hybrid::HybridLayer,
        next_norm: Option<&CudaSlice<f32>>,
        sl: &mut G4DcSlots,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let bits = layer.gemma4.as_ref().unwrap();
        let crate::hybrid::Ffn::Dense {
            ffn_gate,
            ffn_up,
            ffn_down,
        } = &layer.ffn
        else {
            return Err("slotted tail: dense ffn only".into());
        };
        let pnfold = Engine::g4_pnfold_on();
        if pnfold {
            // pn-fold entry (E4B glue, slot-fed): rms(o, post_attn) + residual + ffn_norm
            // + q8 emit in ONE launch — replaces rms_norm + add_rms_norm + quantize.
            let or = unsafe { &*(&sl.o as *const CudaSlice<f32>) };
            let xr = unsafe { &*(&sl.x as *const CudaSlice<f32>) };
            e.rms_pre_add_rms_norm_q8z_into(
                or,
                layer.post_attn_norm.float_data(),
                xr,
                bits.ffn_norm.float_data(),
                &mut sl.attn_out,
                &mut sl.zsh,
                n_embd,
                1,
                eps,
                &mut sl.zq,
                &mut sl.zd,
            )?;
        } else {
            e.add_rms_norm(
                &sl.cur,
                &sl.x,
                bits.ffn_norm.float_data(),
                &mut sl.attn_out,
                &mut sl.zsh,
                n_embd,
                1,
                eps,
            )?;
        }
        let n_ff = ffn_gate.out_features();
        if !pnfold {
            let zshr = unsafe { &*(&sl.zsh as *const CudaSlice<f32>) };
            e.quantize_q8_1_into(zshr, 1, n_embd, &mut sl.zq, &mut sl.zd)?;
        }
        {
            let zq = unsafe { &*(&sl.zq as *const CudaSlice<i8>) };
            let zd = unsafe { &*(&sl.zd as *const CudaSlice<f32>) };
            if !e.matmul_q4_fused2_into(ffn_gate, ffn_up, zq, zd, &mut sl.gate, &mut sl.up)?
                && !e.matmul_nvfp4_fused2_into(
                    ffn_gate,
                    ffn_up,
                    zq,
                    zd,
                    &mut sl.gate,
                    &mut sl.up,
                )?
            {
                return Err("slotted tail: ffn fused2 unavailable".into());
            }
        }
        debug_assert!(e.uses_q8_1_fast(ffn_down));
        {
            let upr = unsafe { &*(&sl.up as *const CudaSlice<f32>) };
            let upv = e.view(upr, n_ff);
            let up_all = upv.slice(0..n_ff);
            let gr = unsafe { &*(&sl.gate as *const CudaSlice<f32>) };
            e.gelu_tanh_mul_q8_1_into(
                gr,
                &up_all,
                &mut sl.act,
                n_ff,
                1,
                &mut sl.actq,
                &mut sl.actd,
            )?;
        }
        {
            let aq = unsafe { &*(&sl.actq as *const CudaSlice<i8>) };
            let ad = unsafe { &*(&sl.actd as *const CudaSlice<f32>) };
            self.g4_matvec_m1_into(e, ffn_down, aq, ad, &mut sl.f0)?;
        }
        if pnfold {
            // pn-fold exit: rms(f0, post_ffw) + scaled residual + next attn_norm + q8
            // emit in ONE launch — replaces rms_norm + add_scale_rms_norm_q8_1.
            if let Some(w) = next_norm {
                let f0r = unsafe { &*(&sl.f0 as *const CudaSlice<f32>) };
                let aor = unsafe { &*(&sl.attn_out as *const CudaSlice<f32>) };
                e.rms_pre_add_scale_rms_norm_q8_1_into(
                    f0r,
                    bits.post_ffw_norm.float_data(),
                    aor,
                    bits.layer_scale,
                    w,
                    &mut sl.xn,
                    n_embd,
                    1,
                    eps,
                    &mut sl.hq,
                    &mut sl.hd_,
                )?;
                return Ok(());
            }
        }
        e.rms_norm(
            &sl.f0,
            bits.post_ffw_norm.float_data(),
            &mut sl.sn,
            n_embd,
            1,
            eps,
        )?;
        match next_norm {
            Some(w) => {
                e.add_scale_rms_norm_q8_1_into(
                    &sl.sn,
                    &sl.attn_out,
                    bits.layer_scale,
                    w,
                    &mut sl.xn,
                    n_embd,
                    1,
                    eps,
                    &mut sl.hq,
                    &mut sl.hd_,
                )?;
            }
            None => {
                e.add_scale(&sl.sn, &sl.attn_out, bits.layer_scale, &mut sl.xn, n_embd)?;
            }
        }
        Ok(())
    }

    /// dc attention: same math as gemma4_decode_attn, KV slot/lengths from device counters.
    #[allow(clippy::too_many_arguments)]
    fn gemma4_decode_attn_dc(
        &self,
        e: &Engine,
        fa: &crate::hybrid::FullAttnLayer,
        il: usize,
        hq: &CudaSlice<i8>,
        hdq: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        cache: &mut Cache,
        cap_bucket_max: Option<(usize, usize)>,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let (hd, nkv, nh, base, scale, swa) = self.gemma4_geom(il);
        let eps = self.cfg.rms_eps;
        let aux = self.gemma4_aux.as_ref().unwrap();
        let ones = aux.ones(e);
        #[cfg(debug_assertions)]
        crate::debug_assert_tensor_stream_device(ones, &e.stream(), "gemma4_decode_attn_dc.ones");
        let (q0, k0, v0) = if swa {
            match e.matmul_q4_fused3(&fa.wq, &fa.wk, &fa.wv, hq, hdq)? {
                Some(t3) => t3,
                // NVFP4mix: wv is Q8_0 (mixed trio) — fuse the (q,k) pair, v single.
                None => match e.matmul_nvfp4_fused2(&fa.wq, &fa.wk, hq, hdq, 1)? {
                    Some((q0, k0)) => {
                        let h0 = e.zeros(0)?;
                        let v0 = e.matmul_pre(&fa.wv, hq, hdq, &h0, 1)?;
                        (q0, k0, v0)
                    }
                    None => {
                        let h0 = e.zeros(0)?;
                        (
                            e.matmul_pre(&fa.wq, hq, hdq, &h0, 1)?,
                            e.matmul_pre(&fa.wk, hq, hdq, &h0, 1)?,
                            e.matmul_pre(&fa.wv, hq, hdq, &h0, 1)?,
                        )
                    }
                },
            }
        } else {
            let (q0, k0) = match e.matmul_q4_fused2(&fa.wq, &fa.wk, hq, hdq)? {
                Some(p) => p,
                None => match e.matmul_nvfp4_fused2(&fa.wq, &fa.wk, hq, hdq, 1)? {
                    Some(p) => p,
                    None => {
                        let h0 = e.zeros(0)?;
                        (
                            e.matmul_pre(&fa.wq, hq, hdq, &h0, 1)?,
                            e.matmul_pre(&fa.wk, hq, hdq, &h0, 1)?,
                        )
                    }
                },
            };
            let v0 = e.clone_dtod(&k0)?;
            (q0, k0, v0)
        };
        let mut q = e.uninit(nh * hd)?;
        let mut k = e.uninit(nkv * hd)?;
        let mut v = e.uninit(nkv * hd)?;
        // E4B wave-3 fold, m=1 completion (2026-07-23): mirrored by the slotted arm.
        let ff = if swa {
            None
        } else {
            Some(
                aux.rope_freqs(e)
                    .expect("gemma4 global rope needs rope_freqs.weight"),
            )
        };
        #[cfg(debug_assertions)]
        if let Some(ff) = ff {
            crate::debug_assert_tensor_stream_device(
                ff,
                &e.stream(),
                "gemma4_decode_attn_dc.rope_freqs",
            );
        }
        let kvl = cache.kv[il].as_mut().unwrap();
        let kv_fp8 = (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on());
        if crate::Engine::qkv_append_on() {
            // append fold (2026-07-23): norm+rope+cache-append in ONE launch.
            e.rms_norm_qkv_rope_append_dc(
                &q0,
                &k0,
                &v0,
                fa.q_norm.float_data(),
                fa.k_norm.float_data(),
                ones,
                &mut q,
                &mut k,
                &mut v,
                hd,
                self.gemma4_rope_dims(il),
                nh,
                nkv,
                pos_d,
                nh,
                nkv,
                base,
                1.0,
                ff,
                eps,
                &mut kvl.k,
                &mut kvl.v,
                &kvl.len_d,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                kv_fp8,
            )?;
        } else {
            e.rms_norm_qkv_rope(
                &q0,
                &k0,
                &v0,
                fa.q_norm.float_data(),
                fa.k_norm.float_data(),
                ones,
                &mut q,
                &mut k,
                &mut v,
                hd,
                self.gemma4_rope_dims(il),
                nh,
                nkv,
                pos_d,
                nh,
                nkv,
                base,
                1.0,
                ff,
                eps,
            )?;
            e.append_kv_quantized_dc(
                &k,
                &v,
                &mut kvl.k,
                &mut kvl.v,
                &kvl.len_d,
                kvl.kv_dim_k,
                kvl.kv_dim_v,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                kv_fp8,
            )?;
        }
        e.inc_seqlen(&mut kvl.len_d)?;
        let mut attn = e.uninit(nh * hd)?;
        // combine-q8 emit carrier (wave-5b m=1 port): rows arms fill this; the tail then
        // rides g4_matvec_m1_into instead of matmul's internal quantize.
        let mut fa_q8: Option<(CudaSlice<i8>, CudaSlice<f32>)> = None;
        // Weight prefetch NOT wired here (26B/31B/qwen probes 2026-07-13: 26B flat
        // 196.6/196.0 vs 196.4/196.1 — MoE ffn dilutes wo; 31B −0.2% — dense decode sits
        // at the DRAM wall, no idle window to front-load into). E4B keeps the arm
        // (gemma4_e4b_attn, +0.65% valid window).
        match cap_bucket_max {
            None => {
                // dc-EAGER: host knows the length — R6 window views EXACTLY like the eager
                // decode (SWA layers attend the last `sliding_window` keys); the device
                // counters carry only the append slot + the graph seam.
                kvl.len += 1;
                let win = self.cfg.gemma4.as_ref().unwrap().sliding_window as usize;
                if !swa
                    && hd == 512
                    && kvl.len >= crate::fa512_min_tkv()
                    && std::env::var("MEMRA_GEMMA_ROWS_W").as_deref() != Ok("0")
                {
                    // global layers: SAME hd512 rows twin as verify, t=1 (parity law).
                    // dc: len_d is live (inc_seqlen) — device-len rides it, plus=-1.
                    let kp = e.view_u8(&kvl.k, kvl.len * kvl.k_tok_bytes);
                    let vp = e.view_u8(&kvl.v, kvl.len * kvl.v_tok_bytes);
                    let (mut aq8, mut ad8) = e.uninit_q8_pair(nh * hd)?;
                    e.fa_decode_rows(
                        &q,
                        &kp,
                        &vp,
                        &mut attn,
                        hd,
                        nh,
                        nkv,
                        kvl.len - 1,
                        1,
                        scale,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                        Some((&kvl.len_d, -1)),
                        false,
                        false,
                        Some((&mut aq8, &mut ad8)),
                    )?;
                    fa_q8 = Some((aq8, ad8));
                } else if swa
                    && kvl.len > win
                    && hd == 256
                    && std::env::var("MEMRA_GEMMA_ROWS_W").as_deref() != Ok("0")
                {
                    // windowed regime: SAME rows_w kernel as verify, t=1 (parity law).
                    let kp = e.view_u8(&kvl.k, kvl.len * kvl.k_tok_bytes);
                    let vp = e.view_u8(&kvl.v, kvl.len * kvl.v_tok_bytes);
                    let (mut aq8, mut ad8) = e.uninit_q8_pair(nh * hd)?;
                    e.fa_decode_rows_w(
                        &q,
                        &kp,
                        &vp,
                        &mut attn,
                        hd,
                        nh,
                        nkv,
                        &kvl.len_d,
                        -1,
                        1,
                        scale,
                        win,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                        Some((&mut aq8, &mut ad8)),
                    )?;
                    fa_q8 = Some((aq8, ad8));
                } else {
                    let (off_tok, t_kv) = if swa && kvl.len > win {
                        (kvl.len - win, win)
                    } else {
                        (0, kvl.len)
                    };
                    let k_view = e.view_u8_range(
                        &kvl.k,
                        off_tok * kvl.k_tok_bytes,
                        (off_tok + t_kv) * kvl.k_tok_bytes,
                    );
                    let v_view = e.view_u8_range(
                        &kvl.v,
                        off_tok * kvl.v_tok_bytes,
                        (off_tok + t_kv) * kvl.v_tok_bytes,
                    );
                    e.fa_decode_kvmod(
                        &q,
                        &k_view,
                        &v_view,
                        &mut attn,
                        hd,
                        nh,
                        nkv,
                        t_kv,
                        scale,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                        swa && crate::Engine::wkv_on(),
                    )?;
                }
            }
            Some((b_swa, b_glob)) => {
                // capture: full-buffer views + device length. PARITY LAW port (graph arc step
                // 3): the SAME regime branches as dc-eager, pinned per arm — b_swa is the
                // exact-key t_kv for the dc family (n_splits constant per bucket), b_glob is
                // the RUNG max for the rows family (kernels derive per-replay splits from
                // kvl.len_d; grid/partials sized for the rung end, excess splits exit).
                let k_view = e.view_u8(&kvl.k, kvl.k.len());
                let v_view = e.view_u8(&kvl.v, kvl.v.len());
                let rows_on = std::env::var("MEMRA_GEMMA_ROWS_W").as_deref() != Ok("0");
                let win = self.cfg.gemma4.as_ref().unwrap().sliding_window as usize;
                if !swa && hd == 512 && b_glob >= crate::fa512_min_tkv() && rows_on {
                    let (mut aq8, mut ad8) = e.uninit_q8_pair(nh * hd)?;
                    e.fa_decode_rows(
                        &q,
                        &k_view,
                        &v_view,
                        &mut attn,
                        hd,
                        nh,
                        nkv,
                        b_glob - 1,
                        1,
                        scale,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                        Some((&kvl.len_d, -1)),
                        false,
                        false,
                        Some((&mut aq8, &mut ad8)),
                    )?;
                    fa_q8 = Some((aq8, ad8));
                } else if swa && b_swa > win && hd == 256 && rows_on {
                    let (mut aq8, mut ad8) = e.uninit_q8_pair(nh * hd)?;
                    e.fa_decode_rows_w(
                        &q,
                        &k_view,
                        &v_view,
                        &mut attn,
                        hd,
                        nh,
                        nkv,
                        &kvl.len_d,
                        -1,
                        1,
                        scale,
                        win,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                        Some((&mut aq8, &mut ad8)),
                    )?;
                    fa_q8 = Some((aq8, ad8));
                } else {
                    let b = if swa { b_swa } else { b_glob };
                    e.fa_decode_dc(
                        &q,
                        &k_view,
                        &v_view,
                        &mut attn,
                        hd,
                        nh,
                        nkv,
                        &kvl.len_d,
                        b,
                        scale,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                        swa && crate::Engine::wkv_on(),
                    )?;
                }
            }
        }
        // combine-q8 emit (wave-5b m=1 port): the rows arms produced the wo activation pair
        // in-combine — ride the slotted arm's exact matvec route (parity by construction).
        if let Some((aq8, ad8)) = fa_q8 {
            let mut y = e.uninit(fa.wo.out_features())?;
            self.g4_matvec_m1_into(e, &fa.wo, &aq8, &ad8, &mut y)?;
            return Ok(y);
        }
        e.matmul(&fa.wo, &attn, 1)
    }

    /// gemma4 GRAPH-REPLAY greedy loop: per (swa-key, global-key) fa bucket, capture ONE full
    /// dc step (self-feeding: argmax writes token_d in-graph) and replay it — one graph launch
    /// per token, one 4B dtoh. V1 scope: whole generation under the sliding window (no window
    /// views in-graph); caller gates and falls back to the dc-eager loop.
    #[allow(clippy::too_many_arguments)]
    // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    #[allow(clippy::map_entry)] // allow: the init body is fallible (`?`); Entry::or_insert_with cannot propagate errors
    pub fn gemma4_generate_graph(
        &self,
        e: &Engine,
        prompt_pos: usize,
        first_token: u32,
        cache: &mut Cache,
        max_new: usize,
        eos: &[u32],
        mut on_token: impl FnMut(u32) -> bool,
    ) -> Result<(Vec<u32>, crate::decode::StopReason), Box<dyn std::error::Error>> {
        if self.is_gemma4_e4b() {
            return Err(
                "E4B graph serving is unwired (HANDOVER-E4B.md) — dc-eager is the serving arm"
                    .into(),
            );
        }
        use crate::decode::StopReason;
        let n_vocab = self.output.out_features();
        let n_embd = self.cfg.n_embd as usize;
        let embd_gpu = self
            .embd_gpu
            .get_or_init(|| e.upload_u8(&self.embd.raw).expect("embed table upload"));
        let (qt, rb) = self.embd.qt_and_row_bytes(n_embd);
        for kvl in cache.kv.iter_mut().flatten() {
            e.set_i32_one(&mut kvl.len_d, kvl.len as i32)?;
        }
        let mut token_d = e.stream().clone_htod(&[first_token])?;
        let mut pos_d = e.htod_i32(&[prompt_pos as i32])?;
        let g4 = self.cfg.gemma4.as_ref().unwrap();
        let (hd_s, hd_g) = (g4.key_length_swa as usize, g4.key_length_global as usize);
        // per-layer nkv: swa vs global counts from the pattern (uniform within class).
        let nkv_s = g4
            .head_count_kv
            .iter()
            .zip(g4.swa_pattern.iter())
            .find(|p| *p.1)
            .map(|p| *p.0 as usize)
            .unwrap_or(8);
        let nkv_g = g4
            .head_count_kv
            .iter()
            .zip(g4.swa_pattern.iter())
            .find(|p| !*p.1)
            .map(|p| *p.0 as usize)
            .unwrap_or(2);
        #[allow(clippy::type_complexity)]
        // allow: one-shot composite type; naming it would hide the shape that matters at the call site
        let mut graphs: std::collections::HashMap<
            ((bool, usize), (bool, usize), bool, bool),
            (
                cudarc::driver::CudaGraph,
                Vec<Box<dyn std::any::Any + Send>>,
            ),
        > = Default::default();
        // ALLOC-FREE capture: persistent transient slots — the captured graph carries zero
        // mem nodes (the 226us/launch tax). Slots must outlive every cached graph.
        let mut slots = self.g4_dc_slots(e)?;
        // Chunked replay ring: tokens park on-device; ONE drain sync per chunk. ring_base is
        // baked at the door entry (the modulo keeps every capture valid indefinitely).
        const RING: usize = 64;
        // DRAIN pinned at 1 (2026-07-23): relaunching the SAME graph exec before its prior
        // launch completes is ILLEGAL (chunk=4 -> ILLEGAL_ADDRESS; chunk=1 clean). Pipelining
        // needs alternating exec instances — and the measured payoff was ~0 (the ~200us
        // cuGraphLaunch host cost already overlaps its own launch's GPU work; llama pays
        // 885us/launch the same way). MEMRA_GRAPH_DRAIN raises it only for experiments.
        const DRAIN: usize = 1;
        let mut ring = e.stream().alloc_zeros::<u32>(RING)?;
        let ring_base = prompt_pos;
        let mut out = Vec::with_capacity(max_new);
        let mut reason = StopReason::MaxNew;
        let mut next = first_token;
        let mut captures = 0usize;
        for _ in 0..max_new {
            out.push(next);
            if eos.contains(&next) {
                reason = StopReason::Eos;
                break;
            }
            if !on_token(next) {
                reason = StopReason::Callback;
                break;
            }
            let t_kv = cache.pos + 1;
            // Bucket key per ARM (graph arc step 3):
            //  - swa component: dc family under the window (exact (fa_vec, n_splits) key —
            //    n_splits must be constant per bucket); rows_w above it (window-constant, so
            //    the component collapses to a single marker).
            //  - global component: dc family under the fa512 floor (exact key); rows_dpl16
            //    at/above it — the kernel derives splits from len_d per replay, so buckets
            //    are power-of-2 RUNGS (one capture per doubling; grid sized for the rung end).
            let win = self.cfg.gemma4.as_ref().unwrap().sliding_window as usize;
            let f512 = crate::fa512_min_tkv();
            let key_s = if t_kv > win {
                (true, usize::MAX)
            } else {
                e.fa_bucket_key(t_kv, hd_s, nkv_s, crate::Engine::wkv_on())
            };
            let (key_g, rung_end) = if t_kv >= f512 {
                // strict upper bound: the rung must ROLL at exact powers (t_kv==1024 starts
                // the [1024,2048) bucket) — sizing covers every replayed T_kv < end.
                let end = (t_kv + 1).next_power_of_two().max(f512 * 2);
                ((true, end), end)
            } else {
                (e.fa_bucket_key(t_kv, hd_g, nkv_g, false), t_kv)
            };
            let key = (key_s, key_g, t_kv >= f512, t_kv > win);
            if !graphs.contains_key(&key) {
                let bucket_max = (t_kv, rung_end);
                // snapshot device+host state (the 3 capture-warmup runs leave no residue).
                let snap = cache.snapshot(e)?;
                let pos_save = e.dtoh_i32_one(&pos_d)?;
                let len_save: Vec<Option<i32>> = cache
                    .kv
                    .iter()
                    .map(|k| k.as_ref().map(|kvl| e.dtoh_i32_one(&kvl.len_d).unwrap()))
                    .collect();
                let tok_save = e.dtoh_u32_one(&token_d)?;
                // RETAINED capture (2026-07-23): the plain capture_graph left pool-transient
                // clones as dead COPY NODES replayed every launch — the E4B 0.74ms/token
                // regression class, and this door's measured -8.8%. The keeper pins warmup
                // transients so the captured graph holds kernel nodes only.
                let graph = {
                    let tok_ref = &mut token_d;
                    let pos_ref = &mut pos_d;
                    let cache_ref = &mut *cache;
                    let slots_ref = &mut slots;
                    let ring_ref = &mut ring;
                    e.capture_graph_retained_flags(
                        cudarc::driver::sys::CUgraphInstantiate_flags::CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY,
                        |e| {
                        // self-feeding: the argmax writes token_d itself.
                        let tok_in = unsafe { &*(tok_ref as *const CudaSlice<u32>) };
                        let sl = unsafe { &mut *(slots_ref as *mut G4DcSlots) };
                        let rg = unsafe { &mut *(ring_ref as *mut CudaSlice<u32>) };
                        self.gemma4_decode_step_dc_slotted(e, tok_in, pos_ref, embd_gpu, qt, rb,
                                                           cache_ref, n_vocab, Some(bucket_max),
                                                           sl, tok_ref, Some((rg, ring_base)))
                    })?
                };
                cache.rollback(e, &snap, 0)?;
                e.set_i32_one(&mut pos_d, pos_save)?;
                for (il, ls) in len_save.iter().enumerate() {
                    if let (Some(kvl), Some(v)) = (cache.kv[il].as_mut(), ls) {
                        e.set_i32_one(&mut kvl.len_d, *v)?;
                    }
                }
                e.set_u32_one(&mut token_d, tok_save)?;
                if std::env::var("MEMRA_GRAPH_CENSUS").as_deref() == Ok("1")
                    && let Ok(c) = crate::graph_update::node_census(&graph.0)
                {
                    eprintln!("[graph-census] {c:?}");
                }
                graphs.insert(key, graph);
                captures += 1;
            }
            // CHUNKED REPLAY: enqueue up to DRAIN launches back-to-back (the ~200us host
            // cost of each cuGraphLaunch overlaps the PREVIOUS launch's ~11ms GPU work),
            // then ONE sync + ring drain. The chunk must stay inside this bucket key and
            // the budget; capture warmups already emitted their tokens through the ring.
            let mut chunk = 1usize;
            let drain_cap: usize = std::env::var("MEMRA_GRAPH_DRAIN")
                .ok()
                .and_then(|v| v.parse().ok())
                .unwrap_or(DRAIN);
            while chunk < drain_cap && out.len() + chunk < max_new {
                let t_next = cache.pos + 1 + chunk;
                let key_s2 = if t_next > win {
                    (true, usize::MAX)
                } else {
                    e.fa_bucket_key(t_next, hd_s, nkv_s, crate::Engine::wkv_on())
                };
                let key_g2 = if t_next >= f512 {
                    (true, (t_next + 1).next_power_of_two().max(f512 * 2))
                } else {
                    e.fa_bucket_key(t_next, hd_g, nkv_g, false)
                };
                if (key_s2, key_g2, t_next >= f512, t_next > win) != key {
                    break;
                }
                chunk += 1;
            }
            let g = &graphs.get(&key).unwrap().0;
            for _ in 0..chunk {
                g.launch()?;
            }
            e.stream().synchronize()?;
            let ringh = e.dtoh_u32(&ring)?;
            for j in 0..chunk {
                let pos_j = cache.pos + j;
                let tok_j = ringh[(pos_j - ring_base) % RING];
                cache.pos += 0; // advanced below in one shot
                if j + 1 == chunk {
                    next = tok_j;
                } else {
                    out.push(tok_j);
                    if eos.contains(&tok_j) || !on_token(tok_j) {
                        reason = if eos.contains(&tok_j) {
                            StopReason::Eos
                        } else {
                            StopReason::Callback
                        };
                        // roll device/host state back to the stop point.
                        let keep = cache.pos + j + 1;
                        e.set_i32_one(&mut pos_d, keep as i32)?;
                        for kvl in cache.kv.iter_mut().filter_map(|k| k.as_mut()) {
                            e.set_i32_one(&mut kvl.len_d, keep as i32)?;
                            kvl.len = keep;
                        }
                        cache.pos = keep;
                        if std::env::var("MEMRA_GRAPH_STATS").is_ok() {
                            eprintln!("[gemma-graph] captures={captures} buckets={}", graphs.len());
                        }
                        return Ok((out, reason));
                    }
                }
            }
            cache.pos += chunk;
            for kvl in cache.kv.iter_mut().filter_map(|k| k.as_mut()) {
                kvl.len += chunk;
            }
        }
        if std::env::var("MEMRA_GRAPH_STATS").is_ok() {
            eprintln!("[gemma-graph] captures={captures} buckets={}", graphs.len());
        }
        Ok((out, reason))
    }

    /// gemma4 VERIFY step (spec decode): t tokens batched through the trunk at positions
    /// pos0..pos0+t-1, K/V rows appended to the quantized cache (caller rolls back rejected
    /// rows via kvl.len), per-token causal windowed attend (fa_decode per token — the same
    /// kernel family as T=1 decode at each token's t_kv). Returns [t, n_vocab] softcapped
    /// logits (host) + advances cache.pos by t.
    pub(crate) fn gemma4_decode_step_t(
        &self,
        e: &Engine,
        tokens: &[u32],
        pos0: usize,
        cache: &mut Cache,
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        Ok(self.gemma4_decode_step_t_h(e, tokens, pos0, cache)?.0)
    }

    /// GREEDY verify: per-row DEVICE argmax (t x 4B host traffic instead of the t x 1MB logits
    /// stack — softcap skipped: tanh is monotonic, per-row argmax unaffected). Returns
    /// (argmax ids [t], post-output_norm hidden stack [t, n_embd]).
    pub(crate) fn gemma4_decode_step_t_am(
        &self,
        e: &Engine,
        tokens: &[u32],
        pos0: usize,
        cache: &mut Cache,
    ) -> Result<(Vec<u32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let (ld, hn) = self.gemma4_verify_trunk(e, tokens, pos0, cache, None)?;
        let t = tokens.len();
        let n_vocab = self.output.out_features();
        let mut toks = e.stream().alloc_zeros::<u32>(t)?;
        for i in 0..t {
            e.argmax_token_device_col(&ld, i, n_vocab, &mut toks, i)?;
        }
        Ok((e.dtoh_u32(&toks)?, hn))
    }

    /// Device-token verify (async spec round): tokens live in tok_d[0..t]; per-row argmax
    /// lands in a DEVICE buffer (no host logits). Returns (vam_d [t] u32 device, hn stack).
    pub(crate) fn gemma4_decode_step_t_am_dev(
        &self,
        e: &Engine,
        tok_d: &CudaSlice<u32>,
        t: usize,
        pos0: usize,
        cache: &mut Cache,
    ) -> Result<(CudaSlice<u32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let (ld, hn) = self.gemma4_verify_trunk(e, &vec![0u32; t], pos0, cache, Some(tok_d))?;
        let n_vocab = self.output.out_features();
        let mut vam = e.stream().alloc_zeros::<u32>(t)?;
        for i in 0..t {
            e.argmax_token_device_col(&ld, i, n_vocab, &mut vam, i)?;
        }
        Ok((vam, hn))
    }

    /// gemma4 verify + the POST-output_norm hidden stack [t, n_embd] (the drafter's h input —
    /// llama's h_nextn convention).
    pub(crate) fn gemma4_decode_step_t_h(
        &self,
        e: &Engine,
        tokens: &[u32],
        pos0: usize,
        cache: &mut Cache,
    ) -> Result<(Vec<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let (mut ld, hn) = self.gemma4_verify_trunk(e, tokens, pos0, cache, None)?;
        let t = tokens.len();
        let cap = self.cfg.gemma4.as_ref().unwrap().final_logit_softcapping;
        e.softcap(&mut ld, cap, t * self.output.out_features())?;
        Ok((e.dtoh(&ld)?, hn))
    }

    /// Persistent device scratch for the BURST verify stream (one per generation): rope pos
    /// rows [cap] + per-row t_kv counters (device-filled from the round counter, zero H2D).
    pub(crate) fn verify_stream_scratch(
        &self,
        e: &Engine,
        cap: usize,
    ) -> Result<VerifyStreamScratch, Box<dyn std::error::Error>> {
        Ok(VerifyStreamScratch {
            pos_d: e.htod_i32(&vec![0i32; cap])?,
            row_ctrs: (0..cap)
                .map(|_| e.htod_i32(&[0]))
                .collect::<Result<_, _>>()?,
        })
    }

    /// BURST verify trunk (device-slot twin): tokens from a device buffer, rope positions
    /// iota'd from `ctr`, appends/attention at the layers' len_d counters (verify_attn_stream),
    /// NO host cache.pos/len advance. Returns (per-row device argmaxes [t], post-norm hidden
    /// stack [t, n_embd]) — the burst's accept/seed inputs, zero host readbacks.
    /// `scr` = PERSISTENT per-gen scratch (pos rows + per-row t_kv counters): the first cut
    /// htod_i32-allocated these per call — 9 pageable H2D copies per round, each a stream
    /// sync, exactly the turnaround the burst exists to remove.
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    pub(crate) fn gemma4_verify_t_am_stream(
        &self,
        e: &Engine,
        tok_d: &CudaSlice<u32>,
        t: usize,
        ctr: &CudaSlice<i32>,
        hint: usize,
        cache: &mut Cache,
        scr: &mut VerifyStreamScratch,
    ) -> Result<(CudaSlice<u32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        assert!(t <= scr.row_ctrs.len() && t <= 64);
        e.i32_iota_from(ctr, &mut scr.pos_d, t)?;
        for i in 0..t {
            e.i32_copy_add(ctr, &mut scr.row_ctrs[i], (i + 1) as i32)?;
        }
        let (pos_d, row_ctrs) = (&scr.pos_d, &scr.row_ctrs);
        let embd_gpu = self
            .embd_gpu
            .get_or_init(|| e.upload_u8(&self.embd.raw).expect("embed table upload"));
        let (qt, rb) = self.embd.qt_and_row_bytes(n_embd);
        let mut x = e.embed_gather_device_td(embd_gpu, tok_d, t, n_embd, qt, rb)?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), t * n_embd)?;
        let mut h_carry: Option<(CudaSlice<i8>, CudaSlice<f32>)> = None;
        let n_layers = self.layers.len();
        for (il, layer) in self.layers.iter().enumerate() {
            let (hq, hdq) = match h_carry.take() {
                Some(p) => p,
                None => {
                    e.rms_norm_q8_1(&x, self.layers[0].attn_norm.float_data(), n_embd, t, eps)?
                }
            };
            let Mixer::Full(fa) = &layer.mixer else {
                panic!("gemma4 layer {il} not full-attn")
            };
            let o = self
                .gemma4_verify_attn_stream(e, fa, il, &hq, &hdq, pos_d, t, cache, hint, row_ctrs)?;
            let next_norm = if il + 1 < n_layers {
                Some(self.layers[il + 1].attn_norm.float_data())
            } else {
                None
            };
            let (xn, hn) = self.gemma4_layer_tail_add_nq_pn(e, layer, &o, &x, t, next_norm)?;
            x = xn;
            h_carry = hn;
            self.dflash_tap(e, cache, il, &x, t)?;
        }
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, t, eps)?;
        let ld = e.matmul(&self.output, &hn, t)?;
        let n_vocab = self.output.out_features();
        let mut vam = e.stream().alloc_zeros::<u32>(t)?;
        for i in 0..t {
            e.argmax_token_device_col(&ld, i, n_vocab, &mut vam, i)?;
        }
        Ok((vam, hn))
    }

    /// Verify trunk core: returns (UN-softcapped logits device [t, n_vocab], post-output_norm
    /// hidden stack [t, n_embd]); appends KV rows + advances cache.pos.
    /// DFlash tap write (dflash lane): copy the post-layer residual rows of `x` into the
    /// armed sink at the tap slot for `il` (row-major [t, n_taps*hidden]). Per-row D2D
    /// copies — t <= block_size on verify; prime pays t*n_taps once per prompt (dedicated
    /// kernel later if it shows in the profile).
    pub(crate) fn dflash_tap(
        &self,
        e: &Engine,
        cache: &mut Cache,
        il: usize,
        x: &CudaSlice<f32>,
        t: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let Some(taps) = cache.dflash_taps.as_mut() else {
            return Ok(());
        };
        let Some(slot) = taps.layer_ids.iter().position(|&l| l == il) else {
            return Ok(());
        };
        let h = taps.hidden;
        let n_taps = taps.layer_ids.len();
        let base = taps.base;
        debug_assert!(
            base + t <= taps.t,
            "tap window {base}+{t} exceeds sink {}",
            taps.t
        );
        let xv = e.view(x, t * h);
        for r in 0..t {
            let row = xv.slice(r * h..(r + 1) * h);
            e.copy_view_into(&mut taps.buf, (base + r) * n_taps * h + slot * h, &row, h)?;
        }
        Ok(())
    }

    fn gemma4_verify_trunk(
        &self,
        e: &Engine,
        tokens: &[u32],
        pos0: usize,
        cache: &mut Cache,
        tok_dev: Option<&CudaSlice<u32>>,
    ) -> Result<(CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let t = tokens.len();
        let pos: Vec<i32> = (0..t).map(|i| (pos0 + i) as i32).collect();
        let pos_d = e.htod_i32(&pos)?;
        let mut x = match tok_dev {
            Some(td) => {
                let embd_gpu = self
                    .embd_gpu
                    .get_or_init(|| e.upload_u8(&self.embd.raw).expect("embed table upload"));
                let (qt, rb) = self.embd.qt_and_row_bytes(n_embd);
                e.embed_gather_device_td(embd_gpu, td, t, n_embd, qt, rb)?
            }
            None => e.htod(&self.embd.gather(n_embd, tokens))?,
        };
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), t * n_embd)?;
        let mut h_carry: Option<(CudaSlice<i8>, CudaSlice<f32>)> = None;
        let n_layers = self.layers.len();
        for (il, layer) in self.layers.iter().enumerate() {
            let (hq, hdq) = match h_carry.take() {
                Some(p) => p,
                None => {
                    e.rms_norm_q8_1(&x, self.layers[0].attn_norm.float_data(), n_embd, t, eps)?
                }
            };
            let Mixer::Full(fa) = &layer.mixer else {
                panic!("gemma4 layer {il} not full-attn")
            };
            let o = self.gemma4_verify_attn(e, fa, il, &hq, &hdq, &pos_d, t, cache)?;
            let next_norm = if il + 1 < n_layers {
                Some(self.layers[il + 1].attn_norm.float_data())
            } else {
                None
            };
            let (xn, hn) = self.gemma4_layer_tail_add_nq_pn(e, layer, &o, &x, t, next_norm)?;
            x = xn;
            h_carry = hn;
            self.dflash_tap(e, cache, il, &x, t)?;
        }
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, t, eps)?;
        let mut ld = e.matmul(&self.output, &hn, t)?;
        self.gemma4_suppress(e, &mut ld, t)?; // before the per-row argmax consumers
        cache.pos += t;
        Ok((ld, hn))
    }

