ferrox_core/

weight_matrix.rs

//! `WeightMatrix`: a weight matrix that may live either as plain f32
//! (small dims, embeddings, synthetic test weights) or as raw
//! Q8_0/Q4_0 block bytes loaded straight from a GGUF file, with no f32
//! expansion at load time. This is what lets ferrox load a
//! multi-billion-parameter checkpoint without first blowing it up 4x
//! in RAM: the loader (ferrox-models) hands tensors over still
//! quantized, and every matmul call here dispatches to the fused
//! dequant+dot kernels in ferrox-quant.

use rayon::prelude::*;
use std::collections::HashMap;
use std::ops::Range;
use std::sync::{Arc, Mutex, OnceLock};

use ferrox_gguf::GgmlType;

use crate::tensor::Tensor;

#[allow(dead_code)]
type Q4kRepackCache = Mutex<HashMap<(usize, usize), Arc<[u8]>>>;
type Q5kRepackCache = Mutex<HashMap<(usize, usize), Arc<[u8]>>>;
type Q6kRepackCache = Mutex<HashMap<(usize, usize), Arc<[u8]>>>;
type Q8x4RepackCache = Mutex<HashMap<(usize, usize), Arc<[u8]>>>;
type Q4x4RepackCache = Mutex<HashMap<(usize, usize), Arc<[u8]>>>;

/// Process-wide cache of interleaved Q4_K (`block_q4_Kx8`) bytes.
/// Retained for when K-quant Q8_K int-dot is re-enabled after parity
/// fixes on real Q4_K_M checkpoints.
#[allow(dead_code)]
fn q4k_repack_cache() -> &'static Q4kRepackCache {
    static CACHE: OnceLock<Q4kRepackCache> = OnceLock::new();
    CACHE.get_or_init(|| Mutex::new(HashMap::new()))
}

#[allow(dead_code)]
fn get_or_repack_q4k(data: &[u8], rows: usize, cols: usize) -> Arc<[u8]> {
    let key = (data.as_ptr() as usize, rows);
    {
        let cache = q4k_repack_cache().lock().unwrap();
        if let Some(hit) = cache.get(&key) {
            return Arc::clone(hit);
        }
    }
    let interleave = ferrox_quant::q4_kx8_interleave();
    let packed = ferrox_quant::pack_q4_k_matrix_x8(data, rows, cols, interleave);
    let arc: Arc<[u8]> = Arc::from(packed.into_boxed_slice());
    let mut cache = q4k_repack_cache().lock().unwrap();
    // Another thread may have won the race; prefer the existing entry.
    Arc::clone(cache.entry(key).or_insert_with(|| Arc::clone(&arc)))
}

/// Process-wide cache of interleaved Q5_K (`block_q5_Kx8`) bytes.
fn q5k_repack_cache() -> &'static Q5kRepackCache {
    static CACHE: OnceLock<Q5kRepackCache> = OnceLock::new();
    CACHE.get_or_init(|| Mutex::new(HashMap::new()))
}

fn get_or_repack_q5k(data: &[u8], rows: usize, cols: usize) -> Arc<[u8]> {
    let key = (data.as_ptr() as usize, rows);
    {
        let cache = q5k_repack_cache().lock().unwrap();
        if let Some(hit) = cache.get(&key) {
            return Arc::clone(hit);
        }
    }
    let interleave = ferrox_quant::q5_kx8_interleave();
    let packed = ferrox_quant::pack_q5_k_matrix_x8(data, rows, cols, interleave);
    let arc: Arc<[u8]> = Arc::from(packed.into_boxed_slice());
    let mut cache = q5k_repack_cache().lock().unwrap();
    Arc::clone(cache.entry(key).or_insert_with(|| Arc::clone(&arc)))
}

fn q6k_repack_cache() -> &'static Q6kRepackCache {
    static CACHE: OnceLock<Q6kRepackCache> = OnceLock::new();
    CACHE.get_or_init(|| Mutex::new(HashMap::new()))
}

fn get_or_repack_q6k(data: &[u8], rows: usize, cols: usize) -> Arc<[u8]> {
    let key = (data.as_ptr() as usize, rows);
    {
        let cache = q6k_repack_cache().lock().unwrap();
        if let Some(hit) = cache.get(&key) {
            return Arc::clone(hit);
        }
    }
    let interleave = ferrox_quant::q6_kx8_interleave();
    let packed = ferrox_quant::pack_q6_k_matrix_x8(data, rows, cols, interleave);
    let arc: Arc<[u8]> = Arc::from(packed.into_boxed_slice());
    let mut cache = q6k_repack_cache().lock().unwrap();
    Arc::clone(cache.entry(key).or_insert_with(|| Arc::clone(&arc)))
}

/// Process-wide cache of interleaved Q8_0 (`block_q8_0x4`) bytes.
fn q8x4_repack_cache() -> &'static Q8x4RepackCache {
    static CACHE: OnceLock<Q8x4RepackCache> = OnceLock::new();
    CACHE.get_or_init(|| Mutex::new(HashMap::new()))
}

fn get_or_repack_q8x4(data: &[u8], rows: usize, cols: usize) -> Arc<[u8]> {
    let key = (data.as_ptr() as usize, rows);
    {
        let cache = q8x4_repack_cache().lock().unwrap();
        if let Some(hit) = cache.get(&key) {
            return Arc::clone(hit);
        }
    }
    let packed =
        ferrox_quant::pack_q8_0_matrix_x4(data, rows, cols, ferrox_quant::q8_0x4_interleave());
    let arc: Arc<[u8]> = Arc::from(packed.into_boxed_slice());
    let mut cache = q8x4_repack_cache().lock().unwrap();
    Arc::clone(cache.entry(key).or_insert_with(|| Arc::clone(&arc)))
}

/// Process-wide cache of interleaved Q4_0 (`block_q4_0x4`) bytes.
fn q4x4_repack_cache() -> &'static Q4x4RepackCache {
    static CACHE: OnceLock<Q4x4RepackCache> = OnceLock::new();
    CACHE.get_or_init(|| Mutex::new(HashMap::new()))
}

fn get_or_repack_q4_0x4(data: &[u8], rows: usize, cols: usize) -> Arc<[u8]> {
    let key = (data.as_ptr() as usize, rows);
    {
        let cache = q4x4_repack_cache().lock().unwrap();
        if let Some(hit) = cache.get(&key) {
            return Arc::clone(hit);
        }
    }
    let packed =
        ferrox_quant::pack_q4_0_matrix_x4(data, rows, cols, ferrox_quant::q4_0x4_interleave());
    let arc: Arc<[u8]> = Arc::from(packed.into_boxed_slice());
    let mut cache = q4x4_repack_cache().lock().unwrap();
    Arc::clone(cache.entry(key).or_insert_with(|| Arc::clone(&arc)))
}

/// Backing storage for a quantized weight matrix's raw bytes: either an
/// owned buffer (synthetic/test weights, or any tensor that had to be
/// copied for some other reason) or a zero-copy view into a shared
/// memory-mapped GGUF file. This is the fix for the "loader read
/// everything into a fresh Vec<u8>" inefficiency: a real checkpoint's
/// resident memory should be the mmap itself, not a second copy of it,
/// which is how llama.cpp's mmap-based loader both avoid
/// doubling a multi-hundred-gigabyte checkpoint's memory footprint.
pub enum WeightBytes {
    Owned(Vec<u8>),
    Mapped {
        mmap: Arc<memmap2::Mmap>,
        range: Range<usize>,
    },
    /// A sub-range of a shared, lease-style buffer (e.g. one matrix
    /// inside an `ferrox_core::expert_store::ExpertLease`'s combined
    /// gate/up/down bytes). Holding the `Arc` here is exactly what
    /// makes the store's lease pinning structural: as long as any
    /// `WeightMatrix` built over these bytes is alive, the cache entry's
    /// strong count stays >1 and eviction cannot reuse it.
    Shared {
        buf: Arc<Vec<u8>>,
        range: Range<usize>,
    },
}

impl WeightBytes {
    pub fn as_slice(&self) -> &[u8] {
        match self {
            WeightBytes::Owned(v) => v,
            WeightBytes::Mapped { mmap, range } => &mmap[range.clone()],
            WeightBytes::Shared { buf, range } => &buf[range.clone()],
        }
    }

    pub fn len(&self) -> usize {
        self.as_slice().len()
    }

    pub fn is_empty(&self) -> bool {
        self.len() == 0
    }

    /// True if this is a zero-copy mmap view rather than an owned
    /// heap allocation -- useful for tests/diagnostics asserting that
    /// the loader actually took the zero-copy path.
    pub fn is_mapped(&self) -> bool {
        matches!(self, WeightBytes::Mapped { .. })
    }
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum QuantKind {
    Q8_0,
    Q4_0,
    /// The dominant real-world GGUF quantization formats (most
    /// published checkpoints ship as Q4_K_M or similar K-quant mixes,
    /// not the legacy Q4_0/Q8_0 formats above). See
    /// `ferrox_quant`'s module docs for the block layout and
    /// independent Python cross-validation.
    Q4K,
    Q5K,
    Q6K,
    /// The two more-aggressive K-quant tiers, used in Q2_K/Q3_K_M/
    /// Q3_K_L-style quant mixes (the far more common Q4_K_M/Q5_K_M
    /// mixes only combine with Q6_K, already covered above). See
    /// `ferrox_quant`'s module docs and independent Python
    /// cross-validation.
    Q2K,
    Q3K,
    /// Legacy, largely-obsolete-for-new-releases formats, still
    /// occasionally encountered. See `ferrox_quant`'s module docs;
    /// byte layouts verified against real `ggml-common.h` source.
    Q4_1,
    Q5_0,
    Q5_1,
    Q8_1,
    /// Non-linear ("codebook") quants: a 4-bit index maps through a
    /// shared 16-entry signed lookup table instead of a linear
    /// `nibble*scale+min` transform. See `ferrox_quant`'s module docs
    /// and independent Python cross-validation.
    IQ4NL,
    IQ4XS,
    /// The codebook-grid low-bit formats used throughout published
    /// "Dynamic" low-bit GGUFs of large MoE models (grid-table
    /// magnitudes + shared sign patterns; scalar kernels only so far).
    /// See `ferrox_quant`'s module docs and the ggml-cross-validated
    /// independent Python reference.
    IQ1S,
    IQ2XXS,
    IQ3XXS,
    /// The second codebook-grid tier (ggml tags 17/21/22/29), which the
    /// published `UD-*` recipes reach for when the `_XXS` tier is too
    /// lossy -- IQ3_S especially, since it is most of what an `IQ3_M`
    /// mix contains. Scalar kernels only; goldens are the real compiled
    /// ggml dequantizers' own output, asserted bit-exactly.
    IQ2XS,
    IQ2S,
    IQ3S,
    IQ1M,
    /// GGUF *block*-MXFP4 (17-byte interleaved blocks, ggml tag 39) --
    /// not the same layout as `WeightMatrix::Mxfp4`'s two-buffer
    /// safetensors form, though the math is identical. Scalar kernel
    /// only so far.
    Mxfp4Gguf,
}

impl QuantKind {
    /// Every variant, so exhaustiveness can be *tested* rather than
    /// trusted. The kernel-coverage tests below iterate this; adding a
    /// variant without adding it here fails to compile (the match in
    /// [`Self::name`] is exhaustive and this list is checked against it).
    pub const ALL: &'static [QuantKind] = &[
        QuantKind::Q8_0,
        QuantKind::Q4_0,
        QuantKind::Q4K,
        QuantKind::Q5K,
        QuantKind::Q6K,
        QuantKind::Q2K,
        QuantKind::Q3K,
        QuantKind::Q4_1,
        QuantKind::Q5_0,
        QuantKind::Q5_1,
        QuantKind::Q8_1,
        QuantKind::IQ4NL,
        QuantKind::IQ4XS,
        QuantKind::IQ1S,
        QuantKind::IQ2XXS,
        QuantKind::IQ3XXS,
        QuantKind::IQ2XS,
        QuantKind::IQ2S,
        QuantKind::IQ3S,
        QuantKind::IQ1M,
        QuantKind::Mxfp4Gguf,
    ];

    /// The GGUF-facing name. Also the key
    /// [`ferrox_metal::gpu::matvec_launch_meta`] is looked up by, which
    /// is why it is one function and not a `Debug` impl.
    pub fn name(self) -> &'static str {
        match self {
            QuantKind::Q8_0 => "Q8_0",
            QuantKind::Q4_0 => "Q4_0",
            QuantKind::Q4K => "Q4_K",
            QuantKind::Q5K => "Q5_K",
            QuantKind::Q6K => "Q6_K",
            QuantKind::Q2K => "Q2_K",
            QuantKind::Q3K => "Q3_K",
            QuantKind::Q4_1 => "Q4_1",
            QuantKind::Q5_0 => "Q5_0",
            QuantKind::Q5_1 => "Q5_1",
            QuantKind::Q8_1 => "Q8_1",
            QuantKind::IQ4NL => "IQ4_NL",
            QuantKind::IQ4XS => "IQ4_XS",
            QuantKind::IQ1S => "IQ1_S",
            QuantKind::IQ2XXS => "IQ2_XXS",
            QuantKind::IQ3XXS => "IQ3_XXS",
            QuantKind::IQ2XS => "IQ2_XS",
            QuantKind::IQ2S => "IQ2_S",
            QuantKind::IQ3S => "IQ3_S",
            QuantKind::IQ1M => "IQ1_M",
            QuantKind::Mxfp4Gguf => "MXFP4",
        }
    }
}

/// Which quant kinds have a **Metal matvec** kernel, as the kernel name
/// [`ferrox_metal::gpu::matvec_launch_meta`] resolves.
///
/// This is the single source of truth for that question. It is *not*
/// `#[cfg(feature = "metal")]`-gated deliberately: the table is a
/// property of the kernel set, and gating it would make it untestable on
/// the builds that run `cargo test --workspace`.
///
/// Duplicating this list is how IQ4_XS batched prefill silently ran on
/// the CPU — `metal_kind_supported` and `apply_gpu_batch`'s kind table
/// disagreed by exactly one entry, and the only symptom was a benchmark
/// row 13.7x behind. Every Metal-kind question now routes through here.
pub fn metal_matvec_kind_name(kind: QuantKind) -> Option<&'static str> {
    match kind {
        QuantKind::Q8_0
        | QuantKind::Q4_0
        | QuantKind::Q4K
        | QuantKind::Q5K
        | QuantKind::Q6K
        | QuantKind::IQ4XS => Some(kind.name()),
        _ => None,
    }
}

/// Which quant kinds have a **Metal batched simdgroup GEMM**
/// (`*_mul_mm_sg`), the prefill path. A kind with a matvec but no GEMM
/// still runs on Metal — as `batch` separate matvecs over the same
/// weights, which is the 13.7x shape.
///
/// The invariant that this set equals [`metal_matvec_kind_name`]'s is
/// asserted by a test, so adding a matvec kernel without a GEMM fails
/// the suite instead of a benchmark.
pub fn metal_mul_mm_kind_supported(kind: QuantKind) -> bool {
    matches!(
        kind,
        QuantKind::Q8_0
            | QuantKind::Q4_0
            | QuantKind::Q4K
            | QuantKind::Q5K
            | QuantKind::Q6K
            | QuantKind::IQ4XS
    )
}

/// Maps a GGUF tensor's on-disk dtype to the [`QuantKind`] a
/// [`WeightMatrix`] uses to pick a fused dequant+dot kernel, or `None`
/// for a dtype with no quantized kernel (F32, or one not implemented at
/// all).
///
/// **The single source of truth for that question**, for the same
/// reason [`metal_mul_mm_kind_supported`] is for its own: this table
/// used to be copied into six GGUF loaders, and the copies drifted.
/// Three of them (`loader`, `glm52_gguf_loader`, `kimi_gguf_loader`)
/// listed 21 dtypes while the other three (`mla_gguf_loader`,
/// `gemma4_gguf_loader`, `hybrid_gguf_loader`) listed 17 -- missing
/// `IQ1_S`, `IQ2_XXS`, `IQ3_XXS` and `MXFP4`. A miss is not a slow
/// path, it is `LoadError::UnsupportedDtype`, so a DeepSeek-MLA
/// checkpoint quantized to `IQ2_XXS` -- an ordinary combination for a
/// model that large -- was refused outright while the identical quant
/// loaded fine on the generic path.
pub fn quant_kind_for(dtype: GgmlType) -> Option<QuantKind> {
    match dtype {
        GgmlType::Q8_0 => Some(QuantKind::Q8_0),
        GgmlType::Q4_0 => Some(QuantKind::Q4_0),
        GgmlType::Q4K => Some(QuantKind::Q4K),
        GgmlType::Q5K => Some(QuantKind::Q5K),
        GgmlType::Q6K => Some(QuantKind::Q6K),
        GgmlType::Q2K => Some(QuantKind::Q2K),
        GgmlType::Q3K => Some(QuantKind::Q3K),
        GgmlType::Q4_1 => Some(QuantKind::Q4_1),
        GgmlType::Q5_0 => Some(QuantKind::Q5_0),
        GgmlType::Q5_1 => Some(QuantKind::Q5_1),
        GgmlType::Q8_1 => Some(QuantKind::Q8_1),
        GgmlType::IQ4NL => Some(QuantKind::IQ4NL),
        GgmlType::IQ4XS => Some(QuantKind::IQ4XS),
        GgmlType::IQ2XS => Some(QuantKind::IQ2XS),
        GgmlType::IQ2S => Some(QuantKind::IQ2S),
        GgmlType::IQ3S => Some(QuantKind::IQ3S),
        GgmlType::IQ1M => Some(QuantKind::IQ1M),
        GgmlType::IQ1S => Some(QuantKind::IQ1S),
        GgmlType::IQ2XXS => Some(QuantKind::IQ2XXS),
        GgmlType::IQ3XXS => Some(QuantKind::IQ3XXS),
        GgmlType::MXFP4 => Some(QuantKind::Mxfp4Gguf),
        _ => None,
    }
}

/// Which quant kinds have a **CUDA matvec** kernel.
pub fn cuda_matvec_kind_supported(kind: QuantKind) -> bool {
    matches!(
        kind,
        QuantKind::Q8_0 | QuantKind::Q4_0 | QuantKind::Q4K | QuantKind::Q5K | QuantKind::Q6K
    )
}

/// Which quant kinds take the CPU integer `vec_dot` path (activation
/// quantized to Q8/Q8_K, int8xint8 dots) rather than the much slower f32
/// dequant-dot. `cols` matters: the K-quant kernels need a whole number
/// of 256-element super-blocks, the legacy ones 32-element blocks.
pub fn cpu_int_dot_kind_supported(kind: QuantKind, cols: usize) -> bool {
    match kind {
        QuantKind::Q8_0 | QuantKind::Q4_0 => cols.is_multiple_of(32),
        QuantKind::Q4K | QuantKind::Q5K | QuantKind::Q6K => cols.is_multiple_of(256),
        _ => false,
    }
}

/// The backend dense matmuls will actually use in this process, decided
/// by the same cached env/probe reads dispatch uses. CUDA wins when both
/// are compiled in, matching [`WeightMatrix::apply_gpu`]'s order.
pub fn active_backend() -> crate::kernel_registry::Backend {
    #[cfg(feature = "cuda")]
    {
        if cuda_dense_enabled() {
            return crate::kernel_registry::Backend::Cuda;
        }
    }
    #[cfg(feature = "metal")]
    {
        if metal_dense_enabled() {
            return crate::kernel_registry::Backend::Metal;
        }
    }
    crate::kernel_registry::Backend::Cpu
}

/// A `ferrox_cuda::gpu::launch_*_matvec` function pointer's signature
/// -- named here purely to keep `apply_gpu`'s CUDA per-kind dispatch
/// table readable (all five real kernels share this exact signature).
#[cfg(feature = "cuda")]
type CudaMatvecLaunchFn =
    fn(&[u8], &[f32], usize, usize, usize) -> Result<Vec<f32>, ferrox_cuda::gpu::CudaError>;

/// Metal matvec launch signature (`weights`/`x` borrowed; row block
/// count is derived inside `ferrox_metal::gpu`).
#[cfg(feature = "metal")]
type MetalMatvecLaunchFn =
    fn(&[u8], &[f32], usize, usize) -> Result<Vec<f32>, ferrox_metal::gpu::MetalError>;
thread_local! {
    /// Elements dotted per output row of the matrix currently being
    /// applied. Set by [`WeightMatrix::with_row_work`] on the calling
    /// thread before a parallel region is opened, and read there -- it is
    /// never consulted from a rayon worker, so it does not need to
    /// propagate into the pool.
    static ROW_WORK: std::cell::Cell<usize> = const { std::cell::Cell::new(0) };
}

/// Minimum multiply-accumulates a rayon task should carry before it is
/// worth its own scheduling. Tunable for A/B; the default was chosen by
/// measurement, not derivation.
fn min_task_macs() -> usize {
    use std::sync::OnceLock;
    static V: OnceLock<usize> = OnceLock::new();
    *V.get_or_init(|| {
        std::env::var("FERROX_MIN_TASK_MACS")
            .ok()
            .and_then(|v| v.trim().parse().ok())
            .unwrap_or(1 << 16)
    })
}

/// Whether dense [`WeightMatrix::apply`] / [`WeightMatrix::apply_batch`]
/// should try Metal first (when built with `--features metal`).
///
/// - `FERROX_METAL=0|false|off|cpu` — force CPU
/// - `FERROX_METAL=1|true|on|metal` — force Metal attempt
/// - unset / `auto` — Metal when [`ferrox_metal::gpu::probe`] finds a device
///
/// Decision is cached for the process lifetime (env read once).
#[cfg(feature = "metal")]
pub fn metal_dense_enabled() -> bool {
    use std::sync::OnceLock;
    static ENABLED: OnceLock<bool> = OnceLock::new();
    *ENABLED.get_or_init(|| match std::env::var("FERROX_METAL").ok().as_deref() {
        Some("0") | Some("false") | Some("off") | Some("cpu") => false,
        Some("1") | Some("true") | Some("on") | Some("metal") => true,
        _ => ferrox_metal::gpu::probe().is_some(),
    })
}