    /// Verify attention: project/norm/rope t rows, append them to the cache, then attend each
    /// row causally over [win_off_i .. base+i] via the SAME fa_decode dispatch as T=1 decode.
    /// BURST verify attention (device-slot twin of `gemma4_verify_attn`): the append lands
    /// at the layer's len_d counter (rows_dc), attention bases ride the counter (rows /
    /// rows_w with base_dev), and NO host len is read or bumped — `hint` is a host UPPER
    /// bound on base_len used only for split sizing and arm gating (the burst loop passes
    /// pos0 + burst slack). Host len mirrors re-sync at the burst drain.
    #[allow(clippy::too_many_arguments)]
    fn gemma4_verify_attn_stream(
        &self,
        e: &Engine,
        fa: &crate::hybrid::FullAttnLayer,
        il: usize,
        hq: &CudaSlice<i8>,
        hdq: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        hint: usize,
        row_ctrs: &[CudaSlice<i32>],
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let (hd, nkv, nh, base, scale, swa) = self.gemma4_geom(il);
        let eps = self.cfg.rms_eps;
        let aux = self.gemma4_aux.as_ref().unwrap();
        let ones = aux.ones(e);
        #[cfg(debug_assertions)]
        crate::debug_assert_tensor_stream_device(
            ones,
            &e.stream(),
            "gemma4_verify_attn_stream.ones",
        );
        let h0 = e.zeros(0)?;
        let h = &h0;
        // BATCHED FUSED qkv (MEMRA_F2B=1, megakernel microcosm): swa layers fuse all three,
        // globals fuse q,k (v := k clone). Bit-identical per row; segments tail-fill.
        static F2B_QKV: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
        let f2b = *F2B_QKV.get_or_init(|| std::env::var("MEMRA_F2B").as_deref() != Ok("0"));
        let fused_qkv = if f2b {
            if swa {
                e.matmul_q4_fused3_batched(&fa.wq, &fa.wk, &fa.wv, hq, hdq, t)?
                    .map(|(a, b, c)| (a, b, Some(c)))
            } else {
                e.matmul_q4_fused2_batched(&fa.wq, &fa.wk, hq, hdq, t)?
                    .map(|(a, b)| (a, b, None))
            }
        } else {
            None
        };
        let (q0, k0, v0) = match fused_qkv {
            Some((a, b, cv)) => {
                let v = match cv {
                    Some(c) => c,
                    None => e.clone_dtod(&b)?,
                };
                (a, b, v)
            }
            None => {
                let q0 = e.matmul_pre(&fa.wq, hq, hdq, h, t)?;
                let k0 = e.matmul_pre(&fa.wk, hq, hdq, h, t)?;
                let v0 = if swa {
                    e.matmul_pre(&fa.wv, hq, hdq, h, t)?
                } else {
                    e.clone_dtod(&k0)?
                };
                (q0, k0, v0)
            }
        };
        let mut q = e.uninit(t * nh * hd)?;
        let mut k = e.uninit(t * nkv * hd)?;
        let mut v = e.uninit(t * nkv * hd)?;
        // E4B wave-3 fold (2026-07-23 decode-dust port): q/k/v norms + q/k rope in ONE
        // launch — rope math verbatim on the normed rows; V ones-rms, never roped.
        let ff = if swa {
            None
        } else {
            Some(
                aux.rope_freqs(e)
                    .expect("gemma4 global rope needs rope_freqs.weight"),
            )
        };
        #[cfg(debug_assertions)]
        if let Some(ff) = ff {
            crate::debug_assert_tensor_stream_device(
                ff,
                &e.stream(),
                "gemma4_verify_attn_stream.rope_freqs",
            );
        }
        e.rms_norm_qkv_rope(
            &q0,
            &k0,
            &v0,
            fa.q_norm.float_data(),
            fa.k_norm.float_data(),
            ones,
            &mut q,
            &mut k,
            &mut v,
            hd,
            self.gemma4_rope_dims(il),
            nh * t,
            nkv * t,
            pos_d,
            nh,
            nkv,
            base,
            1.0,
            ff,
            eps,
        )?;
        let kvl = cache.kv[il].as_mut().unwrap();
        // append at the DEVICE slot; the counter advances by t on-device.
        e.append_kv_quantized_rows_dc(
            &k,
            &v,
            &mut kvl.k,
            &mut kvl.v,
            &kvl.len_d,
            t,
            kvl.kv_dim_k,
            kvl.kv_dim_v,
            kvl.k_tok_bytes,
            kvl.v_tok_bytes,
            (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on()),
        )?;
        // len_d is NOT advanced here: the burst's device rollback (spec_rollback_stream) is
        // the sole len writer after this round's attention (base stays = old len, plus = 0).
        let win = self.cfg.gemma4.as_ref().unwrap().sliding_window as usize;
        let mut attn = e.uninit(t * nh * hd)?;
        let k_view = e.view_u8(&kvl.k, kvl.k.len());
        let v_view = e.view_u8(&kvl.v, kvl.v.len());
        // arm gating on the host HINT (upper bound; burst entry guards hint >= the vec floor
        // and a stable window regime — the same rung/regime keys as the draft graph).
        if swa && hint + 1 >= win {
            // fully-windowed rows: per-row window geometry from the counter (plus = 0: len_d
            // still holds the pre-append len; row r's T_kv = ctr + r + 1).
            e.fa_decode_rows_w(
                &q,
                &k_view,
                &v_view,
                &mut attn,
                hd,
                nh,
                nkv,
                &kvl.len_d,
                0,
                t,
                scale,
                win,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                None,
            )?;
        } else if hd == 512 && hint + t < crate::fa512_min_tkv() {
            // globals UNDER the fa512 crossover: eager runs the per-row fa_decode_kvmod
            // fallback there (parity with t=1 decode) — mirror it with per-row fa_decode_dc
            // (bit-correct for any t_kv <= bucket; the drafter dc arc proved the pairing).
            // Burst entry gates the horizon onto one side of the crossover, so hint decides
            // for every row.
            // NO .max(512): fa_decode_dc gates its hd512 dpl16-vs-scalar pick on bucket_max
            // (mirroring eager's fa512 floor) — forcing 512 here flipped the arm to dpl16
            // while eager ran scalar (il=5 KV drift, the burst's 4/128). SAME LAW capped
            // from above (2026-07-13): a regime-pinned hint near the floor (round-graph
            // captures use f512-1) pow2-rounds PAST it — clamp under the floor, or the arm
            // re-flips to dpl16 (the round-graph 4/64). The scalar unified self-splits, so
            // any bucket >= the live length is exact.
            let bucket = (hint + t + 2)
                .next_power_of_two()
                .min(crate::fa512_min_tkv().saturating_sub(1));
            let qv = e.view(&q, t * nh * hd);
            #[allow(clippy::needless_range_loop)]
            // allow: the explicit index loop keeps the offset arithmetic visible and aligned with the device-side indexing
            for i in 0..t {
                let q_row = qv.slice(i * nh * hd..(i + 1) * nh * hd);
                let mut q_one = e.uninit(nh * hd)?;
                e.copy_view_into(&mut q_one, 0, &q_row, nh * hd)?;
                let mut a_one = e.uninit(nh * hd)?;
                e.fa_decode_dc(
                    &q_one,
                    &k_view,
                    &v_view,
                    &mut a_one,
                    hd,
                    nh,
                    nkv,
                    &row_ctrs[i],
                    bucket,
                    scale,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    false,
                )?;
                e.copy_into(&mut attn, i * nh * hd, &a_one, nh * hd)?;
            }
        } else if hd == 512 {
            // globals past the crossover: dpl16 dc twin via the shared rows wrapper (hint
            // sizes splits — upper bound; splits beyond the device len exit in-kernel).
            e.fa_decode_rows(
                &q,
                &k_view,
                &v_view,
                &mut attn,
                hd,
                nh,
                nkv,
                hint,
                t,
                scale,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                Some((&kvl.len_d, 0)),
                false,
                false,
                None,
            )?;
        } else {
            // hd256 under-window: v4 device-len rows twin.
            e.fa_decode_rows_dc(
                &q,
                &k_view,
                &v_view,
                &mut attn,
                hd,
                nh,
                nkv,
                &kvl.len_d,
                hint + t,
                t,
                scale,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                0,
                swa && crate::Engine::wkv_on(),
            )?;
        }
        e.matmul(&fa.wo, &attn, t)
    }

    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn gemma4_verify_attn(
        &self,
        e: &Engine,
        fa: &crate::hybrid::FullAttnLayer,
        il: usize,
        hq: &CudaSlice<i8>,
        hdq: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let (hd, nkv, nh, base, scale, swa) = self.gemma4_geom(il);
        let eps = self.cfg.rms_eps;
        let aux = self.gemma4_aux.as_ref().unwrap();
        let ones = aux.ones(e);
        #[cfg(debug_assertions)]
        crate::debug_assert_tensor_stream_device(ones, &e.stream(), "gemma4_verify_attn.ones");
        let n_embd = self.cfg.n_embd as usize;
        let _ = n_embd;

        let h0 = e.zeros(0)?;
        let h = &h0;
        // BATCHED FUSED qkv (MEMRA_F2B=1, megakernel microcosm): swa layers fuse all three,
        // globals fuse q,k (v := k clone). Bit-identical per row; segments tail-fill.
        static F2B_QKV: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
        let f2b = *F2B_QKV.get_or_init(|| std::env::var("MEMRA_F2B").as_deref() != Ok("0"));
        let fused_qkv = if f2b {
            if swa {
                e.matmul_q4_fused3_batched(&fa.wq, &fa.wk, &fa.wv, hq, hdq, t)?
                    .map(|(a, b, c)| (a, b, Some(c)))
            } else {
                e.matmul_q4_fused2_batched(&fa.wq, &fa.wk, hq, hdq, t)?
                    .map(|(a, b)| (a, b, None))
            }
        } else {
            None
        };
        let (q0, k0, v0) = match fused_qkv {
            Some((a, b, cv)) => {
                let v = match cv {
                    Some(c) => c,
                    None => e.clone_dtod(&b)?,
                };
                (a, b, v)
            }
            None => {
                let q0 = e.matmul_pre(&fa.wq, hq, hdq, h, t)?;
                let k0 = e.matmul_pre(&fa.wk, hq, hdq, h, t)?;
                let v0 = if swa {
                    e.matmul_pre(&fa.wv, hq, hdq, h, t)?
                } else {
                    e.clone_dtod(&k0)?
                };
                (q0, k0, v0)
            }
        };
        let mut q = e.uninit(t * nh * hd)?;
        let mut k = e.uninit(t * nkv * hd)?;
        let mut v = e.uninit(t * nkv * hd)?;
        // E4B wave-3 fold (2026-07-23 decode-dust port): q/k/v norms + q/k rope in ONE
        // launch — rope math verbatim on the normed rows; V ones-rms, never roped.
        let ff = if swa {
            None
        } else {
            Some(
                aux.rope_freqs(e)
                    .expect("gemma4 global rope needs rope_freqs.weight"),
            )
        };
        #[cfg(debug_assertions)]
        if let Some(ff) = ff {
            crate::debug_assert_tensor_stream_device(
                ff,
                &e.stream(),
                "gemma4_verify_attn.rope_freqs",
            );
        }
        e.rms_norm_qkv_rope(
            &q0,
            &k0,
            &v0,
            fa.q_norm.float_data(),
            fa.k_norm.float_data(),
            ones,
            &mut q,
            &mut k,
            &mut v,
            hd,
            self.gemma4_rope_dims(il),
            nh * t,
            nkv * t,
            pos_d,
            nh,
            nkv,
            base,
            1.0,
            ff,
            eps,
        )?;
        let kvl = cache.kv[il].as_mut().unwrap();
        let base_len = kvl.len;
        e.append_kv_quantized_rows(
            &k,
            &v,
            &mut kvl.k,
            &mut kvl.v,
            base_len,
            t,
            kvl.kv_dim_k,
            kvl.kv_dim_v,
            kvl.k_tok_bytes,
            kvl.v_tok_bytes,
            (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on()),
        )?;
        kvl.len += t;
        let win = self.cfg.gemma4.as_ref().unwrap().sliding_window as usize;
        let mut attn = e.uninit(t * nh * hd)?;
        // ROWS twin when no window offset is in play (globals are hd512-ineligible; SWA rows
        // under the window need no offset): ONE launch, per-row causal == the per-token loop.
        let rows_ok = (hd == 256 && base_len + 1 >= crate::fa_vec_min_tkv())
            // gemma globals: hd512 rows twin in the dpl16 vec regime (row 0 gates the batch);
            // decode rides the SAME symbol at t=1 (parity law).
            || (hd == 512 && !swa && base_len + 1 >= crate::fa512_min_tkv());
        if rows_ok && (!swa || base_len + t <= win) {
            let k_view = e.view_u8(&kvl.k, (base_len + t) * kvl.k_tok_bytes);
            let v_view = e.view_u8(&kvl.v, (base_len + t) * kvl.v_tok_bytes);
            if hd == 512 {
                // device-len twin: sync the counter to the verify base (async arg-store).
                e.i32_set_k(&mut kvl.len_d, base_len as i32)?;
                e.fa_decode_rows(
                    &q,
                    &k_view,
                    &v_view,
                    &mut attn,
                    hd,
                    nh,
                    nkv,
                    base_len,
                    t,
                    scale,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    Some((&kvl.len_d, 0)),
                    false,
                    swa && crate::Engine::wkv_on(),
                    None,
                )?;
            } else {
                // hd256: the SAME v4_dc symbol the burst verify launches (PARITY LAW — the
                // host-base rows_v4 twin compiles apart from v4_dc and the two-symbol split
                // drifted the burst's persisted KV at il=8; one symbol, counter arg-stored).
                e.i32_set_k(&mut kvl.len_d, base_len as i32)?;
                e.fa_decode_rows_dc(
                    &q,
                    &k_view,
                    &v_view,
                    &mut attn,
                    hd,
                    nh,
                    nkv,
                    &kvl.len_d,
                    base_len + t,
                    t,
                    scale,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    0,
                    swa && crate::Engine::wkv_on(),
                )?;
            }
            return e.matmul(&fa.wo, &attn, t);
        }
        // WINDOWED rows twin (deep ctx, every row fully windowed): one launch, ABSOLUTE-index
        // per-row geometry over the prefix view. PARITY LAW (2026-07-10 root-cause): textually
        // identical kernels do NOT compile bit-identically (nvcc unrolls fa_decode_vec_q 2x vs
        // its rows_w clone — SASS-proven; the unpinned score `+=` chain then rounds apart), so
        // decode-vs-verify parity comes from BOTH sides launching THIS SAME rows_w kernel
        // (decode passes t=1) — bitwise equal per position by symbol identity, any lane.
        // MEMRA_GEMMA_ROWS_W=0 -> per-token loop (decode falls back to fa_decode views too).
        if hd == 256
            && swa
            && base_len + 1 >= win
            && std::env::var("MEMRA_GEMMA_ROWS_W").as_deref() != Ok("0")
        {
            let k_view = e.view_u8(&kvl.k, (base_len + t) * kvl.k_tok_bytes);
            let v_view = e.view_u8(&kvl.v, (base_len + t) * kvl.v_tok_bytes);
            e.i32_set_k(&mut kvl.len_d, base_len as i32)?;
            e.fa_decode_rows_w(
                &q,
                &k_view,
                &v_view,
                &mut attn,
                hd,
                nh,
                nkv,
                &kvl.len_d,
                0,
                t,
                scale,
                win,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                None,
            )?;
            return e.matmul(&fa.wo, &attn, t);
        }
        for i in 0..t {
            let avail = base_len + i + 1;
            let (off_tok, t_kv) = if swa && avail > win {
                (avail - win, win)
            } else {
                (0, avail)
            };
            let k_view = e.view_u8_range(
                &kvl.k,
                off_tok * kvl.k_tok_bytes,
                (off_tok + t_kv) * kvl.k_tok_bytes,
            );
            let v_view = e.view_u8_range(
                &kvl.v,
                off_tok * kvl.v_tok_bytes,
                (off_tok + t_kv) * kvl.v_tok_bytes,
            );
            let qi = e.view(&q, t * nh * hd);
            let q_row = qi.slice(i * nh * hd..(i + 1) * nh * hd);
            let mut q_one = e.uninit(nh * hd)?;
            e.copy_view_into(&mut q_one, 0, &q_row, nh * hd)?;
            let mut a_one = e.uninit(nh * hd)?;
            // straddle rounds: windowed rows must ride the SAME rows_w kernel decode uses
            // (parity law above); global hd512 rows past the fa512 floor likewise ride the
            // rows_dpl16 twin; remaining rows keep the gated fa_decode pair.
            if swa
                && avail > win
                && hd == 256
                && std::env::var("MEMRA_GEMMA_ROWS_W").as_deref() != Ok("0")
            {
                let kp = e.view_u8(&kvl.k, avail * kvl.k_tok_bytes);
                let vp = e.view_u8(&kvl.v, avail * kvl.v_tok_bytes);
                e.i32_set_k(&mut kvl.len_d, (avail - 1) as i32)?;
                e.fa_decode_rows_w(
                    &q_one,
                    &kp,
                    &vp,
                    &mut a_one,
                    hd,
                    nh,
                    nkv,
                    &kvl.len_d,
                    0,
                    1,
                    scale,
                    win,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    None,
                )?;
            } else if !swa
                && hd == 512
                && avail >= crate::fa512_min_tkv()
                && std::env::var("MEMRA_GEMMA_ROWS_W").as_deref() != Ok("0")
            {
                let kp = e.view_u8(&kvl.k, avail * kvl.k_tok_bytes);
                let vp = e.view_u8(&kvl.v, avail * kvl.v_tok_bytes);
                e.i32_set_k(&mut kvl.len_d, (avail - 1) as i32)?;
                e.fa_decode_rows(
                    &q_one,
                    &kp,
                    &vp,
                    &mut a_one,
                    hd,
                    nh,
                    nkv,
                    avail - 1,
                    1,
                    scale,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    Some((&kvl.len_d, 0)),
                    false,
                    false,
                    None,
                )?;
            } else {
                e.fa_decode_kvmod(
                    &q_one,
                    &k_view,
                    &v_view,
                    &mut a_one,
                    hd,
                    nh,
                    nkv,
                    t_kv,
                    scale,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    swa && crate::Engine::wkv_on(),
                )?;
            }
            e.copy_into(&mut attn, i * nh * hd, &a_one, nh * hd)?;
        }
        e.matmul(&fa.wo, &attn, t)
    }

    /// gemma4 T=1 decode step: R8 layer graph over the cache; returns (softcapped logits host,
    /// h_seed = pre-output_norm hidden). Advances cache.pos.
    pub(crate) fn gemma4_decode_step_h(
        &self,
        e: &Engine,
        token: u32,
        cache: &mut Cache,
    ) -> Result<(Vec<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        // M1-PP2 door (crate::pp): 2-stage split with an explicit activation handoff.
        // Default OFF — unset env means this branch is never taken. The gemma4 arm stays
        // 2-stage in M2 (the N-stage gate model is the generic-arm 9B); N>2 warns + runs
        // unsplit rather than guessing a fence.
        if let Some(split) = crate::pp::pp2_split(self.layers.len()) {
            let rt = crate::pp::Pp2Rt::get(e)?;
            let _walk = rt.acquire_walk("gemma4_decode_step_h_pp2")?;
            return self.gemma4_decode_step_h_pp2(e, token, cache, split);
        }
        if crate::pp::pp_cuts(self.layers.len()).is_some() {
            crate::pp::warn_unwired_once("gemma4 eager decode (N>2)");
        }
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let pos_d = e.htod_i32(&[cache.pos as i32])?;
        let mut x = e.htod(&self.embd.gather(n_embd, &[token]))?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), n_embd)?;
        // cross-layer fusion: each tail's closing add+scale also EMITS the next layer's
        // attn-normed input pre-quantized q8_1 (the mixer consumes only quantized matmuls).
        let mut h_carry: Option<(CudaSlice<i8>, CudaSlice<f32>)> = None;
        let n_layers = self.layers.len();
        for (il, layer) in self.layers.iter().enumerate() {
            let (hq, hdq) = match h_carry.take() {
                Some(p) => p,
                None => {
                    e.rms_norm_q8_1(&x, self.layers[0].attn_norm.float_data(), n_embd, 1, eps)?
                }
            };
            let Mixer::Full(fa) = &layer.mixer else {
                panic!("gemma4 layer {il} not full-attn")
            };
            let o = self.gemma4_decode_attn(e, fa, il, &hq, &hdq, &pos_d, cache)?;
            let next_norm = if il + 1 < n_layers {
                Some(self.layers[il + 1].attn_norm.float_data())
            } else {
                None
            };
            let (xn, hn) = self.gemma4_layer_tail_add_nq_pn(e, layer, &o, &x, 1, next_norm)?;
            x = xn;
            h_carry = hn;
        }
        let mut hn = e.uninit(n_embd)?;
        e.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, 1, eps)?;
        let h_seed = e.clone_dtod(&x)?;
        let mut ld = e.matmul(&self.output, &hn, 1)?;
        let cap = self.cfg.gemma4.as_ref().unwrap().final_logit_softcapping;
        e.softcap(&mut ld, cap, self.output.out_features())?; // R4 on device (262k host tanh ~ms/step)
        self.gemma4_suppress(e, &mut ld, 1)?;
        let logits = e.dtoh(&ld)?;
        cache.pos += 1;
        Ok((logits, h_seed))
    }

    /// M1-PP2 stage subgraph (gemma4 arm): layers [lo, hi) of the gemma4 T=1 walk with the
    /// pre-quantized next-norm carry LOCAL to the range. Enters with a materialized residual
    /// `x` (the range head runs its own `rms_norm_q8_1` against ITS layer's attn_norm — for
    /// lo == 0 that is exactly the unsplit loop's il==0 arm); exits with the residual
    /// materialized (range tail passes next_norm = None, exactly the unsplit last-layer arm).
    /// Bit-identity of the cut relies on the kernel-check-pinned `add_scale_rms_norm_q8_1 ==
    /// add_scale then rms_norm_q8_1` identity (`pp2-gate` verifies end-to-end).
    fn gemma4_decode_layers(
        &self,
        e: &Engine,
        mut x: CudaSlice<f32>,
        lo: usize,
        hi: usize,
        pos_d: &CudaSlice<i32>,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let mut h_carry: Option<(CudaSlice<i8>, CudaSlice<f32>)> = None;
        for il in lo..hi {
            let layer = &self.layers[il];
            let (hq, hdq) = match h_carry.take() {
                Some(p) => p,
                // range head: il == lo — norm against THIS layer's attn_norm.
                None => {
                    e.rms_norm_q8_1(&x, self.layers[il].attn_norm.float_data(), n_embd, 1, eps)?
                }
            };
            let Mixer::Full(fa) = &layer.mixer else {
                panic!("gemma4 layer {il} not full-attn")
            };
            let o = self.gemma4_decode_attn(e, fa, il, &hq, &hdq, pos_d, cache)?;
            let next_norm = if il + 1 < hi {
                Some(self.layers[il + 1].attn_norm.float_data())
            } else {
                None
            };
            let (xn, hn) = self.gemma4_layer_tail_add_nq_pn(e, layer, &o, &x, 1, next_norm)?;
            x = xn;
            h_carry = hn;
        }
        Ok(x)
    }

    /// M1-PP2 (increment 2): `gemma4_decode_step_h` as TWO stage subgraphs, each on its
    /// own stream (and device, under MEMRA_PP_DEVICES) with the transport-selected
    /// boundary handoff — same choreography as the generic arm (decode.rs), same
    /// ownership contract (crate::pp). Stage 0 = embed+scale + layers [0, split);
    /// stage 1 = layers [split, n) + output_norm + softcapped head.
    /// Each stage uploads its own copy of the step's position scalar on its own stream.
    /// MEMRA_PP_STREAMS=0 = the increment-1 same-stream seam. Gate: `pp2-gate`.
    fn gemma4_decode_step_h_pp2(
        &self,
        e: &Engine,
        token: u32,
        cache: &mut Cache,
        split: usize,
    ) -> Result<(Vec<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        if crate::pp::pp2_streams_off() {
            return self.gemma4_decode_step_h_pp2_samestream(e, token, cache, split);
        }
        let rt = crate::pp::Pp2Rt::get(e)?;
        let e0 = rt.engine(0, e);
        let e1 = rt.engine(1, e);
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let pos = cache.pos as i32;

        // ---- STAGE 0 (its own stream): embed + sqrt(n_embd) scale + layers [0, split) ----
        let slot = {
            let _st0 = rt.enter(0);
            let pos_d = e0.htod_i32(&[pos])?;
            #[cfg(debug_assertions)]
            crate::debug_assert_tensor_stream_device(
                &pos_d,
                &e0.stream(),
                "gemma4_decode_step_h_pp2.stage0.pos_d",
            );
            let mut x = e0.htod(&self.embd.gather(n_embd, &[token]))?;
            e0.scale_inplace(&mut x, (n_embd as f32).sqrt(), n_embd)?;
            let x = self.gemma4_decode_layers(e0, x, 0, split, &pos_d, cache)?;
            rt.tx(0, &x, n_embd)?
        };

        // ---- STAGE 1 (its own stream): RX + layers [split, n) + softcapped head ----
        let _st1 = rt.enter(1);
        let pos_d = e1.htod_i32(&[pos])?;
        #[cfg(debug_assertions)]
        crate::debug_assert_tensor_stream_device(
            &pos_d,
            &e1.stream(),
            "gemma4_decode_step_h_pp2.stage1.pos_d",
        );
        let x = rt.rx(0, slot, n_embd)?;
        let x = self.gemma4_decode_layers(e1, x, split, self.layers.len(), &pos_d, cache)?;

        let mut hn = e1.uninit(n_embd)?;
        e1.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, 1, eps)?;
        let h_seed = e1.clone_dtod(&x)?;
        let mut ld = e1.matmul(&self.output, &hn, 1)?;
        let cap = self.cfg.gemma4.as_ref().unwrap().final_logit_softcapping;
        e1.softcap(&mut ld, cap, self.output.out_features())?;
        self.gemma4_suppress(e1, &mut ld, 1)?;
        let logits = e1.dtoh(&ld)?;
        cache.pos += 1;
        Ok((logits, h_seed))
    }

    /// MEMRA_PP_STREAMS=0 rollback seam: the increment-1 gemma4 pp2 body verbatim — both
    /// stage subgraphs on the ambient compute stream, boundary = two plain dtod copies.
    fn gemma4_decode_step_h_pp2_samestream(
        &self,
        e: &Engine,
        token: u32,
        cache: &mut Cache,
        split: usize,
    ) -> Result<(Vec<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let pos_d = e.htod_i32(&[cache.pos as i32])?;

        // ---- STAGE 0: embed + sqrt(n_embd) scale + layers [0, split) ----
        let mut x = e.htod(&self.embd.gather(n_embd, &[token]))?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), n_embd)?;
        let x = self.gemma4_decode_layers(e, x, 0, split, &pos_d, cache)?;

        // ---- STAGE BOUNDARY: explicit [n_embd] activation handoff (TX copy, RX copy) ----
        let boundary_tx = e.clone_dtod(&x)?;
        let boundary_rx = e.clone_dtod(&boundary_tx)?;

        // ---- STAGE 1: layers [split, n) + output_norm + softcapped head ----
        let x =
            self.gemma4_decode_layers(e, boundary_rx, split, self.layers.len(), &pos_d, cache)?;

        let mut hn = e.uninit(n_embd)?;
        e.rms_norm(&x, self.output_norm.float_data(), &mut hn, n_embd, 1, eps)?;
        let h_seed = e.clone_dtod(&x)?;
        let mut ld = e.matmul(&self.output, &hn, 1)?;
        let cap = self.cfg.gemma4.as_ref().unwrap().final_logit_softcapping;
        e.softcap(&mut ld, cap, self.output.out_features())?;
        self.gemma4_suppress(e, &mut ld, 1)?;
        let logits = e.dtoh(&ld)?;
        cache.pos += 1;
        Ok((logits, h_seed))
    }
}

// ============================ step35 (Step-3.7-Flash) ==================================
// Node-for-node vs llama.cpp src/models/step35.cpp:216-300. WHY THIS IS A DEDICATED MIXER
// FAMILY and not a few branches inside the generic `full_attn*` chain:
//
//   1. `n_head` IS PER LAYER (`step35.attention.head_count` is an ARRAY: 64 on full-attn
//      layers, 96 on SWA). Every generic site reads the `cfg.n_head` scalar, so wq/wo/attn_gate
//      shapes and the FA head counts would be wrong on 33 of 45 layers.
//   2. `cfg.rope_dim_count` is 128 for this arch, but FULL-attn layers rotate only 64 dims
//      (upstream halves `n_rot_full` after the generic loader defaults it). A generic mixer
//      would rotate 128 dims on the full layers — silently wrong, plausible-looking logits.
//   3. Dual rope base (5e6 full / 1e4 SWA) + `rope_freqs.weight` on FULL layers ONLY.
//   4. SWA 3:1 (`sliding_window` 512, pattern [false,true,true,true]).
//   5. The head-wise gate is a SEPARATE `attn_gate.weight [n_embd, n_head_l]` (one pre-sigmoid
//      scalar per head, broadcast over head_dim) applied to the attention output BEFORE wo —
//      not the qwen35 fused-in-wq per-DIM gate that `attn_out_gate()`/`q_gate_split` handle.
//      Its input is the POST-attn_norm hidden (`cur`), not the residual.
//
// Attention scale is the DEFAULT 1/sqrt(n_embd_head_k) (step35.cpp:255) — NOT gemma4's 1.0.
impl HybridModel {
    /// Step35's declarative geometry row. Missing rows are artifact/config errors; never
    /// synthesize a drafter or trunk layer from a neighboring class.
    pub(crate) fn step35_geom(&self, il: usize) -> memra_gguf::config::LayerGeometry {
        let geometry = self
            .cfg
            .layer_geometry(il as u32)
            .unwrap_or_else(|| panic!("step35 layer {il} has no geometry-table row"));
        debug_assert_eq!(
            geometry.attention_gate,
            memra_gguf::config::AttentionGateKind::SeparateHead
        );
        geometry
    }

    /// step35 attention core: everything from the q/k/v projection outputs through the head-wise
    /// gate, returning the GATED attention output PRE-`wo` (the caller runs wo, so this composes
    /// with both the plain `matmul(&fa.wo, ..)` sites and the f16/into-slab GEMM sites).
    ///
    /// `hg` = the POST-attn_norm hidden state (upstream's `cur`) — the gate projection's input
    /// on single-sequence paths. `gt_pre` supplies the already-projected per-head gate for a
    /// cross-request batch; exactly one of `hg` or `gt_pre` must be present.
    /// `cache`:
    ///   * `Some` => PRIME mode: append this chunk's post-rope K / raw V rows into the resident
    ///     quantized cache and attend THROUGH the cache view, exactly like the generic
    ///     `full_attn_prime_fa_dispatch` (quantize-then-attend on chunk 0 too, so chunk size
    ///     cannot decide where a precision edge falls — the grain-free chunk-invariance
    ///     contract, lane/chunkinv-flip).
    ///   * `None` => pure prefill (forward/forward_last/t2probe): attend over this batch's f32
    ///     q/k/v, no cache side effect.
    ///
    /// SWA WINDOW, and why prefill needs `sdpa_naive_w_quantized_view`: the view is trimmed to
    /// the oldest key ANY query in this chunk can reach (`off = base_len - (win-1)`), which
    /// bounds the view at `win-1+t` rows, but the mask is still required — inside the chunk,
    /// query `qt` may only see view keys `[qt, qt+win-1]`, so the earlier keys the trimmed view
    /// still contains must be masked per query. memra's window convention
    /// (`sdpa_naive_w_f32`: mask `t < q_pos-(window-1)`, `q_pos = (T_kv-T)+qt`) is bit-for-bit
    /// upstream's `LLAMA_SWA_TYPE_STANDARD` (`llama-hparams.h:359`: mask `p1-p0 >= n_swa`), and
    /// step35.cpp:6 sets exactly that swa_type — verified verbatim on both sides.
    ///
    /// Note the f32 `sdpa_naive_w` floor's shared memory is `t_kv*4` bytes, so the SWA arm needs
    /// `t_kv = win-1+t <= 12287` — i.e. it relies on chunked prefill (MEMRA_PRIME_CHUNK, default
    /// 4096). A monolithic 32k prime would exceed the 48 KiB dynamic-smem default.
    ///
    /// SWA ARM SELECTION IS KEYED ON `seq_end`, NOT ON THE CHUNK'S OWN `t_kv` (lane/step35-chunkfix,
    /// 2026-08-07). `seq_end` = the ABSOLUTE end position of the whole prime call
    /// (`cache.pos + prompt_len` at entry; `t` on the cacheless prefill path) — a property of the
    /// REQUEST, identical at every MEMRA_PRIME_CHUNK value. The old predicate `swa && t_kv > win`
    /// read the chunk's own extent, which made kernel selection — and therefore the logits, the
    /// hidden rows, and the generated text — a function of the chunk size:
    ///   a chunk [b,e) has off = max(0, b-(win-1)), t_kv = e-off, so b <= win-1 => off=0 => FA iff
    ///   e <= win, while every later chunk has t_kv = t+(win-1) > win. The FA rows were therefore a
    ///   contiguous PREFIX [0,P) with P = c*floor(win/c) for c <= win (else 0), and the output
    ///   depended only on P. Measured (research/step37-p2-20260806, closed-form receipt
    ///   `raw/chunkinv-step35-GAP2-CONFIRMED-20260807.txt`): at T=4883, c=512 and c=64 (P=512) are
    ///   byte-identical to each other but DIFFER from the c=4096 default (P=0) by maxdiff 1.813e0
    ///   with greedy text diverging at step 6; c=513 (P=0) is bit-identical to the default. A
    ///   one-token change in a documented machine-config knob changed the answer.
    /// So the pre-fix comment "same cache bytes, same numeric class" was false as written: same
    /// bytes, DIFFERENT numeric class — swapping `fa_prefill_view_ws` for the f32 windowed floor on
    /// the same rows moves the logits by ~1.8.
    ///
    /// Keying on `seq_end` makes P identically 0 for every chunk size, so MEMRA_PRIME_CHUNK is a
    /// pure memory/transient knob again (research/step35-chunkfix-20260807). It is
    /// correct-by-construction rather than a tolerance argument — the windowed kernel computes the
    /// same masked attention the FA arm computed (for e <= win the window mask is a no-op under
    /// causal, which is exactly why FA was legal there), it is now simply the ONLY arm used once
    /// the request passes the window. It also cannot move the shipped default: at chunk=4096 every
    /// chunk of a `seq_end > win` prime ALREADY had t_kv > win (chunk 0 has t_kv = min(chunk,
    /// seq_end) > win; every later chunk has t_kv >= t+win-1 > win since `PRIME_MIN_T` keeps
    /// t >= 16), and a `seq_end <= win` prime keeps every chunk on FA exactly as before — so this
    /// is a no-op on both default regimes and only removes the FA prefix at small chunk values.
    /// The `t_kv <= 12287` smem ceiling is respected: the chunks whose arm changes are precisely
    /// those with t_kv <= win = 512.
    #[allow(clippy::too_many_arguments)]
    fn step35_attn_pre_wo(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        mut g3: Vec<CudaSlice<f32>>,
        hg: Option<&CudaSlice<f32>>,
        gt_pre: Option<&CudaSlice<f32>>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: Option<&mut Cache>,
        il: usize,
        seq_end: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let geometry = self.cfg.full_attention_geometry_at(il as u32);
        let hd = geometry.head_dim_k as usize;
        let nkv = geometry.n_head_kv as usize;
        let nh = geometry.n_head as usize;
        let rbase = geometry.rope_base;
        let scale = geometry.attention_scale();
        let swa = geometry.window.is_some();
        let eps = self.cfg.rms_eps;
        let win = geometry.window.unwrap_or(0) as usize;
        let n_rot = geometry.n_rot as usize;

        let v = g3.pop().unwrap();
        let k0 = g3.pop().unwrap();
        let q0 = g3.pop().unwrap();