/// `FERROX_METAL_MATMUL=1` opts into the first-cut Q4/Q6 matmul kernels,
/// which can lose to N x matvec for typical chat prompts.
///
/// Read once. These sit inside `apply_gpu_batch`, i.e. once per GEMM per
/// layer per forward pass -- `std::env::var` allocates a `String` and
/// takes the environment lock every time.
#[cfg(feature = "metal")]
fn metal_matmul_opt_in() -> bool {
    use std::sync::OnceLock;
    static V: OnceLock<bool> = OnceLock::new();
    *V.get_or_init(|| {
        matches!(
            std::env::var("FERROX_METAL_MATMUL").ok().as_deref(),
            Some("1") | Some("true") | Some("on")
        )
    })
}

/// Weight-reuse `mul_mm` for prefill. Default on; `FERROX_METAL_MUL_MM=0`
/// forces the N x matvec batch. Read once, same reason as above.
#[cfg(feature = "metal")]
fn metal_mul_mm_enabled() -> bool {
    use std::sync::OnceLock;
    static V: OnceLock<bool> = OnceLock::new();
    *V.get_or_init(|| {
        !matches!(
            std::env::var("FERROX_METAL_MUL_MM").ok().as_deref(),
            Some("0") | Some("false") | Some("off")
        )
    })
}

/// Whether dense [`WeightMatrix::apply`] should try CUDA first (when
/// built with `--features cuda`).
///
/// - `FERROX_CUDA=0|false|off|cpu` — force skip CUDA dense
/// - `FERROX_CUDA=1|true|on|cuda` — force CUDA attempt
/// - unset / `auto` — CUDA when a device probe succeeds
#[cfg(feature = "cuda")]
pub fn cuda_dense_enabled() -> bool {
    use std::sync::OnceLock;
    static ENABLED: OnceLock<bool> = OnceLock::new();
    *ENABLED.get_or_init(|| match std::env::var("FERROX_CUDA").ok().as_deref() {
        Some("0") | Some("false") | Some("off") | Some("cpu") => false,
        Some("1") | Some("true") | Some("on") | Some("cuda") => true,
        _ => ferrox_cuda::gpu::probe().is_some(),
    })
}

/// Whether CPU Q8_0 / Q4_0 / Q4_K / Q5_K / Q6_K matvec should quantize the
/// activation to int8 and use the integer `vec_dot` path. Q4_K
/// additionally lazy-repacks into interleaved `block_q4_Kx8` for 8-wide
/// GEMV; Q8_0 into `block_q8_0x4` and Q4_0 into `block_q4_0x4` for
/// 4-wide GEMV.
///
/// Off by default *as a library*, and turned on by both binaries (see
/// `ferrox_core::threads`'s siblings in `ferrox-cli`/`ferrox-server`,
/// which set `FERROX_CPU_INT_DOT=1` unless the caller already chose).
/// The split is deliberate: this is what llama.cpp's CPU backend does
/// unconditionally -- quantize the activation to Q8, run integer
/// `vec_dot` -- and it is worth 28% of CPU decode on Host B
/// (Qwen2.5-0.5B Q8_0, `-ngl 0 -t 6`: 58.0 -> 80.5 tok/s). But it also
/// perturbs results below the f32 reference's precision, and this
/// crate's golden cross-validation against the independent NumPy
/// reference asserts exact agreement. So the *inference product*
/// defaults to fast and the *library default* stays reference-exact.
pub fn cpu_int_dot_enabled() -> bool {
    use std::sync::OnceLock;
    static ENABLED: OnceLock<bool> = OnceLock::new();
    *ENABLED.get_or_init(|| {
        matches!(
            std::env::var("FERROX_CPU_INT_DOT").ok().as_deref(),
            Some("1") | Some("true") | Some("on")
        )
    })
}

/// Sets `FERROX_CPU_INT_DOT=1` unless the caller already expressed a
/// preference. Call from a binary's startup, before any worker threads
/// exist. See [`cpu_int_dot_enabled`] for why the default lives here
/// rather than in the getter.
///
/// # Safety
/// Must be called while the process is still single-threaded, since it
/// mutates the process environment.
pub unsafe fn default_cpu_int_dot_on() {
    if std::env::var_os("FERROX_CPU_INT_DOT").is_none() {
        unsafe { std::env::set_var("FERROX_CPU_INT_DOT", "1") };
    }
}

/// A batch of activations quantized once for reuse across several
/// [`WeightMatrix::apply_batch_with_acts`] calls that read the same input
/// (q/k/v on one normed batch; gate/up on another). Build with
/// [`WeightMatrix::quantize_batch_acts`]. Q8_0/Q4_0 matrices consume
/// [`BatchActs::Q8`]; the K-quants consume [`BatchActs::Q8K`].
pub enum BatchActs {
    Q8(Vec<ferrox_quant::Q8Activations>),
    Q8K(Vec<ferrox_quant::Q8KActivations>),
}

pub enum WeightMatrix {
    F32(Tensor),
    Quantized {
        data: WeightBytes,
        rows: usize,
        cols: usize,
        kind: QuantKind,
    },
    /// MXFP4 (OCP Microscaling 4-bit float, Kimi K3's real routed-expert
    /// format): unlike every `Quantized` kind above, which store one
    /// interleaved block buffer per row, Kimi K3's real checkpoint
    /// stores the packed 4-bit codes and per-group E8M0 scales as two
    /// *separate* tensors (confirmed against a real shard header, see
    /// `ferrox_quant`'s MXFP4 module docs) -- so this variant holds two
    /// independently zero-copy-mappable buffers instead of `Quantized`'s
    /// single `data` buffer. `apply`/`apply_batch` dispatch to
    /// `ferrox_quant::dot_mxfp4_row_f32`, which reads directly from
    /// these buffers without ever materializing a dequantized f32 copy
    /// of the whole matrix -- the same zero-copy-mmap-plus-fused-dot
    /// discipline as every `Quantized` kind, letting a real MXFP4
    /// checkpoint's resident memory stay close to its on-disk size
    /// instead of the ~8x larger eager-f32-dequant footprint.
    Mxfp4 {
        packed: WeightBytes,
        scale: WeightBytes,
        rows: usize,
        cols: usize,
    },
}

impl WeightMatrix {
    pub fn rows(&self) -> usize {
        match self {
            WeightMatrix::F32(t) => t.rows(),
            WeightMatrix::Quantized { rows, .. } => *rows,
            WeightMatrix::Mxfp4 { rows, .. } => *rows,
        }
    }

    /// The block format, or `None` for the two non-block storages
    /// (`F32`, safetensors-pair `Mxfp4`). This is the key every
    /// kernel-availability table is indexed by.
    pub fn quant_kind(&self) -> Option<QuantKind> {
        match self {
            WeightMatrix::Quantized { kind, .. } => Some(*kind),
            WeightMatrix::F32(_) | WeightMatrix::Mxfp4 { .. } => None,
        }
    }

    pub fn cols(&self) -> usize {
        match self {
            WeightMatrix::F32(t) => t.cols(),
            WeightMatrix::Quantized { cols, .. } => *cols,
            WeightMatrix::Mxfp4 { cols, .. } => *cols,
        }
    }

    fn block_bytes_per_row(&self, kind: QuantKind, cols: usize) -> usize {
        match kind {
            QuantKind::Q8_0 => {
                (cols / ferrox_quant::Q8_0_BLOCK_ELEMS) * ferrox_quant::Q8_0_BLOCK_BYTES
            }
            QuantKind::Q4_0 => {
                (cols / ferrox_quant::Q4_0_BLOCK_ELEMS) * ferrox_quant::Q4_0_BLOCK_BYTES
            }
            QuantKind::Q4K => {
                (cols / ferrox_quant::Q4_K_BLOCK_ELEMS) * ferrox_quant::Q4_K_BLOCK_BYTES
            }
            QuantKind::Q5K => {
                (cols / ferrox_quant::Q5_K_BLOCK_ELEMS) * ferrox_quant::Q5_K_BLOCK_BYTES
            }
            QuantKind::Q6K => {
                (cols / ferrox_quant::Q6_K_BLOCK_ELEMS) * ferrox_quant::Q6_K_BLOCK_BYTES
            }
            QuantKind::Q2K => {
                (cols / ferrox_quant::Q2_K_BLOCK_ELEMS) * ferrox_quant::Q2_K_BLOCK_BYTES
            }
            QuantKind::Q3K => {
                (cols / ferrox_quant::Q3_K_BLOCK_ELEMS) * ferrox_quant::Q3_K_BLOCK_BYTES
            }
            QuantKind::Q4_1 => {
                (cols / ferrox_quant::Q4_1_BLOCK_ELEMS) * ferrox_quant::Q4_1_BLOCK_BYTES
            }
            QuantKind::Q5_0 => {
                (cols / ferrox_quant::Q5_0_BLOCK_ELEMS) * ferrox_quant::Q5_0_BLOCK_BYTES
            }
            QuantKind::Q5_1 => {
                (cols / ferrox_quant::Q5_1_BLOCK_ELEMS) * ferrox_quant::Q5_1_BLOCK_BYTES
            }
            QuantKind::Q8_1 => {
                (cols / ferrox_quant::Q8_1_BLOCK_ELEMS) * ferrox_quant::Q8_1_BLOCK_BYTES
            }
            QuantKind::IQ4NL => {
                (cols / ferrox_quant::IQ4_NL_BLOCK_ELEMS) * ferrox_quant::IQ4_NL_BLOCK_BYTES
            }
            QuantKind::IQ4XS => {
                (cols / ferrox_quant::IQ4_XS_BLOCK_ELEMS) * ferrox_quant::IQ4_XS_BLOCK_BYTES
            }
            QuantKind::IQ1S => {
                (cols / ferrox_quant::IQ1_S_BLOCK_ELEMS) * ferrox_quant::IQ1_S_BLOCK_BYTES
            }
            QuantKind::IQ2XXS => {
                (cols / ferrox_quant::IQ2_XXS_BLOCK_ELEMS) * ferrox_quant::IQ2_XXS_BLOCK_BYTES
            }
            QuantKind::IQ3XXS => {
                (cols / ferrox_quant::IQ3_XXS_BLOCK_ELEMS) * ferrox_quant::IQ3_XXS_BLOCK_BYTES
            }
            QuantKind::IQ2XS => {
                (cols / ferrox_quant::IQ2_XS_BLOCK_ELEMS) * ferrox_quant::IQ2_XS_BLOCK_BYTES
            }
            QuantKind::IQ2S => {
                (cols / ferrox_quant::IQ2_S_BLOCK_ELEMS) * ferrox_quant::IQ2_S_BLOCK_BYTES
            }
            QuantKind::IQ3S => {
                (cols / ferrox_quant::IQ3_S_BLOCK_ELEMS) * ferrox_quant::IQ3_S_BLOCK_BYTES
            }
            QuantKind::IQ1M => {
                (cols / ferrox_quant::IQ1_M_BLOCK_ELEMS) * ferrox_quant::IQ1_M_BLOCK_BYTES
            }
            QuantKind::Mxfp4Gguf => {
                (cols / ferrox_quant::MXFP4_GGUF_BLOCK_ELEMS) * ferrox_quant::MXFP4_GGUF_BLOCK_BYTES
            }
        }
    }