        // q/k RMSNorm over head_dim rows (attn_q_norm/attn_k_norm [128] F32), then the
        // per-layer PARTIAL NEOX rope: n_rot = 64 on full layers (dims 64..127 pass through
        // unrotated), 128 on SWA. rope_freqs (llama3-style per-dim factors) on FULL only.
        let mut q = e.uninit(t * nh * hd)?;
        e.rms_norm(&q0, fa.q_norm.float_data(), &mut q, hd, nh * t, eps)?;
        let mut k = e.uninit(t * nkv * hd)?;
        e.rms_norm(&k0, fa.k_norm.float_data(), &mut k, hd, nkv * t, eps)?;
        let ff = if geometry.rope_factors {
            self.step35_aux.as_ref().and_then(|a| a.rope_freqs(e))
        } else {
            None
        };
        #[cfg(debug_assertions)]
        if let Some(ff) = ff {
            crate::debug_assert_tensor_stream_device(
                ff,
                &e.stream(),
                "step35_attn_pre_wo.rope_freqs",
            );
        }
        e.rope_neox2(&mut q, &mut k, pos_d, hd, n_rot, nh, nkv, t, rbase, 1.0, ff)?;

        let mut attn = e.uninit(t * nh * hd)?;
        match cache {
            Some(cache) => {
                let base_len = cache.kv[il].as_ref().unwrap().len;
                // Read per layer call, never in a measured default.
                let legacy_tkv = std::env::var("MEMRA_STEP35_SWA_TKV").as_deref() == Ok("1");
                let legacy_calllocal = std::env::var("MEMRA_PRIME_CALLLOCAL").as_deref() == Ok("1");
                let off = if swa {
                    let raw = base_len.saturating_sub(win - 1);
                    if legacy_tkv || legacy_calllocal {
                        raw
                    } else {
                        raw & !31usize
                    }
                } else {
                    0
                };
                {
                    let kvl = cache.kv[il].as_mut().unwrap();
                    assert!(kvl.len + t <= cache.max_ctx, "step35 prime: KV overflow");
                    let write_row = e.prepare_kv_append(kvl, off, t)?;
                    e.append_kv_quantized_rows(
                        &k,
                        &v,
                        &mut kvl.k,
                        &mut kvl.v,
                        write_row,
                        t,
                        kvl.kv_dim_k,
                        kvl.kv_dim_v,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                        crate::Engine::kv_fp8_on(),
                    )?;
                    kvl.len += t;
                    let new_len = kvl.len as i32;
                    e.set_i32_one(&mut kvl.len_d, new_len)?;
                }
                let kvl = cache.kv[il].as_ref().unwrap();
                // Both segmentation seams restore the FULL pre-fix arithmetic: their legacy
                // arm predicate (`t_kv` below, per-call `seq_end` in prime_cache) AND the
                // unaligned view offset here. Both halves are load-bearing for the canaries:
                // on the FA default the predicate arms agree bitwise wherever they can differ
                // (a t_kv<=win view has no maskable key, so windowed==unwindowed FA bit-for-bit).
                // That made predicate-only seams INERT under Lever A — first chunkinv35c, then
                // tickinv35c. The raw offset restores the live segmentation-variant mechanism
                // on the current FA path: its tile grid starts at the chunk/call boundary.
                // SWA: trim the view to the oldest key any query in this chunk can reach —
                // ALIGNED DOWN to the FA tile size (BK=32). The raw trim is chunk-DEPENDENT
                // (off = base_len-(win-1), and base_len is a chunk boundary), and the FA
                // kernel's online-softmax recurrence groups keys into BK tiles relative to
                // the VIEW START — so an unaligned off regroups the same absolute keys into
                // different tiles at different chunk sizes = different (m,l) rounding =
                // chunk-dependent bits. chunkinv35 caught exactly this on the first FA-arm
                // battery (first_div == chunk size; raw/leverA-gates-20260807T135541Z.log).
                // Aligning off to 32 pins tiles to ABSOLUTE key positions for every chunk
                // size; the <=31 extra leading keys are older than EVERY query's window
                // (all queries sit at >= base_len, so keys < base_len-(win-1) are masked
                // for all of them) and a fully-masked key is an exact-0.0 no-op in both
                // kernels (NEG_INF -> p=0.0; l+=0.0 and O+=0.0 are bitwise identity), so
                // the floor arm's bits do not move either (gated: G2f, battery 2).
                let t_kv = base_len + t - off;
                let physical = kvl.physical_rows(off, off + t_kv)?;
                let k_view = e.view_u8_range(
                    &kvl.k,
                    physical.start * kvl.k_tok_bytes,
                    physical.end * kvl.k_tok_bytes,
                );
                let v_view = e.view_u8_range(
                    &kvl.v,
                    physical.start * kvl.v_tok_bytes,
                    physical.end * kvl.v_tok_bytes,
                );
                // CHUNK-INVARIANT PREDICATE: `seq_end` (the request's absolute end position), not
                // this chunk's `t_kv`. See the doc note — keying on t_kv made P = c*floor(win/c)
                // rows take FA and the output a function of MEMRA_PRIME_CHUNK.
                // MEMRA_STEP35_SWA_TKV=1 is the ROLLBACK SEAM to the pre-fix arithmetic — BOTH
                // halves: this predicate AND the unaligned view offset above (`legacy_tkv`).
                // It is what gives the step35 chunkinv gate its canary teeth: chunk-VARIANT by
                // construction, so the invariance assertion MUST break under it (the seam whose
                // absence made the original canary inert — GAP 1 in research/step37-p2-20260806;
                // and a predicate-only seam went inert AGAIN under the FA default, battery 2's
                // CANARY UNEXPECTEDLY MATCHED — see the offset comment). Read per call, not
                // cached (probes flip it in-process). Never on in a measured default run.
                let swa_naive = if legacy_tkv {
                    t_kv > win
                } else {
                    seq_end > win
                };
                if swa && swa_naive {
                    // Windowed mask needed (see the doc note). DEFAULT since lane/pp-prefill
                    // 2026-08-07: the windowed hd128 FA stamp (`fa_prefill_view_ws_w_hd128`) —
                    // the anatomy profile measured the f32 floor at 565 ms/layer on a pp4096
                    // (41% of the whole prime) while the unwindowed hd128 FA family did the
                    // strictly harder causal-4096 in 3.3 ms. NOTE t_kv can be <= win here
                    // (a chunk-0 view on a small chunk); the windowed kernel handles that
                    // identically to the unwindowed one modulo the mask, which is the point.
                    // NEW NUMERIC CLASS vs the floor (bf16-MMA online softmax vs f32 serial),
                    // selected on `seq_end` like every arm here, so the class is uniform for
                    // the whole request at every MEMRA_PRIME_CHUNK — chunkinv holds by the
                    // same construction as the chunkfix. MEMRA_STEP35_SWA_FA=0 = rollback to
                    // the f32 floor (the previous numeric config, kept as the A/B seam).
                    if std::env::var("MEMRA_STEP35_SWA_FA").as_deref() == Ok("0") {
                        e.sdpa_naive_w_quantized_view(
                            &q,
                            &k_view,
                            &v_view,
                            &mut attn,
                            hd,
                            nh,
                            nkv,
                            t,
                            t_kv,
                            scale,
                            true,
                            win,
                            kvl.k_tok_bytes,
                            kvl.v_tok_bytes,
                        )?;
                    } else {
                        e.fa_prefill_view_ws_w_hd128(
                            &q,
                            &k_view,
                            &v_view,
                            &mut attn,
                            hd,
                            nh,
                            nkv,
                            t,
                            t_kv,
                            scale,
                            true,
                            win,
                            kvl.k_tok_bytes,
                            kvl.v_tok_bytes,
                        )?;
                    }
                } else if std::env::var("MEMRA_NOFA").is_ok() {
                    e.sdpa_naive_quantized_view(
                        &q,
                        &k_view,
                        &v_view,
                        &mut attn,
                        hd,
                        nh,
                        nkv,
                        t,
                        t_kv,
                        scale,
                        true,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                    )?;
                } else {
                    // seq_end <= win (or a full-attn layer): no query in the WHOLE request can
                    // reach past the window, so the window mask is a no-op under causal and every
                    // chunk rides the hd128 dequant-once FA prefill — one arm for the whole
                    // request either way, which is what makes the chunk size arithmetic-free.
                    e.fa_prefill_view_ws(
                        &q,
                        &k_view,
                        &v_view,
                        &mut attn,
                        hd,
                        nh,
                        nkv,
                        t,
                        t_kv,
                        scale,
                        true,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                        crate::Engine::kv_fp8_on(),
                    )?;
                }
            }
            None => {
                // Cacheless prefill (forward/forward_last/t2probe) is MONOLITHIC — there is no
                // chunk loop on this path, so seq_end == t and the predicate is unchanged. The
                // assert pins that: if a chunked cacheless prefill is ever added, it must thread
                // seq_end here too or it re-opens the same door.
                debug_assert_eq!(
                    seq_end, t,
                    "step35 cacheless prefill is monolithic (seq_end == t)"
                );
                if swa && seq_end > win {
                    e.sdpa_naive_w(&q, &k, &v, &mut attn, hd, nh, nkv, t, t, scale, true, win)?;
                } else if std::env::var("MEMRA_NOFA").is_ok() {
                    e.sdpa_naive(&q, &k, &v, &mut attn, hd, nh, nkv, t, t, scale, true)?;
                } else {
                    e.fa_prefill(&q, &k, &v, &mut attn, hd, nh, nkv, t, t, scale, true)?;
                }
            }
        }

        // HEAD-WISE GATE (step35.cpp:267-285): gate = attn_gate(cur) -> [t, n_head_l];
        // attn *= sigmoid(gate) broadcast over head_dim. BEFORE wo.
        let gw = fa
            .attn_gate
            .as_ref()
            .ok_or("step35 layer is missing attn_gate.weight (head-wise attention gate)")?;
        let gt_owned = if gt_pre.is_none() {
            Some(e.matmul(
                gw,
                hg.ok_or("step35 attention needs hg when gt_pre is absent")?,
                t,
            )?)
        } else {
            None
        };
        let gt = gt_pre.or(gt_owned.as_ref()).unwrap();
        let mut ag = e.uninit(t * nh * hd)?;
        e.attn_head_gate(&attn, gt, &mut ag, None, hd, nh, t)?;
        Ok(ag)
    }

    /// step35 PREFILL mixer, no cache side effect (the `full_attn` contract: `forward`,
    /// `forward_last`, t2probe). Post-`wo`.
    pub(crate) fn step35_attn(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        il: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let g3 = match self.full_attn_tp_qkv(e, fa, h, t)? {
            Some(g3) => g3,
            None => e.matmul_group(&[&fa.wq, &fa.wk, &fa.wv], h, t)?,
        };
        // Monolithic path: the request ends at t (no chunk loop). See step35_attn_pre_wo's note.
        let ag = self.step35_attn_pre_wo(e, fa, g3, Some(h), None, pos_d, t, None, il, t)?;
        self.full_attn_o(e, fa, &ag, t)
    }

    /// step35 PRIME mixer (the `full_attn_prime` contract: append this chunk's K/V into the
    /// resident quantized cache, attend through the cache view). Post-`wo`.
    ///
    /// `seq_end` = the ABSOLUTE end position of the whole prime request (chunk-invariant); it
    /// selects the SWA arm. See `step35_attn_pre_wo`'s doc note for why it cannot be this chunk's
    /// own extent.
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn step35_attn_prime(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        h: &CudaSlice<f32>,
        hx: Option<&CudaSlice<u8>>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        il: usize,
        seq_end: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        if step_tp_prefill_enabled()? && fa.step_tp_qkv.is_some() {
            if hx.is_some() {
                return Err(
                    "rank-local Step prefill preserves BF16 activations and refuses the q8_1 \
                     pre-quantized prime path"
                        .into(),
                );
            }
            return self.step35_tp_prefill_attn_resident(e, fa, il, h, pos_d, t, cache, seq_end);
        }
        let g3 = if fa.step_tp_qkv.is_some() {
            if hx.is_some() {
                return Err(
                    "Step Q/K/V TP preserves BF16 activations and refuses the q8_1 \
                     pre-quantized prime path"
                        .into(),
                );
            }
            self.full_attn_tp_qkv(e, fa, h, t)?
                .expect("Step Q/K/V TP disappeared after the presence check")
        } else {
            match hx {
                Some(xh) => e.matmul_group_xh(&[&fa.wq, &fa.wk, &fa.wv], h, xh, t)?,
                None => e.matmul_group(&[&fa.wq, &fa.wk, &fa.wv], h, t)?,
            }
        };
        let ag =
            self.step35_attn_pre_wo(e, fa, g3, Some(h), None, pos_d, t, Some(cache), il, seq_end)?;
        self.full_attn_o(e, fa, &ag, t)
    }

    fn ensure_step_tp_kv_cache(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        il: usize,
        cache: &mut Cache,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        let tp = fa
            .step_tp_qkv
            .as_ref()
            .ok_or("Step TP cache hydration lost its resident projections")?;
        let geometry = self.cfg.full_attention_geometry_at(il as u32);
        let window = geometry.window.map(|window| window as usize);
        let ranks = tp.runtime.devices().len();
        let head_dim = geometry.head_dim_k as usize;
        let kv_heads = geometry.n_head_kv as usize;
        let max_ctx = cache.max_ctx;

        if cache.tp_kv[il].is_some() {
            return Ok(false);
        }
        let local = cache.kv[il]
            .as_ref()
            .ok_or_else(|| format!("Step TP layer {il} has no owning-stage KV cache"))?;
        if local.kv_dim_k != kv_heads * head_dim || local.kv_dim_v != kv_heads * head_dim {
            return Err(format!(
                "Step TP layer {il} local KV geometry k={} v={} != {}",
                local.kv_dim_k,
                local.kv_dim_v,
                kv_heads * head_dim
            )
            .into());
        }
        let resident_start = window
            .map(|window| local.len.saturating_sub(window.saturating_sub(1)) & !31usize)
            .unwrap_or(0);
        let resident_rows = local.len - resident_start;
        let physical = local.physical_rows(resident_start, local.len)?;
        let k_rows = if resident_rows == 0 {
            Vec::new()
        } else {
            e.dtoh_u8_view(&e.view_u8_range(
                &local.k,
                physical.start * local.k_tok_bytes,
                physical.end * local.k_tok_bytes,
            ))?
        };
        let v_rows = if resident_rows == 0 {
            Vec::new()
        } else {
            e.dtoh_u8_view(&e.view_u8_range(
                &local.v,
                physical.start * local.v_tok_bytes,
                physical.end * local.v_tok_bytes,
            ))?
        };
        let mut distributed = match window {
            Some(window) => tp.runtime.allocate_tp_swa_kv_cache(
                kv_heads * head_dim,
                kv_heads * head_dim,
                max_ctx,
                window,
            )?,
            None => tp.runtime.allocate_tp_kv_cache(
                kv_heads * head_dim,
                kv_heads * head_dim,
                max_ctx,
            )?,
        };
        if distributed.k_tok_bytes() * ranks != local.k_tok_bytes
            || distributed.v_tok_bytes() * ranks != local.v_tok_bytes
        {
            return Err(format!(
                "Step TP layer {il} distributed/local KV token bytes disagree: \
                 k={}x{ranks}/{} v={}x{ranks}/{}",
                distributed.k_tok_bytes(),
                local.k_tok_bytes,
                distributed.v_tok_bytes(),
                local.v_tok_bytes,
            )
            .into());
        }
        tp.runtime.hydrate_tp_kv_cache_from(
            &mut distributed,
            local.len,
            resident_start,
            &k_rows,
            &v_rows,
        )?;
        cache.tp_kv[il] = Some(distributed);
        Ok(true)
    }

    #[allow(clippy::too_many_arguments)]
    #[allow(clippy::manual_is_multiple_of)] // allow: divisor is runtime-derived; the modulo form keeps a zero divisor loud (a panic), where is_multiple_of would return false silently
    fn step35_tp_prefill_attn_resident(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        il: usize,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        tokens: usize,
        cache: &mut Cache,
        seq_end: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let tp = fa
            .step_tp_qkv
            .as_ref()
            .ok_or("Step TP prefill lost its resident projections")?;
        let attention = tp
            .attention
            .as_ref()
            .ok_or("Step TP prefill lost its resident attention auxiliaries")?;
        let ranks = tp.runtime.devices().len();
        if !step_tp_prefill_shape(
            true,
            tokens,
            ranks,
            tp.runtime.native_p2p(),
            true,
            crate::Engine::kv_fp8_on(),
        ) {
            return Err(format!(
                "rank-local Step prefill requires tokens>={PRIME_MIN_T}, TP2/TP4 native P2P, \
                 rank-local attention, and q8_0/q5_1 KV; got tokens={tokens} ranks={ranks} \
                 native_p2p={} fp8_kv={}",
                tp.runtime.native_p2p(),
                crate::Engine::kv_fp8_on(),
            )
            .into());
        }
        for seam in [
            "MEMRA_STEP35_SWA_TKV",
            "MEMRA_PRIME_CALLLOCAL",
            "MEMRA_PRIME_F32CHUNK0",
        ] {
            if std::env::var(seam).as_deref() == Ok("1") {
                return Err(format!(
                    "rank-local Step prefill has not qualified the legacy seam {seam}=1"
                )
                .into());
            }
        }

        let geometry = self.cfg.full_attention_geometry_at(il as u32);
        let window = geometry.window.map(|window| window as usize);
        let head_dim = geometry.head_dim_k as usize;
        let heads = geometry.n_head as usize;
        let kv_heads = geometry.n_head_kv as usize;
        if heads % ranks != 0 || kv_heads % ranks != 0 {
            return Err(format!(
                "Step attention heads q={heads} kv={kv_heads} are not divisible by TP={ranks}"
            )
            .into());
        }
        let local_heads = heads / ranks;
        let local_kv_heads = kv_heads / ranks;
        let local_kv_dim = local_kv_heads * head_dim;
        let hidden = self.cfg.n_embd as usize;
        let expected_input = tokens
            .checked_mul(hidden)
            .ok_or("Step TP prefill input size overflow")?;
        if h.len() < expected_input {
            return Err(format!(
                "Step TP prefill input {} is shorter than {tokens}x{hidden}",
                h.len()
            )
            .into());
        }
        let positions = e.dtoh_i32(pos_d)?;
        if positions.len() != tokens {
            return Err(format!(
                "rank-local Step prefill positions {} != tokens {tokens}",
                positions.len()
            )
            .into());
        }

        let mut active_input = e.uninit(expected_input)?;
        e.copy_view_into(
            &mut active_input,
            0,
            &h.slice(0..expected_input),
            expected_input,
        )?;
        let mut input = tp.runtime.allocate_replicated_device_rows(tokens, hidden)?;
        // PRODUCER FENCE (2026-08-20 flake fix): active_input was written on the MODEL engine's
        // stream; the refresh below reads it from the runtime root engine's stream (same device,
        // different stream). Unfenced, the peer read can overtake the in-flight copy — the
        // layer-count-amplified arm of the boot flake.
        e.stream().synchronize()?;
        tp.runtime
            .refresh_replicated_device_rows_from_root(&mut input, &active_input)?;
        let q_raw = tp
            .runtime
            .bf16_column_parallel_resident_replicated_device_shards(&tp.q, &input)?;
        let k_raw = tp
            .runtime
            .bf16_column_parallel_resident_replicated_device_shards(&tp.k, &input)?;
        let v_raw = tp
            .runtime
            .bf16_column_parallel_resident_replicated_device_shards(&tp.v, &input)?;
        let mut q = Vec::with_capacity(ranks);
        let mut k = Vec::with_capacity(ranks);
        for rank in 0..ranks {
            let engine = tp
                .runtime
                .rank_engine(rank)
                .ok_or_else(|| format!("Step TP layer {il} has no engine for rank {rank}"))?;
            let _main = engine.gpu.enter_main()?;
            let mut q_rank = engine.uninit(tokens * local_heads * head_dim)?;
            engine.rms_norm(
                &q_raw[rank],
                &attention.q_norm[rank],
                &mut q_rank,
                head_dim,
                tokens * local_heads,
                self.cfg.rms_eps,
            )?;
            let mut k_rank = engine.uninit(tokens * local_kv_dim)?;
            engine.rms_norm(
                &k_raw[rank],
                &attention.k_norm[rank],
                &mut k_rank,
                head_dim,
                tokens * local_kv_heads,
                self.cfg.rms_eps,
            )?;
            let position = engine.htod_i32(&positions)?;
            let rope_freqs = if geometry.rope_factors {
                self.step35_aux
                    .as_ref()
                    .and_then(|aux| aux.rope_freqs(engine))
            } else {
                None
            };
            engine.rope_neox2(
                &mut q_rank,
                &mut k_rank,
                &position,
                head_dim,
                geometry.n_rot as usize,
                local_heads,
                local_kv_heads,
                tokens,
                geometry.rope_base,
                1.0,
                rope_freqs,
            )?;
            q.push(q_rank);
            k.push(k_rank);
        }

        let gate_weight = fa
            .attn_gate
            .as_ref()
            .ok_or("step35 layer is missing attn_gate.weight")?;
        let gate = e.dtoh(&e.matmul(gate_weight, h, tokens)?)?;
        if gate.len() != tokens * heads {
            return Err(format!(
                "Step TP layer {il} gate output {} != {tokens}x{heads}",
                gate.len()
            )
            .into());
        }

        let hydrated = self.ensure_step_tp_kv_cache(e, fa, il, cache)?;
        let base_len = cache.kv[il]
            .as_ref()
            .ok_or_else(|| format!("Step TP layer {il} lost its local KV cache"))?
            .len;
        let distributed = cache.tp_kv[il]
            .as_ref()
            .ok_or_else(|| format!("Step TP layer {il} lost its distributed KV cache"))?;
        if distributed.committed_len() != base_len || distributed.staged_len() != base_len {
            return Err(format!(
                "Step TP layer {il} cache lengths diverged before prefill: \
                 local={base_len} distributed={}/{}",
                distributed.committed_len(),
                distributed.staged_len()
            )
            .into());
        }
        let target_len = base_len
            .checked_add(tokens)
            .ok_or("Step TP prefill cache length overflow")?;
        if target_len > cache.max_ctx {
            return Err(format!(
                "Step TP layer {il} prefill exceeds cache: {base_len}+{tokens}>{}",
                cache.max_ctx
            )
            .into());
        }
        if seq_end < target_len {
            return Err(format!(
                "Step TP layer {il} request end {seq_end} precedes chunk end {target_len}"
            )
            .into());
        }

        let transaction = cache.tp_kv[il]
            .as_mut()
            .expect("distributed cache checked above")
            .begin_transaction()?;
        if let Err(error) = tp.runtime.append_tp_kv_transaction(
            cache.tp_kv[il]
                .as_mut()
                .expect("distributed cache checked above"),
            transaction,
            &k,
            &v_raw,
            tokens,
        ) {
            let _ = tp.runtime.rollback_tp_kv_transaction(
                cache.tp_kv[il]
                    .as_mut()
                    .expect("distributed cache checked above"),
                transaction,
            );
            return Err(error);
        }

        let staged = (|| -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
            let distributed = cache.tp_kv[il]
                .as_ref()
                .expect("distributed cache checked above");
            let staged_len = distributed.staged_len();
            let view_start = window
                .map(|window| base_len.saturating_sub(window.saturating_sub(1)) & !31usize)
                .unwrap_or(0);
            let physical = distributed.physical_range(view_start, staged_len)?;
            let t_kv = staged_len - view_start;
            let swa_naive = window.is_some_and(|window| seq_end > window);
            let mut gated = Vec::with_capacity(ranks);
            #[allow(clippy::needless_range_loop)]
            // allow: the explicit index loop keeps the offset arithmetic visible and aligned with the device-side indexing
            for rank in 0..ranks {
                let engine = tp
                    .runtime
                    .rank_engine(rank)
                    .ok_or_else(|| format!("Step TP layer {il} has no engine for rank {rank}"))?;
                let _main = engine.gpu.enter_main()?;
                let rank_cache = distributed
                    .rank(rank)
                    .ok_or_else(|| format!("Step TP layer {il} has no KV cache rank {rank}"))?;
                let k_view = engine.view_u8_range(
                    rank_cache.k(),
                    physical.start * distributed.k_tok_bytes(),
                    physical.end * distributed.k_tok_bytes(),
                );
                let v_view = engine.view_u8_range(
                    rank_cache.v(),
                    physical.start * distributed.v_tok_bytes(),
                    physical.end * distributed.v_tok_bytes(),
                );
                let mut attention_out = engine.uninit(tokens * local_heads * head_dim)?;
                if swa_naive {
                    let window = window.expect("SWA predicate requires a window");
                    if std::env::var("MEMRA_STEP35_SWA_FA").as_deref() == Ok("0") {
                        engine.sdpa_naive_w_quantized_view(
                            &q[rank],
                            &k_view,
                            &v_view,
                            &mut attention_out,
                            head_dim,
                            local_heads,
                            local_kv_heads,
                            tokens,
                            t_kv,
                            geometry.attention_scale(),
                            true,
                            window,
                            distributed.k_tok_bytes(),
                            distributed.v_tok_bytes(),
                        )?;
                    } else {
                        engine.fa_prefill_view_ws_w_hd128(
                            &q[rank],
                            &k_view,
                            &v_view,
                            &mut attention_out,
                            head_dim,
                            local_heads,
                            local_kv_heads,
                            tokens,
                            t_kv,
                            geometry.attention_scale(),
                            true,
                            window,
                            distributed.k_tok_bytes(),
                            distributed.v_tok_bytes(),
                        )?;
                    }
                } else if std::env::var("MEMRA_NOFA").is_ok() {
                    engine.sdpa_naive_quantized_view(
                        &q[rank],
                        &k_view,
                        &v_view,
                        &mut attention_out,
                        head_dim,
                        local_heads,
                        local_kv_heads,
                        tokens,
                        t_kv,
                        geometry.attention_scale(),
                        true,
                        distributed.k_tok_bytes(),
                        distributed.v_tok_bytes(),
                    )?;
                } else {
                    engine.fa_prefill_view_ws(
                        &q[rank],
                        &k_view,
                        &v_view,
                        &mut attention_out,
                        head_dim,
                        local_heads,
                        local_kv_heads,
                        tokens,
                        t_kv,
                        geometry.attention_scale(),
                        true,
                        distributed.k_tok_bytes(),
                        distributed.v_tok_bytes(),
                        false,
                    )?;
                }

                let gate_start = rank * local_heads;
                let mut gate_rank = Vec::with_capacity(tokens * local_heads);
                for token in 0..tokens {
                    let start = token * heads + gate_start;
                    gate_rank.extend_from_slice(&gate[start..start + local_heads]);
                }
                let gate_rank = engine.htod(&gate_rank)?;
                let mut gated_rank = engine.uninit(tokens * local_heads * head_dim)?;
                engine.attn_head_gate(
                    &attention_out,
                    &gate_rank,
                    &mut gated_rank,
                    None,
                    head_dim,
                    local_heads,
                    tokens,
                )?;
                gated.push(gated_rank);
            }
            for rank in 1..ranks {
                let engine = tp
                    .runtime
                    .rank_engine(rank)
                    .ok_or_else(|| format!("Step TP layer {il} has no engine for rank {rank}"))?;
                let _main = engine.gpu.enter_main()?;
                engine.stream().synchronize()?;
            }

            let (output, k_shadow, v_shadow) = if tp.runtime.bulk_p2p() {
                let output = tp
                    .runtime
                    .step_bf16_row_parallel_resident_root_device(&tp.o, &gated, tokens)?;
                let k_shadow =
                    tp.runtime
                        .gather_native_column_shards_device(&k, tokens, local_kv_dim)?;
                let v_shadow =
                    tp.runtime
                        .gather_native_column_shards_device(&v_raw, tokens, local_kv_dim)?;
                let root = tp
                    .runtime
                    .rank_engine(0)
                    .ok_or("Step TP prefill lost its root engine")?;
                let _main = root.gpu.enter_main()?;
                root.stream().synchronize()?;
                (output, k_shadow, v_shadow)
            } else {
                let attention = tp.runtime.gather_native_column_shards(
                    &gated,
                    tokens,
                    local_heads * head_dim,
                )?;
                let output = tp
                    .runtime
                    .step_bf16_row_parallel_resident_native(&tp.o, &attention, tokens)?;
                let k_shadow = tp
                    .runtime
                    .gather_native_column_shards(&k, tokens, local_kv_dim)?;
                let v_shadow =
                    tp.runtime
                        .gather_native_column_shards(&v_raw, tokens, local_kv_dim)?;
                (e.htod(&output)?, e.htod(&k_shadow)?, e.htod(&v_shadow)?)
            };
            let local = cache.kv[il]
                .as_mut()
                .ok_or_else(|| format!("Step TP layer {il} lost its local KV cache"))?;
            if local.len != base_len {
                return Err(format!(
                    "Step TP layer {il} local cache changed during prefill: \
                     len={} base={base_len}",
                    local.len
                )
                .into());
            }
            let retain_from = window
                .map(|window| {
                    let staged_retain = staged_len.saturating_sub(window) & !31usize;
                    let rollback_retain =
                        base_len.saturating_sub(window.saturating_sub(1)) & !31usize;
                    staged_retain.min(rollback_retain)
                })
                .unwrap_or(0);
            let write_row = e.prepare_kv_append(local, retain_from, tokens)?;
            e.append_kv_quantized_rows(
                &k_shadow,
                &v_shadow,
                &mut local.k,
                &mut local.v,
                write_row,
                tokens,
                local.kv_dim_k,
                local.kv_dim_v,
                local.k_tok_bytes,
                local.v_tok_bytes,
                false,
            )?;
            local.len = staged_len;
            e.set_i32_one(&mut local.len_d, staged_len as i32)?;
            Ok(output)
        })();

        let output = match staged {
            Ok(output) => output,
            Err(error) => {
                let _ = tp.runtime.rollback_tp_kv_transaction(
                    cache.tp_kv[il]
                        .as_mut()
                        .expect("distributed cache checked above"),
                    transaction,
                );
                if let Some(local) = cache.kv[il].as_mut() {
                    local.len = base_len;
                    let _ = e.set_i32_one(&mut local.len_d, base_len as i32);
                }
                return Err(error);
            }
        };
        if let Err(error) = tp.runtime.commit_tp_kv_transaction(
            cache.tp_kv[il]
                .as_mut()
                .expect("distributed cache checked above"),
            transaction,
            tokens,
        ) {
            let _ = tp.runtime.rollback_tp_kv_transaction(
                cache.tp_kv[il]
                    .as_mut()
                    .expect("distributed cache checked above"),
                transaction,
            );
            let local = cache.kv[il].as_mut().expect("local cache checked above");
            local.len = base_len;
            e.set_i32_one(&mut local.len_d, base_len as i32)?;
            return Err(error);
        }

        let committed = cache.tp_kv[il]
            .as_ref()
            .expect("distributed cache checked above")
            .committed_len();
        let local_len = cache.kv[il]
            .as_ref()
            .expect("local cache checked above")
            .len;
        if committed != local_len {
            return Err(format!(
                "Step TP layer {il} committed cache length {committed} != local shadow {local_len}"
            )
            .into());
        }
        eprintln!(
            "[step-tp-prefill-attn] execute layer={} devices={:?} tokens={tokens} \
             qkv_tensor_parallel=true qk_norm_rank_local=true rope_rank_local=true \
             kv_cache_distributed=true kv_cache_hydrated={} attention_tensor_parallel=true \
             attention_scope={} input_path=root-device-replicated gate_tensor_parallel=false \
             gate_shards=host-canonical o_tensor_parallel=true local_cache_shadow=true \
             cache_commit=chunk transport={} native_p2p=true bulk_p2p={} \
             output={} performance_claim=false",
            tp.layer,
            tp.devices,
            hydrated,
            if window.is_some() {
                "rank-local-swa-ring"
            } else {
                "rank-local-global"
            },
            tp.runtime.transport_label(),
            tp.runtime.bulk_p2p(),
            if tp.runtime.bulk_p2p() {
                "root-device"
            } else {
                "root-readback"
            },
        );
        Ok(output)
    }

    fn step35_tp_decode_attn_resident(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        il: usize,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        // MEMRA_STEP_TP_TIMING=1: cumulative wall-clock of the rank-local TP attention decode,
        // printed every ~10 decode steps' worth of layers — the wall-decomposition twin of the
        // nvfp4-dev-routes counter.
        static ATTN_NS: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static ATTN_CALLS: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        let timing = std::env::var("MEMRA_STEP_TP_TIMING").as_deref() == Ok("1");
        let started = timing.then(std::time::Instant::now);
        let result = if crate::tp::step_tp_decode_v2_enabled()? {
            self.step35_tp_decode_attn_resident_v2(e, fa, il, h, pos_d, cache)
        } else {
            self.step35_tp_decode_attn_resident_inner(e, fa, il, h, pos_d, cache)
        };
        if let Some(started) = started {
            use std::sync::atomic::Ordering;
            let ns = ATTN_NS.fetch_add(started.elapsed().as_nanos() as u64, Ordering::Relaxed)
                + started.elapsed().as_nanos() as u64;
            let calls = ATTN_CALLS.fetch_add(1, Ordering::Relaxed) + 1;
            if calls.is_multiple_of(430) {
                eprintln!(
                    "[step-tp-attn-timing] calls={calls} total_ms={:.1} avg_us={:.1}",
                    ns as f64 / 1.0e6,
                    ns as f64 / calls as f64 / 1.0e3,
                );
            }
        }
        result
    }

    #[allow(clippy::too_many_arguments)]
    fn step35_tp_decode_attn_resident_inner(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        il: usize,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        // MEMRA_STEP_TP_TIMING=1 phase decomposition of the 1550us/layer decode wall. Each lap
        // drains every stream so queued async work is billed to the phase that queued it — the
        // drains perturb absolute wall, but v1 already ends most phases on a host sync, so the
        // relative split is honest. Timing OFF is the measured configuration: zero extra syncs.
        static T_POS: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static T_QKV: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static T_NORMROPE: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static T_GATE: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static T_APPEND: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static T_ATTN: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static T_OPROJ: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static T_SHADOW: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        static T_PHASE_CALLS: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
        let timing = std::env::var("MEMRA_STEP_TP_TIMING").as_deref() == Ok("1");
        #[allow(clippy::manual_is_multiple_of)] // allow: divisor is runtime-derived; the modulo form keeps a zero divisor loud (a panic), where is_multiple_of would return false silently
        fn lap(
            runtime: &crate::tp::TpE4m3HostBounce,
            e: &Engine,
            timer: &std::sync::atomic::AtomicU64,
            started: &mut Option<std::time::Instant>,
        ) -> Result<(), Box<dyn std::error::Error>> {
            let Some(start) = started.as_mut() else {
                return Ok(());
            };
            for rank in 0..runtime.devices().len() {
                if let Some(engine) = runtime.rank_engine(rank) {
                    let _main = engine.gpu.enter_main()?;
                    engine.stream().synchronize()?;
                }
            }
            e.stream().synchronize()?;
            timer.fetch_add(
                start.elapsed().as_nanos() as u64,
                std::sync::atomic::Ordering::Relaxed,
            );
            *start = std::time::Instant::now();
            Ok(())
        }
        let tp = fa
            .step_tp_qkv
            .as_ref()
            .ok_or("Step TP decode lost its resident projections")?;
        let attention = tp
            .attention
            .as_ref()
            .ok_or("Step TP decode lost its resident attention auxiliaries")?;
        if !tp.runtime.native_p2p() {
            return Err("rank-local Step attention requires native P2P".into());
        }
        if crate::Engine::kv_fp8_on() {
            return Err("rank-local Step attention has not qualified the FP8 KV cache".into());
        }

        let geometry = self.step35_geom(il);
        let window = geometry.window.map(|window| window as usize);
        let ranks = tp.runtime.devices().len();
        let head_dim = geometry.head_dim_k as usize;
        let heads = geometry.n_head as usize;
        let kv_heads = geometry.n_head_kv as usize;
        if !heads.is_multiple_of(ranks) || !kv_heads.is_multiple_of(ranks) {
            return Err(format!(
                "Step attention heads q={heads} kv={kv_heads} are not divisible by TP={ranks}"
            )
            .into());
        }
        let local_heads = heads / ranks;
        let local_kv_heads = kv_heads / ranks;
        let local_kv_dim = local_kv_heads * head_dim;
        let max_ctx = cache.max_ctx;

        let hydrated = self.ensure_step_tp_kv_cache(e, fa, il, cache)?;

        let base_len = cache.kv[il]
            .as_ref()
            .ok_or_else(|| format!("Step TP layer {il} lost its local KV cache"))?
            .len;
        let distributed = cache.tp_kv[il]
            .as_ref()
            .ok_or_else(|| format!("Step TP layer {il} lost its distributed KV cache"))?;
        if distributed.committed_len() != base_len || distributed.staged_len() != base_len {
            return Err(format!(
                "Step TP layer {il} cache lengths diverged before decode: \
                 local={base_len} distributed={}/{}",
                distributed.committed_len(),
                distributed.staged_len()
            )
            .into());
        }

        let mut lap_start = timing.then(std::time::Instant::now);
        let positions = e.dtoh_i32(pos_d)?;
        if positions.len() != 1 {
            return Err(format!(
                "rank-local Step decode requires one position, got {}",
                positions.len()
            )
            .into());
        }
        lap(&tp.runtime, e, &T_POS, &mut lap_start)?;
        let (q_raw, k_raw, v_raw, input_path) = if let Some(decode_input) =
            attention.decode_input.as_ref()
        {
            let mut decode_input = decode_input
                .lock()
                .map_err(|_| "Step TP replicated decode input lock is poisoned")?;
            // PRODUCER FENCE (2026-08-20 flake fix): h is the layer hidden written on the MODEL
            // engine's stream; the refresh reads it from the runtime root engine's stream. This
            // per-layer-per-token seam is the layer-count-amplified arm of the boot flake.
            e.stream().synchronize()?;
            tp.runtime
                .refresh_replicated_device_rows_from_root(&mut decode_input, h)?;
            let q_raw = tp
                .runtime
                .bf16_column_parallel_resident_replicated_device_shards(&tp.q, &decode_input)?;
            let k_raw = tp
                .runtime
                .bf16_column_parallel_resident_replicated_device_shards(&tp.k, &decode_input)?;
            let v_raw = tp
                .runtime
                .bf16_column_parallel_resident_replicated_device_shards(&tp.v, &decode_input)?;
            (q_raw, k_raw, v_raw, "root-device-replicated")
        } else {
            let activation = e.dtoh(h)?;
            let q_raw =
                tp.runtime
                    .bf16_column_parallel_resident_device_shards(&tp.q, &activation, 1)?;
            let k_raw =
                tp.runtime
                    .bf16_column_parallel_resident_device_shards(&tp.k, &activation, 1)?;
            let v_raw =
                tp.runtime
                    .bf16_column_parallel_resident_device_shards(&tp.v, &activation, 1)?;
            (q_raw, k_raw, v_raw, "host-replicated")
        };
        lap(&tp.runtime, e, &T_QKV, &mut lap_start)?;
        let mut q = Vec::with_capacity(ranks);
        let mut k = Vec::with_capacity(ranks);
        for rank in 0..ranks {
            let engine = tp
                .runtime
                .rank_engine(rank)
                .ok_or_else(|| format!("Step TP layer {il} has no engine for rank {rank}"))?;
            let _main = engine.gpu.enter_main()?;
            let mut q_rank = engine.uninit(local_heads * head_dim)?;
            engine.rms_norm(
                &q_raw[rank],
                &attention.q_norm[rank],
                &mut q_rank,
                head_dim,
                local_heads,
                self.cfg.rms_eps,
            )?;
            let mut k_rank = engine.uninit(local_kv_dim)?;
            engine.rms_norm(
                &k_raw[rank],
                &attention.k_norm[rank],
                &mut k_rank,
                head_dim,
                local_kv_heads,
                self.cfg.rms_eps,
            )?;
            let position = engine.htod_i32(&positions)?;
            let rope_freqs = if geometry.rope_factors {
                self.step35_aux
                    .as_ref()
                    .and_then(|aux| aux.rope_freqs(engine))
            } else {
                None
            };
            engine.rope_neox2(
                &mut q_rank,
                &mut k_rank,
                &position,
                head_dim,
                geometry.n_rot as usize,
                local_heads,
                local_kv_heads,
                1,
                geometry.rope_base,
                1.0,
                rope_freqs,
            )?;
            q.push(q_rank);
            k.push(k_rank);
        }
        lap(&tp.runtime, e, &T_NORMROPE, &mut lap_start)?;

        let gate_weight = fa
            .attn_gate
            .as_ref()
            .ok_or("step35 layer is missing attn_gate.weight")?;
        let gate = e.matmul(gate_weight, h, 1)?;
        let gate = e.dtoh(&gate)?;
        if gate.len() != heads {
            return Err(format!("Step TP layer {il} gate output {} != {heads}", gate.len()).into());
        }
        lap(&tp.runtime, e, &T_GATE, &mut lap_start)?;

        let transaction = cache.tp_kv[il]
            .as_mut()
            .expect("distributed cache checked above")
            .begin_transaction()?;
        if let Err(error) = tp.runtime.append_tp_kv_transaction(
            cache.tp_kv[il]
                .as_mut()
                .expect("distributed cache checked above"),
            transaction,
            &k,
            &v_raw,
            1,
        ) {
            let _ = tp.runtime.rollback_tp_kv_transaction(
                cache.tp_kv[il]
                    .as_mut()
                    .expect("distributed cache checked above"),
                transaction,
            );
            return Err(error);
        }
        lap(&tp.runtime, e, &T_APPEND, &mut lap_start)?;

        let staged = (|| -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
            let distributed = cache.tp_kv[il]
                .as_ref()
                .expect("distributed cache checked above");
            let staged_len = distributed.staged_len();
            let view_start = window
                .map(|window| staged_len.saturating_sub(window))
                .unwrap_or(0);
            let physical = distributed.physical_range(view_start, staged_len)?;
            let t_kv = staged_len - view_start;
            let mut gated = Vec::with_capacity(ranks);
            #[allow(clippy::needless_range_loop)]
            // allow: the explicit index loop keeps the offset arithmetic visible and aligned with the device-side indexing
            for rank in 0..ranks {
                let engine = tp
                    .runtime
                    .rank_engine(rank)
                    .ok_or_else(|| format!("Step TP layer {il} has no engine for rank {rank}"))?;
                let _main = engine.gpu.enter_main()?;
                let rank_cache = distributed
                    .rank(rank)
                    .ok_or_else(|| format!("Step TP layer {il} has no KV cache rank {rank}"))?;
                let k_view = engine.view_u8_range(
                    rank_cache.k(),
                    physical.start * distributed.k_tok_bytes(),
                    physical.end * distributed.k_tok_bytes(),
                );
                let v_view = engine.view_u8_range(
                    rank_cache.v(),
                    physical.start * distributed.v_tok_bytes(),
                    physical.end * distributed.v_tok_bytes(),
                );
                let mut attention_out = engine.uninit(local_heads * head_dim)?;
                engine.fa_decode_kvmod(
                    &q[rank],
                    &k_view,
                    &v_view,
                    &mut attention_out,
                    head_dim,
                    local_heads,
                    local_kv_heads,
                    t_kv,
                    geometry.attention_scale(),
                    distributed.k_tok_bytes(),
                    distributed.v_tok_bytes(),
                    false,
                )?;
                let gate_start = rank * local_heads;
                let gate_rank = engine.htod(&gate[gate_start..gate_start + local_heads])?;
                let mut gated_rank = engine.uninit(local_heads * head_dim)?;
                engine.attn_head_gate(
                    &attention_out,
                    &gate_rank,
                    &mut gated_rank,
                    None,
                    head_dim,
                    local_heads,
                    1,
                )?;
                gated.push(gated_rank);
            }
            lap(&tp.runtime, e, &T_ATTN, &mut lap_start)?;

            let gathered =
                tp.runtime
                    .gather_native_column_shards(&gated, 1, local_heads * head_dim)?;
            let output = tp
                .runtime
                .step_bf16_row_parallel_resident_native(&tp.o, &gathered, 1)?;
            let output = e.htod(&output)?;
            lap(&tp.runtime, e, &T_OPROJ, &mut lap_start)?;

            let k_shadow = tp
                .runtime
                .gather_native_column_shards(&k, 1, local_kv_dim)?;
            let v_shadow = tp
                .runtime
                .gather_native_column_shards(&v_raw, 1, local_kv_dim)?;
            let k_shadow = e.htod(&k_shadow)?;
            let v_shadow = e.htod(&v_shadow)?;
            let local = cache.kv[il]
                .as_mut()
                .ok_or_else(|| format!("Step TP layer {il} lost its local KV cache"))?;
            if local.len != base_len || base_len + 1 > max_ctx {
                return Err(format!(
                    "Step TP layer {il} local cache changed during decode: \
                     len={} base={base_len} max={max_ctx}",
                    local.len
                )
                .into());
            }
            let retain_from = window
                .map(|window| {
                    let staged_retain = (base_len + 1).saturating_sub(window) & !31usize;
                    let rollback_retain =
                        base_len.saturating_sub(window.saturating_sub(1)) & !31usize;
                    staged_retain.min(rollback_retain)
                })
                .unwrap_or(0);
            let write_row = e.prepare_kv_append(local, retain_from, 1)?;
            e.append_kv_quantized(
                &k_shadow,
                &v_shadow,
                &mut local.k,
                &mut local.v,
                write_row,
                local.kv_dim_k,
                local.kv_dim_v,
                local.k_tok_bytes,
                local.v_tok_bytes,
                false,
            )?;
            local.len = base_len + 1;
            e.set_i32_one(&mut local.len_d, local.len as i32)?;
            Ok(output)
        })();

        let output = match staged {
            Ok(output) => output,
            Err(error) => {
                let _ = tp.runtime.rollback_tp_kv_transaction(
                    cache.tp_kv[il]
                        .as_mut()
                        .expect("distributed cache checked above"),
                    transaction,
                );
                if let Some(local) = cache.kv[il].as_mut() {
                    local.len = base_len;
                    let _ = e.set_i32_one(&mut local.len_d, base_len as i32);
                }
                return Err(error);
            }
        };
        if let Err(error) = tp.runtime.commit_tp_kv_transaction(
            cache.tp_kv[il]
                .as_mut()
                .expect("distributed cache checked above"),
            transaction,
            1,
        ) {
            let _ = tp.runtime.rollback_tp_kv_transaction(
                cache.tp_kv[il]
                    .as_mut()
                    .expect("distributed cache checked above"),
                transaction,
            );
            let local = cache.kv[il].as_mut().expect("local cache checked above");
            local.len = base_len;
            e.set_i32_one(&mut local.len_d, base_len as i32)?;
            return Err(error);
        }

        let committed = cache.tp_kv[il]
            .as_ref()
            .expect("distributed cache checked above")
            .committed_len();
        let local_len = cache.kv[il]
            .as_ref()
            .expect("local cache checked above")
            .len;
        if committed != local_len {
            return Err(format!(
                "Step TP layer {il} committed cache length {committed} != local shadow {local_len}"
            )
            .into());
        }
        lap(&tp.runtime, e, &T_SHADOW, &mut lap_start)?;
        if timing {
            use std::sync::atomic::Ordering;
            let calls = T_PHASE_CALLS.fetch_add(1, Ordering::Relaxed) + 1;
            if calls.is_multiple_of(430) {
                let avg = |t: &std::sync::atomic::AtomicU64| {
                    t.load(Ordering::Relaxed) as f64 / calls as f64 / 1.0e3
                };
                eprintln!(
                    "[step-tp-attn-phase] calls={calls} avg_us pos={:.1} qkv={:.1} \
                     normrope={:.1} gate={:.1} append={:.1} attn={:.1} oproj={:.1} shadow={:.1}",
                    avg(&T_POS),
                    avg(&T_QKV),
                    avg(&T_NORMROPE),
                    avg(&T_GATE),
                    avg(&T_APPEND),
                    avg(&T_ATTN),
                    avg(&T_OPROJ),
                    avg(&T_SHADOW),
                );
            }
        }
        eprintln!(
            "[step-tp-attn] execute layer={} devices={:?} tokens=1 \
             qkv_tensor_parallel=true qk_norm_rank_local=true rope_rank_local=true \
             kv_cache_distributed=true kv_cache_hydrated={} attention_tensor_parallel=true \
             attention_scope={} input_path={} kv_physical_rows={} \
             gate_tensor_parallel=false gate_shards=host-canonical o_tensor_parallel=true \
             local_cache_shadow=true cache_commit=immediate transport={} native_p2p=true \
             bulk_p2p={} output=root-readback performance_claim=false",
            tp.layer,
            tp.devices,
            hydrated,
            if window.is_some() {
                "rank-local-swa-ring"
            } else {
                "rank-local-global"
            },
            input_path,
            cache.tp_kv[il]
                .as_ref()
                .expect("distributed cache checked above")
                .physical_capacity(),
            tp.runtime.transport_label(),
            tp.runtime.bulk_p2p(),
        );
        Ok(output)
    }

    /// v2 rank-local decode attention (MEMRA_STEP_TP_DECODE_V2): the same kernels, operand
    /// values, and canonical reduction order as `step35_tp_decode_attn_resident_inner`,
    /// restructured onto a persistent per-runtime workspace with evented cross-stream ordering.
    /// Per layer per token this path performs exactly one cuMemAlloc (the returned e-context
    /// output row), no host round-trip, and no host stream synchronize — the phase timers
    /// measured v1 spending 81% of its 1550us/layer wall on those three classes.
    #[allow(clippy::too_many_arguments)]
    /// T-COLUMN verify precompute for layer `il`: weight-amortized QKV(+gate) over the T
    /// verify columns into the ws slabs (per-column rope/append/fa run later through the
    /// unmodified t=1 program via the col-select door). Ok(false) when the layer is not on
    /// the resident fused TP2 class (caller falls back to the per-row walk).
    pub(crate) fn step35_verify_qkv_precompute(
        &self,
        e: &Engine,
        il: usize,
        h_t: &CudaSlice<f32>,
        t: usize,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Ok(false);
        };
        let Some(tp) = fa.step_tp_qkv.as_ref() else {
            return Ok(false);
        };
        let Some(attention) = tp.attention.as_ref() else {
            return Ok(false);
        };
        if !tp.runtime.native_p2p() || !crate::tp::step_tp_qkv_fused_enabled()? {
            return Ok(false);
        }
        let geometry = self.step35_geom(il);
        let heads = geometry.n_head as usize;
        let ws_index = tp
            .runtime
            .decode_v2_ensure(e, &tp.q, &tp.k, &tp.v, &tp.o, heads)?;
        let gate_shards = attention
            .gate_shards_bf16
            .as_deref()
            .map(crate::tp::StepTpGateShards::Bf16);
        tp.runtime.decode_v2_input_qkv_tcol(
            ws_index,
            e,
            h_t,
            t,
            &tp.q,
            &tp.k,
            &tp.v,
            gate_shards,
        )?;
        Ok(true)
    }

    /// MEMRA_TCOL_OPROJ join for the verify walk: after every column of layer `il`
    /// stashed its `gated` rows, produce the [t, o_out] `mixed` slab on `e` via the
    /// weight-amortized b4_tcol + slab join. Callers only reach this after the stash
    /// flag confirmed the defer engaged for every column.
    pub(crate) fn step35_verify_oproj_tcol(
        &self,
        e: &Engine,
        il: usize,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Err("tcol o_proj join expects full attention".into());
        };
        let tp = fa
            .step_tp_qkv
            .as_ref()
            .ok_or("tcol o_proj join lost its resident projections")?;
        let heads = self.step35_geom(il).n_head as usize;
        let ws_index = tp
            .runtime
            .decode_v2_ensure(e, &tp.q, &tp.k, &tp.v, &tp.o, heads)?;
        tp.runtime.decode_v2_oproj_tcol(ws_index, e, &tp.o, t)
    }

    /// MEMRA_SPEC_FA2 precheck: decide BEFORE arming the defer whether both verify
    /// columns of layer `il` will take the dcw arm AND the T=2 launch is bit-safe —
    /// stashing is unrecoverable (no per-column output exists), so every dynamic input
    /// to the engine-side dcw decision is evaluated here, plus the equal-partition
    /// guard fa_decode_dcw2's contract requires. Boundary rounds return false and the
    /// walk runs the ordinary per-column program.
    #[allow(dead_code)] // allow: banked MEMRA_SPEC_FA2 arm; kept as the named seam for the spec-FA2 join program
    pub(crate) fn step35_spec_fa2_precheck(
        &self,
        cache: &Cache,
        il: usize,
        pos0: usize,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        // MEMRA_SPEC_FA2_DEBUG=1: print the first failing clause once per clause id —
        // a silently-vacuous door is indistinguishable from a slow one without this.
        fn nope(clause: &str, il: usize, pos0: usize) -> bool {
            static DBG: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
            static SEEN: std::sync::Mutex<Vec<&'static str>> = std::sync::Mutex::new(Vec::new());
            if *DBG.get_or_init(|| std::env::var("MEMRA_SPEC_FA2_DEBUG").as_deref() == Ok("1")) {
                let mut seen = SEEN.lock().unwrap();
                if !seen.contains(&clause) {
                    // leak: bounded by the clause-id set
                    seen.push(Box::leak(clause.to_string().into_boxed_str()));
                    eprintln!("[spec-fa2] precheck FAIL clause={clause} il={il} pos0={pos0}");
                }
            }
            false
        }
        // MEMRA_SPEC_FA2_LAYER=<il>: engage on ONE layer only (divergence bisection).
        static ONLY: std::sync::OnceLock<Option<usize>> = std::sync::OnceLock::new();
        if let Some(only) =
            ONLY.get_or_init(|| std::env::var("MEMRA_SPEC_FA2_LAYER").ok()?.parse().ok())
            && *only != il
        {
            return Ok(false);
        }
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Ok(nope("mixer", il, pos0));
        };
        let Some(tp) = fa.step_tp_qkv.as_ref() else {
            return Ok(nope("step_tp", il, pos0));
        };
        let Some(attention) = tp.attention.as_ref() else {
            return Ok(nope("attention", il, pos0));
        };
        if !tp.runtime.native_p2p()
            || crate::Engine::kv_fp8_on()
            || !crate::tp::step_tp_dcw_enabled()?
            || !crate::tp::step_tp_qkv_fused_enabled()?
            || (attention.gate_shards.is_none() && attention.gate_shards_bf16.is_none())
        {
            return Ok(nope("runtime-doors", il, pos0));
        }
        let geometry = self.step35_geom(il);
        let head_dim = geometry.head_dim_k as usize;
        if head_dim > 256 || !head_dim.is_multiple_of(32) || !crate::fa_v3_on() {
            return Ok(nope("fa-class", il, pos0));
        }
        let Some(distributed) = cache.tp_kv[il].as_ref() else {
            return Ok(nope("tp-kv", il, pos0));
        };
        if distributed.staged_len() != pos0 {
            return Ok(nope("staged-len", il, pos0));
        }
        // Both appends must land without a ring rebase (rebase columns take the
        // host-row path, which cannot stash).
        let (_, would_rebase) = distributed.peek_append_ring(2)?;
        if would_rebase {
            return Ok(nope("rebase", il, pos0));
        }
        let window = geometry.window.map(|w| w as usize);
        // Capped SWA is REDUCTION-CLASS in the joined kernel (the two rows' windows
        // shift by one key, so one shared tile grid cannot reproduce both rows'
        // per-column FP grouping) — and drifted verify logits change accept decisions,
        // breaking the spec==target contract. Engage only when BOTH rows' views start
        // at 0 (global, or SWA still inside its window): bitwise per row under the
        // partition guard below. At agentic ctx this keeps the global layers — ~3/4 of
        // the per-key fa work — and leaves capped-SWA layers on the per-column program.
        if let Some(w) = window
            && pos0 + 2 > w
        {
            return Ok(nope("swa-capped", il, pos0));
        }
        // Row r's own per-column launch sees the POST-append view: T_r = pos0 + 1 + r
        // (kernel T_kv = len_dev - lstart; the host bucket matches it — the one-partition
        // law). Both dcw eligibility (t_kv_eff >= 96) and the vec floor key off T0.
        let (t0, t1) = (pos0 + 1, pos0 + 2);
        if t0 < 96 {
            return Ok(nope("dcw-floor", il, pos0));
        }
        if std::env::var("MEMRA_NO_FA_VEC").is_ok() || t0 < crate::fa_vec_min_tkv() {
            return Ok(nope("vec-floor", il, pos0));
        }
        // Equal-partition guard, on the KERNEL's derivation: split width (sp), effective
        // count (ns = ceil(T/sp)) and stride (per = ceil(T/ns)) must all match between
        // the two rows' own launches — the joined kernel derives one grid from T1 and
        // row0 inherits it, so any difference shifts row0's split boundaries and changes
        // the combine's merge rounding. Boundary rounds fall back per column.
        let ranks = tp.runtime.devices().len();
        let local_kv_heads = (geometry.n_head_kv as usize / ranks).max(1);
        let sp0 = crate::fa_split_keys_pub(t0, local_kv_heads);
        let sp1 = crate::fa_split_keys_pub(t1, local_kv_heads);
        if sp0 != sp1 {
            return Ok(nope("partition-sp", il, pos0));
        }
        let (ns0, ns1) = (t0.div_ceil(sp0), t1.div_ceil(sp1));
        if ns0 != ns1 {
            return Ok(nope("partition-ns", il, pos0));
        }
        if t0.div_ceil(ns0) != t1.div_ceil(ns1) {
            return Ok(nope("partition-per", il, pos0));
        }
        Ok(true)
    }

    /// T-ROW fa precheck (the rows kernel supersedes the dcw2 pair-join): every dynamic
    /// input of the engine-side dcw decision must hold for EVERY row — stashing is
    /// unrecoverable — plus the rows-launcher guards (big-rig ladder, no env split
    /// overrides). No partition or capped-SWA clauses: each row derives its OWN geometry.
    pub(crate) fn step35_fa_rows_precheck(
        &self,
        cache: &Cache,
        il: usize,
        pos0: usize,
        t: usize,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Ok(false);
        };
        let Some(tp) = fa.step_tp_qkv.as_ref() else {
            return Ok(false);
        };
        let Some(attention) = tp.attention.as_ref() else {
            return Ok(false);
        };
        if !tp.runtime.native_p2p()
            || crate::Engine::kv_fp8_on()
            || !crate::tp::step_tp_dcw_enabled()?
            || !crate::tp::step_tp_qkv_fused_enabled()?
            || (attention.gate_shards.is_none() && attention.gate_shards_bf16.is_none())
        {
            return Ok(false);
        }
        let geometry = self.step35_geom(il);
        let head_dim = geometry.head_dim_k as usize;
        if head_dim > 256 || !head_dim.is_multiple_of(32) || !crate::fa_v3_on() {
            return Ok(false);
        }
        if crate::fa_sm_count() < 128
            || std::env::var("MEMRA_FA_SPLIT").is_ok()
            || std::env::var("MEMRA_FA_SP_SHORT").is_ok()
            || std::env::var("MEMRA_FA_SP16").is_ok()
            || std::env::var("MEMRA_NO_FA_VEC").is_ok()
        {
            return Ok(false);
        }
        let Some(distributed) = cache.tp_kv[il].as_ref() else {
            return Ok(false);
        };
        if distributed.staged_len() != pos0 {
            return Ok(false);
        }
        let (_, would_rebase) = distributed.peek_append_ring(t)?;
        if would_rebase {
            return Ok(false);
        }
        // Row 0 sees the smallest view: its post-append effective t_kv must clear both
        // the dcw floor and the vec-class floor (later rows only grow).
        let window = geometry.window.map(|w| w as usize);
        let t0 = window.map(|w| (pos0 + 1).min(w)).unwrap_or(pos0 + 1);
        if t0 < 96 || t0 < crate::fa_vec_min_tkv() {
            return Ok(false);
        }
        Ok(true)
    }

    /// T-ROW fa join for the verify walk (same-session rows: shared ring/len with
    /// len_back = t-1-r). Tables stage once per (layer, rank, ring, t) and live on the
    /// owning rank.
    pub(crate) fn step35_verify_fa_rows_join(
        &self,
        e: &Engine,
        il: usize,
        cache: &Cache,
        pos0: usize,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        use cudarc::driver::DevicePtr;
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Err("fa rows join expects full attention".into());
        };
        let tp = fa
            .step_tp_qkv
            .as_ref()
            .ok_or("fa rows join lost its resident projections")?;
        let geometry = self.step35_geom(il);
        let heads = geometry.n_head as usize;
        let head_dim = geometry.head_dim_k as usize;
        let window = geometry.window.map(|w| w as usize);
        let distributed = cache.tp_kv[il]
            .as_ref()
            .ok_or("fa rows join lost its distributed KV cache")?;
        let (k_tok_bytes, v_tok_bytes) = (distributed.k_tok_bytes(), distributed.v_tok_bytes());
        // Host mirror of the kernel's big-rig ladder (launcher-guarded identical).
        let ladder = |t_kv: usize| -> usize {
            if t_kv <= 2048 {
                16
            } else if t_kv <= 16384 {
                64
            } else {
                128
            }
        };
        let mut max_ns = 1usize;
        for r in 0..t {
            let t_kv = window
                .map(|w| (pos0 + r + 1).min(w))
                .unwrap_or(pos0 + r + 1);
            max_ns = max_ns.max(t_kv.div_ceil(ladder(t_kv)));
        }
        // Rebuild the tiny raw-pointer table from the live distributed cache immediately
        // before launch. A process-lifetime map cannot prove allocation generation: CUDA may
        // recycle len/base independently of the large K/V rings, making a pointer-key cache
        // hit refer to another session (Hermes `11339f5cd3c132a3`).
        let ranks = tp.runtime.devices().len();
        let mut tables = Vec::with_capacity(ranks);
        for rank in 0..ranks {
            let engine = tp
                .runtime
                .rank_engine(rank)
                .ok_or("fa rows join lost a rank engine")?;
            let rank_cache = distributed
                .rank(rank)
                .ok_or("fa rows join lost a KV cache rank")?;
            let _main = engine.gpu.enter_main()?;
            let s = engine.stream();
            let (kp, _g0) = rank_cache.k().device_ptr(&s);
            let (vp, _g1) = rank_cache.v().device_ptr(&s);
            let (lp, _g2) = rank_cache.len_d().device_ptr(&s);
            let bp = match rank_cache.base_d() {
                Some(b) => {
                    let (p, _g) = b.device_ptr(&s);
                    p
                }
                None => 0u64,
            };
            let mut host = Vec::with_capacity(t * 6);
            for r in 0..t {
                host.extend_from_slice(&[kp, vp, lp, bp, 0u64, (t - 1 - r) as u64]);
            }
            tables.push(engine.stream().clone_htod(&host)?);
        }
        let tabs: Vec<&CudaSlice<u64>> = tables.iter().collect();
        let ws_index = tp
            .runtime
            .decode_v2_ensure(e, &tp.q, &tp.k, &tp.v, &tp.o, heads)?;
        tp.runtime.decode_v2_fa_rows_join(
            ws_index,
            e,
            &tp.o,
            &tabs,
            t,
            head_dim,
            window.unwrap_or(0),
            max_ns,
            geometry.attention_scale(),
            k_tok_bytes,
            v_tok_bytes,
        )
    }

    /// Multi-session t-row fa precheck (the batched serving walk): the static doors of
    /// the rows kernel plus per-SESSION dynamic checks — every row's own cache must be
    /// hydrated, in sync, rebase-free and above both floors.
    pub(crate) fn step35_batch_fa_rows_precheck(
        &self,
        caches: &[&mut Cache],
        row_to_cache: impl Fn(usize) -> usize,
        positions: &[i32],
        il: usize,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Ok(false);
        };
        let Some(tp) = fa.step_tp_qkv.as_ref() else {
            return Ok(false);
        };
        let Some(attention) = tp.attention.as_ref() else {
            return Ok(false);
        };
        if !tp.runtime.native_p2p()
            || crate::Engine::kv_fp8_on()
            || !crate::tp::step_tp_dcw_enabled()?
            || !crate::tp::step_tp_qkv_fused_enabled()?
            || (attention.gate_shards.is_none() && attention.gate_shards_bf16.is_none())
        {
            return Ok(false);
        }
        let geometry = self.step35_geom(il);
        let head_dim = geometry.head_dim_k as usize;
        if head_dim > 256 || !head_dim.is_multiple_of(32) || !crate::fa_v3_on() {
            return Ok(false);
        }
        if crate::fa_sm_count() < 128
            || std::env::var("MEMRA_FA_SPLIT").is_ok()
            || std::env::var("MEMRA_FA_SP_SHORT").is_ok()
            || std::env::var("MEMRA_FA_SP16").is_ok()
            || std::env::var("MEMRA_NO_FA_VEC").is_ok()
        {
            return Ok(false);
        }
        let window = geometry.window.map(|w| w as usize);
        for (r, &pos) in positions.iter().enumerate() {
            let cache = &caches[row_to_cache(r)];
            let Some(distributed) = cache.tp_kv[il].as_ref() else {
                return Ok(false);
            };
            if distributed.staged_len() != pos as usize {
                return Ok(false);
            }
            if distributed.peek_append_ring(1)?.1 {
                return Ok(false);
            }
            let t0 = window
                .map(|w| (pos as usize + 1).min(w))
                .unwrap_or(pos as usize + 1);
            if t0 < 96 || t0 < crate::fa_vec_min_tkv() {
                return Ok(false);
            }
        }
        Ok(true)
    }

    /// FULL t-row attention pass for the VERIFY walk (same-session rows): rope/append +
    /// fa + combine + o_proj join in 3 launches/rank/layer. Row r appends at slot
    /// len-base+r and one last block advances len by t; the fa rows read len_back =
    /// t-1-r. Returns None when the fused-rope class does not hold (the walk keeps the
    /// per-column stash flow). Caller has passed `step35_fa_rows_precheck`.
    pub(crate) fn step35_verify_rope_fa_pass(
        &self,
        e: &Engine,
        il: usize,
        cache: &Cache,
        pos0: usize,
        t: usize,
        stage_pos: bool,
    ) -> Result<Option<CudaSlice<f32>>, Box<dyn std::error::Error>> {
        use cudarc::driver::DevicePtr;
        if !crate::tp::fuse_rope_append_on() {
            return Ok(None);
        }
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Ok(None);
        };
        let Some(tp) = fa.step_tp_qkv.as_ref() else {
            return Ok(None);
        };
        let Some(attention) = tp.attention.as_ref() else {
            return Ok(None);
        };
        let geometry = self.step35_geom(il);
        let head_dim = geometry.head_dim_k as usize;
        if head_dim != 128 {
            return Ok(None);
        }
        let heads = geometry.n_head as usize;
        let window = geometry.window.map(|w| w as usize);
        let ranks = tp.runtime.devices().len();
        let Some(distributed) = cache.tp_kv[il].as_ref() else {
            return Ok(None);
        };
        if distributed.kv_dim_k() != distributed.kv_dim_v() {
            return Ok(None);
        }
        {
            let rank0 = distributed.rank(0).ok_or("verify rope pass lost rank 0")?;
            if rank0.base_d().is_none()
                && distributed.staged_len() + t > distributed.physical_capacity()
            {
                return Ok(None);
            }
        }
        let mut rope_freqs = Vec::with_capacity(ranks);
        for rank in 0..ranks {
            let engine = tp
                .runtime
                .rank_engine(rank)
                .ok_or("verify rope pass lost a rank engine")?;
            rope_freqs.push(if geometry.rope_factors {
                match self
                    .step35_aux
                    .as_ref()
                    .and_then(|aux| aux.rope_freqs(engine))
                {
                    Some(f) => Some(f),
                    None => return Ok(None),
                }
            } else {
                None
            });
        }
        let ladder = |t_kv: usize| -> usize {
            if t_kv <= 2048 {
                16
            } else if t_kv <= 16384 {
                64
            } else {
                128
            }
        };
        let (k_tok_bytes, v_tok_bytes) = (distributed.k_tok_bytes(), distributed.v_tok_bytes());
        let mut max_ns = 1usize;
        let mut positions = Vec::with_capacity(t);
        for r in 0..t {
            positions.push((pos0 + r) as i32);
            let t_kv = window
                .map(|w| (pos0 + r + 1).min(w))
                .unwrap_or(pos0 + r + 1);
            max_ns = max_ns.max(t_kv.div_ceil(ladder(t_kv)));
        }
        let mut session_parts: Vec<Vec<[u64; 4]>> = vec![Vec::with_capacity(t); ranks];
        let mut tab_keys = vec![0u64; ranks];
        for rank in 0..ranks {
            let engine = tp
                .runtime
                .rank_engine(rank)
                .ok_or("verify rope pass lost a rank engine")?;
            let rank_cache = distributed
                .rank(rank)
                .ok_or("verify rope pass lost a KV cache rank")?;
            let _main = engine.gpu.enter_main()?;
            let s = engine.stream();
            let (kp, _g0) = rank_cache.k().device_ptr(&s);
            let (vp, _g1) = rank_cache.v().device_ptr(&s);
            let (lp, _g2) = rank_cache.len_d().device_ptr(&s);
            let bp = match rank_cache.base_d() {
                Some(b) => {
                    let (p, _g) = b.device_ptr(&s);
                    p
                }
                None => 0u64,
            };
            tab_keys[rank] = kp
                .rotate_left(17)
                .wrapping_add(bp)
                .wrapping_add((il as u64) << 32)
                .wrapping_add(t as u64)
                .wrapping_add(1 << 63);
            for _r in 0..t {
                session_parts[rank].push([kp, vp, lp, bp]);
            }
        }
        let ws_index = tp
            .runtime
            .decode_v2_ensure(e, &tp.q, &tp.k, &tp.v, &tp.o, heads)?;
        tp.runtime
            .decode_v2_rope_fa_rows(
                ws_index,
                e,
                &tp.o,
                &session_parts,
                &tab_keys,
                &positions,
                stage_pos,
                true,
                &attention.q_norm,
                &attention.k_norm,
                &rope_freqs,
                t,
                head_dim,
                geometry.n_rot as usize,
                window.unwrap_or(0),
                max_ns,
                geometry.attention_scale(),
                k_tok_bytes,
                v_tok_bytes,
                self.cfg.rms_eps,
                geometry.rope_base,
            )
            .map(Some)
    }

    /// FULL t-row attention pass for the batched walk (rope/append + fa + combine +
    /// o_proj join, 3 launches/rank/layer): returns None when the fused-rope class does
    /// not hold — the caller falls back to the per-row stash flow. The caller has
    /// already passed `step35_batch_fa_rows_precheck`.
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn step35_batch_rope_fa_pass(
        &self,
        e: &Engine,
        il: usize,
        caches: &[&mut Cache],
        row_to_cache: impl Fn(usize) -> usize,
        positions: &[i32],
        t: usize,
        stage_pos: bool,
    ) -> Result<Option<CudaSlice<f32>>, Box<dyn std::error::Error>> {
        use cudarc::driver::DevicePtr;
        if !crate::tp::fuse_rope_append_on() {
            return Ok(None);
        }
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Ok(None);
        };
        let Some(tp) = fa.step_tp_qkv.as_ref() else {
            return Ok(None);
        };
        let Some(attention) = tp.attention.as_ref() else {
            return Ok(None);
        };
        let geometry = self.step35_geom(il);
        let head_dim = geometry.head_dim_k as usize;
        if head_dim != 128 {
            return Ok(None);
        }
        let heads = geometry.n_head as usize;
        let window = geometry.window.map(|w| w as usize);
        let ranks = tp.runtime.devices().len();
        // The rows kernels never arm base_d; refuse once a ring could have rebased
        // without an armed base (the table would read base=0 after a real rebase).
        for r in 0..t {
            let cache = &caches[row_to_cache(r)];
            let Some(distributed) = cache.tp_kv[il].as_ref() else {
                return Ok(None);
            };
            if distributed.kv_dim_k() != distributed.kv_dim_v() {
                return Ok(None);
            }
            let rank0 = distributed.rank(0).ok_or("rope fa pass lost rank 0")?;
            if rank0.base_d().is_none()
                && distributed.staged_len() + t > distributed.physical_capacity()
            {
                return Ok(None);
            }
        }
        let mut rope_freqs = Vec::with_capacity(ranks);
        for rank in 0..ranks {
            let engine = tp
                .runtime
                .rank_engine(rank)
                .ok_or("rope fa pass lost a rank engine")?;
            rope_freqs.push(if geometry.rope_factors {
                match self
                    .step35_aux
                    .as_ref()
                    .and_then(|aux| aux.rope_freqs(engine))
                {
                    Some(f) => Some(f),
                    None => return Ok(None),
                }
            } else {
                None
            });
        }
        let ladder = |t_kv: usize| -> usize {
            if t_kv <= 2048 {
                16
            } else if t_kv <= 16384 {
                64
            } else {
                128
            }
        };
        let (mut max_ns, mut k_tok_bytes, mut v_tok_bytes) = (1usize, 0usize, 0usize);
        let mut session_parts: Vec<Vec<[u64; 4]>> = vec![Vec::with_capacity(t); ranks];
        let mut tab_keys = vec![0u64; ranks];
        for (r, &pos) in positions.iter().enumerate().take(t) {
            let cache = &caches[row_to_cache(r)];
            let distributed = cache.tp_kv[il]
                .as_ref()
                .ok_or("rope fa pass lost a distributed KV cache")?;
            (k_tok_bytes, v_tok_bytes) = (distributed.k_tok_bytes(), distributed.v_tok_bytes());
            let t_kv = window
                .map(|w| (pos as usize + 1).min(w))
                .unwrap_or(pos as usize + 1);
            max_ns = max_ns.max(t_kv.div_ceil(ladder(t_kv)));
            for rank in 0..ranks {
                let engine = tp
                    .runtime
                    .rank_engine(rank)
                    .ok_or("rope fa pass lost a rank engine")?;
                let rank_cache = distributed
                    .rank(rank)
                    .ok_or("rope fa pass lost a KV cache rank")?;
                let _main = engine.gpu.enter_main()?;
                let s = engine.stream();
                let (kp, _g0) = rank_cache.k().device_ptr(&s);
                let (vp, _g1) = rank_cache.v().device_ptr(&s);
                let (lp, _g2) = rank_cache.len_d().device_ptr(&s);
                let bp = match rank_cache.base_d() {
                    Some(b) => {
                        let (p, _g) = b.device_ptr(&s);
                        p
                    }
                    None => 0u64,
                };
                tab_keys[rank] = tab_keys[rank]
                    .rotate_left(9)
                    .wrapping_add(kp)
                    .wrapping_add(bp)
                    .wrapping_add(il as u64);
                session_parts[rank].push([kp, vp, lp, bp]);
            }
        }
        let ws_index = tp
            .runtime
            .decode_v2_ensure(e, &tp.q, &tp.k, &tp.v, &tp.o, heads)?;
        tp.runtime
            .decode_v2_rope_fa_rows(
                ws_index,
                e,
                &tp.o,
                &session_parts,
                &tab_keys,
                positions,
                stage_pos,
                false,
                &attention.q_norm,
                &attention.k_norm,
                &rope_freqs,
                t,
                head_dim,
                geometry.n_rot as usize,
                window.unwrap_or(0),
                max_ns,
                geometry.attention_scale(),
                k_tok_bytes,
                v_tok_bytes,
                self.cfg.rms_eps,
                geometry.rope_base,
            )
            .map(Some)
    }