    /// A reasonable minimum number of rows for one rayon task to
    /// process, to avoid rayon's work-stealing splitter fragmenting a
    /// matmul into tasks so small that scheduling/synchronization
    /// overhead dominates the real per-row work (a fused dequant+dot,
    /// not free). This is a real, measured fix, not speculative
    /// tuning: naive per-row splitting (rayon's default) caused a
    /// 13-16x throughput regression on a host configured with far more
    /// rayon threads than a small model's matrices have useful
    /// parallelism for (observed directly on a shared-core rented
    /// host, where auto-detected high thread counts collapsed
    /// throughput ~13-16x on a small model). Aims for ~4 tasks per thread
    /// -- enough that rayon's work-stealing can still load-balance
    /// across threads that finish early, without going all the way
    /// down to one task per row.
    ///
    /// Floor of 8 avoids Rayon thrash on tiny mats (SmolLM2 attn_kv
    /// has 192 rows → without a floor, ~48 one-row tasks on 10 cores).
    ///
    /// The floor is also **work-aware**, which matters for decode. A row
    /// count alone says nothing about how much arithmetic a task carries:
    /// SmolLM2's 576-wide projections split into ~24 tasks of ~14K MACs
    /// each, far too little to pay for a fork-join. Measured on this host
    /// (both engines back to back, thread count as the only variable):
    /// ferrox scales 1.40x / 2.93x from 1 to 6 threads on TinyLlama /
    /// Mistral-7B where llama.cpp scales 1.99x / 4.39x, and the deficit
    /// grows as the model shrinks -- the signature of tasks too small to
    /// amortise their own scheduling, not of slow kernels (ferrox is
    /// *ahead* of llama at one thread on Mistral-7B).
    ///
    /// [`Self::with_row_work`] supplies the elements-per-row so a task
    /// can be required to carry at least `FERROX_MIN_TASK_MACS`
    /// multiply-accumulates. Zero (unset) keeps the old row-only
    /// behaviour, so any call site that has not opted in is unchanged.
    fn min_rows_per_task(rows: usize) -> usize {
        let threads = rayon::current_num_threads().max(1);
        let by_threads = (rows / (threads * 4)).max(8.min(rows.max(1)));
        let per_row = ROW_WORK.with(|c| c.get());
        if per_row == 0 {
            return by_threads;
        }
        let need = min_task_macs().div_ceil(per_row.max(1));
        by_threads.max(need.min(rows.max(1)))
    }

    /// Runs `f` with the per-row work (elements dotted per output row)
    /// published for [`Self::min_rows_per_task`]. Restores the previous
    /// value, so nesting is safe.
    fn with_row_work<R>(per_row: usize, f: impl FnOnce() -> R) -> R {
        let prev = ROW_WORK.with(|c| c.replace(per_row));
        let out = f();
        ROW_WORK.with(|c| c.set(prev));
        out
    }

    /// Run `body(g, t0, t1)` for every row-group `g` and activation-tile
    /// range `[t0, t1)` of a llama-style 2D chunk grid over
    /// (row-groups × batch tiles).
    ///
    /// This is the port of `ggml_compute_forward_mul_mat`'s chunking
    /// (`ggml-cpu.c`): ~16 rows / 16 batch positions per chunk, and if
    /// that grid is smaller than `4 × threads`, re-chunk by thread along
    /// the larger dimension. llama walks the grid with an atomic
    /// `current_chunk` because its threadpool has no scheduler; Rayon
    /// already work-steals, so handing it the same chunks (`min_len 1`)
    /// gets the same load balancing. The point is the *batch* dimension:
    /// splitting only by rows leaves a 192-row projection with ~3 tasks
    /// no matter how many positions are in flight.
    fn par_chunked_groups(
        n_groups: usize,
        group_rows: usize,
        n_tiles: usize,
        tile_batch: usize,
        body: impl Fn(usize, usize, usize) + Sync,
    ) {
        if n_groups == 0 || n_tiles == 0 {
            return;
        }
        let nth = rayon::current_num_threads().max(1);
        const CHUNK_ELEMS: usize = 16;
        let g_per_chunk = (CHUNK_ELEMS / group_rows).max(1);
        let t_per_chunk = (CHUNK_ELEMS / tile_batch).max(1);
        let mut nchunk_g = n_groups.div_ceil(g_per_chunk);
        let mut nchunk_t = n_tiles.div_ceil(t_per_chunk);
        if nchunk_g * nchunk_t < nth * 4 {
            // llama's fallback: one chunk per thread along the larger dim.
            if n_groups * group_rows > n_tiles * tile_batch {
                nchunk_g = nth.min(n_groups);
                nchunk_t = 1;
            } else {
                nchunk_g = 1;
                nchunk_t = nth.min(n_tiles);
            }
        }
        let dg = n_groups.div_ceil(nchunk_g);
        let dt = n_tiles.div_ceil(nchunk_t);
        (0..nchunk_g * nchunk_t)
            .into_par_iter()
            .with_min_len(1)
            .for_each(|chunk| {
                let g0 = (chunk % nchunk_g) * dg;
                let g1 = (g0 + dg).min(n_groups);
                let t0 = (chunk / nchunk_g) * dt;
                let t1 = (t0 + dt).min(n_tiles);
                for g in g0..g1 {
                    body(g, t0, t1);
                }
            });
    }

    /// Prefer serial when the mat is too small for fork-join to pay off.
    fn prefer_serial_matvec(rows: usize, cols: usize) -> bool {
        // ~256k f32-equivalent ops: below this, Rayon overhead dominates
        // on Host B-class cores for Q8/Q4 decode GEMVs.
        rows.saturating_mul(cols) < 256_000
    }

    fn dot(kind: QuantKind, row: &[u8], x: &[f32]) -> f32 {
        match kind {
            QuantKind::Q8_0 => ferrox_quant::dot_q8_0_f32(row, x),
            QuantKind::Q4_0 => ferrox_quant::dot_q4_0_f32(row, x),
            QuantKind::Q4K => ferrox_quant::dot_q4_k_f32(row, x),
            QuantKind::Q5K => ferrox_quant::dot_q5_k_f32(row, x),
            QuantKind::Q6K => ferrox_quant::dot_q6_k_f32(row, x),
            QuantKind::Q2K => ferrox_quant::dot_q2_k_f32(row, x),
            QuantKind::Q3K => ferrox_quant::dot_q3_k_f32(row, x),
            QuantKind::Q4_1 => ferrox_quant::dot_q4_1_f32(row, x),
            QuantKind::Q5_0 => ferrox_quant::dot_q5_0_f32(row, x),
            QuantKind::Q5_1 => ferrox_quant::dot_q5_1_f32(row, x),
            QuantKind::Q8_1 => ferrox_quant::dot_q8_1_f32(row, x),
            QuantKind::IQ4NL => ferrox_quant::dot_iq4_nl_f32(row, x),
            QuantKind::IQ4XS => ferrox_quant::dot_iq4_xs_f32(row, x),
            QuantKind::IQ1S => ferrox_quant::dot_iq1_s_f32(row, x),
            QuantKind::IQ2XXS => ferrox_quant::dot_iq2_xxs_f32(row, x),
            QuantKind::IQ3XXS => ferrox_quant::dot_iq3_xxs_f32(row, x),
            QuantKind::IQ2XS => ferrox_quant::dot_iq2_xs_f32(row, x),
            QuantKind::IQ2S => ferrox_quant::dot_iq2_s_f32(row, x),
            QuantKind::IQ3S => ferrox_quant::dot_iq3_s_f32(row, x),
            QuantKind::IQ1M => ferrox_quant::dot_iq1_m_f32(row, x),
            QuantKind::Mxfp4Gguf => ferrox_quant::dot_mxfp4_gguf_f32(row, x),
        }
    }

    /// Per-kind full-buffer dequantization -- the row-lookup counterpart
    /// of `dot`'s fused per-kind dispatch below.
    fn dequant(kind: QuantKind, bytes: &[u8]) -> Vec<f32> {
        let out = match kind {
            QuantKind::Q8_0 => ferrox_quant::dequant_q8_0(bytes),
            QuantKind::Q4_0 => ferrox_quant::dequant_q4_0(bytes),
            QuantKind::Q4K => ferrox_quant::dequant_q4_k(bytes),
            QuantKind::Q5K => ferrox_quant::dequant_q5_k(bytes),
            QuantKind::Q6K => ferrox_quant::dequant_q6_k(bytes),
            QuantKind::Q2K => ferrox_quant::dequant_q2_k(bytes),
            QuantKind::Q3K => ferrox_quant::dequant_q3_k(bytes),
            QuantKind::Q4_1 => ferrox_quant::dequant_q4_1(bytes),
            QuantKind::Q5_0 => ferrox_quant::dequant_q5_0(bytes),
            QuantKind::Q5_1 => ferrox_quant::dequant_q5_1(bytes),
            QuantKind::Q8_1 => ferrox_quant::dequant_q8_1(bytes),
            QuantKind::IQ4NL => ferrox_quant::dequant_iq4_nl(bytes),
            QuantKind::IQ4XS => ferrox_quant::dequant_iq4_xs(bytes),
            QuantKind::IQ1S => ferrox_quant::dequant_iq1_s(bytes),
            QuantKind::IQ2XXS => ferrox_quant::dequant_iq2_xxs(bytes),
            QuantKind::IQ3XXS => ferrox_quant::dequant_iq3_xxs(bytes),
            QuantKind::IQ2XS => ferrox_quant::dequant_iq2_xs(bytes),
            QuantKind::IQ2S => ferrox_quant::dequant_iq2_s(bytes),
            QuantKind::IQ3S => ferrox_quant::dequant_iq3_s(bytes),
            QuantKind::IQ1M => ferrox_quant::dequant_iq1_m(bytes),
            QuantKind::Mxfp4Gguf => ferrox_quant::dequant_mxfp4_gguf(bytes),
        };
        out.expect("row byte length is block-aligned by construction (block_bytes_per_row)")
    }

    /// Dequantizes exactly one row to f32, without touching any other
    /// row's bytes. This is what makes a *quantized* embedding table
    /// usable directly: token lookup reads `row_bytes` bytes and
    /// dequantizes `cols` values, instead of the whole vocabulary
    /// tensor ever being widened to f32 (which for a large-vocab model
    /// is a multi-GB allocation that exists only to be indexed one row
    /// at a time).
    pub fn dequant_row(&self, r: usize) -> Vec<f32> {
        assert!(r < self.rows(), "row {r} out of range ({})", self.rows());
        match self {
            WeightMatrix::F32(t) => t.row(r).to_vec(),
            WeightMatrix::Quantized {
                data, cols, kind, ..
            } => {
                let row_bytes = self.block_bytes_per_row(*kind, *cols);
                let bytes = &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                let out = Self::dequant(*kind, bytes);
                debug_assert_eq!(out.len(), *cols);
                out
            }
            WeightMatrix::Mxfp4 {
                packed,
                scale,
                cols,
                ..
            } => {
                let packed_per_row = cols / 2;
                let scales_per_row = cols / ferrox_quant::MXFP4_GROUP_SIZE;
                let p = &packed.as_slice()[r * packed_per_row..(r + 1) * packed_per_row];
                let sc = &scale.as_slice()[r * scales_per_row..(r + 1) * scales_per_row];
                ferrox_quant::dequant_mxfp4_row(p, sc)
                    .expect("row slices are group-aligned by construction")
            }
        }
    }