    /// Multi-session t-row fa join (batched serving): per-row table entries point at
    /// each row's OWN session rings/counters (len_back = 0 — every session appended
    /// exactly its one row). Tables stage once per (layer, rank, session-set, t).
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn step35_batch_fa_rows_join(
        &self,
        e: &Engine,
        il: usize,
        caches: &[&mut Cache],
        row_to_cache: impl Fn(usize) -> usize,
        positions: &[i32],
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        use cudarc::driver::DevicePtr;
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Err("batch fa rows join expects full attention".into());
        };
        let tp = fa
            .step_tp_qkv
            .as_ref()
            .ok_or("batch fa rows join lost its resident projections")?;
        let geometry = self.step35_geom(il);
        let heads = geometry.n_head as usize;
        let head_dim = geometry.head_dim_k as usize;
        let window = geometry.window.map(|w| w as usize);
        let ladder = |t_kv: usize| -> usize {
            if t_kv <= 2048 {
                16
            } else if t_kv <= 16384 {
                64
            } else {
                128
            }
        };
        let (mut max_ns, mut k_tok_bytes, mut v_tok_bytes) = (1usize, 0usize, 0usize);
        for (r, &pos) in positions.iter().enumerate() {
            let cache = &caches[row_to_cache(r)];
            let distributed = cache.tp_kv[il]
                .as_ref()
                .ok_or("batch fa rows join lost a distributed KV cache")?;
            (k_tok_bytes, v_tok_bytes) = (distributed.k_tok_bytes(), distributed.v_tok_bytes());
            let t_kv = window
                .map(|w| (pos as usize + 1).min(w))
                .unwrap_or(pos as usize + 1);
            max_ns = max_ns.max(t_kv.div_ceil(ladder(t_kv)));
        }
        // Multi-session tables also rebuild from every live K/V/len/base tuple. Keeping a
        // process-lifetime raw-pointer cache here omitted V and len identity and had no
        // allocation generation, so allocator reuse could bind one request to another.
        let ranks = tp.runtime.devices().len();
        let mut tables = Vec::with_capacity(ranks);
        for rank in 0..ranks {
            let engine = tp
                .runtime
                .rank_engine(rank)
                .ok_or("batch fa rows join lost a rank engine")?;
            let _main = engine.gpu.enter_main()?;
            let s = engine.stream();
            let mut host = Vec::with_capacity(t * 6);
            for r in 0..t {
                let cache = &caches[row_to_cache(r)];
                let distributed = cache.tp_kv[il]
                    .as_ref()
                    .ok_or("batch fa rows join lost a distributed KV cache")?;
                let rank_cache = distributed
                    .rank(rank)
                    .ok_or("batch fa rows join lost a KV cache rank")?;
                let (kp, _g0) = rank_cache.k().device_ptr(&s);
                let (vp, _g1) = rank_cache.v().device_ptr(&s);
                let (lp, _g2) = rank_cache.len_d().device_ptr(&s);
                let bp = match rank_cache.base_d() {
                    Some(b) => {
                        let (p, _g) = b.device_ptr(&s);
                        p
                    }
                    None => 0u64,
                };
                host.extend_from_slice(&[kp, vp, lp, bp, 0u64, 0u64]);
            }
            tables.push(engine.stream().clone_htod(&host)?);
        }
        let tabs: Vec<&CudaSlice<u64>> = tables.iter().collect();
        let ws_index = tp
            .runtime
            .decode_v2_ensure(e, &tp.q, &tp.k, &tp.v, &tp.o, heads)?;
        tp.runtime.decode_v2_fa_rows_join(
            ws_index,
            e,
            &tp.o,
            &tabs,
            t,
            head_dim,
            window.unwrap_or(0),
            max_ns,
            geometry.attention_scale(),
            k_tok_bytes,
            v_tok_bytes,
        )
    }

    /// MEMRA_SPEC_FA2 join for the verify walk: both columns stashed; one shared-KV T=2
    /// fa per rank + the weight-amortized o_proj join produce the [2, o_out] `mixed`
    /// slab on `e`.
    #[allow(dead_code)] // allow: banked MEMRA_SPEC_FA2 arm; kept as the named seam for the spec-FA2 join program
    pub(crate) fn step35_verify_spec_fa2_join(
        &self,
        e: &Engine,
        il: usize,
        cache: &Cache,
        pos0: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let crate::hybrid::Mixer::Full(fa) = &self.layers[il].mixer else {
            return Err("spec fa2 join expects full attention".into());
        };
        let tp = fa
            .step_tp_qkv
            .as_ref()
            .ok_or("spec fa2 join lost its resident projections")?;
        let geometry = self.step35_geom(il);
        let heads = geometry.n_head as usize;
        let head_dim = geometry.head_dim_k as usize;
        let window = geometry.window.map(|w| w as usize);
        // POST-append view of the second row (kernel T1 = len - lstart with len =
        // pos0 + 2): sp/ns derive from it, and the precheck proved row0 shares them.
        let bucket = window.map(|w| (pos0 + 2).min(w)).unwrap_or(pos0 + 2);
        let distributed = cache.tp_kv[il]
            .as_ref()
            .ok_or("spec fa2 join lost its distributed KV cache")?;
        let ws_index = tp
            .runtime
            .decode_v2_ensure(e, &tp.q, &tp.k, &tp.v, &tp.o, heads)?;
        tp.runtime.decode_v2_spec_fa2_join(
            ws_index,
            e,
            &tp.o,
            distributed,
            head_dim,
            window.unwrap_or(0),
            bucket,
            geometry.attention_scale(),
        )
    }

    pub(crate) fn step35_tp_decode_attn_resident_v2(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        il: usize,
        h: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let tp = fa
            .step_tp_qkv
            .as_ref()
            .ok_or("Step TP decode lost its resident projections")?;
        let attention = tp
            .attention
            .as_ref()
            .ok_or("Step TP decode lost its resident attention auxiliaries")?;
        if !tp.runtime.native_p2p() {
            return Err("rank-local Step attention requires native P2P".into());
        }
        if crate::Engine::kv_fp8_on() {
            return Err("rank-local Step attention has not qualified the FP8 KV cache".into());
        }

        let geometry = self.cfg.full_attention_geometry_at(il as u32);
        let window = geometry.window.map(|window| window as usize);
        let ranks = tp.runtime.devices().len();
        let head_dim = geometry.head_dim_k as usize;
        let heads = geometry.n_head as usize;
        let kv_heads = geometry.n_head_kv as usize;
        if !heads.is_multiple_of(ranks) || !kv_heads.is_multiple_of(ranks) {
            return Err(format!(
                "Step attention heads q={heads} kv={kv_heads} are not divisible by TP={ranks}"
            )
            .into());
        }
        let local_heads = heads / ranks;
        let local_kv_heads = kv_heads / ranks;
        let max_ctx = cache.max_ctx;

        let hydrated = self.ensure_step_tp_kv_cache(e, fa, il, cache)?;

        let base_len = cache.kv[il]
            .as_ref()
            .ok_or_else(|| format!("Step TP layer {il} lost its local KV cache"))?
            .len;
        {
            let distributed = cache.tp_kv[il]
                .as_ref()
                .ok_or_else(|| format!("Step TP layer {il} lost its distributed KV cache"))?;
            if distributed.committed_len() != base_len || distributed.staged_len() != base_len {
                return Err(format!(
                    "Step TP layer {il} cache lengths diverged before decode: \
                     local={base_len} distributed={}/{}",
                    distributed.committed_len(),
                    distributed.staged_len()
                )
                .into());
            }
        }
        if pos_d.len() != 1 {
            return Err(format!(
                "rank-local Step decode requires one position, got {}",
                pos_d.len()
            )
            .into());
        }

        let decode_input = attention
            .decode_input
            .as_ref()
            .ok_or("Step TP decode v2 requires the replicated decode input")?;
        let mut decode_input = decode_input
            .lock()
            .map_err(|_| "Step TP replicated decode input lock is poisoned")?;

        let has_gate = fa.attn_gate.is_some();
        // Gate: with per-rank shards loaded (fused door), the fused QKV+gate kernel computes
        // it rank-locally and the model-engine matmul (and its staging copies) disappears.
        // Otherwise it queues on e's stream BEFORE decode_v2_input_qkv records the entry
        // event, so the rank-stream reads of the staged gate are ordered without a host sync.
        let use_gate_shards = has_gate
            && (attention.gate_shards.is_some() || attention.gate_shards_bf16.is_some())
            && crate::tp::step_tp_qkv_fused_enabled()?;
        let gate_raw = if !has_gate || use_gate_shards {
            None
        } else {
            let gate_weight = fa
                .attn_gate
                .as_ref()
                .ok_or("step35 layer is missing attn_gate.weight")?;
            let gate_raw = e.matmul(gate_weight, h, 1)?;
            if gate_raw.len() != heads {
                return Err(format!(
                    "Step TP layer {il} gate output {} != {heads}",
                    gate_raw.len()
                )
                .into());
            }
            Some(gate_raw)
        };

        let ws_index = tp
            .runtime
            .decode_v2_ensure(e, &tp.q, &tp.k, &tp.v, &tp.o, heads)?;
        let mut ws_guard = tp
            .runtime
            .decode_v2_workspace()
            .lock()
            .map_err(|_| "Step TP decode v2 workspace lock is poisoned")?;
        let ws = ws_guard
            .get_mut(ws_index)
            .ok_or("Step TP decode v2 workspace missing after ensure")?;

        let mut rope_freqs = Vec::with_capacity(ranks);
        for rank in 0..ranks {
            let engine = tp
                .runtime
                .rank_engine(rank)
                .ok_or_else(|| format!("Step TP layer {il} has no engine for rank {rank}"))?;
            rope_freqs.push(if geometry.rope_factors {
                self.step35_aux
                    .as_ref()
                    .and_then(|aux| aux.rope_freqs(engine))
            } else {
                None
            });
        }
        // DCW arm (MEMRA_STEP_TP_DCW=1): device-counter append + counter-derived fa — the
        // exact captured-child content, run eagerly. bucket = effective t_kv keeps the fa
        // split geometry identical to the kvmod path (one-partition law) -> bit-identical.
        // Rebase tokens and sub-vec-floor contexts take the host-row path below.
        // (Eligibility computed BEFORE input_qkv so FUSION #1 can defer the norm+rope into
        // the fused rope+append+inc launch on dcw tokens.)
        let staged_next = base_len + 1;
        let t_kv_eff = window
            .map(|window| staged_next.min(window))
            .unwrap_or(staged_next);
        let dcw = crate::tp::step_tp_dcw_enabled()?
            && (use_gate_shards || (!has_gate && crate::tp::step_tp_qkv_fused_enabled()?))
            && t_kv_eff >= 96
            && {
                let (write_row, would_rebase) = cache.tp_kv[il]
                    .as_ref()
                    .expect("distributed cache checked above")
                    .peek_append_ring(1)?;
                if !would_rebase {
                    // Arm the base mirrors on first use: base = logical staged - physical row.
                    let base = (base_len - write_row) as i32;
                    let distributed = cache.tp_kv[il]
                        .as_mut()
                        .expect("distributed cache checked above");
                    for rank in 0..ranks {
                        let engine = tp.runtime.rank_engine(rank).ok_or_else(|| {
                            format!("Step TP layer {il} has no engine for rank {rank}")
                        })?;
                        let _main = engine.gpu.enter_main()?;
                        let rank_cache = distributed.rank_mut(rank).ok_or_else(|| {
                            format!("Step TP layer {il} has no KV cache rank {rank}")
                        })?;
                        if rank_cache.base_d().is_none() {
                            rank_cache.arm_base_d(engine.htod_i32(&[base])?);
                        }
                    }
                }
                !would_rebase
            };
        let fuse_rope = dcw
            && crate::tp::fuse_rope_append_on()
            && head_dim == 128
            && cache.tp_kv[il]
                .as_ref()
                .map(|d| d.kv_dim_k() == d.kv_dim_v() && d.kv_dim_k() == local_kv_heads * head_dim)
                .unwrap_or(false);

        let tcol_col = crate::tp::take_verify_tcol();
        // MEMRA_SPEC_FA2 defer: the verify walk armed this column for the shared-KV T=2
        // attention. On dcw tokens the per-rank pass still norms/ropes/APPENDS (cache
        // state must advance per column) but skips the fa+gate launch; post-rope q and
        // gate rows are stashed instead, and ONE fa_decode_dcw2 per rank joins both
        // columns after the second append. Non-dcw tokens ignore the defer (the fa runs
        // normally and the walk consumes the real output — stash flag stays unset).
        let fa2_col = crate::tp::take_spec_fa2_defer();
        tp.runtime.decode_v2_input_qkv(
            ws,
            e,
            h,
            pos_d,
            gate_raw.as_ref(),
            if !use_gate_shards {
                None
            } else if let Some(shards) = attention.gate_shards.as_deref() {
                Some(crate::tp::StepTpGateShards::F32(shards))
            } else {
                attention
                    .gate_shards_bf16
                    .as_deref()
                    .map(crate::tp::StepTpGateShards::Bf16)
            },
            &mut decode_input,
            &tp.q,
            &tp.k,
            &tp.v,
            &attention.q_norm,
            &attention.k_norm,
            head_dim,
            geometry.n_rot as usize,
            geometry.rope_base,
            &rope_freqs,
            self.cfg.rms_eps,
            has_gate,
            fuse_rope,
            tcol_col,
        )?;

        let transaction = cache.tp_kv[il]
            .as_mut()
            .expect("distributed cache checked above")
            .begin_transaction()?;
        let append_result = tp.runtime.append_tp_kv_transaction_inner(
            cache.tp_kv[il]
                .as_mut()
                .expect("distributed cache checked above"),
            transaction,
            &ws.k,
            &ws.v_raw,
            1,
            dcw,
        );
        if let Err(error) = append_result {
            let _ = tp.runtime.rollback_tp_kv_transaction(
                cache.tp_kv[il]
                    .as_mut()
                    .expect("distributed cache checked above"),
                transaction,
            );
            return Err(error);
        }

        let staged = (|| -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
            // Copy the view scalars out and DROP the shared borrow — the merged dcw arm
            // reborrows the cache mutably per rank.
            let (staged_len, physical, k_tok_bytes_c, v_tok_bytes_c, capacity) = {
                let distributed = cache.tp_kv[il]
                    .as_ref()
                    .expect("distributed cache checked above");
                let staged_len = distributed.staged_len();
                let view_start = window
                    .map(|window| staged_len.saturating_sub(window))
                    .unwrap_or(0);
                (
                    staged_len,
                    distributed.physical_range(view_start, staged_len)?,
                    distributed.k_tok_bytes(),
                    distributed.v_tok_bytes(),
                    distributed.physical_capacity(),
                )
            };
            let view_start = window
                .map(|window| staged_len.saturating_sub(window))
                .unwrap_or(0);
            let t_kv = staged_len - view_start;
            for rank in 0..ranks {
                let engine = tp
                    .runtime
                    .rank_engine(rank)
                    .ok_or_else(|| format!("Step TP layer {il} has no engine for rank {rank}"))?;
                let _main = engine.gpu.enter_main()?;
                if dcw {
                    // MERGED per-rank pass (the capture unit): append + inc + fa + gate on ONE
                    // stream visit. distributed is borrowed shared here; the planes need mut —
                    // reborrow through the cache Option (the closure holds cache mutably).
                    {
                        let distributed_mut = cache.tp_kv[il]
                            .as_mut()
                            .expect("distributed cache checked above");
                        let (kv_dim_k, kv_dim_v) =
                            (distributed_mut.kv_dim_k(), distributed_mut.kv_dim_v());
                        let (k_tok_bytes, v_tok_bytes) =
                            (distributed_mut.k_tok_bytes(), distributed_mut.v_tok_bytes());
                        let rank_cache = distributed_mut.rank_mut(rank).ok_or_else(|| {
                            format!("Step TP layer {il} has no KV cache rank {rank}")
                        })?;
                        let (k_plane, v_plane, len_d, base_d) =
                            rank_cache.planes_and_counters_mut();
                        if fuse_rope {
                            // FUSION #1: norms + rope (deferred out of input_qkv) + append
                            // + last-block len inc in ONE launch. Bit-identical bodies.
                            let same_dev = engine.ctx().ordinal() == e.ctx().ordinal();
                            let crate::tp::StepTpDecodeV2Ws {
                                q_raw,
                                k_raw,
                                v_raw,
                                q,
                                k,
                                pos,
                                pos_stage,
                                fuse_ctr,
                                ..
                            } = &mut *ws;
                            // Same-device rank: the staged-copy elision leaves pos[rank]
                            // stale — read the e-context pos stage directly (mirrors the
                            // rope elision in input_qkv_rank).
                            let pos_ref: &CudaSlice<i32> = if same_dev {
                                pos_stage
                                    .as_ref()
                                    .ok_or("step TP decode v2 pos stage not armed")?
                            } else {
                                &pos[rank]
                            };
                            engine.qk_norm_rope_append_inc_dcw(
                                &q_raw[rank],
                                &k_raw[rank],
                                &v_raw[rank],
                                &attention.q_norm[rank],
                                &attention.k_norm[rank],
                                &mut q[rank],
                                &mut k[rank],
                                pos_ref,
                                k_plane,
                                v_plane,
                                len_d,
                                base_d,
                                &mut fuse_ctr[rank],
                                kv_dim_k,
                                kv_dim_v,
                                k_tok_bytes,
                                v_tok_bytes,
                                head_dim,
                                geometry.n_rot as usize,
                                local_heads,
                                local_kv_heads,
                                self.cfg.rms_eps,
                                geometry.rope_base,
                                1.0,
                                rope_freqs[rank],
                            )?;
                        } else {
                            engine.append_kv_quantized_dcw(
                                &ws.k[rank],
                                &ws.v_raw[rank],
                                k_plane,
                                v_plane,
                                len_d,
                                base_d,
                                kv_dim_k,
                                kv_dim_v,
                                k_tok_bytes,
                                v_tok_bytes,
                            )?;
                        }
                        if !fuse_rope {
                            let rank_cache = distributed_mut.rank_mut(rank).ok_or_else(|| {
                                format!("Step TP layer {il} has no KV cache rank {rank}")
                            })?;
                            engine.inc_i32(rank_cache.len_d_mut())?;
                        }
                    }
                    let distributed = cache.tp_kv[il]
                        .as_ref()
                        .expect("distributed cache checked above");
                    let rank_cache = distributed
                        .rank(rank)
                        .ok_or_else(|| format!("Step TP layer {il} has no KV cache rank {rank}"))?;
                    let k_ring = engine.view_u8_range(rank_cache.k(), 0, capacity * k_tok_bytes_c);
                    let v_ring = engine.view_u8_range(rank_cache.v(), 0, capacity * v_tok_bytes_c);
                    if fa2_col.is_some() {
                        // SPEC_FA2 defer: append landed above; the fa for this column
                        // runs in the T=2 joined launch after the pair's second append.
                        continue;
                    }
                    {
                        // FUSION #2d: combine + head gate in the dcw tail — `gated` receives
                        // the gated output directly (bit-identical; one launch saved).
                        let crate::tp::StepTpDecodeV2Ws { q, gate, gated, .. } = &mut *ws;
                        engine.fa_decode_dcw(
                            &q[rank],
                            &k_ring,
                            &v_ring,
                            &mut gated[rank],
                            head_dim,
                            local_heads,
                            local_kv_heads,
                            rank_cache.len_d(),
                            rank_cache.base_d(),
                            window.unwrap_or(0),
                            t_kv,
                            geometry.attention_scale(),
                            k_tok_bytes_c,
                            v_tok_bytes_c,
                            has_gate.then_some(&gate[rank]),
                        )?;
                    }
                    continue;
                }
                let distributed = cache.tp_kv[il]
                    .as_ref()
                    .expect("distributed cache checked above");
                let rank_cache = distributed
                    .rank(rank)
                    .ok_or_else(|| format!("Step TP layer {il} has no KV cache rank {rank}"))?;
                let k_view = engine.view_u8_range(
                    rank_cache.k(),
                    physical.start * k_tok_bytes_c,
                    physical.end * k_tok_bytes_c,
                );
                let v_view = engine.view_u8_range(
                    rank_cache.v(),
                    physical.start * v_tok_bytes_c,
                    physical.end * v_tok_bytes_c,
                );
                if has_gate {
                    engine.fa_decode_kvmod(
                        &ws.q[rank],
                        &k_view,
                        &v_view,
                        &mut ws.attn_out[rank],
                        head_dim,
                        local_heads,
                        local_kv_heads,
                        t_kv,
                        geometry.attention_scale(),
                        k_tok_bytes_c,
                        v_tok_bytes_c,
                        false,
                    )?;
                    engine.attn_head_gate(
                        &ws.attn_out[rank],
                        &ws.gate[rank],
                        &mut ws.gated[rank],
                        None,
                        head_dim,
                        local_heads,
                        1,
                    )?;
                } else {
                    engine.fa_decode_kvmod(
                        &ws.q[rank],
                        &k_view,
                        &v_view,
                        &mut ws.gated[rank],
                        head_dim,
                        local_heads,
                        local_kv_heads,
                        t_kv,
                        geometry.attention_scale(),
                        k_tok_bytes_c,
                        v_tok_bytes_c,
                        false,
                    )?;
                }
            }

            // MEMRA_TCOL_OPROJ defer: the verify driver armed a column — stash this
            // column's `gated` rows and skip the per-column finish choreography entirely
            // (the batched b4_tcol + join runs after every column). The returned buffer
            // is UNWRITTEN in that mode (oproj-tail precedent); the driver reads the
            // stashed flag, never this buffer. Ineligible configs fall back to the
            // normal finish and the driver consumes the real `mixed` per column.
            let output = if let Some(col) = fa2_col.filter(|_| dcw) {
                // SPEC_FA2 stash: q + gate rows to the fa2 slabs; fa, o_proj and the
                // finish all run in the joined pass. Returned buffer is UNWRITTEN
                // (oproj-defer precedent — the walk reads the stash flag, never this).
                tp.runtime.decode_v2_stash_fa2(ws, e, col)?;
                crate::tp::set_spec_fa2_stashed();
                e.uninit(ws.o_out)?
            } else if let Some(col) = crate::tp::take_tcol_oproj_defer() {
                if tp.runtime.decode_v2_oproj_tcol_eligible(ws, &tp.o) {
                    tp.runtime.decode_v2_stash_gated(ws, e, col)?;
                    crate::tp::set_tcol_oproj_stashed();
                    e.uninit(ws.o_out)?
                } else {
                    tp.runtime.decode_v2_finish(ws, e, &tp.o)?
                }
            } else {
                tp.runtime.decode_v2_finish(ws, e, &tp.o)?
            };

            // Local shadow append: reads ws.k_shadow/ws.v_shadow on e's stream, which
            // decode_v2_finish ordered behind the root event. Same math and cache state
            // transitions as v1.
            let local = cache.kv[il]
                .as_mut()
                .ok_or_else(|| format!("Step TP layer {il} lost its local KV cache"))?;
            if local.len != base_len || base_len + 1 > max_ctx {
                return Err(format!(
                    "Step TP layer {il} local cache changed during decode: \
                     len={} base={base_len} max={max_ctx}",
                    local.len
                )
                .into());
            }
            if crate::tp::no_local_shadow_on() {
                // Lengths advance, contents stay stale (graph-door precedent: decode reads
                // only the distributed TP caches; local contents feed spec/MTP scratch).
                local.len = base_len + 1;
                // MEMRA_LEN_MIRROR_LAZY=1: skip the 4B in-stream htod — nothing reads the
                // LOCAL device mirror in TP decode (the dcw fa reads the RANK counters),
                // and each tiny copy costs a compute->copy engine turnaround mid-layer.
                if !crate::tp::len_mirror_lazy_on() {
                    e.set_i32_one(&mut local.len_d, local.len as i32)?;
                }
            } else {
                let retain_from = window
                    .map(|window| {
                        let staged_retain = (base_len + 1).saturating_sub(window) & !31usize;
                        let rollback_retain =
                            base_len.saturating_sub(window.saturating_sub(1)) & !31usize;
                        staged_retain.min(rollback_retain)
                    })
                    .unwrap_or(0);
                let write_row = e.prepare_kv_append(local, retain_from, 1)?;
                e.append_kv_quantized(
                    &ws.k_shadow,
                    &ws.v_shadow,
                    &mut local.k,
                    &mut local.v,
                    write_row,
                    local.kv_dim_k,
                    local.kv_dim_v,
                    local.k_tok_bytes,
                    local.v_tok_bytes,
                    false,
                )?;
                local.len = base_len + 1;
                e.set_i32_one(&mut local.len_d, local.len as i32)?;
            }
            Ok(output)
        })();

        let output = match staged {
            Ok(output) => output,
            Err(error) => {
                let _ = tp.runtime.rollback_tp_kv_transaction(
                    cache.tp_kv[il]
                        .as_mut()
                        .expect("distributed cache checked above"),
                    transaction,
                );
                if let Some(local) = cache.kv[il].as_mut() {
                    local.len = base_len;
                    let _ = e.set_i32_one(&mut local.len_d, base_len as i32);
                }
                return Err(error);
            }
        };
        // MEMRA_LEN_MIRROR_LAZY under FUSE_ROPE_APPEND: the fused append atomicInc owns
        // the rank counters (same value as the absolute re-set on full accept), so commit
        // host bookkeeping only — kills two 4B in-stream htods per layer. Non-fused dcw
        // keeps the absolute set (its appends do NOT inc).
        let lazy_commit = fuse_rope && crate::tp::len_mirror_lazy_on();
        if lazy_commit {
            if let Err(error) = tp.runtime.commit_tp_kv_transaction_external(
                cache.tp_kv[il]
                    .as_mut()
                    .expect("distributed cache checked above"),
                transaction,
                1,
            ) {
                let _ = tp.runtime.rollback_tp_kv_transaction(
                    cache.tp_kv[il]
                        .as_mut()
                        .expect("distributed cache checked above"),
                    transaction,
                );
                let local = cache.kv[il].as_mut().expect("local cache checked above");
                local.len = base_len;
                e.set_i32_one(&mut local.len_d, base_len as i32)?;
                return Err(error);
            }
        } else if let Err(error) = tp.runtime.commit_tp_kv_transaction(
            cache.tp_kv[il]
                .as_mut()
                .expect("distributed cache checked above"),
            transaction,
            1,
        ) {
            let _ = tp.runtime.rollback_tp_kv_transaction(
                cache.tp_kv[il]
                    .as_mut()
                    .expect("distributed cache checked above"),
                transaction,
            );
            let local = cache.kv[il].as_mut().expect("local cache checked above");
            local.len = base_len;
            e.set_i32_one(&mut local.len_d, base_len as i32)?;
            return Err(error);
        }

        let committed = cache.tp_kv[il]
            .as_ref()
            .expect("distributed cache checked above")
            .committed_len();
        let local_len = cache.kv[il]
            .as_ref()
            .expect("local cache checked above")
            .len;
        if committed != local_len {
            return Err(format!(
                "Step TP layer {il} committed cache length {committed} != local shadow {local_len}"
            )
            .into());
        }
        static V2_LOGGED: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
        if !V2_LOGGED.swap(true, std::sync::atomic::Ordering::Relaxed) {
            eprintln!(
                "[step-tp-attn-v2] execute layer={} devices={:?} tokens=1 driver=v2 \
                 qkv_tensor_parallel=true qk_norm_rank_local=true rope_rank_local=true \
                 kv_cache_distributed=true kv_cache_hydrated={hydrated} \
                 attention_tensor_parallel=true attention_scope={} \
                 input_path=root-device-replicated gate={} gate_tensor_parallel={} \
                 gate_shards={} o_tensor_parallel=true o_reduce=root-device \
                 local_cache_shadow=true cache_commit=immediate transport={} native_p2p=true \
                 bulk_p2p={} workspace=persistent ordering=evented output=e-device \
                 performance_claim=false (logged once; every decode layer runs this driver)",
                tp.layer,
                tp.devices,
                if window.is_some() {
                    "rank-local-swa-ring"
                } else {
                    "rank-local-global"
                },
                has_gate,
                use_gate_shards,
                if use_gate_shards {
                    "device-staged"
                } else if has_gate {
                    "root-staged"
                } else {
                    "none"
                },
                tp.runtime.transport_label(),
                tp.runtime.bulk_p2p(),
            );
        }
        Ok(output)
    }

    /// step35 T=1 decode attention (post-`wo`, matching `full_attn_decode_pre`'s contract).
    /// `pre_q` = the attn-input norm's q8_1 pair when the caller took the norm-fusion lever
    /// (then `h` is a zero-length placeholder and EVERY projection here — including the gate —
    /// must be on the q8_1 fast path; `mixer_in_q8_1_fast` enforces that for step35 by also
    /// requiring `attn_gate`).
    ///
    /// SWA decode is a token-aligned VIEW OFFSET into the quantized cache (the gemma4 R6
    /// pattern): keys carry absolute rope and the mask is purely positional, so the single
    /// query at `len-1` attending the last `win` rows IS the windowed result — no mask kernel.
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn step35_decode_attn(
        &self,
        e: &Engine,
        fa: &FullAttnLayer,
        il: usize,
        h: &CudaSlice<f32>,
        pre_q: Option<(&CudaSlice<i8>, &CudaSlice<f32>)>,
        pos_d: &CudaSlice<i32>,
        cache: &mut Cache,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        if fa
            .step_tp_qkv
            .as_ref()
            .is_some_and(|tp| tp.attention.is_some())
        {
            if pre_q.is_some() {
                return Err(
                    "rank-local Step attention preserves BF16 activations and refuses the q8_1 \
                     pre-quantized decode path"
                        .into(),
                );
            }
            return self.step35_tp_decode_attn_resident(e, fa, il, h, pos_d, cache);
        }

        let geometry = self.step35_geom(il);
        let hd = geometry.head_dim_k as usize;
        let nkv = geometry.n_head_kv as usize;
        let nh = geometry.n_head as usize;
        let rbase = geometry.rope_base;
        let scale = geometry.attention_scale();
        let swa = geometry.window.is_some();
        let eps = self.cfg.rms_eps;
        let win = geometry.window.unwrap_or(0) as usize;
        let n_rot = geometry.n_rot as usize;
        let n_embd = self.cfg.n_embd as usize;
        let gw = fa
            .attn_gate
            .as_ref()
            .ok_or("step35 layer is missing attn_gate.weight (head-wise attention gate)")?;

        let tp_qkv = if fa.step_tp_qkv.is_some() {
            if pre_q.is_some() {
                return Err(
                    "Step Q/K/V TP preserves BF16 activations and refuses the q8_1 \
                     pre-quantized decode path"
                        .into(),
                );
            }
            self.full_attn_tp_qkv(e, fa, h, 1)?
        } else {
            None
        };

        let (q0, k0, v0, gt) = match tp_qkv {
            Some(mut g3) => {
                let v = g3.pop().unwrap();
                let k = g3.pop().unwrap();
                let q = g3.pop().unwrap();
                let gt = e.matmul(gw, h, 1)?;
                (q, k, v, gt)
            }
            None => match pre_q {
                Some((hq, hdq)) => {
                    debug_assert!(
                        e.uses_q8_1_fast(gw),
                        "step35 pre-quantized decode requires attn_gate on the q8_1 fast path \
                         (h is a zero-length placeholder here) — see mixer_in_q8_1_fast"
                    );
                    let (a, b, c) = match e.matmul_q8_fused3(&fa.wq, &fa.wk, &fa.wv, hq, hdq)? {
                        Some(t3) => t3,
                        None => (
                            e.matmul_pre(&fa.wq, hq, hdq, h, 1)?,
                            e.matmul_pre(&fa.wk, hq, hdq, h, 1)?,
                            e.matmul_pre(&fa.wv, hq, hdq, h, 1)?,
                        ),
                    };
                    let gt = e.matmul_pre(gw, hq, hdq, h, 1)?;
                    (a, b, c, gt)
                }
                None => {
                    if e.uses_q8_1_fast(&fa.wq)
                        && e.uses_q8_1_fast(&fa.wk)
                        && e.uses_q8_1_fast(&fa.wv)
                        && e.uses_q8_1_fast(gw)
                    {
                        let (hq, hdq) = e.quantize_q8_1(h, 1, n_embd)?;
                        let (a, b, c) =
                            match e.matmul_q8_fused3(&fa.wq, &fa.wk, &fa.wv, &hq, &hdq)? {
                                Some(t3) => t3,
                                None => (
                                    e.matmul_pre(&fa.wq, &hq, &hdq, h, 1)?,
                                    e.matmul_pre(&fa.wk, &hq, &hdq, h, 1)?,
                                    e.matmul_pre(&fa.wv, &hq, &hdq, h, 1)?,
                                ),
                            };
                        let gt = e.matmul_pre(gw, &hq, &hdq, h, 1)?;
                        (a, b, c, gt)
                    } else {
                        (
                            e.matmul(&fa.wq, h, 1)?,
                            e.matmul(&fa.wk, h, 1)?,
                            e.matmul(&fa.wv, h, 1)?,
                            e.matmul(gw, h, 1)?,
                        )
                    }
                }
            },
        };

        let mut q = e.uninit(nh * hd)?;
        e.rms_norm(&q0, fa.q_norm.float_data(), &mut q, hd, nh, eps)?;
        let mut k = e.uninit(nkv * hd)?;
        e.rms_norm(&k0, fa.k_norm.float_data(), &mut k, hd, nkv, eps)?;
        let ff = if swa {
            None
        } else {
            self.step35_aux.as_ref().and_then(|a| a.rope_freqs(e))
        };
        #[cfg(debug_assertions)]
        if let Some(ff) = ff {
            crate::debug_assert_tensor_stream_device(
                ff,
                &e.stream(),
                "step35_decode_attn.rope_freqs",
            );
        }
        e.rope_neox2(&mut q, &mut k, pos_d, hd, n_rot, nh, nkv, 1, rbase, 1.0, ff)?;

        if std::env::var("MEMRA_NOFA").is_ok() {
            return Err(
                "MEMRA_NOFA (naive f32 SDPA) is incompatible with the quantized KV \
                        cache; unset MEMRA_NOFA to use fa_decode"
                    .into(),
            );
        }
        let kvl = cache.kv[il].as_mut().unwrap();
        let next_len = kvl.len + 1;
        let (off, t_kv) = if swa && next_len > win {
            (next_len - win, win)
        } else {
            (0, next_len)
        };
        let write_row = e.prepare_kv_append(kvl, off & !31usize, 1)?;
        e.append_kv_quantized(
            &k,
            &v0,
            &mut kvl.k,
            &mut kvl.v,
            write_row,
            kvl.kv_dim_k,
            kvl.kv_dim_v,
            kvl.k_tok_bytes,
            kvl.v_tok_bytes,
            crate::Engine::kv_fp8_on(),
        )?;
        kvl.len = next_len;
        let physical = kvl.physical_rows(off, off + t_kv)?;
        let k_view = e.view_u8_range(
            &kvl.k,
            physical.start * kvl.k_tok_bytes,
            physical.end * kvl.k_tok_bytes,
        );
        let v_view = e.view_u8_range(
            &kvl.v,
            physical.start * kvl.v_tok_bytes,
            physical.end * kvl.v_tok_bytes,
        );
        let mut attn = e.uninit(nh * hd)?;
        e.fa_decode_kvmod(
            &q,
            &k_view,
            &v_view,
            &mut attn,
            hd,
            nh,
            nkv,
            t_kv,
            scale,
            kvl.k_tok_bytes,
            kvl.v_tok_bytes,
            crate::Engine::kv_fp8_on(),
        )?;

        let mut ag = e.uninit(nh * hd)?;
        e.attn_head_gate(&attn, &gt, &mut ag, None, hd, nh, 1)?;
        self.full_attn_o(e, fa, &ag, 1)
    }
}

// ===================================================================================== //
//  gemma-4 E4B (per-layer embeddings + KV-sharing) — FIRST-LIGHT forward.               //
//  Dedicated simple path (Stage-B matmuls, per-row causal attention over the quantized  //
//  cache) so the tuned 26B/31B paths stay untouched. Arch: research/gemma4-bringup/     //
//  e4b-arch-map.md; llama reference: src/models/gemma4.cpp (E4B arms). Wired: forward   //
//  (prefill logits), prime (tokenwise-equivalent batched trunk), eager decode_step_h.   //
//  NOT wired: dc/graph serving arms, verify/spec, chunked prime (HANDOVER-E4B.md).      //
// ===================================================================================== //
impl HybridModel {
    pub fn is_gemma4_e4b(&self) -> bool {
        self.gemma4_aux.as_ref().is_some_and(|a| a.e4b.is_some())
    }

    /// E4B per-layer geometry: nh/nkv derive from the layer's OWN tensor shapes (the E4B GGUF
    /// ships a scalar head_count_kv; swa q 8x256 / kv 2x256, global q 4x512 / kv 1x512).
    /// KV-shared layers report the SHARE TARGET's kv count (their wk IS the target's tensor).
    fn gemma4_e4b_geom(&self, il: usize) -> (usize, usize, usize, f32, f32, bool) {
        let g = self.cfg.gemma4.as_ref().unwrap();
        let swa = g.swa_pattern[il];
        let hd = if swa {
            g.key_length_swa
        } else {
            g.key_length_global
        } as usize;
        let Mixer::Full(fa) = &self.layers[il].mixer else {
            panic!("e4b layer {il} not full-attn")
        };
        let nh = fa.wq.out_features() / hd;
        let nkv = fa.wk.out_features() / hd;
        (
            hd,
            nkv,
            nh,
            if swa {
                g.rope_base_swa
            } else {
                g.rope_base_global
            },
            1.0,
            swa,
        )
    }

    /// E4B KV-share target: Some(target) for the trailing shared layers, None = own cache.
    fn gemma4_e4b_kv_target(&self, il: usize) -> Option<usize> {
        self.layers[il]
            .gemma4
            .as_ref()
            .and_then(|b| b.e4b.as_ref())
            .and_then(|e4| e4.kv_share.map(|t| t as usize))
    }

    /// E4B prologue: inp_pl [t][n_layer][n_epl] =
    ///   ( gather(per_layer_tok_embd, tok)*sqrt(n_epl)
    ///   + rms_norm(model_proj . x_scaled * 1/sqrt(n_embd), proj_norm) ) * 1/sqrt(2)
    ///     (llama gemma4.cpp build_inp_per_layer + project_per_layer_inputs, exact order).
    fn gemma4_e4b_inp_pl(
        &self,
        e: &Engine,
        tokens: &[u32],
        x_scaled: &CudaSlice<f32>,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let tok_d = e.stream().clone_htod(&tokens.to_vec())?;
        self.gemma4_e4b_inp_pl_dev(e, &tok_d, x_scaled, t)
    }

    /// Device-token prologue core (dc arm shares it: token ids never touch the host).
    fn gemma4_e4b_inp_pl_dev(
        &self,
        e: &Engine,
        tok_d: &CudaSlice<u32>,
        x_scaled: &CudaSlice<f32>,
        t: usize,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let aux = self.gemma4_aux.as_ref().unwrap();
        let m = aux.e4b.as_ref().unwrap();
        let n_embd = self.cfg.n_embd as usize;
        let n_layer = self.layers.len();
        let width = m.n_epl * n_layer;
        let tbl = m.tok_tbl_gpu.get_or_init(|| {
            e.upload_u8(&m.tok_embd_bytes)
                .expect("e4b per-layer token table upload")
        });
        let mut a =
            e.embed_gather_device_td(tbl, tok_d, t, width, m.tok_embd_qt, m.tok_embd_row_bytes)?;
        e.scale_inplace(&mut a, (m.n_epl as f32).sqrt(), t * width)?;
        let mut p = e.matmul(&m.model_proj, x_scaled, t)?;
        e.scale_inplace(&mut p, 1.0 / (n_embd as f32).sqrt(), t * width)?;
        let mut pn = e.uninit(t * width)?;
        e.rms_norm(
            &p,
            m.proj_norm.float_data(),
            &mut pn,
            m.n_epl,
            t * n_layer,
            self.cfg.rms_eps,
        )?;
        let mut out = e.uninit(t * width)?;
        e.add_scale(&a, &pn, 1.0 / 2f32.sqrt(), &mut out, t * width)?;
        Ok(out)
    }

    /// E4B attention (t-wide, causal, per-row fa over the QUANTIZED cache — first-light
    /// correctness path; the fa rows arms come later). Own-KV layers project+norm+rope k/v
    /// and append t rows; KV-shared layers are Q-only over the target layer's cache (which
    /// already holds this forward's rows — the target runs earlier in the stack).
    #[allow(clippy::too_many_arguments)]
    fn gemma4_e4b_attn(
        &self,
        e: &Engine,
        il: usize,
        hq: &CudaSlice<i8>,
        hdq: &CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        dc_bucket: Option<usize>,
    ) -> Result<CudaSlice<f32>, Box<dyn std::error::Error>> {
        let (hd, nkv, nh, base, scale, swa) = self.gemma4_e4b_geom(il);
        let eps = self.cfg.rms_eps;
        let aux = self.gemma4_aux.as_ref().unwrap();
        let ones = aux.ones(e);
        #[cfg(debug_assertions)]
        crate::debug_assert_tensor_stream_device(ones, &e.stream(), "gemma4_e4b_attn.ones");
        let Mixer::Full(fa) = &self.layers[il].mixer else {
            unreachable!()
        };
        // pre-quantized layer input (E4B fusion port, 2026-07-12): ONE norm+quant feeds
        // wq/wk/wv via matmul_pre — the first-light path quantized the same h three times
        // (the profile's 342 quantize_q8_1/token; glue = 26% of the 6.1ms token).
        let h0 = e.zeros(0)?;
        let h = &h0;

        let ff = if swa {
            None
        } else {
            Some(
                aux.rope_freqs(e)
                    .expect("e4b global rope needs rope_freqs.weight"),
            )
        };
        #[cfg(debug_assertions)]
        if let Some(ff) = ff {
            crate::debug_assert_tensor_stream_device(ff, &e.stream(), "gemma4_e4b_attn.rope_freqs");
        }
        let share = self.gemma4_e4b_kv_target(il);
        // Own-KV arms keep the f32 (k, v) alive for the prime fa arm below.
        let mut kv_f32: Option<(CudaSlice<f32>, CudaSlice<f32>)> = None;
        let mut q;
        if let Some(_tgt) = share {
            let q0 = e.matmul_pre(&fa.wq, hq, hdq, h, t)?;
            q = e.uninit(t * nh * hd)?;
            // Q-only through the same fused norm+rope kernel (rk = 0: the k/v segments are
            // empty; q0 stands in for the unused k/v pointers).
            let mut kdummy = e.uninit(1)?;
            let mut vdummy = e.uninit(1)?;
            e.rms_norm_qkv_rope(
                &q0,
                &q0,
                &q0,
                fa.q_norm.float_data(),
                fa.q_norm.float_data(),
                ones,
                &mut q,
                &mut kdummy,
                &mut vdummy,
                hd,
                self.gemma4_rope_dims(il),
                nh * t,
                0,
                pos_d,
                nh,
                1,
                base,
                1.0,
                ff,
                eps,
            )?;
        } else {
            // wave-4b: ONE concat matvec (wq|wk|wv) at t == 1 when the cat tensor exists;
            // else the fused3 grid launch; else per-matvec. The cat output is contiguous
            // q|k|v rows — the cat norm+rope twin consumes it directly.
            let e4bits = self.layers[il].gemma4.as_ref().and_then(|g| g.e4b.as_ref());
            let cat = e4bits.and_then(|e4| e4.qkv_cat.as_ref());
            q = e.uninit(t * nh * hd)?;
            let mut k = e.uninit(t * nkv * hd)?;
            let mut v = e.uninit(t * nkv * hd)?;
            if t == 1 && cat.is_some() {
                #[allow(clippy::unnecessary_unwrap)]
                // allow: the Some-guard sits in a multi-clause regime gate; if-let would reshape the arm structure
                let qkv0 = e.matmul_pre(cat.unwrap(), hq, hdq, h, 1)?;
                e.rms_norm_qkv_rope_cat(
                    &qkv0,
                    fa.q_norm.float_data(),
                    fa.k_norm.float_data(),
                    ones,
                    &mut q,
                    &mut k,
                    &mut v,
                    hd,
                    self.gemma4_rope_dims(il),
                    nh,
                    nkv,
                    pos_d,
                    nh,
                    nkv,
                    base,
                    1.0,
                    ff,
                    eps,
                )?;
            } else {
                let (q0, k0, v0) = match if t == 1 {
                    e.matmul_q4_fused3(&fa.wq, &fa.wk, &fa.wv, hq, hdq)?
                } else {
                    // E4B verify f3 port (2026-07-14): the 31B segmented-grid batched qkv
                    // on E4B's real-V triple — same MEMRA_F2B seam, bit-identical per row.
                    static F2B_QKV: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
                    if *F2B_QKV.get_or_init(|| std::env::var("MEMRA_F2B").as_deref() != Ok("0")) {
                        e.matmul_q4_fused3_batched(&fa.wq, &fa.wk, &fa.wv, hq, hdq, t)?
                    } else {
                        None
                    }
                } {
                    Some(triple) => triple,
                    None => (
                        e.matmul_pre(&fa.wq, hq, hdq, h, t)?,
                        e.matmul_pre(&fa.wk, hq, hdq, h, t)?,
                        e.matmul_pre(&fa.wv, hq, hdq, h, t)?,
                    ), // E4B: real v (K != V)
                };
                // wave-3 fold: q/k/v norms + q/k rope in ONE launch (rope math verbatim on
                // the normed rows; V ones-rms, never roped).
                e.rms_norm_qkv_rope(
                    &q0,
                    &k0,
                    &v0,
                    fa.q_norm.float_data(),
                    fa.k_norm.float_data(),
                    ones,
                    &mut q,
                    &mut k,
                    &mut v,
                    hd,
                    self.gemma4_rope_dims(il),
                    nh * t,
                    nkv * t,
                    pos_d,
                    nh,
                    nkv,
                    base,
                    1.0,
                    ff,
                    eps,
                )?;
            }
            let kvl = cache.kv[il].as_mut().unwrap();
            // class flag must match the cache dims (the g-threading sweep hardcoded `false`
            // here and the wkv default corrupted E4B: q8_0 bytes into an e4m3 cache — the
            // degenerate tok-0 stream, 2026-07-12).
            let cls = (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on());
            if dc_bucket.is_some() {
                // DC arm (graph serving): append at the len_d slot, advance the counter
                // in-stream — replay-correct, no host len in the launch args. Host mirrors
                // are NOT touched here (the replay loop owns them; a bump at capture-record
                // time would double-count the capture iteration).
                debug_assert!(t == 1);
                // wave 5c: append + len_d inc fused (one launch; single-block ordering).
                e.append_kv_quantized_row_dc_inc(
                    &k,
                    &v,
                    &mut kvl.k,
                    &mut kvl.v,
                    &mut kvl.len_d,
                    kvl.kv_dim_k,
                    kvl.kv_dim_v,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    cls,
                )?;
            } else {
                e.append_kv_quantized_rows(
                    &k,
                    &v,
                    &mut kvl.k,
                    &mut kvl.v,
                    kvl.len,
                    t,
                    kvl.kv_dim_k,
                    kvl.kv_dim_v,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    cls,
                )?;
                kvl.len += t;
            }
            kv_f32 = Some((k, v));
        }
        // attention: per-row causal fa over the (own or target) quantized cache. The cache
        // already contains this forward's rows in both arms; row i attends [.., base+i].
        let kvl_idx = share.unwrap_or(il);
        let kvl = cache.kv[kvl_idx].as_ref().unwrap();
        let base_len = kvl.len - t; // pre-append length (target appended this forward too)
        let win = self.cfg.gemma4.as_ref().unwrap().sliding_window as usize;
        let mut attn = e.uninit(t * nh * hd)?;
        // PRIME FA ARMS (perf lane 3, completed 2026-07-31 — the H100 board pinned E4B
        // prefill at 482 tok/s: the per-row loop ran the DECODE kernel once per token per
        // layer). Fresh-prompt (base_len == 0) batched attention, one launch per layer:
        //   - own-KV hd256 under the window: f32 fa_prefill (full causal exact — 26B pattern)
        //   - own-KV hd256 above the window (swa): fa_prefill_w windowed twin (12B pattern)
        //   - own-KV hd512 globals: fa_prefill_hd512 (12B/31B globals pattern)
        //   - KV-shared layers (no f32 k/v): fa_prefill_view over the target's QUANTIZED
        //     rows (the T=K verify kernel; the target appended this forward's rows already).
        //     swa-shared above the window keeps the per-row loop (no windowed view twin).
        // Same numeric class split as the 26B prime (f32/batched prefill vs per-row
        // quantized decode); run-gen argmax + chat gates arbitrate. MEMRA_NOFA=1 reverts all.
        if t > 1 && base_len == 0 && std::env::var("MEMRA_NOFA").is_err() {
            if let Some((kf, vf)) = &kv_f32 {
                if hd == 256 && t <= win {
                    e.fa_prefill(&q, kf, vf, &mut attn, hd, nh, nkv, t, t, scale, true)?;
                    return e.matmul(&fa.wo, &attn, t);
                }
                if hd == 256 && swa && t > win {
                    e.fa_prefill_w(&q, kf, vf, &mut attn, hd, nh, nkv, t, t, scale, true, win)?;
                    return e.matmul(&fa.wo, &attn, t);
                }
                if hd == 512 && !swa {
                    e.fa_prefill_hd512(&q, kf, vf, &mut attn, hd, nh, nkv, t, t, scale, true)?;
                    return e.matmul(&fa.wo, &attn, t);
                }
            } else if share.is_some() {
                let g = (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on());
                let k_view = e.view_u8(&kvl.k, kvl.k.len());
                let v_view = e.view_u8(&kvl.v, kvl.v.len());
                if hd == 256 && (!swa || t <= win) {
                    // inline-dequant quantized-view prefill (fa_prefill_q stamps 256/128)
                    e.fa_prefill_view(
                        &q,
                        &k_view,
                        &v_view,
                        &mut attn,
                        hd,
                        nh,
                        nkv,
                        t,
                        t,
                        scale,
                        true,
                        kvl.k_tok_bytes,
                        kvl.v_tok_bytes,
                        g,
                    )?;
                    return e.matmul(&fa.wo, &attn, t);
                }
                // remaining shared classes (swa above the window; hd512 globals): dequant
                // the target's t rows ONCE to f32, then the same f32 twins as own-KV.
                let kv_dim = nkv * hd;
                let mut kf = e.uninit(t * kv_dim)?;
                let mut vf = e.uninit(t * kv_dim)?;
                e.fa_dequant_kv_view_f32(
                    &k_view,
                    &v_view,
                    &mut kf,
                    &mut vf,
                    kv_dim,
                    kv_dim,
                    t,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    g,
                )?;
                if hd == 512 {
                    e.fa_prefill_hd512(&q, &kf, &vf, &mut attn, hd, nh, nkv, t, t, scale, true)?;
                } else {
                    e.fa_prefill_w(&q, &kf, &vf, &mut attn, hd, nh, nkv, t, t, scale, true, win)?;
                }
                return e.matmul(&fa.wo, &attn, t);
            }
        }
        if let Some(bucket) = dc_bucket {
            // DC arm (t == 1, under-window regime — the generate gate enforces it): ONE
            // fa_decode_dc over the live counter. len_d already advanced past this token
            // (t_kv = len_d[0], the cache.rs contract). KV-shared layers read the target's
            // counter (advanced when the target ran earlier in the stack).
            assert!(t == 1);
            // hd512 globals: eager (kvmod) picks the SCALAR unified below the fa512 floor,
            // and under the window every live t_kv sits below it — cap the capture bucket
            // under the floor so fa_decode_dc bakes the same scalar symbol, or the graph
            // rides the dpl16 twin and its numeric class diverges from the dc-eager stream
            // (E4B-GRAPH-GATE 2/64, 2026-07-12).
            let bucket = if hd == 512 && win <= crate::fa512_min_tkv() {
                bucket.min(crate::fa512_min_tkv().saturating_sub(1))
            } else {
                bucket
            };
            let k_view = e.view_u8(&kvl.k, kvl.k.len());
            let v_view = e.view_u8(&kvl.v, kvl.v.len());
            let g = (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on());
            // Weight prefetch (SOTA item 3, 2026-07-13): wo's decode plane prefetched into
            // L2 across the fa window — fa reads KV only, the weight DRAM lanes are idle
            // there (E4B valid window +0.65%: 196.8 vs 195.6). Value-free scheduling op,
            // captured into the dc graph like any other launch. Extending the cascade to
            // the ffn gate/up planes measured NEGATIVE (193.9 vs 195.8 — 29MB/layer floods
            // the fill path and evicts still-hot lines); wo-only is the shipped shape.
            // MEMRA_WPF=0 rollback seam.
            if crate::Engine::wpf_level() >= 1 {
                e.prefetch_weight_l2(&fa.wo)?;
            }
            // wave 5b: the combine emits the wo input q8 pair directly — the standalone
            // quantize launch + the f32 attn round-trip fold away (t=1 fast path only).
            if e.uses_q8_1_fast(&fa.wo) {
                let mut oq = e.alloc_i8_uninit(nh * hd)?;
                let mut od = e.zeros(nh * hd / 32)?;
                e.fa_decode_dc_q8(
                    &q,
                    &k_view,
                    &v_view,
                    &mut attn,
                    hd,
                    nh,
                    nkv,
                    &kvl.len_d,
                    bucket,
                    scale,
                    kvl.k_tok_bytes,
                    kvl.v_tok_bytes,
                    g,
                    Some((&mut oq, &mut od)),
                )?;
                return e.matmul_pre(&fa.wo, &oq, &od, &attn, t);
            }
            e.fa_decode_dc(
                &q,
                &k_view,
                &v_view,
                &mut attn,
                hd,
                nh,
                nkv,
                &kvl.len_d,
                bucket,
                scale,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                g,
            )?;
            return e.matmul(&fa.wo, &attn, t);
        }
        for i in 0..t {
            let avail = base_len + i + 1;
            let (off_tok, t_kv) = if swa && avail > win {
                (avail - win, win)
            } else {
                (0, avail)
            };
            let k_view = e.view_u8_range(
                &kvl.k,
                off_tok * kvl.k_tok_bytes,
                (off_tok + t_kv) * kvl.k_tok_bytes,
            );
            let v_view = e.view_u8_range(
                &kvl.v,
                off_tok * kvl.v_tok_bytes,
                (off_tok + t_kv) * kvl.v_tok_bytes,
            );
            let qv = e.view(&q, t * nh * hd);
            let q_row = qv.slice(i * nh * hd..(i + 1) * nh * hd);
            let mut q_one = e.uninit(nh * hd)?;
            e.copy_view_into(&mut q_one, 0, &q_row, nh * hd)?;
            let mut a_one = e.uninit(nh * hd)?;
            // read class MUST match the append class (globals are e4m3 under gkv): the
            // swa-only flag decoded global-layer e4m3 bytes as q8_0/q5_1 — attention over
            // garbage on EVERY E4B global layer, the cross-mode maxdiff-30 root (2026-07-12).
            e.fa_decode_kvmod(
                &q_one,
                &k_view,
                &v_view,
                &mut a_one,
                hd,
                nh,
                nkv,
                t_kv,
                scale,
                kvl.k_tok_bytes,
                kvl.v_tok_bytes,
                (!swa && crate::Engine::gkv_on()) || (swa && crate::Engine::wkv_on()),
            )?;
            e.copy_into(&mut attn, i * nh * hd, &a_one, nh * hd)?;
        }
        e.matmul(&fa.wo, &attn, t)
    }

    /// E4B trunk: embed -> prologue -> layers (attn + dense ffn via the 31B tail_core + the
    /// per-layer-embedding tail + layer scale) -> output_norm. Returns (softcapped logits
    /// device [t, n_vocab], pre-output_norm hidden [t, n_embd]). Appends t rows per own-KV
    /// layer; does NOT advance cache.pos (caller owns pos).
    fn gemma4_e4b_trunk(
        &self,
        e: &Engine,
        tokens: &[u32],
        pos0: usize,
        cache: &mut Cache,
        head_last: bool,
    ) -> Result<(CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let t = tokens.len();
        let pos: Vec<i32> = (0..t).map(|i| (pos0 + i) as i32).collect();
        let pos_d = e.htod_i32(&pos)?;
        let mut x = e.htod(&self.embd.gather(n_embd, tokens))?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), t * n_embd)?;
        let inp_pl = self.gemma4_e4b_inp_pl(e, tokens, &x, t)?;
        self.gemma4_e4b_trunk_core(e, x, inp_pl, &pos_d, t, cache, None, true, head_last)
    }

    /// Layer stack + head over prebuilt (x_scaled, inp_pl, device pos) — everything below
    /// here is device-driven, so the dc arm shares it verbatim (stream identity with the
    /// eager chain by construction: SAME functions, not twins).
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    fn gemma4_e4b_trunk_core(
        &self,
        e: &Engine,
        x_in: CudaSlice<f32>,
        inp_pl: CudaSlice<f32>,
        pos_d: &CudaSlice<i32>,
        t: usize,
        cache: &mut Cache,
        dc_bucket: Option<usize>,
        cap_logits: bool,
        head_last: bool,
    ) -> Result<(CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let n_layer = self.layers.len();
        let mut x = x_in;
        let aux_e4b = self.gemma4_aux.as_ref().unwrap().e4b.as_ref().unwrap();
        let n_epl = aux_e4b.n_epl;

        // cross-layer fusion (2026-07-12 port of the 26B/31B trunk structure): each layer's
        // closing add_scale also EMITS the next layer's attn-normed input pre-quantized
        // q8_1 (add_scale_rms_norm_q8_1); the LAST layer emits through output_norm so the
        // head rides matmul_pre too. First layer's pair comes from a standalone fused
        // norm+quant.
        let mut h_carry: Option<(CudaSlice<i8>, CudaSlice<f32>)> = None;
        for il in 0..n_layer {
            let layer = &self.layers[il];
            let (hq, hdq) = match h_carry.take() {
                Some(p) => p,
                None => e.rms_norm_q8_1(&x, layer.attn_norm.float_data(), n_embd, t, eps)?,
            };
            let o = self.gemma4_e4b_attn(e, il, &hq, &hdq, pos_d, t, cache, dc_bucket)?;
            // dense ffn tail with the post-attn norm FOLDED into its entry (one launch for
            // rms(o, post_attn_norm) + residual add + ffn_norm — glue-fusion lane).
            let bits = layer.gemma4.as_ref().unwrap();
            let e4b = bits.e4b.as_ref().expect("e4b layer bits");
            // glue wave 5: the tail DEFERS its post_ffw norm — the FFN exit fuses
            // rms(f0, post_ffw) + residual add + q8 emit into ONE launch (rms_pre_add_q8_1),
            // killing the rms_norm + add_q8_1 pair per layer. T-GENERIC since 2026-07-13:
            // the fused single-phase reduction is NOT FP-order-identical to the unfused
            // rms_norm+add pair (the original "bit-identical chain" claim was FALSE — the
            // t==1 gate left the batched VERIFY on the unfused chain and split E4B verify
            // from decode by logit maxdiff ~0.45, the greedy tie-flip at depth: gate 135/256.
            // NOFUSE bisect: disabling ONLY this fusion -> verify maxdiff 0.000e0). With the
            // gate dropped, decode AND verify ride the same fused chain — parity by
            // construction, VERIFY-GATE 0.000e0.
            let fuse_exit = e.uses_q8_1_fast(&e4b.inp_gate);
            let (sn, attn_out) = self.gemma4_layer_tail_core_pn(
                e,
                layer,
                &o,
                &x,
                t,
                Some(layer.post_attn_norm.float_data()),
                fuse_exit,
            )?;
            let mut resid = e.uninit(t * n_embd)?;
            // per-layer-embedding tail: resid += rms(proj . (gelu(inp_gate . resid) * inp_pl[il]))
            // wave-2: the residual add emits q8_1 alongside — inp_gate rides matmul_pre.
            // (PLE one-block mega-fusion PROBED NEGATIVE 2026-07-13: argmax-correct but
            // 126 vs 189 tok/s — one SM pulling 0.74MB of weights loses to the multi-block
            // launch chain it replaced; jsonl row. Kernel deleted per doctrine.)
            let g = if fuse_exit {
                // sn here = RAW f0 (post_ffw deferred).
                let (rq, rd) = e.rms_pre_add_q8_1(
                    &sn,
                    bits.post_ffw_norm.float_data(),
                    &attn_out,
                    &mut resid,
                    n_embd,
                    t,
                    self.cfg.rms_eps,
                )?;
                e.matmul_pre(&e4b.inp_gate, &rq, &rd, &resid, t)?
            } else {
                e.add(&sn, &attn_out, &mut resid, t * n_embd)?;
                e.matmul(&e4b.inp_gate, &resid, t)?
            };
            let mut act = e.uninit(t * n_epl)?;
            let y = if t == 1 && e.uses_q8_1_fast(&e4b.proj) {
                let ipv = e.view(&inp_pl, n_epl * n_layer);
                let row = ipv.slice(il * n_epl..(il + 1) * n_epl);
                let (aq, ad) = e.gelu_tanh_mul_q8_1(&g, &row, &mut act, n_epl, 1)?;
                e.matmul_pre(&e4b.proj, &aq, &ad, &act, t)?
            } else {
                let mut inp_this = e.uninit(t * n_epl)?;
                e.copy_rows_strided(
                    &inp_pl,
                    &mut inp_this,
                    n_epl,
                    t,
                    n_epl * n_layer,
                    il * n_epl,
                )?;
                e.gelu_tanh_mul(&g, &inp_this, &mut act, t * n_epl)?;
                e.matmul(&e4b.proj, &act, t)?
            };
            // rms(y, post_norm) + (yn + resid)*layer_scale + next-layer norm+quant emit,
            // ONE launch (glue-fusion lane; last layer emits through output_norm).
            let next_norm = if il + 1 < n_layer {
                self.layers[il + 1].attn_norm.float_data()
            } else {
                self.output_norm.float_data()
            };
            let mut xn = e.uninit(t * n_embd)?;
            let pair = e.rms_pre_add_scale_rms_norm_q8_1(
                &y,
                e4b.post_norm.float_data(),
                &resid,
                bits.layer_scale,
                next_norm,
                &mut xn,
                n_embd,
                t,
                eps,
            )?;
            h_carry = Some(pair);
            x = xn;
        }
        // the head consumes the last layer's fused (output_norm) emit. head_last callers
        // (prime, last_only forward) need only the final row's logits — the all-T head is
        // t*n_vocab of discarded work (E4B prime: ~134ms GEMM + a 2.26GB dtoh kept 1 row).
        let (oq, odq) = h_carry.take().unwrap();
        let h0 = e.zeros(0)?;
        let hm = if head_last { 1 } else { t };
        let (hq, hd) = if head_last && t > 1 {
            let mut q1 = e.uninit_i8(n_embd)?;
            e.dtod_copy_view_i8(&oq.slice((t - 1) * n_embd..t * n_embd), &mut q1)?;
            let nb = n_embd / 32;
            let mut d1 = e.uninit(nb)?;
            e.dtod_copy_view(&odq.slice((t - 1) * nb..t * nb), &mut d1)?;
            (q1, d1)
        } else {
            (oq, odq)
        };
        let mut ld = e.matmul_pre(&self.output, &hq, &hd, &h0, hm)?;
        // softcap is strictly monotonic — greedy (argmax-only) consumers skip it, matching
        // the 26B/31B dc precedent (their dc head goes matmul -> argmax with no cap).
        // Logit-returning callers (host logits / spec prime) keep the capped emit.
        if cap_logits {
            let cap = self.cfg.gemma4.as_ref().unwrap().final_logit_softcapping;
            e.softcap(&mut ld, cap, hm * self.output.out_features())?;
        }
        self.gemma4_suppress(e, &mut ld, hm)?; // mask both capped and argmax-only consumers
        Ok((ld, x))
    }

    /// E4B batched VERIFY (device tokens, the spec round's t=K+1 step): t rows through the
    /// e4b trunk (per-row causal attention; own-KV layers append t rows host-len, KV-shared
    /// layers ride their targets), per-row device argmax + the POST-output_norm hidden
    /// stack (the drafter's h convention). Advances cache.pos/kvl.len by t — the spec
    /// round rolls back rejected rows (shared layers have no KvLayer, so the plain rewind
    /// covers exactly the layers that appended).
    pub fn gemma4_e4b_decode_step_t_am_dev(
        &self,
        e: &Engine,
        tok_d: &CudaSlice<u32>,
        t: usize,
        pos0: usize,
        cache: &mut Cache,
    ) -> Result<(CudaSlice<u32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let pos: Vec<i32> = (0..t).map(|i| (pos0 + i) as i32).collect();
        let pos_d = e.htod_i32(&pos)?;
        let embd_gpu = self
            .embd_gpu
            .get_or_init(|| e.upload_u8(&self.embd.raw).expect("embed table upload"));
        let (qt, rb) = self.embd.qt_and_row_bytes(n_embd);
        let mut x = e.embed_gather_device_td(embd_gpu, tok_d, t, n_embd, qt, rb)?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), t * n_embd)?;
        let inp_pl = self.gemma4_e4b_inp_pl_dev(e, tok_d, &x, t)?;
        let (ld, xp) =
            self.gemma4_e4b_trunk_core(e, x, inp_pl, &pos_d, t, cache, None, true, false)?;
        // softcap is monotonic — the per-row argmax is invariant to it (the trunk's head
        // emit is already capped, matching the eager chain bit-for-bit).
        let n_vocab = self.output.out_features();
        let mut vam = e.stream().alloc_zeros::<u32>(t)?;
        for i in 0..t {
            e.argmax_token_device_col(&ld, i, n_vocab, &mut vam, i)?;
        }
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(&xp, self.output_norm.float_data(), &mut hn, n_embd, t, eps)?;
        cache.pos += t;
        Ok((vam, hn))
    }

    /// E4B verify + host logits + POST-output_norm hidden stack (the short-prompt spec
    /// prime path — mirror of `gemma4_decode_step_t_h`).
    pub(crate) fn gemma4_e4b_decode_step_t_h(
        &self,
        e: &Engine,
        tokens: &[u32],
        pos0: usize,
        cache: &mut Cache,
    ) -> Result<(Vec<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let t = tokens.len();
        let (ld, xp) = self.gemma4_e4b_trunk(e, tokens, pos0, cache, false)?;
        let mut hn = e.uninit(t * n_embd)?;
        e.rms_norm(&xp, self.output_norm.float_data(), &mut hn, n_embd, t, eps)?;
        cache.pos += t;
        Ok((e.dtoh(&ld)?, hn))
    }

    /// E4B GRAPH-CAPTURABLE dc step: same trunk as the dc step but token_d is updated IN
    /// PLACE (self-feeding replay) and every launch arg is a device counter — pos from
    /// pos_d (inc'd in-stream), KV slots from len_d (advanced in-stream), attention from
    /// fa_decode_dc at `bucket`. Host mirrors (cache.pos / kvl.len) advance in the caller's
    /// replay loop. UNDER-WINDOW regime only (the caller gates pos + budget < window).
    #[allow(clippy::too_many_arguments)] // allow: the parameter list mirrors the kernel/FFI/call contract; bundling into a struct is a refactor, not a lint fix
    pub fn gemma4_e4b_decode_step_dcg(
        &self,
        e: &Engine,
        token_d: &mut CudaSlice<u32>,
        pos_d: &mut CudaSlice<i32>,
        embd_gpu: &CudaSlice<u8>,
        embd_qt: i32,
        embd_rb: usize,
        cache: &mut Cache,
        n_vocab: usize,
        bucket: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let mut x = e.embed_gather_device(embd_gpu, token_d, n_embd, embd_qt, embd_rb)?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), n_embd)?;
        let inp_pl = self.gemma4_e4b_inp_pl_dev(e, token_d, &x, 1)?;
        let (ld, _x) =
            self.gemma4_e4b_trunk_core(e, x, inp_pl, pos_d, 1, cache, Some(bucket), false, false)?;
        e.argmax_token_device_into(&ld, token_d, n_vocab)?;
        e.inc_seqlen(pos_d)?;
        Ok(())
    }

    /// E4B DEVICE-COUNTER decode step (the dc serving arm): token id rides `token_d`, the
    /// greedy argmax lands in the returned device buffer — 4B/token host traffic. The layer
    /// stack is gemma4_e4b_trunk_core, i.e. the SAME functions the eager chain runs (stream
    /// identity by construction, not by twin-kernel parity). Host KV mirrors advance like the
    /// 26B dc-eager arm (window views are host math); len_d stays synced by the caller's
    /// entry sync + the appends here don't read it. Graph capture is NOT wired (no
    /// cap_bucket_max) — the E4B graph arc comes after the perf gates.
    #[allow(clippy::too_many_arguments)]
    pub fn gemma4_e4b_decode_step_dc(
        &self,
        e: &Engine,
        token_d: &CudaSlice<u32>,
        pos_d: &mut CudaSlice<i32>,
        embd_gpu: &CudaSlice<u8>,
        embd_qt: i32,
        embd_rb: usize,
        cache: &mut Cache,
        n_vocab: usize,
    ) -> Result<CudaSlice<u32>, Box<dyn std::error::Error>> {
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let mut x = e.embed_gather_device(embd_gpu, token_d, n_embd, embd_qt, embd_rb)?;
        e.scale_inplace(&mut x, (n_embd as f32).sqrt(), n_embd)?;
        let inp_pl = self.gemma4_e4b_inp_pl_dev(e, token_d, &x, 1)?;
        let (ld, _x) =
            self.gemma4_e4b_trunk_core(e, x, inp_pl, pos_d, 1, cache, None, false, false)?;
        let mut tok_out = e.stream().alloc_zeros::<u32>(1)?;
        e.argmax_token_device_into(&ld, &mut tok_out, n_vocab)?;
        e.inc_seqlen(pos_d)?;
        cache.pos += 1;
        let _ = eps;
        Ok(tok_out)
    }

    /// E4B eager T=1 decode step (decode_step_h contract): returns (softcapped logits host,
    /// pre-output_norm hidden). Advances cache.pos.
    pub(crate) fn gemma4_e4b_decode_step_h(
        &self,
        e: &Engine,
        token: u32,
        cache: &mut Cache,
    ) -> Result<(Vec<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        let (ld, x) = self.gemma4_e4b_trunk(e, &[token], cache.pos, cache, false)?;
        let logits = e.dtoh(&ld)?;
        cache.pos += 1;
        Ok((logits, x))
    }

    /// E4B batched prime (prime_cache contract: (last-row logits host, h_seed pre-norm last
    /// row, hidden stack)). First-light: the t-wide trunk (per-row attention) — correct, not
    /// fast; the prefill fa arms come later.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    pub(crate) fn gemma4_e4b_prime(
        &self,
        e: &Engine,
        tokens: &[u32],
        cache: &mut Cache,
    ) -> Result<(Vec<f32>, CudaSlice<f32>, CudaSlice<f32>), Box<dyn std::error::Error>> {
        // Err, not assert (2026-08-07, lane/gemma4-serve-gaps): same served-chunked-prompt
        // process-kill as gemma4_prime — refuse per-request.
        if cache.pos != 0 {
            return Err(
                "e4b prime is fresh-prompt only (v0) — prime the full prompt in one \
                        call or decode tokenwise"
                    .into(),
            );
        }
        let n_embd = self.cfg.n_embd as usize;
        let t = tokens.len();
        let (ld, x) = self.gemma4_e4b_trunk(e, tokens, 0, cache, true)?;
        cache.pos += t;
        let last = e.dtoh(&ld)?; // head_last: ld is already the final row only
        let xv = e.view(&x, t * n_embd);
        let row = xv.slice((t - 1) * n_embd..t * n_embd);
        let mut h_seed = e.uninit(n_embd)?;
        e.copy_view_into(&mut h_seed, 0, &row, n_embd)?;
        Ok((last, h_seed, x))
    }

    /// E4B prefill logits (forward contract — no persistent cache; scratch cache internally).
    pub(crate) fn gemma4_e4b_forward(
        &self,
        e: &Engine,
        tokens: &[u32],
        last_only: bool,
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        let mut cache = Cache::new(e, &self.cfg, tokens.len() + 8)?;
        let (ld, _x) = self.gemma4_e4b_trunk(e, tokens, 0, &mut cache, last_only)?;
        e.dtoh(&ld) // head_last already reduced to the final row when last_only
    }
}

#[cfg(test)]
mod prime_chunk_schedule_tests {
    use super::{
        PRIME_MIN_T, PRIME_PIPE_MIN_CHUNK, PrimePpSignal, PrimePpStageChannels, PrimePpWaveCredits,
        PrimePpWaveSlot, active_matrix_values, align_prime_ranges_to_gdn,
        dynamic_prime_chunk_ranges, fixed_prime_chunk_ranges, fixed_prime_chunk_ranges_for_ring,
        move_prime_cache_layers, parse_step_ep_grouped_prefill, parse_step_tp_prefill,
        prime_cache_stage_for_layer, recv_prime_pp_signal, restore_prime_cache_layers,
        step_grouped_decode_shape, step_grouped_prefill_shape, step_tp_prefill_shape,
        validate_step_prime_batch_modes,
    };

    fn sizes(ranges: &[(usize, usize)]) -> Vec<usize> {
        ranges.iter().map(|(start, end)| end - start).collect()
    }

    #[allow(clippy::manual_clamp)] // allow: the min/max chain mirrors the reference arithmetic order in pinned sizing/quant math
    fn auto_chunk(t: usize) -> usize {
        t.div_ceil(8).max(PRIME_PIPE_MIN_CHUNK).min(4096)
    }

    #[test]
    fn ppn_prime_cache_partition_moves_and_restores_every_layer() {
        let round_trip = |fence: &[usize], layers: usize| {
            let original: Vec<Option<usize>> = (0..layers).map(Some).collect();
            let mut parent = original.clone();
            let mut stages: Vec<Vec<Option<usize>>> =
                (0..fence.len() - 1).map(|_| vec![None; layers]).collect();

            move_prime_cache_layers(&mut parent, &mut stages, fence);
            assert!(parent.iter().all(Option::is_none));
            for layer in 0..layers {
                let owner = prime_cache_stage_for_layer(fence, layer);
                for (stage, values) in stages.iter().enumerate() {
                    assert_eq!(values[layer], (stage == owner).then_some(layer));
                }
            }

            restore_prime_cache_layers(&mut parent, &mut stages, fence);
            assert_eq!(parent, original);
            assert!(stages.iter().flatten().all(Option::is_none));
        };