    /// Whether batching this matrix during prefill beats running the
    /// fused per-position dense-FFN launch once per token.
    ///
    /// Measured, not assumed. Every kind with a simdgroup GEMM
    /// (`*_mul_mm_sg`) batches: Q4_K, Q5_K, Q6_K, Q8_0, Q4_0, IQ4_XS.
    /// The remaining IQ codebook kinds have no GEMM, and their batched
    /// *matvec* loses to the fused per-position launch — IQ4_XS
    /// regressed 72.1 -> 33.2 on Llama-3.2-1B while it was in that
    /// state — so they keep the per-position path until a GEMM exists
    /// for them too.
    /// This matrix as a Metal simdgroup-GEMM descriptor, or `None` if
    /// its quant kind has no GEMM (so it must stay on the matvec path).
    /// Lets several matmuls be encoded into one command buffer instead
    /// of one launch each.
    #[cfg(feature = "metal")]
    pub fn mul_mm_sg_launch(&self) -> Option<ferrox_metal::gpu::MulMmSgLaunch<'_>> {
        let WeightMatrix::Quantized {
            data,
            rows,
            cols,
            kind,
        } = self
        else {
            return None;
        };
        let kind_name = match kind {
            QuantKind::Q8_0 => "Q8_0",
            QuantKind::Q4_0 => "Q4_0",
            QuantKind::Q5_0 => "Q5_0",
            QuantKind::Q4K => "Q4_K",
            QuantKind::Q5K => "Q5_K",
            QuantKind::Q6K => "Q6_K",
            QuantKind::IQ4XS => "IQ4_XS",
            _ => return None,
        };
        let (fn_name, block_bytes, block_elems) = ferrox_metal::gpu::mul_mm_sg_meta(kind_name)?;
        Some(ferrox_metal::gpu::MulMmSgLaunch {
            weights: data.as_slice(),
            rows: *rows,
            row_bytes: self.block_bytes_per_row(*kind, *cols),
            fn_name,
            block_bytes,
            block_elems,
        })
    }

    #[cfg(any(feature = "metal", feature = "cuda"))]
    pub fn prefers_gpu_batch(&self) -> bool {
        !matches!(
            self,
            WeightMatrix::Quantized {
                kind: QuantKind::IQ4NL
                    | QuantKind::IQ1S
                    | QuantKind::IQ2XXS
                    | QuantKind::IQ3XXS
                    | QuantKind::IQ2XS
                    | QuantKind::IQ2S
                    | QuantKind::IQ3S
                    | QuantKind::IQ1M,
                ..
            }
        )
    }

    /// Computes `W @ x` for a single activation vector `x` of length
    /// `self.cols()`, returning a vector of length `self.rows()`.
    /// Parallelized over output rows with rayon, same decomposition as
    /// `matmul_f32`.
    ///
    /// With `--features metal` / `--features cuda`, when the matching
    /// dense GPU env selects a device (see [`metal_dense_enabled`] /
    /// [`cuda_dense_enabled`]), quantized kinds that have a GPU kernel
    /// go through [`Self::apply_gpu`] first so dense Llama-class
    /// decode uses the GPU instead of only MoE expert placement.
    pub fn apply(&self, x: &[f32]) -> Vec<f32> {
        assert_eq!(
            x.len(),
            self.cols(),
            "activation length must match matrix column count"
        );
        #[cfg(feature = "cuda")]
        {
            if cuda_dense_enabled() {
                if let Some(out) = self.apply_gpu(x) {
                    return out;
                }
            }
        }
        #[cfg(feature = "metal")]
        {
            if metal_dense_enabled() {
                if let Some(out) = self.apply_gpu(x) {
                    return out;
                }
            }
        }
        self.apply_cpu(x)
    }

    /// CPU-only matvec (NEON/AVX/scalar via `ferrox-quant`). Used by
    /// [`Self::apply`] after Metal miss/disable, and by GPU parity tests
    /// that must not recurse into [`Self::apply_gpu`].
    /// Applies three independent matrices to the same activation,
    /// overlapping their parallel regions instead of running them one
    /// after another.
    ///
    /// Decode opens one rayon fork-join per weight matrix -- roughly
    /// seven per layer -- and the measured CPU decode deficit is
    /// scheduling, not kernels (ferrox scales 1.40x/2.93x from 1 to 6
    /// threads where llama.cpp scales 1.99x/4.39x, while *beating* llama
    /// at one thread). q/k/v share an input and are independent, so
    /// their regions can coexist and let rayon's work-stealing fill
    /// threads that would otherwise idle at the tail of each one.
    ///
    /// CPU only. On a GPU backend each `apply` submits and waits on its
    /// own command buffer, and Metal decode is already at or ahead of
    /// parity -- there is nothing to win and a live path to disturb.
    pub fn apply_three(a: &Self, b: &Self, c: &Self, x: &[f32]) -> (Vec<f32>, Vec<f32>, Vec<f32>) {
        #[cfg(feature = "metal")]
        let gpu = metal_dense_enabled();
        #[cfg(not(feature = "metal"))]
        let gpu = false;
        #[cfg(feature = "cuda")]
        let gpu = gpu || cuda_dense_enabled();
        if gpu {
            return (a.apply(x), b.apply(x), c.apply(x));
        }
        let (ra, (rb, rc)) =
            rayon::join(|| a.apply(x), || rayon::join(|| b.apply(x), || c.apply(x)));
        (ra, rb, rc)
    }

    pub fn apply_cpu(&self, x: &[f32]) -> Vec<f32> {
        assert_eq!(
            x.len(),
            self.cols(),
            "activation length must match matrix column count"
        );
        // Decode: one activation, so a task's work is (rows in task) x cols.
        // Publish `cols` so task sizing can be work-aware, not row-count-aware.
        Self::with_row_work(x.len(), || self.apply_cpu_inner(x))
    }

    fn apply_cpu_inner(&self, x: &[f32]) -> Vec<f32> {
        match self {
            WeightMatrix::F32(t) => {
                let xt = Tensor::new(x.to_vec(), vec![1, x.len()]);
                crate::matmul::matmul_f32(&xt, t).data
            }
            WeightMatrix::Quantized {
                data,
                rows,
                cols,
                kind,
            } => {
                let row_bytes = self.block_bytes_per_row(*kind, *cols);
                let mut out = vec![0f32; *rows];
                // FERROX_CPU_INT_DOT=1: quantize the shared activation once,
                // then every row dot is int8×int8 → i32 (llama.cpp CPU matmul).
                // Q8_0/Q4_0 use 32-elem Q8_0 acts; Q4_K/Q5_K/Q6_K use Q8_K.
                if cpu_int_dot_enabled() {
                    match *kind {
                        QuantKind::Q8_0 if x.len().is_multiple_of(32) => {
                            let act = ferrox_quant::quantize_activations_q8(x);
                            let n_groups = *rows / ferrox_quant::Q8_0X4_NROWS;
                            let serial = Self::prefer_serial_matvec(*rows, *cols);
                            if n_groups > 0 {
                                let packed = get_or_repack_q8x4(data.as_slice(), *rows, *cols);
                                if serial {
                                    for (g, chunk) in out[..n_groups * ferrox_quant::Q8_0X4_NROWS]
                                        .chunks_mut(ferrox_quant::Q8_0X4_NROWS)
                                        .enumerate()
                                    {
                                        ferrox_quant::gemv_q8_0x4_group(
                                            &packed,
                                            g,
                                            &act,
                                            *cols,
                                            ferrox_quant::q8_0x4_interleave(),
                                            chunk,
                                        );
                                    }
                                } else {
                                    out[..n_groups * ferrox_quant::Q8_0X4_NROWS]
                                        .par_chunks_mut(ferrox_quant::Q8_0X4_NROWS)
                                        .with_min_len(Self::min_rows_per_task(n_groups).max(1))
                                        .enumerate()
                                        .for_each(|(g, chunk)| {
                                            ferrox_quant::gemv_q8_0x4_group(
                                                &packed,
                                                g,
                                                &act,
                                                *cols,
                                                ferrox_quant::q8_0x4_interleave(),
                                                chunk,
                                            );
                                        });
                                }
                                let data_slice = data.as_slice();
                                let tail_len = *rows - n_groups * ferrox_quant::Q8_0X4_NROWS;
                                if tail_len > 0 {
                                    let tail = &mut out[n_groups * ferrox_quant::Q8_0X4_NROWS..];
                                    if serial || Self::prefer_serial_matvec(tail_len, *cols) {
                                        for (i, o) in tail.iter_mut().enumerate() {
                                            let r = n_groups * ferrox_quant::Q8_0X4_NROWS + i;
                                            let row =
                                                &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                            *o = ferrox_quant::dot_q8_0_q8(row, &act);
                                        }
                                    } else {
                                        let min_len = Self::min_rows_per_task(tail_len);
                                        tail.par_iter_mut()
                                            .with_min_len(min_len)
                                            .enumerate()
                                            .for_each(|(i, o)| {
                                                let r = n_groups * ferrox_quant::Q8_0X4_NROWS + i;
                                                let row =
                                                    &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                                *o = ferrox_quant::dot_q8_0_q8(row, &act);
                                            });
                                    }
                                }
                                return out;
                            }
                            if serial {
                                for (r, o) in out.iter_mut().enumerate() {
                                    let row = &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                    *o = ferrox_quant::dot_q8_0_q8(row, &act);
                                }
                            } else {
                                out.par_iter_mut()
                                    .with_min_len(Self::min_rows_per_task(*rows))
                                    .enumerate()
                                    .for_each(|(r, o)| {
                                        let row =
                                            &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                        *o = ferrox_quant::dot_q8_0_q8(row, &act);
                                    });
                            }
                            return out;
                        }
                        QuantKind::Q4_0 if x.len().is_multiple_of(32) => {
                            let act = ferrox_quant::quantize_activations_q8(x);
                            let n_groups = *rows / ferrox_quant::Q4_0X4_NROWS;
                            let serial = Self::prefer_serial_matvec(*rows, *cols);
                            if n_groups > 0 {
                                let packed = get_or_repack_q4_0x4(data.as_slice(), *rows, *cols);
                                if serial {
                                    for (g, chunk) in out[..n_groups * ferrox_quant::Q4_0X4_NROWS]
                                        .chunks_mut(ferrox_quant::Q4_0X4_NROWS)
                                        .enumerate()
                                    {
                                        ferrox_quant::gemv_q4_0x4_group(
                                            &packed,
                                            g,
                                            &act,
                                            *cols,
                                            ferrox_quant::q4_0x4_interleave(),
                                            chunk,
                                        );
                                    }
                                } else {
                                    out[..n_groups * ferrox_quant::Q4_0X4_NROWS]
                                        .par_chunks_mut(ferrox_quant::Q4_0X4_NROWS)
                                        .with_min_len(Self::min_rows_per_task(n_groups).max(1))
                                        .enumerate()
                                        .for_each(|(g, chunk)| {
                                            ferrox_quant::gemv_q4_0x4_group(
                                                &packed,
                                                g,
                                                &act,
                                                *cols,
                                                ferrox_quant::q4_0x4_interleave(),
                                                chunk,
                                            );
                                        });
                                }
                                let data_slice = data.as_slice();
                                let tail_len = *rows - n_groups * ferrox_quant::Q4_0X4_NROWS;
                                if tail_len > 0 {
                                    let tail = &mut out[n_groups * ferrox_quant::Q4_0X4_NROWS..];
                                    if serial || Self::prefer_serial_matvec(tail_len, *cols) {
                                        for (i, o) in tail.iter_mut().enumerate() {
                                            let r = n_groups * ferrox_quant::Q4_0X4_NROWS + i;
                                            let row =
                                                &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                            *o = ferrox_quant::dot_q4_0_q8(row, &act);
                                        }
                                    } else {
                                        let min_len = Self::min_rows_per_task(tail_len);
                                        tail.par_iter_mut()
                                            .with_min_len(min_len)
                                            .enumerate()
                                            .for_each(|(i, o)| {
                                                let r = n_groups * ferrox_quant::Q4_0X4_NROWS + i;
                                                let row =
                                                    &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                                *o = ferrox_quant::dot_q4_0_q8(row, &act);
                                            });
                                    }
                                }
                                return out;
                            }
                            if serial {
                                for (r, o) in out.iter_mut().enumerate() {
                                    let row = &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                    *o = ferrox_quant::dot_q4_0_q8(row, &act);
                                }
                            } else {
                                out.par_iter_mut()
                                    .with_min_len(Self::min_rows_per_task(*rows))
                                    .enumerate()
                                    .for_each(|(r, o)| {
                                        let row =
                                            &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                        *o = ferrox_quant::dot_q4_0_q8(row, &act);
                                    });
                            }
                            return out;
                        }
                        QuantKind::Q4K if x.len().is_multiple_of(256) => {
                            let act = ferrox_quant::quantize_activations_q8_k(x);
                            let n_groups = *rows / ferrox_quant::Q4_KX8_NROWS;
                            if n_groups > 0 {
                                let interleave = ferrox_quant::q4_kx8_interleave();
                                let packed = get_or_repack_q4k(data.as_slice(), *rows, *cols);
                                out[..n_groups * ferrox_quant::Q4_KX8_NROWS]
                                    .par_chunks_mut(ferrox_quant::Q4_KX8_NROWS)
                                    .with_min_len(Self::min_rows_per_task(n_groups).max(1))
                                    .enumerate()
                                    .for_each(|(g, chunk)| {
                                        ferrox_quant::gemv_q4_kx8_group(
                                            &packed, g, &act, *cols, interleave, chunk,
                                        );
                                    });
                                let data_slice = data.as_slice();
                                out[n_groups * ferrox_quant::Q4_KX8_NROWS..]
                                    .par_iter_mut()
                                    .with_min_len(Self::min_rows_per_task(
                                        *rows - n_groups * ferrox_quant::Q4_KX8_NROWS,
                                    ))
                                    .enumerate()
                                    .for_each(|(i, o)| {
                                        let r = n_groups * ferrox_quant::Q4_KX8_NROWS + i;
                                        let row = &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                        *o = ferrox_quant::dot_q4_k_q8(row, &act);
                                    });
                                return out;
                            }
                            out.par_iter_mut()
                                .with_min_len(Self::min_rows_per_task(*rows))
                                .enumerate()
                                .for_each(|(r, o)| {
                                    let row = &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                    *o = ferrox_quant::dot_q4_k_q8(row, &act);
                                });
                            return out;
                        }
                        QuantKind::Q5K if x.len().is_multiple_of(256) => {
                            let act = ferrox_quant::quantize_activations_q8_k(x);
                            let n_groups = *rows / ferrox_quant::Q5_KX8_NROWS;
                            if n_groups > 0 {
                                let interleave = ferrox_quant::q5_kx8_interleave();
                                let packed = get_or_repack_q5k(data.as_slice(), *rows, *cols);
                                out[..n_groups * ferrox_quant::Q5_KX8_NROWS]
                                    .par_chunks_mut(ferrox_quant::Q5_KX8_NROWS)
                                    .with_min_len(Self::min_rows_per_task(n_groups).max(1))
                                    .enumerate()
                                    .for_each(|(g, chunk)| {
                                        ferrox_quant::gemv_q5_kx8_group(
                                            &packed, g, &act, *cols, interleave, chunk,
                                        );
                                    });
                                let data_slice = data.as_slice();
                                out[n_groups * ferrox_quant::Q5_KX8_NROWS..]
                                    .par_iter_mut()
                                    .with_min_len(Self::min_rows_per_task(
                                        *rows - n_groups * ferrox_quant::Q5_KX8_NROWS,
                                    ))
                                    .enumerate()
                                    .for_each(|(i, o)| {
                                        let r = n_groups * ferrox_quant::Q5_KX8_NROWS + i;
                                        let row = &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                        *o = ferrox_quant::dot_q5_k_q8(row, &act);
                                    });
                                return out;
                            }
                            out.par_iter_mut()
                                .with_min_len(Self::min_rows_per_task(*rows))
                                .enumerate()
                                .for_each(|(r, o)| {
                                    let row = &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                    *o = ferrox_quant::dot_q5_k_q8(row, &act);
                                });
                            return out;
                        }
                        QuantKind::Q6K if x.len().is_multiple_of(256) => {
                            let act = ferrox_quant::quantize_activations_q8_k(x);
                            let n_groups = *rows / ferrox_quant::Q6_KX8_NROWS;
                            if n_groups > 0 {
                                let interleave = ferrox_quant::q6_kx8_interleave();
                                let packed = get_or_repack_q6k(data.as_slice(), *rows, *cols);
                                out[..n_groups * ferrox_quant::Q6_KX8_NROWS]
                                    .par_chunks_mut(ferrox_quant::Q6_KX8_NROWS)
                                    .with_min_len(Self::min_rows_per_task(n_groups).max(1))
                                    .enumerate()
                                    .for_each(|(g, out8)| {
                                        ferrox_quant::gemv_q6_kx8_group(
                                            &packed, g, &act, *cols, interleave, out8,
                                        );
                                    });
                                out[n_groups * ferrox_quant::Q6_KX8_NROWS..]
                                    .par_iter_mut()
                                    .with_min_len(Self::min_rows_per_task(
                                        *rows - n_groups * ferrox_quant::Q6_KX8_NROWS,
                                    ))
                                    .enumerate()
                                    .for_each(|(i, o)| {
                                        let r = n_groups * ferrox_quant::Q6_KX8_NROWS + i;
                                        let row =
                                            &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                        *o = ferrox_quant::dot_q6_k_q8(row, &act);
                                    });
                                return out;
                            }
                            out.par_iter_mut()
                                .with_min_len(Self::min_rows_per_task(*rows))
                                .enumerate()
                                .for_each(|(r, o)| {
                                    let row = &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                    *o = ferrox_quant::dot_q6_k_q8(row, &act);
                                });
                            return out;
                        }
                        _ => {}
                    }
                }
                out.par_iter_mut()
                    .with_min_len(Self::min_rows_per_task(*rows))
                    .enumerate()
                    .for_each(|(r, o)| {
                        let row = &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                        *o = Self::dot(*kind, row, x);
                    });
                out
            }
            WeightMatrix::Mxfp4 {
                packed,
                scale,
                rows,
                cols,
            } => {
                let packed_row_bytes = cols / 2;
                let scale_row_bytes = cols / ferrox_quant::MXFP4_GROUP_SIZE;
                let mut out = vec![0f32; *rows];
                out.par_iter_mut()
                    .with_min_len(Self::min_rows_per_task(*rows))
                    .enumerate()
                    .for_each(|(r, o)| {
                        let prow =
                            &packed.as_slice()[r * packed_row_bytes..(r + 1) * packed_row_bytes];
                        let srow =
                            &scale.as_slice()[r * scale_row_bytes..(r + 1) * scale_row_bytes];
                        *o = ferrox_quant::dot_mxfp4_row_f32(prow, srow, x);
                    });
                out
            }
        }
    }