        // Layers beyond the trunk fence end model MTP/tail state and remain last-stage owned.
        round_trip(&[0, 5, 8], 10);
        round_trip(&[0, 2, 5, 8], 10);
        round_trip(&[0, 1, 3, 6, 8], 10);
    }

    #[test]
    fn ppn_prime_wave_credit_requires_the_exact_oldest_wave_and_slot() {
        let mut credits = PrimePpWaveCredits::default();
        let wave0 = PrimePpWaveSlot { wave: 0, slot: 1 };
        let wave1 = PrimePpWaveSlot { wave: 1, slot: 0 };
        credits.record_send(wave0).unwrap();
        assert_eq!(credits.release_required(), None);
        credits.record_send(wave1).unwrap();
        assert_eq!(credits.release_required(), Some(wave0));

        assert!(
            credits
                .record_release(PrimePpWaveSlot { wave: 0, slot: 0 })
                .unwrap_err()
                .contains("does not match oldest pending")
        );
        assert_eq!(credits.release_required(), Some(wave0));
        credits.record_release(wave0).unwrap();
        credits
            .record_send(PrimePpWaveSlot { wave: 2, slot: 1 })
            .unwrap();
        assert!(
            credits
                .record_send(PrimePpWaveSlot { wave: 4, slot: 0 })
                .unwrap_err()
                .contains("while wave 3 was next")
        );
        assert!(
            credits
                .record_send(PrimePpWaveSlot { wave: 3, slot: 1 })
                .unwrap_err()
                .contains("reused slot 1")
        );
    }

    #[test]
    fn ppn_prime_wave_signal_reports_order_error_injected_error_and_closure() {
        let expected = PrimePpWaveSlot { wave: 2, slot: 1 };

        let (sender, receiver) = std::sync::mpsc::channel();
        sender.send(PrimePpSignal::Slot(expected)).unwrap();
        assert_eq!(
            recv_prime_pp_signal(&receiver, expected, true, "test").unwrap(),
            expected
        );

        let (sender, receiver) = std::sync::mpsc::channel();
        sender
            .send(PrimePpSignal::Slot(PrimePpWaveSlot { wave: 3, slot: 1 }))
            .unwrap();
        assert!(
            recv_prime_pp_signal(&receiver, expected, true, "test")
                .unwrap_err()
                .contains("expected wave/slot")
        );

        let (sender, receiver) = std::sync::mpsc::channel();
        sender
            .send(PrimePpSignal::Error("injected stage failure".into()))
            .unwrap();
        assert_eq!(
            recv_prime_pp_signal(&receiver, expected, true, "test").unwrap_err(),
            "injected stage failure"
        );

        let (upstream_sender, upstream_receiver) = std::sync::mpsc::channel();
        let (outgoing_sender, outgoing_receiver) = std::sync::mpsc::channel();
        let (_release_sender, released_downstream) = std::sync::mpsc::channel();
        PrimePpStageChannels {
            incoming: None,
            release_upstream: Some(upstream_sender),
            outgoing: outgoing_sender,
            released_downstream,
        }
        .notify_failure("injected worker error");
        assert_eq!(
            recv_prime_pp_signal(&upstream_receiver, expected, false, "test").unwrap_err(),
            "injected worker error"
        );
        assert_eq!(
            recv_prime_pp_signal(&outgoing_receiver, expected, false, "test").unwrap_err(),
            "injected worker error"
        );

        let (sender, receiver) = std::sync::mpsc::channel::<PrimePpSignal>();
        drop(sender);
        assert!(
            recv_prime_pp_signal(&receiver, expected, true, "test")
                .unwrap_err()
                .contains("channel closed while waiting for wave 2")
        );
    }

    /// TOOTH for the PP-auto-ranges GDN grid law (lane/hermes-perf-fixes, 2026-08-23;
    /// primegrid pattern): the AUTO schedules put internal prime-call boundaries OFF the
    /// WY-chunk grid — the broken arm must be demonstrably off-grid, and the aligned twin
    /// must land every boundary on it without changing coverage.
    #[test]
    fn auto_prime_ranges_align_to_the_gdn_grid() {
        let c = 32usize; // shipped MEMRA_GDN_CHUNK default/clamp floor
        let assert_covers = |ranges: &[(usize, usize)], t: usize| {
            assert_eq!(ranges.first().map(|&(s, _)| s), Some(0));
            assert_eq!(ranges.last().map(|&(_, e)| e), Some(t));
            for w in ranges.windows(2) {
                assert_eq!(w[0].1, w[1].0, "ranges must stay contiguous");
            }
            assert!(ranges.iter().all(|&(s, e)| e > s), "no empty range");
        };

        // The PP-2 auto geometry at a real agentic length: t=9510 -> fill = 1189 (div_ceil
        // by 8), every internal boundary off the 32 grid — the falsified-identity arm.
        let t = 9510usize;
        let fill = auto_chunk(t);
        let fixed = fixed_prime_chunk_ranges(t, fill);
        assert!(
            fixed[..fixed.len() - 1].iter().any(|&(_, e)| e % c != 0),
            "broken arm vanished: fixed auto boundaries all landed on-grid"
        );
        let dynamic = dynamic_prime_chunk_ranges(t, fill, &fixed);
        assert!(
            dynamic[..dynamic.len() - 1]
                .iter()
                .any(|&(_, e)| e % c != 0),
            "broken arm vanished: dynamic auto boundaries all landed on-grid"
        );

        for ranges in [&fixed, &dynamic] {
            let aligned = align_prime_ranges_to_gdn(ranges, t, c);
            assert_covers(&aligned, t);
            for &(_, e) in &aligned[..aligned.len() - 1] {
                assert_eq!(e % c, 0, "internal boundary {e} off the {c}-grid");
            }
            // boundaries only move DOWN, at most c-1 tokens.
            for (&(_, a), &(_, b)) in aligned.iter().zip(ranges.iter()) {
                assert!(a <= b && b - a < c);
            }
        }

        // Collapse/merge: boundaries inside one grid cell fuse instead of emitting an
        // empty range; the schedule survives degenerate short fills.
        let tight = vec![(0usize, 33usize), (33, 40), (40, 200)];
        let aligned = align_prime_ranges_to_gdn(&tight, 200, c);
        assert_covers(&aligned, 200);
        assert_eq!(aligned, vec![(0, 32), (32, 200)]);

        // No-ops: single range, c=0 (grid off), already-aligned schedules.
        assert_eq!(align_prime_ranges_to_gdn(&[(0, 200)], 200, c), [(0, 200)]);
        assert_eq!(align_prime_ranges_to_gdn(&tight, 200, 0), tight.as_slice());
        let on_grid = vec![(0usize, 128usize), (128, 256), (256, 300)];
        assert_eq!(
            align_prime_ranges_to_gdn(&on_grid, 300, c),
            on_grid.as_slice()
        );
    }

    #[test]
    fn active_matrix_prefix_scopes_reused_prime_slabs() {
        assert_eq!(
            active_matrix_values(40 * 4096, 29, 4096, "activation").unwrap(),
            29 * 4096
        );
        assert_eq!(
            active_matrix_values(29 * 4096, 29, 4096, "activation").unwrap(),
            29 * 4096
        );
        assert_eq!(
            active_matrix_values(29 * 4096, 24, 4096, "activation").unwrap(),
            24 * 4096
        );
        assert!(active_matrix_values(28 * 4096, 29, 4096, "activation").is_err());
        assert!(active_matrix_values(usize::MAX, usize::MAX, 2, "activation").is_err());
    }

    #[test]
    fn step_tp_prefill_batch_refuses_before_scheduler_fallback() {
        assert!(validate_step_prime_batch_modes(false, false).is_ok());

        let grouped_without_tp = validate_step_prime_batch_modes(false, true).unwrap_err();
        assert!(grouped_without_tp.contains("requires MEMRA_STEP_TP_PREFILL=1"));

        for grouped in [false, true] {
            let err = validate_step_prime_batch_modes(true, grouped).unwrap_err();
            assert!(err.contains("did not clear the live-server performance gate"));
            assert!(err.contains("per-session grouped prefill"));
        }
    }

    #[test]
    fn step_grouped_path_is_eager_single_token_only() {
        assert!(step_grouped_decode_shape(false, 1));
        assert!(!step_grouped_decode_shape(true, 1));
        assert!(!step_grouped_decode_shape(false, 2));
        assert!(!step_grouped_decode_shape(true, 2));
    }

    #[test]
    fn step_grouped_prefill_door_is_strict_and_capacity_bounded() {
        assert!(!parse_step_ep_grouped_prefill(None).unwrap());
        assert!(!parse_step_ep_grouped_prefill(Some("")).unwrap());
        assert!(!parse_step_ep_grouped_prefill(Some("0")).unwrap());
        assert!(parse_step_ep_grouped_prefill(Some("1")).unwrap());
        assert!(parse_step_ep_grouped_prefill(Some("true")).is_err());
        assert!(parse_step_ep_grouped_prefill(Some("2")).is_err());

        assert!(step_grouped_prefill_shape(true, true, PRIME_MIN_T));
        assert!(step_grouped_prefill_shape(
            true,
            true,
            crate::cache::PRIME_CHUNK_MAX_TOKENS,
        ));
        assert!(!step_grouped_prefill_shape(true, true, PRIME_MIN_T - 1,));
        assert!(!step_grouped_prefill_shape(
            true,
            true,
            crate::cache::PRIME_CHUNK_MAX_TOKENS + 1,
        ));
        assert!(!step_grouped_prefill_shape(false, true, PRIME_MIN_T));
        assert!(!step_grouped_prefill_shape(true, false, PRIME_MIN_T));
    }

    #[test]
    fn step_tp_prefill_door_is_strict_and_default_off() {
        assert!(!parse_step_tp_prefill(None).unwrap());
        assert!(!parse_step_tp_prefill(Some("")).unwrap());
        assert!(!parse_step_tp_prefill(Some("0")).unwrap());
        assert!(parse_step_tp_prefill(Some("1")).unwrap());
        assert!(parse_step_tp_prefill(Some("true")).is_err());
        assert!(parse_step_tp_prefill(Some("2")).is_err());
    }

    #[test]
    fn step_tp_prefill_requires_a_qualified_even_rank_shape() {
        assert!(step_tp_prefill_shape(
            true,
            PRIME_MIN_T,
            4,
            true,
            true,
            false,
        ));
        assert!(!step_tp_prefill_shape(
            false,
            PRIME_MIN_T,
            4,
            true,
            true,
            false,
        ));
        assert!(!step_tp_prefill_shape(
            true,
            PRIME_MIN_T - 1,
            4,
            true,
            true,
            false,
        ));
        // TP2 admits (2026-08-25); odd/1-card placements still refuse.
        assert!(step_tp_prefill_shape(
            true,
            PRIME_MIN_T,
            2,
            true,
            true,
            false
        ));
        assert!(!step_tp_prefill_shape(
            true,
            PRIME_MIN_T,
            1,
            true,
            true,
            false
        ));
        assert!(!step_tp_prefill_shape(
            true,
            PRIME_MIN_T,
            3,
            true,
            true,
            false
        ));
        assert!(!step_tp_prefill_shape(
            true,
            PRIME_MIN_T,
            4,
            false,
            true,
            false,
        ));
        assert!(!step_tp_prefill_shape(
            true,
            PRIME_MIN_T,
            4,
            true,
            false,
            false,
        ));
        assert!(!step_tp_prefill_shape(
            true,
            PRIME_MIN_T,
            4,
            true,
            true,
            true,
        ));
    }

    #[test]
    fn fixed_schedule_retains_measured_geometry() {
        assert_eq!(
            sizes(&fixed_prime_chunk_ranges(461, 128)),
            vec![128, 128, 128, 77]
        );
        assert_eq!(
            sizes(&fixed_prime_chunk_ranges(1833, 230)),
            vec![230, 230, 230, 230, 230, 230, 230, 223]
        );
        assert_eq!(sizes(&fixed_prime_chunk_ranges(4096, 512)), vec![512; 8]);
        let capped = sizes(&fixed_prime_chunk_ranges_for_ring(8200, 4096, true));
        assert_eq!(capped, vec![4096, 4088, 16]);
        assert!(capped.iter().all(|&rows| rows <= 4096));
        assert_eq!(
            sizes(&fixed_prime_chunk_ranges_for_ring(4100, 4096, false)),
            vec![4100],
            "flag-off schedule remains byte-for-byte the legacy monolithic tail",
        );
    }

    #[test]
    fn dynamic_schedule_matches_registered_shapes() {
        let cases = [
            (461, vec![64, 141, 132, 124]),
            (1833, vec![115, 269, 260, 252, 244, 237, 231, 225]),
            (4096, vec![256, 602, 580, 563, 545, 531, 516, 503]),
        ];
        for (t, expected) in cases {
            let chunk = auto_chunk(t);
            let fixed = fixed_prime_chunk_ranges(t, chunk);
            assert_eq!(
                sizes(&dynamic_prime_chunk_ranges(t, chunk, &fixed)),
                expected
            );
        }
    }

    #[test]
    fn dynamic_schedule_covers_exactly_and_shrinks_after_fill() {
        for t in 256..=8192 {
            let chunk = auto_chunk(t);
            let fixed = fixed_prime_chunk_ranges(t, chunk);
            let dynamic = dynamic_prime_chunk_ranges(t, chunk, &fixed);
            assert_eq!(dynamic.len(), fixed.len(), "T={t}");
            assert_eq!(dynamic.first().unwrap().0, 0, "T={t}");
            assert_eq!(dynamic.last().unwrap().1, t, "T={t}");
            for pair in dynamic.windows(2) {
                assert_eq!(pair[0].1, pair[1].0, "T={t}");
            }
            assert!(
                dynamic
                    .iter()
                    .all(|(start, end)| end - start >= PRIME_MIN_T),
                "T={t} sizes={:?}",
                sizes(&dynamic)
            );
            if dynamic.len() >= 3 {
                let chunk_sizes = sizes(&dynamic);
                assert!(
                    chunk_sizes[0] < chunk_sizes[1],
                    "T={t} sizes={chunk_sizes:?}"
                );
                assert!(
                    chunk_sizes[1..].windows(2).all(|pair| pair[0] >= pair[1]),
                    "T={t} sizes={chunk_sizes:?}"
                );
            }
        }
    }
}

#[cfg(test)]
mod page_prefetch_tests {
    use super::{
        grouped_worker_prefetch_position, page_prefetch_positions,
        page_prefetch_window_from_values, worker_prefetch_positions,
    };

    #[test]
    fn page_prefetch_window_keeps_existing_opt_in_default() {
        assert_eq!(page_prefetch_window_from_values(false, None), 0);
        assert_eq!(page_prefetch_window_from_values(false, Some("8")), 0);
        assert_eq!(page_prefetch_window_from_values(true, None), 1);
        assert_eq!(page_prefetch_window_from_values(true, Some("bad")), 1);
        assert_eq!(page_prefetch_window_from_values(true, Some("0")), 0);
        assert_eq!(page_prefetch_window_from_values(true, Some("8")), 8);
    }

    #[test]
    fn rolling_page_prefetch_advises_each_future_expert_once() {
        let advised: Vec<_> = (0..7)
            .flat_map(|position| page_prefetch_positions(position, 7, 3))
            .collect();
        assert_eq!(advised, vec![1, 2, 3, 4, 5, 6]);

        let one_ahead: Vec<_> = (0..4)
            .flat_map(|position| page_prefetch_positions(position, 4, 1))
            .collect();
        assert_eq!(one_ahead, vec![1, 2, 3]);
        assert!(page_prefetch_positions(0, 4, 0).is_empty());
    }

    #[test]
    fn grouped_worker_prefetch_primes_first_then_each_known_next_once() {
        assert_eq!(grouped_worker_prefetch_position(0, None), None);
        let positions: Vec<_> = std::iter::once(grouped_worker_prefetch_position(4, None).unwrap())
            .chain(
                (0..4).filter_map(|position| grouped_worker_prefetch_position(4, Some(position))),
            )
            .collect();
        assert_eq!(positions, vec![0, 1, 2, 3]);
        assert_eq!(grouped_worker_prefetch_position(1, Some(0)), None);
    }

    #[test]
    fn rolling_worker_prefetch_primes_current_and_each_future_expert_once() {
        let queued: Vec<_> = (0..8)
            .flat_map(|position| worker_prefetch_positions(position, 8, 5))
            .collect();
        assert_eq!(queued, (0..8).collect::<Vec<_>>());

        let one_at_a_time: Vec<_> = (0..4)
            .flat_map(|position| worker_prefetch_positions(position, 4, 1))
            .collect();
        assert_eq!(one_at_a_time, vec![0, 1, 2, 3]);
        assert!(worker_prefetch_positions(0, 4, 0).is_empty());
    }
}

pub struct G4DcSlots {
    x: CudaSlice<f32>,
    xn: CudaSlice<f32>,
    cur: CudaSlice<f32>,
    hq: CudaSlice<i8>,
    hd_: CudaSlice<f32>,
    q0: CudaSlice<f32>,
    k0: CudaSlice<f32>,
    v0: CudaSlice<f32>,
    q: CudaSlice<f32>,
    k: CudaSlice<f32>,
    v: CudaSlice<f32>,
    attn: CudaSlice<f32>,
    o: CudaSlice<f32>,
    attn_out: CudaSlice<f32>,
    zsh: CudaSlice<f32>,
    zq: CudaSlice<i8>,
    zd: CudaSlice<f32>,
    gate: CudaSlice<f32>,
    up: CudaSlice<f32>,
    act: CudaSlice<f32>,
    actq: CudaSlice<i8>,
    actd: CudaSlice<f32>,
    f0: CudaSlice<f32>,
    sn: CudaSlice<f32>,
    hn: CudaSlice<f32>,
    logits: CudaSlice<f32>,
}

/// Whole-token decode graph state (step TP graph increment B). One stitched multi-device
/// parent per fa bucket, plus the persistent host->graph plumbing: the device token id the
/// in-graph embed gathers, the device position the ropes read (advanced in-graph), and the
/// fixed logits stage the head writes.
pub struct Step35TokenGraphState {
    /// (bucket_max, graph) — bucket keyed by the fa split geometry (fa_geom_eager).
    pub graphs: Vec<(usize, crate::tp::TokenGraph)>,
    pub token_d: cudarc::driver::CudaSlice<u32>,
    pub pos_d: cudarc::driver::CudaSlice<i32>,
    pub logits_stage: cudarc::driver::CudaSlice<f32>,
    /// Cross-child intermediates MUST live at fixed addresses (graph mem nodes remap at
    /// launch, so an alloc made inside one captured child is not referable from another):
    /// the running residual, the post-attention pair, the shared-expert row, and the
    /// e-context mirrors of the root-produced attention output and K/V shadow rows.
    pub x: cudarc::driver::CudaSlice<f32>,
    pub x1: cudarc::driver::CudaSlice<f32>,
    pub mixed_stage: cudarc::driver::CudaSlice<f32>,
    pub sh_stage: cudarc::driver::CudaSlice<f32>,
    pub k_shadow_stage: cudarc::driver::CudaSlice<f32>,
    pub v_shadow_stage: cudarc::driver::CudaSlice<f32>,
    /// Alloc-free e-section scratch (child graphs cannot contain mem nodes): router logits,
    /// shared-expert gate/up/act rows + sigmoid scalar, dense-FFN z/gate/up/act, head hidden.
    pub router_logits: cudarc::driver::CudaSlice<f32>,
    pub shexp_gate: cudarc::driver::CudaSlice<f32>,
    pub shexp_up: cudarc::driver::CudaSlice<f32>,
    pub shexp_act: cudarc::driver::CudaSlice<f32>,
    pub gate_sig: cudarc::driver::CudaSlice<f32>,
    pub dense_z: cudarc::driver::CudaSlice<f32>,
    pub dense_gate: cudarc::driver::CudaSlice<f32>,
    pub dense_up: cudarc::driver::CudaSlice<f32>,
    pub dense_act: cudarc::driver::CudaSlice<f32>,
    pub hn: cudarc::driver::CudaSlice<f32>,
    /// MEMRA_TG_PROBE_LAYER diagnostics: capture-time copies of layer K's attention output
    /// and post-FFN residual, dumped after replay for graph-vs-eager layer bisection.
    pub probe_mixed: cudarc::driver::CudaSlice<f32>,
    pub probe_x: cudarc::driver::CudaSlice<f32>,
    /// Chunk-loop (F-lite): device token history ring + its device write index, filled by
    /// the in-graph tail argmax chain; host reads the ring once per chunk.
    pub token_hist: cudarc::driver::CudaSlice<u32>,
    pub hist_idx: cudarc::driver::CudaSlice<i32>,
}

impl HybridModel {
    /// Whole-token decode graph (step TP graph increment B): ONE stitched multi-device parent
    /// replays the entire 45-layer token — the launch-collapse the per-layer minis could not
    /// reach. Returns Some(logits) when the graph handled the token, None for eager fallback
    /// (door off, ineligible class, any layer's ring would rebase, or a bucket boundary that
    /// needs a rebuild this token).
    ///
    /// v1 SCOPE (diagnostic door, default OFF): the LOCAL shadow caches advance their lengths
    /// but not their contents under this door (the TP rank caches are fully maintained
    /// in-graph via the dcw counters). Sessions relying on shadow CONTENT (save/rollback)
    /// must not run with the door on until the local-dcw twin lands.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    pub(crate) fn step35_token_graph_step(
        &self,
        e: &Engine,
        token: u32,
        cache: &mut Cache,
    ) -> Result<Option<(Vec<f32>, CudaSlice<f32>)>, Box<dyn std::error::Error>> {
        if !self.uses_sliding_gated_moe_program()
            || !crate::tp::step_tp_graph_enabled()?
            || !crate::tp::step_tp_dcw_enabled()?
            || !crate::tp::step_tp_qkv_fused_enabled()?
            || !crate::tp::step_tp_dev_router_enabled()?
            || !crate::tp::step_nvfp4_dev_routes_enabled()?
        {
            return Ok(None);
        }
        // GRAPH-LAUNCH HEADROOM GUARD (see spec::GRAPH_LAUNCH_MIN_FREE): the eager
        // token step is this route's byte-identical twin — warmup and rebase tokens
        // already ride it — so below the driver-free floor the token goes eager
        // (`Ok(None)` = the caller's eager fallback) instead of feeding cuGraphLaunch
        // an exhausted card (lane/graph-launch-guard-sweep-20260831).
        if !crate::spec::graph_launch_headroom_ok(e) {
            static NOTED: std::sync::Once = std::sync::Once::new();
            NOTED.call_once(|| crate::spec::graph_replay_suspended_note("step-tp-token"));
            return Ok(None);
        }
        let n_embd = self.cfg.n_embd as usize;
        let n_vocab = self.cfg.n_vocab as usize;
        let n_layers = self.layers.len();
        let pos = cache.pos;
        let staged_next = pos + 1;
        if staged_next < 96 {
            return Ok(None); // sub-vec-floor contexts keep eager (fa kernel-class boundary)
        }

        // Per-layer eligibility: every TP layer contiguous-appends this token (any rebase ->
        // eager fallback for the whole token; the host path also updates base_d there).
        for il in 0..n_layers {
            let Some(tp_kv) = cache.tp_kv[il].as_ref() else {
                return Ok(None); // caches not hydrated yet — eager warms them
            };
            if tp_kv.peek_append_ring(1)?.1 {
                return Ok(None);
            }
        }

        // Bucket key: the global layers' fa split geometry at this depth (SWA layers cap at
        // their window and share one bucket forever after ctx > window).
        let (fa_vec, n_splits) = e.fa_geom_eager(staged_next, 128, 8, false);
        if !fa_vec {
            return Ok(None);
        }
        let sp = crate::fa_split_keys(staged_next, 8);
        let bucket_max = (n_splits * sp).max(staged_next);

        let mut state_guard = self
            .step35_token_graph
            .lock()
            .map_err(|_| "step35 token graph lock is poisoned")?;
        if state_guard.is_none() {
            let _main = e.gpu.enter_main()?;
            let n_expert = self
                .cfg
                .moe
                .as_ref()
                .map(|m| m.expert_count as usize)
                .unwrap_or(0);
            let n_ff_sh = self
                .layers
                .iter()
                .find_map(|l| match &l.ffn {
                    crate::hybrid::Ffn::Moe(m) => m.gate_shexp.as_ref().map(|g| g.out_features()),
                    _ => None,
                })
                .unwrap_or(0);
            let n_ff_dense = self
                .layers
                .iter()
                .find_map(|l| match &l.ffn {
                    crate::hybrid::Ffn::Dense { ffn_gate, .. } => Some(ffn_gate.out_features()),
                    _ => None,
                })
                .unwrap_or(0);
            *state_guard = Some(Step35TokenGraphState {
                graphs: Vec::new(),
                token_d: e.stream().clone_htod(&[0u32])?,
                pos_d: e.htod_i32(&[pos as i32])?,
                logits_stage: e.htod(&vec![0.0f32; n_vocab])?,
                x: e.htod(&vec![0.0f32; n_embd])?,
                x1: e.htod(&vec![0.0f32; n_embd])?,
                mixed_stage: e.htod(&vec![0.0f32; n_embd])?,
                sh_stage: e.htod(&vec![0.0f32; n_embd])?,
                k_shadow_stage: e.htod(&vec![0.0f32; 2048])?,
                v_shadow_stage: e.htod(&vec![0.0f32; 2048])?,
                router_logits: e.htod(&vec![0.0f32; n_expert.max(1)])?,
                shexp_gate: e.htod(&vec![0.0f32; n_ff_sh.max(1)])?,
                shexp_up: e.htod(&vec![0.0f32; n_ff_sh.max(1)])?,
                shexp_act: e.htod(&vec![0.0f32; n_ff_sh.max(1)])?,
                gate_sig: e.htod(&[1.0f32; 1])?,
                dense_z: e.htod(&vec![0.0f32; n_embd])?,
                dense_gate: e.htod(&vec![0.0f32; n_ff_dense.max(1)])?,
                dense_up: e.htod(&vec![0.0f32; n_ff_dense.max(1)])?,
                dense_act: e.htod(&vec![0.0f32; n_ff_dense.max(1)])?,
                hn: e.htod(&vec![0.0f32; n_embd])?,
                probe_mixed: e.htod(&vec![0.0f32; n_embd])?,
                probe_x: e.htod(&vec![0.0f32; n_embd])?,
                token_hist: e.stream().clone_htod(&[0u32; 16])?,
                hist_idx: e.htod_i32(&[0])?,
            });
        }
        let state = state_guard.as_mut().expect("state armed above");
        // Pre-arm the argmax partials OUTSIDE any capture: the launcher allocates them on
        // first use, and an alloc inside a captured section is a mem node (child graphs
        // reject those — the tail argmax chain needs them already resident).
        {
            let _main = e.gpu.enter_main()?;
            let Step35TokenGraphState {
                logits_stage,
                token_d,
                ..
            } = &mut *state;
            e.argmax_token_device_into(logits_stage, token_d, n_vocab)?;
        }

        // ONE graph, retargeted per bucket (increment C): the per-16-token whole rebuild was
        // ~55ms (3.4ms/token persistent); the M1 exec update path moves nsp/ski/gridDimY and
        // the partial-pool memset widths in ~1ms. The partial pool is pre-grown to the run
        // ceiling at build so the baked pointers never move.
        if state.graphs.is_empty() {
            // Build the parent at this bucket. Capture executes nothing; correctness is
            // pinned at replay by the token-identity gate.
            self.step35_token_graph_build(e, cache, state, bucket_max)?;
        }
        {
            let (b, g) = state.graphs.first_mut().expect("graph built above");
            if *b != bucket_max {
                g.retarget_bucket(bucket_max)?;
                *b = bucket_max;
            }
        }
        let graph = state
            .graphs
            .first()
            .map(|(_, g)| g)
            .expect("graph built above");

        let tg_timing = std::env::var("MEMRA_STEP_TP_TIMING").as_deref() == Ok("1");
        let t_fence = tg_timing.then(std::time::Instant::now);
        // Rank-stream fence: an eager token (warmup, rebase fallback) leaves len-mirror sets
        // queued on the rank streams, and graph children carry no ordering edge to those
        // streams. Graph tokens themselves enqueue nothing there (external commit), so this
        // sync is a no-op between consecutive replays.
        {
            let fa0 = match &self.layers[0].mixer {
                Mixer::Full(fa) => fa,
                _ => return Err("step35 token graph expects full-attention layers".into()),
            };
            let tp0 = fa0
                .step_tp_qkv
                .as_ref()
                .ok_or("step35 token graph lost its TP state")?;
            for rank in 0..tp0.runtime.devices().len() {
                let engine = tp0
                    .runtime
                    .rank_engine(rank)
                    .ok_or("step35 token graph lost a rank engine")?;
                let _main = engine.gpu.enter_main()?;
                engine.stream().synchronize()?;
            }
        }

        // Replay: feed the token, launch, read the logits, mirror the host bookkeeping.
        {
            let _main = e.gpu.enter_main()?;
            e.set_u32_one(&mut state.token_d, token)?;
            e.set_i32_one(&mut state.pos_d, pos as i32)?;
        }
        let t_launch = tg_timing.then(std::time::Instant::now);
        graph.launch(e)?;
        let t_book = tg_timing.then(std::time::Instant::now);
        // Host bookkeeping OVERLAPS the replay (nsys 2026-08-21: the 45-layer txn loop was
        // 5.4ms/token of inter-token gap when it ran after the sync). Host-only work except
        // the local len_d set, which is stream-ordered AFTER the graph on e's stream. On a
        // replay error the counters are already advanced — acceptable: the decode aborts.
        for il in 0..n_layers {
            let tp_kv = cache.tp_kv[il].as_mut().expect("eligibility checked above");
            let transaction = tp_kv.begin_transaction()?;
            let fa = match &self.layers[il].mixer {
                Mixer::Full(fa) => fa,
                _ => return Err("step35 token graph expects full-attention layers".into()),
            };
            let tp = fa
                .step_tp_qkv
                .as_ref()
                .ok_or("step35 token graph lost its TP state")?;
            // Bookkeeping-only txn (external appends; mirror sets skipped — the in-graph
            // incs own the counters). Shards unused.
            let empty: [CudaSlice<f32>; 0] = [];
            tp.runtime.append_tp_kv_transaction_inner(
                tp_kv,
                transaction,
                &empty,
                &empty,
                1,
                true,
            )?;
            tp.runtime
                .commit_tp_kv_transaction_external(tp_kv, transaction, 1)?;
            // Local shadow: lengths advance (v1 keeps contents stale under the door).
            if let Some(local) = cache.kv[il].as_mut() {
                local.len = pos + 1;
                let _main = e.gpu.enter_main()?;
                e.set_i32_one(&mut local.len_d, (pos + 1) as i32)?;
            }
        }
        cache.pos = pos + 1;
        let t_sync = tg_timing.then(std::time::Instant::now);
        let (logits, h_seed) = {
            let _main = e.gpu.enter_main()?;
            e.stream().synchronize()?;
            (e.dtoh(&state.logits_stage)?, e.clone_dtod(&state.x)?)
        };
        if let (Some(f), Some(l), Some(b), Some(sy)) = (t_fence, t_launch, t_book, t_sync) {
            use std::sync::atomic::{AtomicU64, Ordering};
            static NS: [AtomicU64; 5] = [
                AtomicU64::new(0),
                AtomicU64::new(0),
                AtomicU64::new(0),
                AtomicU64::new(0),
                AtomicU64::new(0),
            ];
            static CALLS: AtomicU64 = AtomicU64::new(0);
            let now = std::time::Instant::now();
            NS[0].fetch_add((l - f).as_nanos() as u64, Ordering::Relaxed); // fence+set
            NS[1].fetch_add((b - l).as_nanos() as u64, Ordering::Relaxed); // launch call
            NS[2].fetch_add((sy - b).as_nanos() as u64, Ordering::Relaxed); // bookkeeping
            NS[3].fetch_add((now - sy).as_nanos() as u64, Ordering::Relaxed); // sync+dtoh
            NS[4].fetch_add((now - f).as_nanos() as u64, Ordering::Relaxed); // total
            let calls = CALLS.fetch_add(1, Ordering::Relaxed) + 1;
            if calls.is_multiple_of(100) {
                let avg = |i: usize| NS[i].load(Ordering::Relaxed) as f64 / calls as f64 / 1e3;
                eprintln!(
                    "[tg-timing] calls={calls} fence_us={:.0} launch_us={:.0} book_us={:.0} \
                     syncdtoh_us={:.0} total_us={:.0}",
                    avg(0),
                    avg(1),
                    avg(2),
                    avg(3),
                    avg(4)
                );
            }
        }
        // MEMRA_TG_PROBE_LAYER diagnostics: append the captured layer-K probes.
        if std::env::var("MEMRA_TG_PROBE_LAYER").is_ok() {
            use std::io::Write;
            let (pm, px) = {
                let _main = e.gpu.enter_main()?;
                (e.dtoh(&state.probe_mixed)?, e.dtoh(&state.probe_x)?)
            };
            for (path, data) in [
                ("/root/tg-probe-mixed.bin", &pm),
                ("/root/tg-probe-x.bin", &px),
            ] {
                let mut fo = std::fs::OpenOptions::new()
                    .create(true)
                    .append(true)
                    .open(path)?;
                for v in data {
                    fo.write_all(&v.to_le_bytes())?;
                }
            }
        }
        // MEMRA_DUMP_HN twin of the eager tail's dump (same format: appended raw LE f32 rows)
        // so a graph arm and an eager arm produce position-aligned pre-head hidden streams.
        if let Ok(path) = std::env::var("MEMRA_DUMP_HN") {
            let hh = {
                let _main = e.gpu.enter_main()?;
                e.dtoh(&state.hn)?
            };
            use std::io::Write;
            let mut fo = std::fs::OpenOptions::new()
                .create(true)
                .append(true)
                .open(path)?;
            for v in &hh {
                fo.write_all(&v.to_le_bytes())?;
            }
        }
        // MEMRA_STEP_TP_GRAPH_DEBUG=1: per-token device-counter dump (drift hunts). One dtoh
        // per rank per token; diagnostics only.
        if std::env::var("MEMRA_STEP_TP_GRAPH_DEBUG").as_deref() == Ok("1") {
            for il in [0usize, 1, 44] {
                let tp_kv = cache.tp_kv[il].as_ref().expect("eligibility checked above");
                let host_len = tp_kv.staged_len();
                let fa = match &self.layers[il].mixer {
                    Mixer::Full(fa) => fa,
                    _ => continue,
                };
                let tp = fa
                    .step_tp_qkv
                    .as_ref()
                    .ok_or("step35 token graph lost its TP state")?;
                for rank in 0..tp.runtime.devices().len() {
                    let engine = tp
                        .runtime
                        .rank_engine(rank)
                        .ok_or("step35 token graph lost a rank engine")?;
                    let rank_cache = tp_kv.rank(rank).ok_or("debug rank cache missing")?;
                    let _main = engine.gpu.enter_main()?;
                    engine.stream().synchronize()?;
                    let len_d = engine.dtoh_i32_one(rank_cache.len_d())?;
                    let base_d = match rank_cache.base_d() {
                        Some(b) => engine.dtoh_i32_one(b)?,
                        None => -1,
                    };
                    eprintln!(
                        "[graph-debug] pos={pos} il={il} rank={rank} host_len={host_len} \
                         len_d={len_d} base_d={base_d}"
                    );
                }
            }
        }
        Ok(Some((logits, h_seed)))
    }

    /// MEMRA_HEAD_SPLIT worker: dev0 computes lm-head rows [0, half), rank1 computes
    /// [half, n_vocab) concurrently on its otherwise-idle tail, and the halves concatenate
    /// on e — bit-identical per logit to the single-device matvec. Process-static workspace
    /// (SHEXP_WS pattern) pinned by the head tensor pointer; rank1 holds a one-time 0.5GB
    /// replica of its row half. Returns None when ineligible (no bf16 head / no rank1).
    pub(crate) fn head_split_matvec(
        &self,
        e: &Engine,
        hn: &CudaSlice<f32>,
    ) -> Result<Option<Vec<f32>>, Box<dyn std::error::Error>> {
        if self.head_split_fill_device(e, hn)?.is_none() {
            return Ok(None);
        }
        let guard = HEAD_SPLIT_WS
            .lock()
            .map_err(|_| "head split lock is poisoned")?;
        let ws = guard.as_ref().expect("filled above");
        let _main = e.gpu.enter_main()?;
        Ok(Some(e.dtoh(&ws.logits_e)?))
    }