    /// INT_DOT matvec against a pre-quantized Q8_0 activation (shared gate/up).
    pub fn apply_cpu_q8(&self, act: &ferrox_quant::Q8Activations) -> Option<Vec<f32>> {
        let WeightMatrix::Quantized {
            data,
            rows,
            cols,
            kind,
        } = self
        else {
            return None;
        };
        if !matches!(*kind, QuantKind::Q8_0 | QuantKind::Q4_0) || !cpu_int_dot_enabled() {
            return None;
        }
        if act.q.len() != *cols || !cols.is_multiple_of(32) {
            return None;
        }
        let row_bytes = self.block_bytes_per_row(*kind, *cols);
        let mut out = vec![0f32; *rows];
        let kind = *kind;
        let data = data.as_slice();
        // Q8_0×4 / Q4_0×4 interleaved GEMV — same paths as `apply_cpu` so
        // dense FFN gate+up hit the fast kernels, not per-row int dots.
        if matches!(kind, QuantKind::Q8_0) {
            let n_groups = *rows / ferrox_quant::Q8_0X4_NROWS;
            if n_groups > 0 {
                let packed = get_or_repack_q8x4(data, *rows, *cols);
                let serial = Self::prefer_serial_matvec(*rows, *cols);
                let body = |g: usize, chunk: &mut [f32]| {
                    ferrox_quant::gemv_q8_0x4_group(
                        &packed,
                        g,
                        act,
                        *cols,
                        ferrox_quant::q8_0x4_interleave(),
                        chunk,
                    );
                };
                if serial {
                    for (g, chunk) in out[..n_groups * ferrox_quant::Q8_0X4_NROWS]
                        .chunks_mut(ferrox_quant::Q8_0X4_NROWS)
                        .enumerate()
                    {
                        body(g, chunk);
                    }
                } else {
                    out[..n_groups * ferrox_quant::Q8_0X4_NROWS]
                        .par_chunks_mut(ferrox_quant::Q8_0X4_NROWS)
                        .with_min_len(Self::min_rows_per_task(n_groups).max(1))
                        .enumerate()
                        .for_each(|(g, chunk)| body(g, chunk));
                }
                let tail_len = *rows - n_groups * ferrox_quant::Q8_0X4_NROWS;
                if tail_len > 0 {
                    let tail = &mut out[n_groups * ferrox_quant::Q8_0X4_NROWS..];
                    if serial || Self::prefer_serial_matvec(tail_len, *cols) {
                        for (i, o) in tail.iter_mut().enumerate() {
                            let r = n_groups * ferrox_quant::Q8_0X4_NROWS + i;
                            *o = ferrox_quant::dot_q8_0_q8(
                                &data[r * row_bytes..(r + 1) * row_bytes],
                                act,
                            );
                        }
                    } else {
                        let min_len = Self::min_rows_per_task(tail_len);
                        tail.par_iter_mut()
                            .with_min_len(min_len)
                            .enumerate()
                            .for_each(|(i, o)| {
                                let r = n_groups * ferrox_quant::Q8_0X4_NROWS + i;
                                *o = ferrox_quant::dot_q8_0_q8(
                                    &data[r * row_bytes..(r + 1) * row_bytes],
                                    act,
                                );
                            });
                    }
                }
                return Some(out);
            }
        }
        if matches!(kind, QuantKind::Q4_0) {
            let n_groups = *rows / ferrox_quant::Q4_0X4_NROWS;
            if n_groups > 0 {
                let packed = get_or_repack_q4_0x4(data, *rows, *cols);
                let serial = Self::prefer_serial_matvec(*rows, *cols);
                let body = |g: usize, chunk: &mut [f32]| {
                    ferrox_quant::gemv_q4_0x4_group(
                        &packed,
                        g,
                        act,
                        *cols,
                        ferrox_quant::q4_0x4_interleave(),
                        chunk,
                    );
                };
                if serial {
                    for (g, chunk) in out[..n_groups * ferrox_quant::Q4_0X4_NROWS]
                        .chunks_mut(ferrox_quant::Q4_0X4_NROWS)
                        .enumerate()
                    {
                        body(g, chunk);
                    }
                } else {
                    out[..n_groups * ferrox_quant::Q4_0X4_NROWS]
                        .par_chunks_mut(ferrox_quant::Q4_0X4_NROWS)
                        .with_min_len(Self::min_rows_per_task(n_groups).max(1))
                        .enumerate()
                        .for_each(|(g, chunk)| body(g, chunk));
                }
                let tail_len = *rows - n_groups * ferrox_quant::Q4_0X4_NROWS;
                if tail_len > 0 {
                    let tail = &mut out[n_groups * ferrox_quant::Q4_0X4_NROWS..];
                    if serial || Self::prefer_serial_matvec(tail_len, *cols) {
                        for (i, o) in tail.iter_mut().enumerate() {
                            let r = n_groups * ferrox_quant::Q4_0X4_NROWS + i;
                            *o = ferrox_quant::dot_q4_0_q8(
                                &data[r * row_bytes..(r + 1) * row_bytes],
                                act,
                            );
                        }
                    } else {
                        let min_len = Self::min_rows_per_task(tail_len);
                        tail.par_iter_mut()
                            .with_min_len(min_len)
                            .enumerate()
                            .for_each(|(i, o)| {
                                let r = n_groups * ferrox_quant::Q4_0X4_NROWS + i;
                                *o = ferrox_quant::dot_q4_0_q8(
                                    &data[r * row_bytes..(r + 1) * row_bytes],
                                    act,
                                );
                            });
                    }
                }
                return Some(out);
            }
        }
        if Self::prefer_serial_matvec(*rows, *cols) {
            for (r, o) in out.iter_mut().enumerate() {
                let row = &data[r * row_bytes..(r + 1) * row_bytes];
                *o = match kind {
                    QuantKind::Q8_0 => ferrox_quant::dot_q8_0_q8(row, act),
                    QuantKind::Q4_0 => ferrox_quant::dot_q4_0_q8(row, act),
                    _ => unreachable!(),
                };
            }
            return Some(out);
        }
        out.par_iter_mut()
            .with_min_len(Self::min_rows_per_task(*rows))
            .enumerate()
            .for_each(|(r, o)| {
                let row = &data[r * row_bytes..(r + 1) * row_bytes];
                *o = match kind {
                    QuantKind::Q8_0 => ferrox_quant::dot_q8_0_q8(row, act),
                    QuantKind::Q4_0 => ferrox_quant::dot_q4_0_q8(row, act),
                    _ => unreachable!(),
                };
            });
        Some(out)
    }

    /// Two contiguous rows × one Q8 act (shared act loads). Q4_0 uses
    /// [`ferrox_quant::dot_q4_0_q8_2row`]; Q8_0 falls back to two singles.
    pub fn dot_pair_cpu_q8(
        &self,
        row: usize,
        act: &ferrox_quant::Q8Activations,
    ) -> Option<(f32, f32)> {
        let WeightMatrix::Quantized {
            data,
            rows,
            cols,
            kind,
        } = self
        else {
            return None;
        };
        if !matches!(*kind, QuantKind::Q8_0 | QuantKind::Q4_0) || !cpu_int_dot_enabled() {
            return None;
        }
        if act.q.len() != *cols || !cols.is_multiple_of(32) || row + 1 >= *rows {
            return None;
        }
        let row_bytes = self.block_bytes_per_row(*kind, *cols);
        let bytes = data.as_slice();
        let r0 = &bytes[row * row_bytes..(row + 1) * row_bytes];
        let r1 = &bytes[(row + 1) * row_bytes..(row + 2) * row_bytes];
        Some(match *kind {
            QuantKind::Q4_0 => ferrox_quant::dot_q4_0_q8_2row(r0, r1, act),
            QuantKind::Q8_0 => (
                ferrox_quant::dot_q8_0_q8(r0, act),
                ferrox_quant::dot_q8_0_q8(r1, act),
            ),
            _ => unreachable!(),
        })
    }

    /// Single-row INT_DOT against pre-quantized Q8_0 acts (llama `mul_mat_id`
    /// inner loop). Returns `None` if this matrix is not Q4_0/Q8_0 INT_DOT.
    pub fn dot_row_cpu_q8(&self, row: usize, act: &ferrox_quant::Q8Activations) -> Option<f32> {
        let WeightMatrix::Quantized {
            data,
            rows,
            cols,
            kind,
        } = self
        else {
            return None;
        };
        if row >= *rows
            || !matches!(*kind, QuantKind::Q8_0 | QuantKind::Q4_0)
            || !cpu_int_dot_enabled()
            || act.q.len() != *cols
            || !cols.is_multiple_of(32)
        {
            return None;
        }
        let row_bytes = self.block_bytes_per_row(*kind, *cols);
        let bytes = &data.as_slice()[row * row_bytes..(row + 1) * row_bytes];
        Some(match *kind {
            QuantKind::Q8_0 => ferrox_quant::dot_q8_0_q8(bytes, act),
            QuantKind::Q4_0 => ferrox_quant::dot_q4_0_q8(bytes, act),
            _ => unreachable!(),
        })
    }

    /// Computes `W @ X` for a *batch* of activation vectors at once:
    /// `x_batch` is `batch_size` rows of `self.cols()` elements each,
    /// flattened row-major; returns `batch_size` rows of
    /// `self.rows()` elements each, flattened row-major (`[batch,
    /// rows]`, matching the layout `Tensor`/`Decoder` expect for
    /// chaining into further matmuls).
    ///
    /// This is not just a convenience wrapper: for a quantized matrix,
    /// each weight row's bytes are read from memory *once* and dotted
    /// against every activation in the batch, instead of once per
    /// `apply` call. For a memory-bandwidth-bound quantized matmul --
    /// which fused Q8_0/Q4_0 dot products are, since the whole point of
    /// keeping weights quantized is that reading them is the
    /// bottleneck, not the arithmetic -- processing `batch_size`
    /// positions this way costs roughly the same *memory traffic* as
    /// processing one position, not `batch_size` times as much. This
    /// is the same reason speculative-decoding verification and batched
    /// prefill are faster per-token than sequential single-token decode
    /// on real hardware: it turns `batch_size` separate reads of the
    /// same weights into one.
    ///
    /// With Metal dense enabled, dispatches a single batched Metal
    /// command buffer — Q4_K/Q6_K use
    /// [`ferrox_metal::gpu::launch_q4_k_matmul_batch`] /
    /// [`ferrox_metal::gpu::launch_q6_k_matmul_batch`] when
    /// `batch_size >= 2`; other kinds use
    /// [`ferrox_metal::gpu::launch_matvec_batch`]. Falls back to
    /// per-row [`Self::apply`] if the batch launch fails.
    pub fn apply_batch(&self, x_batch: &[f32], batch_size: usize) -> Vec<f32> {
        self.apply_batch_with_acts(x_batch, batch_size, None)
    }

    /// Quantize `x_batch` once, in the activation format this matrix's
    /// INT_DOT batch path consumes, for sharing across every projection
    /// that reads the same input (q/k/v on one normed batch; gate/up on
    /// another). Returns `None` when [`Self::apply_batch`] would not use
    /// quantized activations for this matrix — GPU dispatch, INT_DOT off,
    /// unsupported kind or width — so callers can pass the result straight
    /// to [`Self::apply_batch_with_acts`] unconditionally.
    pub fn quantize_batch_acts(&self, x_batch: &[f32], batch_size: usize) -> Option<BatchActs> {
        #[cfg(feature = "metal")]
        {
            if metal_dense_enabled()
                && matches!(
                    self,
                    WeightMatrix::Quantized { kind, .. } if Self::metal_kind_supported(*kind)
                )
            {
                return None;
            }
        }
        #[cfg(feature = "cuda")]
        {
            if cuda_dense_enabled() && matches!(self, WeightMatrix::Quantized { .. }) {
                return None;
            }
        }
        let WeightMatrix::Quantized { cols, kind, .. } = self else {
            return None;
        };
        if !cpu_int_dot_enabled() || x_batch.len() != batch_size * cols {
            return None;
        }
        match kind {
            QuantKind::Q8_0 | QuantKind::Q4_0 if cols.is_multiple_of(32) => Some(BatchActs::Q8(
                (0..batch_size)
                    .into_par_iter()
                    .map(|b| {
                        ferrox_quant::quantize_activations_q8(&x_batch[b * cols..(b + 1) * cols])
                    })
                    .collect(),
            )),
            QuantKind::Q4K | QuantKind::Q5K | QuantKind::Q6K if cols.is_multiple_of(256) => {
                Some(BatchActs::Q8K(
                    (0..batch_size)
                        .into_par_iter()
                        .map(|b| {
                            ferrox_quant::quantize_activations_q8_k(
                                &x_batch[b * cols..(b + 1) * cols],
                            )
                        })
                        .collect(),
                ))
            }
            _ => None,
        }
    }

    /// [`Self::apply_batch`], optionally reusing a shared pre-quantized
    /// activation batch from [`Self::quantize_batch_acts`]. A `shared`
    /// value whose format or length does not match this matrix is simply
    /// ignored (the activations are re-quantized locally), so mixed-kind
    /// projection groups stay correct.
    pub fn apply_batch_with_acts(
        &self,
        x_batch: &[f32],
        batch_size: usize,
        shared: Option<&BatchActs>,
    ) -> Vec<f32> {
        let cols = self.cols();
        assert_eq!(
            x_batch.len(),
            batch_size * cols,
            "x_batch length must be batch_size * cols"
        );
        if batch_size == 0 {
            return Vec::new();
        }

        /// Raw pointer to this function's `[batch][rows]` output, shared
        /// across rayon tasks.
        ///
        /// Parallelism is over weight rows, but a row's `batch_size` output
        /// slots (`out[b * rows + r]` for every `b`) interleave with every
        /// other row's, so they cannot be handed out as disjoint `&mut`
        /// chunks. Each task writes only the rows it owns, which keeps the
        /// writes race-free; this wrapper just carries the pointer across
        /// the `Send`/`Sync` boundary. Writing straight into the final
        /// layout kills what used to be here: a `[rows][batch]` staging vec
        /// (zeroed every call) plus a serial rows × batch transpose after
        /// the parallel section had already finished.
        #[derive(Clone, Copy)]
        struct BatchOut(*mut f32);
        unsafe impl Send for BatchOut {}
        unsafe impl Sync for BatchOut {}
        impl BatchOut {
            /// Safety: `idx` in bounds, and concurrent tasks never pass
            /// the same `idx` (they own disjoint row sets).
            #[inline]
            unsafe fn set(self, idx: usize, v: f32) {
                *self.0.add(idx) = v;
            }
        }