    /// Compute body of the split head: arms the replica + staging on first use, then fills
    /// the persistent full-logits row (e's half by view matvec, rank1's half by raw P2P
    /// push) and orders e's stream behind it. None = ineligible.
    fn head_split_fill_device(
        &self,
        e: &Engine,
        hn: &CudaSlice<f32>,
    ) -> Result<Option<()>, Box<dyn std::error::Error>> {
        use cudarc::driver::DevicePtr;
        let crate::model::GpuTensor::FloatBf16 { data: head, .. } = &self.output else {
            return Ok(None);
        };
        let Some(rank1) = self.layers.first().and_then(|l| match &l.mixer {
            Mixer::Full(fa) => fa
                .step_tp_qkv
                .as_ref()
                .and_then(|tp| tp.runtime.rank_engine(1)),
            _ => None,
        }) else {
            return Ok(None);
        };
        let n_embd = self.cfg.n_embd as usize;
        let n_vocab = self.cfg.n_vocab as usize;
        let half = n_vocab / 2;
        let mut guard = HEAD_SPLIT_WS
            .lock()
            .map_err(|_| "head split lock is poisoned")?;
        let pin = {
            let _main = e.gpu.enter_main()?;
            let stream = e.stream();
            let (ptr, _g) = head.device_ptr(&stream);
            ptr
        };
        if guard.as_ref().is_none_or(|ws| ws.pin != pin) {
            // One-time: upload rank1's row half + persistent staging.
            let hi_rows = n_vocab - half;
            let (w1, hn1, y1, ev_done) = {
                let _r1 = rank1.gpu.enter_main()?;
                (
                    rank1.alloc_u8_uninit(hi_rows * n_embd * 2)?,
                    rank1.htod(&vec![0.0f32; n_embd])?,
                    rank1.htod(&vec![0.0f32; hi_rows])?,
                    rank1.ctx().new_event(None)?,
                )
            };
            {
                use cudarc::driver::sys;
                let src = pin + (half * n_embd * 2) as u64;
                let dst = {
                    let _r1 = rank1.gpu.enter_main()?;
                    let rstream = rank1.stream();
                    let (d, _g) = w1.device_ptr(&rstream);
                    d
                };
                let _r1 = rank1.gpu.enter_main()?;
                let r = unsafe {
                    sys::cuMemcpyAsync(
                        dst as sys::CUdeviceptr,
                        src as sys::CUdeviceptr,
                        hi_rows * n_embd * 2,
                        rank1.stream().cu_stream() as sys::CUstream,
                    )
                };
                if r != sys::CUresult::CUDA_SUCCESS {
                    return Err(format!("head split replica upload: {r:?}").into());
                }
                rank1.stream().synchronize()?;
            }
            let (logits_e, ev_hn) = {
                let _main = e.gpu.enter_main()?;
                (e.htod(&vec![0.0f32; n_vocab])?, e.ctx().new_event(None)?)
            };
            let (raw_hn1, raw_y1) = {
                let _r1 = rank1.gpu.enter_main()?;
                let rstream = rank1.stream();
                let (a, _g0) = hn1.device_ptr(&rstream);
                let (b, _g1) = y1.device_ptr(&rstream);
                (a, b)
            };
            let raw_logits_hi = {
                let _main = e.gpu.enter_main()?;
                let stream = e.stream();
                let (l, _g) = logits_e.device_ptr(&stream);
                l + (half * 4) as u64
            };
            *guard = Some(HeadSplit {
                pin,
                w1,
                hn1,
                y1,
                logits_e,
                ev_hn,
                ev_done,
                raw_hn1,
                raw_y1,
                raw_logits_hi,
                samp: None,
            });
        }
        let ws = guard.as_mut().expect("armed above");
        let hi_rows = n_vocab - half;
        // e: signal hn ready; rank1: pull hn, matvec its half, push the logits half back.
        let raw_hn = {
            let _main = e.gpu.enter_main()?;
            let stream = e.stream();
            let (h, _g) = hn.device_ptr(&stream);
            ws.ev_hn.record(&stream)?;
            h
        };
        {
            let _r1 = rank1.gpu.enter_main()?;
            rank1.stream().wait(&ws.ev_hn)?;
            crate::tp::raw_copy_bytes(ws.raw_hn1, raw_hn, n_embd * 4, rank1)?;
            let HeadSplit { w1, hn1, y1, .. } = &mut *ws;
            rank1.matvec_bf16_into(w1, hn1, y1, n_embd, hi_rows)?;
            crate::tp::raw_copy_bytes(ws.raw_logits_hi, ws.raw_y1, hi_rows * 4, rank1)?;
            ws.ev_done.record(&rank1.stream())?;
        }
        {
            let _main = e.gpu.enter_main()?;
            let head_lo = head.slice(0..half * n_embd * 2);
            let HeadSplit { logits_e, .. } = &mut *ws;
            // Writes rows [0, half) of logits_e; rank1's raw push fills [half, n_vocab).
            e.matvec_bf16_view_into(&head_lo, hn, logits_e, n_embd, half)?;
            e.stream().wait(&ws.ev_done)?;
            Ok(Some(()))
        }
    }

    /// Device twin of `head_split_matvec` for the chain: fills the persistent full-logits
    /// row exactly like the host variant (identical halves, identical concat) and runs the
    /// device argmax into `token_d` — NO host readback. Returns false when the split is
    /// ineligible (caller falls back to the plain matmul head).
    pub(crate) fn head_split_argmax_device(
        &self,
        e: &Engine,
        hn: &CudaSlice<f32>,
        token_d: &mut CudaSlice<u32>,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        if self.head_split_fill_device(e, hn)?.is_none() {
            return Ok(false);
        }
        let n_vocab = self.cfg.n_vocab as usize;
        let guard = HEAD_SPLIT_WS
            .lock()
            .map_err(|_| "head split lock is poisoned")?;
        let ws = guard.as_ref().expect("filled above");
        let _main = e.gpu.enter_main()?;
        e.argmax_token_device_into(&ws.logits_e, token_d, n_vocab)?;
        Ok(true)
    }

    /// SAMPLED twin of `head_split_argmax_device`. The split head already materializes the
    /// full concatenated row in `ws.logits_e`, so sampling does NOT have to give up HEAD_SPLIT
    /// — it draws from that row on device (filter thresholds, Gumbel perturbation, argmax)
    /// exactly as the serve tick does. Worth ~0.2 ms/token: the post-W8 census had the
    /// unsplit q8 head at ~364 us against ~82 us per half.
    pub(crate) fn head_split_sample_device(
        &self,
        e: &Engine,
        hn: &CudaSlice<f32>,
        token_d: &mut CudaSlice<u32>,
        samp: &crate::decode_batch::DevSamp,
        ctr: u32,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        if self.head_split_fill_device(e, hn)?.is_none() {
            return Ok(false);
        }
        let n_vocab = self.cfg.n_vocab as usize;
        let guard = HEAD_SPLIT_WS
            .lock()
            .map_err(|_| "head split lock is poisoned")?;
        let mut guard = guard;
        let ws = guard.as_mut().expect("filled above");
        let _main = e.gpu.enter_main()?;
        if ws.samp.is_none() {
            ws.samp = Some(SampScratch {
                pb: e.zeros(n_vocab)?,
                th: e.zeros(1)?,
                z: e.zeros(1)?,
                mx: e.zeros(1)?,
                rows: e.htod_i32(&[0i32])?,
            });
        }
        let filtered = samp.top_k > 0 || samp.top_p < 1.0 || samp.min_p > 0.0;
        let HeadSplit {
            logits_e,
            samp: scratch,
            ..
        } = &mut *ws;
        let sc = scratch.as_mut().expect("armed above");
        if filtered {
            e.filter_stats(
                logits_e, n_vocab, &sc.rows, &mut sc.th, &mut sc.z, &mut sc.mx, n_vocab, 1,
                samp.temp, samp.top_k, samp.top_p, samp.min_p,
            )?;
            let SampScratch { pb, th, mx, .. } = sc;
            e.gumbel_perturb_filtered_col(
                logits_e, 0, pb, n_vocab, samp.seed, ctr, samp.temp, mx, th, 0,
            )?;
        } else {
            e.gumbel_perturb_col(logits_e, 0, &mut sc.pb, n_vocab, samp.seed, ctr, samp.temp)?;
        }
        e.argmax_token_device_col(&sc.pb, 0, n_vocab, token_d, 0)?;
        Ok(true)
    }

    /// One-per-chunk host readback of the persistent full-logits row (the LAST chain
    /// token's row).
    pub(crate) fn head_split_logits_dtoh(
        &self,
        e: &Engine,
    ) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
        let guard = HEAD_SPLIT_WS
            .lock()
            .map_err(|_| "head split lock is poisoned")?;
        let ws = guard.as_ref().ok_or("head split logits not armed")?;
        let _main = e.gpu.enter_main()?;
        e.dtoh(&ws.logits_e)
    }

    /// Chunk-loop replay (F-lite, MEMRA_STEP_TP_GRAPH_LOOP): run up to `k_target` greedy
    /// tokens as back-to-back graph launches chained through the in-graph tail argmax —
    /// ONE host sync, ONE history readback, and ONE bulk KV transaction per layer per
    /// chunk. Returns None when ineligible (caller falls back to the per-token path).
    /// The chunk consumes `token` (already emitted by the caller) as launch 0's input and
    /// returns the ids the chain argmax'd (hist[0..k]) plus the LAST launch's logits row —
    /// hist[k-1] is exactly argmax(logits), so the caller emits hist[..k-1] and lets its
    /// own loop re-derive hist[k-1] from the returned row.
    #[allow(clippy::type_complexity)] // allow: one-shot composite type; naming it would hide the shape that matters at the call site
    pub fn step35_token_graph_chunk(
        &self,
        e: &Engine,
        token: u32,
        k_target: usize,
        cache: &mut Cache,
    ) -> Result<Option<(Vec<u32>, Vec<f32>)>, Box<dyn std::error::Error>> {
        if !self.uses_sliding_gated_moe_program()
            || !crate::tp::step_tp_graph_enabled()?
            || !crate::tp::step_tp_dcw_enabled()?
            || !crate::tp::step_tp_qkv_fused_enabled()?
            || !crate::tp::step_tp_dev_router_enabled()?
            || !crate::tp::step_nvfp4_dev_routes_enabled()?
        {
            return Ok(None);
        }
        // GRAPH-LAUNCH HEADROOM GUARD: same guard, same eager twin as
        // `step35_token_graph_step` (the chunk is that step replayed k times).
        if !crate::spec::graph_launch_headroom_ok(e) {
            static NOTED: std::sync::Once = std::sync::Once::new();
            NOTED.call_once(|| crate::spec::graph_replay_suspended_note("step-tp-token"));
            return Ok(None);
        }
        let n_layers = self.layers.len();
        let pos = cache.pos;
        let staged_next = pos + 1;
        if staged_next < 96 {
            return Ok(None);
        }
        // Bucket for the FIRST token; the chunk must not cross the bucket boundary (the
        // exec's n_splits ladder must match eager per depth).
        let (fa_vec, n_splits) = e.fa_geom_eager(staged_next, 128, 8, false);
        if !fa_vec {
            return Ok(None);
        }
        let sp = crate::fa_split_keys(staged_next, 8);
        let bucket_max = (n_splits * sp).max(staged_next);
        let to_boundary = bucket_max.saturating_sub(staged_next) + 1;
        let mut k = k_target.min(to_boundary).min(16);
        if k < 2 {
            return Ok(None);
        }
        // Every layer must contiguous-append all k rows (no rebase inside the chunk).
        for il in 0..n_layers {
            let Some(tp_kv) = cache.tp_kv[il].as_ref() else {
                return Ok(None);
            };
            while k >= 2 && tp_kv.peek_append_ring(k)?.1 {
                k -= 1;
            }
            if k < 2 {
                return Ok(None);
            }
        }

        let mut state_guard = self
            .step35_token_graph
            .lock()
            .map_err(|_| "step35 token graph lock is poisoned")?;
        let Some(state) = state_guard.as_mut() else {
            return Ok(None); // per-token path arms the state + stages first
        };
        if state.graphs.is_empty() {
            return Ok(None);
        }
        {
            let (b, g) = state.graphs.first_mut().expect("checked above");
            if *b != bucket_max {
                g.retarget_bucket(bucket_max)?;
                *b = bucket_max;
            }
        }
        let graph = state.graphs.first().map(|(_, g)| g).expect("checked above");

        // Rank-stream fence (eager stragglers; see the per-token path).
        {
            let fa0 = match &self.layers[0].mixer {
                Mixer::Full(fa) => fa,
                _ => return Err("step35 token graph expects full-attention layers".into()),
            };
            let tp0 = fa0
                .step_tp_qkv
                .as_ref()
                .ok_or("step35 token graph lost its TP state")?;
            for rank in 0..tp0.runtime.devices().len() {
                let engine = tp0
                    .runtime
                    .rank_engine(rank)
                    .ok_or("step35 token graph lost a rank engine")?;
                let _main = engine.gpu.enter_main()?;
                engine.stream().synchronize()?;
            }
        }

        // Seed the chain and fire k launches back-to-back: launch i embeds the token the
        // PREVIOUS launch's tail argmax wrote (launch 0 embeds the host-seeded `token`).
        {
            let _main = e.gpu.enter_main()?;
            e.set_u32_one(&mut state.token_d, token)?;
            e.set_i32_one(&mut state.pos_d, pos as i32)?;
            e.set_i32_one(&mut state.hist_idx, 0)?;
        }
        for _ in 0..k {
            graph.launch(e)?;
        }
        // Bulk host bookkeeping overlaps the replays: one k-row txn per layer.
        for il in 0..n_layers {
            let tp_kv = cache.tp_kv[il].as_mut().expect("eligibility checked above");
            let transaction = tp_kv.begin_transaction()?;
            let fa = match &self.layers[il].mixer {
                Mixer::Full(fa) => fa,
                _ => return Err("step35 token graph expects full-attention layers".into()),
            };
            let tp = fa
                .step_tp_qkv
                .as_ref()
                .ok_or("step35 token graph lost its TP state")?;
            let empty: [CudaSlice<f32>; 0] = [];
            tp.runtime.append_tp_kv_transaction_inner(
                tp_kv,
                transaction,
                &empty,
                &empty,
                k,
                true,
            )?;
            tp.runtime
                .commit_tp_kv_transaction_external(tp_kv, transaction, k)?;
            if let Some(local) = cache.kv[il].as_mut() {
                local.len = pos + k;
                let _main = e.gpu.enter_main()?;
                e.set_i32_one(&mut local.len_d, (pos + k) as i32)?;
            }
        }
        cache.pos = pos + k;
        let (hist, logits) = {
            let _main = e.gpu.enter_main()?;
            e.stream().synchronize()?;
            (e.dtoh_u32(&state.token_hist)?, e.dtoh(&state.logits_stage)?)
        };
        Ok(Some((hist[..k].to_vec(), logits)))
    }
}

impl HybridModel {
    /// Capture the whole-token parent for one fa bucket. Capture executes nothing; the
    /// section closures issue the SAME calls the eager dcw path runs (bit-proven), with the
    /// per-token operands living at the fixed stage addresses. Groups: the two rank sections
    /// of each phase fork in parallel and merge into the following root section.
    #[allow(clippy::too_many_arguments)]
    fn step35_token_graph_build(
        &self,
        e: &Engine,
        cache: &mut Cache,
        state: &mut Step35TokenGraphState,
        bucket_max: usize,
    ) -> Result<(), Box<dyn std::error::Error>> {
        use cudarc::driver::DevicePtr;
        let n_embd = self.cfg.n_embd as usize;
        let eps = self.cfg.rms_eps;
        let n_layers = self.layers.len();
        let started = std::time::Instant::now();
        if !crate::router_kernel_on() {
            return Err(
                "step35 token graph requires the router kernel (MEMRA_ROUTER_KERNEL=0)".into(),
            );
        }
        if !Engine::bf16_mmv_on() || !n_embd.is_multiple_of(8) {
            return Err("step35 token graph requires MEMRA_BF16_MMV bf16-resident matvecs".into());
        }

        // Device embed table (the spec/graph lanes' lazily-uploaded copy).
        let embd_gpu = self
            .embd_gpu_try(e)
            .ok_or("step35 token graph could not upload the device embed table")?;
        let embd_qtype = match self.embd.ggml_type {
            memra_gguf::GgmlType::BF16 => crate::QT_BF16,
            memra_gguf::GgmlType::Q8_0 => crate::QT_Q8_0,
            other => return Err(format!("token graph embed dtype {other:?} unhandled").into()),
        };
        let embd_row_bytes = self.embd.raw.len() / self.cfg.n_vocab as usize;

        // Fixed-stage pointers the sections reference.
        let (p_mixed, p_kshadow, p_vshadow) = {
            let _main = e.gpu.enter_main()?;
            let stream = e.stream();
            let (a, _g) = state.mixed_stage.device_ptr(&stream);
            let (b, _g) = state.k_shadow_stage.device_ptr(&stream);
            let (c, _g) = state.v_shadow_stage.device_ptr(&stream);
            (a, b, c)
        };

        crate::tp::token_graph_build_begin()?;
        let mut group_id: u32 = 0;
        for il in 0..n_layers {
            let layer = &self.layers[il];
            let fa = match &layer.mixer {
                Mixer::Full(fa) => fa,
                _ => return Err("step35 token graph expects full-attention layers".into()),
            };
            let tp = fa
                .step_tp_qkv
                .as_ref()
                .ok_or("step35 token graph lost its TP state")?;
            let attention = tp
                .attention
                .as_ref()
                .ok_or("step35 token graph lost its attention aux")?;
            let geometry = self.step35_geom(il);
            let window = geometry.window.map(|w| w as usize);
            let head_dim = geometry.head_dim_k as usize;
            let heads = geometry.n_head as usize;
            let kv_heads = geometry.n_head_kv as usize;
            let ranks = tp.runtime.devices().len();
            let local_heads = heads / ranks;
            let local_kv_heads = kv_heads / ranks;
            let layer_bucket = window.map(|w| bucket_max.min(w)).unwrap_or(bucket_max);
            let use_gate_shards =
                attention.gate_shards.is_some() || attention.gate_shards_bf16.is_some();
            if !use_gate_shards {
                return Err("step35 token graph requires the fused gate shards".into());
            }

            let ws_index = tp
                .runtime
                .decode_v2_ensure(e, &tp.q, &tp.k, &tp.v, &tp.o, heads)?;
            let ws_mutex = tp.runtime.decode_v2_workspace();
            let mut ws_guard = ws_mutex
                .lock()
                .map_err(|_| "step TP decode v2 workspace lock is poisoned")?;
            let ws = ws_guard
                .get_mut(ws_index)
                .ok_or("step TP decode v2 workspace missing after ensure")?;
            tp.runtime
                .decode_v2_arm_token_mirrors(ws, p_mixed, (p_kshadow, p_vshadow))?;
            let mut rope_freqs = Vec::with_capacity(ranks);
            for rank in 0..ranks {
                let engine = tp
                    .runtime
                    .rank_engine(rank)
                    .ok_or("step35 token graph lost a rank engine")?;
                rope_freqs.push(if geometry.rope_factors {
                    self.step35_aux
                        .as_ref()
                        .and_then(|aux| aux.rope_freqs(engine))
                } else {
                    None
                });
            }
            let gate_shards_arg = if let Some(shards) = attention.gate_shards.as_deref() {
                Some(crate::tp::StepTpGateShards::F32(shards))
            } else {
                attention
                    .gate_shards_bf16
                    .as_deref()
                    .map(crate::tp::StepTpGateShards::Bf16)
            };

            // ---- E1: embed (layer 0) / attn norm into h_stage + pos copy ----
            let decode_input = attention
                .decode_input
                .as_ref()
                .ok_or("step35 token graph requires the replicated decode input")?;
            let mut decode_input = decode_input
                .lock()
                .map_err(|_| "replicated decode input lock is poisoned")?;
            // Stage arming happens through the eager stage flow once; require it here.
            if ws.h_stage.is_none() {
                return Err(
                    "step35 token graph requires the stage flow armed (run eager dcw first)".into(),
                );
            }
            {
                let state_x = &mut state.x;
                let token_d = &state.token_d;
                let pos_d = &state.pos_d;
                crate::tp::graph_section(e, None, || {
                    let _main = e.gpu.enter_main()?;
                    if il == 0 {
                        e.embed_gather_device_into(
                            embd_gpu,
                            token_d,
                            state_x,
                            n_embd,
                            embd_qtype,
                            embd_row_bytes,
                        )?;
                    }
                    {
                        let h_stage = ws.h_stage.as_mut().expect("stage armed checked above");
                        e.rms_norm(
                            state_x,
                            layer.attn_norm.float_data(),
                            h_stage,
                            n_embd,
                            1,
                            eps,
                        )?;
                    }
                    {
                        let pos_stage = ws.pos_stage.as_mut().expect("stage armed above");
                        let mut dst = pos_stage.slice_mut(0..1);
                        e.stream().memcpy_dtod(&pos_d.slice(0..1), &mut dst)?;
                    }
                    Ok(())
                })?;
            }

            // ---- R0/R1 (parallel): projections + dcw attention interior ----
            group_id += 1;
            for rank in 0..ranks {
                let engine = tp
                    .runtime
                    .rank_engine(rank)
                    .ok_or("step35 token graph lost a rank engine")?;
                {
                    // fa partial pool must reach the RUN CEILING before capture — an
                    // in-capture grow is a mem node (child graphs reject those), and the
                    // retarget path (increment C) widens the baked memsets up to the ceiling
                    // without moving the pool pointers. Two ensures cover both sp rungs.
                    let ceiling = window
                        .map(|w| cache.max_ctx.min(w))
                        .unwrap_or(cache.max_ctx);
                    let _main = engine.gpu.enter_main()?;
                    engine.fa_dcw_pool_ensure(
                        head_dim,
                        local_heads,
                        local_kv_heads,
                        ceiling.min(2048),
                    )?;
                    engine.fa_dcw_pool_ensure(head_dim, local_heads, local_kv_heads, ceiling)?;
                    engine.fa_dcw_pool_ensure(
                        head_dim,
                        local_heads,
                        local_kv_heads,
                        layer_bucket,
                    )?;
                }
                let runtime = &tp.runtime;
                let q_norm = &attention.q_norm;
                let k_norm = &attention.k_norm;
                let gate_ref = gate_shards_arg.as_ref();
                crate::tp::graph_section(engine, Some(group_id), || {
                    runtime.decode_v2_input_qkv_rank(
                        ws,
                        &state.pos_d,
                        &mut decode_input,
                        &tp.q,
                        &tp.k,
                        &tp.v,
                        q_norm,
                        k_norm,
                        head_dim,
                        geometry.n_rot as usize,
                        geometry.rope_base,
                        &rope_freqs,
                        eps,
                        gate_ref,
                        true,
                        true,
                        false,
                        rank,
                        None,
                    )?;
                    // Merged dcw interior at the BUCKET geometry (one-partition law makes the
                    // replayed values track the live counters).
                    let distributed = cache.tp_kv[il]
                        .as_mut()
                        .ok_or("step35 token graph lost a TP cache")?;
                    let (kv_dim_k, kv_dim_v) = (distributed.kv_dim_k(), distributed.kv_dim_v());
                    let (ktb, vtb) = (distributed.k_tok_bytes(), distributed.v_tok_bytes());
                    let capacity = distributed.physical_capacity();
                    {
                        let rank_cache = distributed
                            .rank_mut(rank)
                            .ok_or("step35 token graph lost a rank cache")?;
                        let (k_plane, v_plane, len_d, base_d) =
                            rank_cache.planes_and_counters_mut();
                        engine.append_kv_quantized_dcw(
                            &ws.k[rank],
                            &ws.v_raw[rank],
                            k_plane,
                            v_plane,
                            len_d,
                            base_d,
                            kv_dim_k,
                            kv_dim_v,
                            ktb,
                            vtb,
                        )?;
                    }
                    {
                        let rank_cache = distributed
                            .rank_mut(rank)
                            .ok_or("step35 token graph lost a rank cache")?;
                        engine.inc_i32(rank_cache.len_d_mut())?;
                    }
                    let rank_cache = distributed
                        .rank(rank)
                        .ok_or("step35 token graph lost a rank cache")?;
                    let k_ring = engine.view_u8_range(rank_cache.k(), 0, capacity * ktb);
                    let v_ring = engine.view_u8_range(rank_cache.v(), 0, capacity * vtb);
                    // Graph build keeps the UNFUSED combine + gate pair: the bucket
                    // retarget addresses combine's nsp at arg slot 6, and the fused
                    // combine_gate kernel shifts it to 7 — bit-identical either way, so
                    // only the eager arm takes FUSION #2d.
                    engine.fa_decode_dcw(
                        &ws.q[rank],
                        &k_ring,
                        &v_ring,
                        &mut ws.attn_out[rank],
                        head_dim,
                        local_heads,
                        local_kv_heads,
                        rank_cache.len_d(),
                        rank_cache.base_d(),
                        window.unwrap_or(0),
                        layer_bucket,
                        geometry.attention_scale(),
                        ktb,
                        vtb,
                        None,
                    )?;
                    engine.attn_head_gate(
                        &ws.attn_out[rank],
                        &ws.gate[rank],
                        &mut ws.gated[rank],
                        None,
                        head_dim,
                        local_heads,
                        1,
                    )?;
                    runtime.decode_v2_finish_rank_partial(ws, &tp.o, true, rank)?;
                    Ok(())
                })?;
            }

            // ---- ROOT: combine + shadows + e-mirrors ----
            {
                let root = tp
                    .runtime
                    .rank_engine(0)
                    .ok_or("step35 token graph lost the root engine")?;
                let runtime = &tp.runtime;
                crate::tp::graph_section(root, None, || runtime.decode_v2_finish_root_fused(ws))?;
            }
            drop(ws_guard);
            drop(decode_input);

            let probe_layer: Option<usize> = std::env::var("MEMRA_TG_PROBE_LAYER")
                .ok()
                .and_then(|v| v.parse().ok());
            if probe_layer == Some(il) {
                let Step35TokenGraphState {
                    mixed_stage,
                    probe_mixed,
                    ..
                } = &mut *state;
                crate::tp::graph_section(e, None, || {
                    let _main = e.gpu.enter_main()?;
                    let mut dst = probe_mixed.slice_mut(0..n_embd);
                    e.stream()
                        .memcpy_dtod(&mixed_stage.slice(0..n_embd), &mut dst)?;
                    Ok(())
                })?;
            }

            // ---- FFN half ----
            match &layer.ffn {
                crate::hybrid::Ffn::Dense {
                    ffn_gate,
                    ffn_up,
                    ffn_down,
                } => {
                    let n_ff = ffn_gate.out_features();
                    let lim = self.cfg.clamp_shexp_at(il as u32);
                    // Alloc-free inline of ffn_swiglu_decode's bf16 tail: dual gate/up matvec is
                    // bit-identical per row to the two matmul-dispatched matvec_bf16 launches.
                    // A clamped dense layer would take eager's q8_1 branch instead -- refuse.
                    if lim.is_some() {
                        return Err("step35 token graph dense FFN with clamp unsupported".into());
                    }
                    let (wg_d, wu_d, wd_d) = match (ffn_gate, ffn_up, ffn_down) {
                        (
                            crate::model::GpuTensor::FloatBf16 { data: wg, .. },
                            crate::model::GpuTensor::FloatBf16 { data: wu, .. },
                            crate::model::GpuTensor::FloatBf16 { data: wd, .. },
                        ) => (wg, wu, wd),
                        _ => {
                            return Err(
                                "step35 token graph dense FFN requires bf16-resident weights"
                                    .into(),
                            );
                        }
                    };
                    crate::tp::graph_section(e, None, || {
                        let _main = e.gpu.enter_main()?;
                        let Step35TokenGraphState {
                            x,
                            x1,
                            mixed_stage,
                            dense_z,
                            dense_gate,
                            dense_up,
                            dense_act,
                            sh_stage,
                            ..
                        } = &mut *state;
                        e.add_rms_norm(
                            x,
                            mixed_stage,
                            layer.post_attn_norm.float_data(),
                            x1,
                            dense_z,
                            n_embd,
                            1,
                            eps,
                        )?;
                        // TWO SINGLE matvecs, not the dual: eager dense rides two
                        // matmul-dispatched matvec_bf16 launches; the dual twin measured a
                        // ~2e-9 residual difference here (token-graph bisection, 2026-08-21).
                        e.matvec_bf16_into(wg_d, dense_z, dense_gate, n_embd, n_ff)?;
                        e.matvec_bf16_into(wu_d, dense_z, dense_up, n_embd, n_ff)?;
                        Self::ffn_act_lim(
                            e, &self.cfg, dense_gate, dense_up, 1.0, 1.0, lim, dense_act, n_ff,
                        )?;
                        e.matvec_bf16_into(wd_d, dense_act, sh_stage, n_ff, n_embd)?;
                        e.add(x1, sh_stage, x, n_embd)?;
                        Ok(())
                    })?;
                }
                crate::hybrid::Ffn::Moe(m) => {
                    let moe = self
                        .cfg
                        .moe
                        .as_ref()
                        .ok_or("step35 token graph needs moe cfg")?;
                    let n_expert = moe.expert_count as usize;
                    let n_used = moe.expert_used_count as usize;
                    let sigmoid = self
                        .cfg
                        .sigmoid_router()
                        .ok_or("step35 token graph needs the sigmoid router")?;
                    let step_tp = m
                        .step_tp
                        .as_ref()
                        .ok_or("step35 token graph needs TP experts")?;
                    let bank = match &step_tp.experts {
                        crate::hybrid::StepTpExpertBank::Nvfp4(bank) => bank,
                        _ => return Err("step35 token graph needs the NVFP4 bank".into()),
                    };
                    let routes_ws_mutex = bank.device_workspace_handle();
                    let mut routes_guard = routes_ws_mutex
                        .lock()
                        .map_err(|_| "routes workspace lock is poisoned")?;
                    let routes_ws = routes_guard
                        .as_mut()
                        .ok_or("step35 token graph requires the routes workspace warmed")?;
                    routes_ws.arm_stages(e, bank.input_width, n_used)?;
                    step_tp.runtime.routes_arm_raw(bank, routes_ws)?;
                    let p_z = {
                        let root = step_tp
                            .runtime
                            .rank_engine(0)
                            .ok_or("routes root engine missing")?;
                        let _main = root.gpu.enter_main()?;
                        let stream = root.stream();
                        let in_stage = routes_ws
                            .in_stage_handle()
                            .ok_or("routes in stage not armed")?;
                        let (a, _g) = in_stage.device_ptr(&stream);
                        a
                    };
                    let local_out = bank.expert_width / ranks;

                    // ---- E2: post-attn norm into the routes in-stage + router + staging ----
                    crate::tp::graph_section(e, None, || {
                        let _main = e.gpu.enter_main()?;
                        {
                            let in_stage = routes_ws
                                .in_stage_mut()
                                .ok_or("routes in stage not armed")?;
                            let Step35TokenGraphState {
                                x, x1, mixed_stage, ..
                            } = &mut *state;
                            e.add_rms_norm(
                                x,
                                mixed_stage,
                                layer.post_attn_norm.float_data(),
                                x1,
                                in_stage,
                                n_embd,
                                1,
                                eps,
                            )?;
                        }
                        {
                            let z_ref = routes_ws
                                .in_stage_handle()
                                .ok_or("routes in stage not armed")?;
                            e.router_gemv_into(
                                m.gate_inp.float_data(),
                                z_ref,
                                &mut state.router_logits,
                                n_embd,
                                n_expert,
                                1,
                            )?;
                        }
                        let (sel_e, w_e) = routes_ws
                            .dev_route_e_mut()
                            .ok_or("routes staging not armed")?;
                        e.moe_router_sigmoid_topk_into(
                            &state.router_logits,
                            1,
                            n_expert,
                            n_used,
                            m.active_count(),
                            &m.exp_probs_b_dev,
                            &m.active_experts_dev,
                            sigmoid.0,
                            sigmoid.1,
                            sel_e,
                            w_e,
                        )?;
                        Ok(())
                    })?;

                    // ---- R0r/R1r (parallel): routes sweeps ----
                    group_id += 1;
                    for rank in 0..ranks {
                        let engine = step_tp
                            .runtime
                            .rank_engine(rank)
                            .ok_or("routes rank engine missing")?;
                        let runtime = &step_tp.runtime;
                        crate::tp::graph_section(engine, Some(group_id), || {
                            runtime.routes_rank_section(
                                bank,
                                routes_ws,
                                p_z,
                                local_out,
                                n_used,
                                step_tp.activation_limit,
                                rank,
                            )
                        })?;
                    }

                    // ---- ROOTr: combine into the out stage ----
                    {
                        let root = step_tp
                            .runtime
                            .rank_engine(0)
                            .ok_or("routes root engine missing")?;
                        let runtime = &step_tp.runtime;
                        crate::tp::graph_section(root, None, || {
                            runtime.routes_root_section(bank, routes_ws)
                        })?;
                    }

                    // ---- E3: shexp + add_shared onto the out stage + residual ----
                    // Alloc-free inline of moe_ffn_grouped_add_shared's bf16_dual arm (the arm
                    // eager takes under MEMRA_BF16_MMV, guarded at fn entry).
                    let lim_sh = self.cfg.clamp_shexp_at(il as u32);
                    let (wg_sh, wu_sh, wd_sh) = match (&m.gate_shexp, &m.up_shexp, &m.down_shexp) {
                        (
                            Some(crate::model::GpuTensor::FloatBf16 { data: wg, .. }),
                            Some(crate::model::GpuTensor::FloatBf16 { data: wu, .. }),
                            Some(crate::model::GpuTensor::FloatBf16 { data: wd, .. }),
                        ) => (wg, wu, wd),
                        _ => {
                            return Err(
                                "step35 token graph shexp requires bf16-resident weights".into()
                            );
                        }
                    };
                    let n_ff_sh = m
                        .gate_shexp
                        .as_ref()
                        .expect("matched Some above")
                        .out_features();
                    // No sigmoid gate on this model's shexp -> gate_sig stays at its 1.0
                    // init, reproducing eager's ones vector without a launch.
                    let gate_inp_shexp = m.gate_inp_shexp.as_ref();
                    crate::tp::graph_section(e, None, || {
                        let _main = e.gpu.enter_main()?;
                        let (z_ref, out_stage) = routes_ws
                            .in_and_out_stages_mut()
                            .ok_or("routes stages not armed")?;
                        let Step35TokenGraphState {
                            x,
                            x1,
                            sh_stage,
                            shexp_gate,
                            shexp_up,
                            shexp_act,
                            gate_sig,
                            ..
                        } = &mut *state;
                        e.matvec_bf16_dual_into(
                            wg_sh, wu_sh, z_ref, shexp_gate, shexp_up, n_embd, n_ff_sh,
                        )?;
                        Self::ffn_act_lim(
                            e, &self.cfg, shexp_gate, shexp_up, 1.0, 1.0, lim_sh, shexp_act,
                            n_ff_sh,
                        )?;
                        e.matvec_bf16_into(wd_sh, shexp_act, sh_stage, n_ff_sh, n_embd)?;
                        if let Some(gate_w) = gate_inp_shexp {
                            e.sigmoid_dot_rows_into(
                                z_ref,
                                gate_w.float_data(),
                                gate_sig,
                                n_embd,
                                1,
                            )?;
                        }
                        e.add_scaled_rows(sh_stage, gate_sig, out_stage, n_embd, 1)?;
                        e.add(x1, out_stage, x, n_embd)?;
                        Ok(())
                    })?;
                }
            }
            if probe_layer == Some(il) {
                let Step35TokenGraphState { x, probe_x, .. } = &mut *state;
                crate::tp::graph_section(e, None, || {
                    let _main = e.gpu.enter_main()?;
                    let mut dst = probe_x.slice_mut(0..n_embd);
                    e.stream().memcpy_dtod(&x.slice(0..n_embd), &mut dst)?;
                    Ok(())
                })?;
            }
        }

        // ---- Tail: output norm + head into the logits stage ----
        let head = match &self.output {
            crate::model::GpuTensor::FloatBf16 { data, .. } => data,
            _ => return Err("step35 token graph head requires the bf16-resident output".into()),
        };
        crate::tp::graph_section(e, None, || {
            let _main = e.gpu.enter_main()?;
            let Step35TokenGraphState {
                x,
                hn,
                logits_stage,
                token_d,
                pos_d,
                token_hist,
                hist_idx,
                ..
            } = &mut *state;
            e.rms_norm(x, self.output_norm.float_data(), hn, n_embd, 1, eps)?;
            e.matvec_bf16_into(head, hn, logits_stage, n_embd, self.cfg.n_vocab as usize)?;
            // Chunk-loop tail: greedy argmax feeds token_d (host-identical tie-break,
            // argmax_gate-validated), the id lands in the history ring, and pos advances on
            // device — consecutive launches chain with NO host sync. Single-token mode
            // overwrites token_d/pos_d from the host before each launch, so these nodes are
            // harmless there.
            e.argmax_token_device_into(logits_stage, token_d, self.cfg.n_vocab as usize)?;
            e.u32_hist_append(token_d, token_hist, hist_idx)?;
            e.inc_i32(pos_d)?;
            Ok(())
        })?;

        let graph = crate::tp::token_graph_build_finish()?;
        state.graphs.push((bucket_max, graph));
        eprintln!(
            "[step35-token-graph] built bucket={bucket_max} layers={n_layers} \
             build_ms={:.0} performance_claim=false",
            started.elapsed().as_secs_f64() * 1e3
        );
        Ok(())
    }
}