        #[cfg(feature = "metal")]
        {
            if metal_dense_enabled()
                && matches!(
                    self,
                    WeightMatrix::Quantized { kind, .. } if Self::metal_kind_supported(*kind)
                )
            {
                if let Some(out) = self.apply_gpu_batch(x_batch, batch_size) {
                    return out;
                }
                // The kind is Metal-supported, so reaching here means a
                // launch failed and the batch degrades to `batch_size`
                // separate `apply` calls -- each its own command buffer,
                // commit and wait.
                crate::kernel_registry::miss(
                    crate::kernel_registry::Lookup::new(
                        crate::kernel_registry::Backend::Metal,
                        crate::kernel_registry::op::GEMM_PREFILL,
                        self.quant_kind(),
                    ),
                    "N x apply (one command buffer each)",
                );
                let rows = self.rows();
                let mut out = vec![0f32; batch_size * rows];
                for b in 0..batch_size {
                    let y = self.apply(&x_batch[b * cols..(b + 1) * cols]);
                    out[b * rows..(b + 1) * rows].copy_from_slice(&y);
                }
                return out;
            } else if metal_dense_enabled() {
                // Metal is on but this matrix has no Metal kernel at
                // all, so the whole GEMM runs on the CPU. For a
                // quantized weight that is the IQ4_XS shape exactly; for
                // an F32 one it is the documented host GEMM.
                let look = crate::kernel_registry::Lookup::new(
                    crate::kernel_registry::Backend::Metal,
                    crate::kernel_registry::op::GEMM_PREFILL,
                    self.quant_kind(),
                );
                if self.quant_kind().is_some() {
                    crate::kernel_registry::miss(look, "CPU apply_batch");
                } else {
                    crate::kernel_registry::miss_by_design(look, "CPU f32 GEMM");
                }
            }
        }

        // CUDA has no batched GEMM yet, but `apply` does dispatch a real
        // CUDA matvec per position. Without this arm a batched prefill
        // fell through to the CPU branch below and never touched the
        // GPU at all -- measured on an RTX 4090, SmolLM2 `pp512` ran at
        // 28 tok/s against llama.cpp's 57466. Per-position matvec is
        // still the wrong shape (see ROADMAP: CUDA needs `mul_mm`), but
        // it is the GPU rather than 26 idle SMs.
        #[cfg(feature = "cuda")]
        {
            if cuda_dense_enabled()
                && matches!(self, WeightMatrix::Quantized { .. })
                && self.apply_gpu(&x_batch[..cols]).is_some()
            {
                let rows = self.rows();
                let mut out = vec![0f32; batch_size * rows];
                for b in 0..batch_size {
                    match self.apply_gpu(&x_batch[b * cols..(b + 1) * cols]) {
                        Some(y) => out[b * rows..(b + 1) * rows].copy_from_slice(&y),
                        None => {
                            let y = self.apply(&x_batch[b * cols..(b + 1) * cols]);
                            out[b * rows..(b + 1) * rows].copy_from_slice(&y);
                        }
                    }
                }
                return out;
            }
        }

        match self {
            WeightMatrix::F32(t) => {
                let xt = Tensor::new(x_batch.to_vec(), vec![batch_size, cols]);
                crate::matmul::matmul_f32(&xt, t).data
            }
            WeightMatrix::Quantized {
                data,
                rows,
                cols: _,
                kind,
            } => {
                let row_bytes = self.block_bytes_per_row(*kind, cols);
                // Written directly in the [batch, rows] layout the function
                // returns: each parallel task owns a disjoint set of rows
                // `r` and scatters `out[b * rows + r]` for every `b`
                // through `BatchOut`.
                let mut out = vec![0f32; batch_size * rows];
                let out_w = BatchOut(out.as_mut_ptr());

                // Prefill INT_DOT: quantize each activation once, then
                // reuse Q8 packs across all weight rows (llama CPU path).
                if cpu_int_dot_enabled() {
                    match *kind {
                        QuantKind::Q8_0 if cols.is_multiple_of(32) => {
                            let acts_owned: Vec<_>;
                            let acts: &[ferrox_quant::Q8Activations] = match shared {
                                Some(BatchActs::Q8(a)) if a.len() == batch_size => a,
                                _ => {
                                    acts_owned = (0..batch_size)
                                        .into_par_iter()
                                        .map(|b| {
                                            ferrox_quant::quantize_activations_q8(
                                                &x_batch[b * cols..(b + 1) * cols],
                                            )
                                        })
                                        .collect();
                                    &acts_owned
                                }
                            };
                            let n_groups = *rows / ferrox_quant::Q8_0X4_NROWS;
                            if n_groups > 0 {
                                let packed = get_or_repack_q8x4(data.as_slice(), *rows, cols);
                                let nrows_g = ferrox_quant::Q8_0X4_NROWS;
                                let interleave = ferrox_quant::q8_0x4_interleave();
                                if ferrox_quant::q8_0x4_gemm_uses_acts_x4(interleave) {
                                    // i8mm: interleave each quad of
                                    // activations once per matmul (llama.cpp
                                    // `ggml_quantize_mat_q8_0_4x8` into
                                    // `wdata`); every row-group reuses it.
                                    let nc = ferrox_quant::Q8K_ACTS_X4_NC;
                                    let act_tiles: Vec<ferrox_quant::Q8ActsX4> = acts
                                        .par_chunks(nc)
                                        .map(|chunk| ferrox_quant::prepare_q8_acts_x4(chunk, cols))
                                        .collect();
                                    Self::par_chunked_groups(
                                        n_groups,
                                        nrows_g,
                                        act_tiles.len(),
                                        nc,
                                        |g, t0, t1| {
                                            let mut tmp = [0f32;
                                                ferrox_quant::Q8_0X4_NROWS
                                                    * ferrox_quant::Q8K_ACTS_X4_NC];
                                            for (t, tile) in act_tiles[t0..t1].iter().enumerate() {
                                                let t = t0 + t;
                                                let n = tile.na;
                                                let tmp = &mut tmp[..nrows_g * n];
                                                ferrox_quant::gemm_q8_0x4_group_x4(
                                                    &packed, g, tile, cols, interleave, tmp,
                                                );
                                                for j in 0..n {
                                                    let col = (t * nc + j) * rows + g * nrows_g;
                                                    for r in 0..nrows_g {
                                                        unsafe {
                                                            out_w.set(col + r, tmp[r * n + j]);
                                                        }
                                                    }
                                                }
                                            }
                                        },
                                    );
                                } else {
                                    // GEMM, not a GEMV per position: the
                                    // batched kernel writes a `[row][batch]`
                                    // span, and the group's weight vectors
                                    // stay in registers across a tile of
                                    // activations. The span is then scattered
                                    // into the [batch][rows] output right
                                    // here, in parallel.
                                    let span = ferrox_quant::Q8_0X4_GEMM_NC;
                                    let n_tiles = batch_size.div_ceil(span);
                                    Self::par_chunked_groups(
                                        n_groups,
                                        nrows_g,
                                        n_tiles,
                                        span,
                                        |g, t0, t1| {
                                            let b0 = t0 * span;
                                            let b1 = (t1 * span).min(batch_size);
                                            let n = b1 - b0;
                                            let mut group = vec![0f32; nrows_g * n];
                                            ferrox_quant::gemm_q8_0x4_group(
                                                &packed,
                                                g,
                                                &acts[b0..b1],
                                                cols,
                                                interleave,
                                                &mut group,
                                            );
                                            for (bi, b) in (b0..b1).enumerate() {
                                                for r in 0..nrows_g {
                                                    unsafe {
                                                        out_w.set(
                                                            b * rows + g * nrows_g + r,
                                                            group[r * n + bi],
                                                        );
                                                    }
                                                }
                                            }
                                        },
                                    );
                                }
                                let data_slice = data.as_slice();
                                let tail = *rows - n_groups * ferrox_quant::Q8_0X4_NROWS;
                                (0..tail)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(tail))
                                    .for_each(|i| {
                                        let r = n_groups * ferrox_quant::Q8_0X4_NROWS + i;
                                        let row = &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                        for (b, act) in acts.iter().enumerate() {
                                            unsafe {
                                                out_w.set(
                                                    b * rows + r,
                                                    ferrox_quant::dot_q8_0_q8(row, act),
                                                );
                                            }
                                        }
                                    });
                            } else {
                                (0..*rows)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(*rows))
                                    .for_each(|r| {
                                        let row =
                                            &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                        for (b, act) in acts.iter().enumerate() {
                                            unsafe {
                                                out_w.set(
                                                    b * rows + r,
                                                    ferrox_quant::dot_q8_0_q8(row, act),
                                                );
                                            }
                                        }
                                    });
                            }
                            return out;
                        }
                        QuantKind::Q4_0 if cols.is_multiple_of(32) => {
                            let acts_owned: Vec<_>;
                            let acts: &[ferrox_quant::Q8Activations] = match shared {
                                Some(BatchActs::Q8(a)) if a.len() == batch_size => a,
                                _ => {
                                    acts_owned = (0..batch_size)
                                        .into_par_iter()
                                        .map(|b| {
                                            ferrox_quant::quantize_activations_q8(
                                                &x_batch[b * cols..(b + 1) * cols],
                                            )
                                        })
                                        .collect();
                                    &acts_owned
                                }
                            };
                            let n_groups = *rows / ferrox_quant::Q4_0X4_NROWS;
                            if n_groups > 0 {
                                let packed = get_or_repack_q4_0x4(data.as_slice(), *rows, cols);
                                let nrows_g = ferrox_quant::Q4_0X4_NROWS;
                                let interleave = ferrox_quant::q4_0x4_interleave();
                                if ferrox_quant::q4_0x4_gemm_uses_acts_x4(interleave) {
                                    // i8mm: same once-per-matmul activation
                                    // quad hoist as the Q8_0 arm above.
                                    let nc = ferrox_quant::Q8K_ACTS_X4_NC;
                                    let act_tiles: Vec<ferrox_quant::Q8ActsX4> = acts
                                        .par_chunks(nc)
                                        .map(|chunk| ferrox_quant::prepare_q8_acts_x4(chunk, cols))
                                        .collect();
                                    Self::par_chunked_groups(
                                        n_groups,
                                        nrows_g,
                                        act_tiles.len(),
                                        nc,
                                        |g, t0, t1| {
                                            let mut tmp = [0f32;
                                                ferrox_quant::Q4_0X4_NROWS
                                                    * ferrox_quant::Q8K_ACTS_X4_NC];
                                            for (t, tile) in act_tiles[t0..t1].iter().enumerate() {
                                                let t = t0 + t;
                                                let n = tile.na;
                                                let tmp = &mut tmp[..nrows_g * n];
                                                ferrox_quant::gemm_q4_0x4_group_x4(
                                                    &packed, g, tile, cols, interleave, tmp,
                                                );
                                                for j in 0..n {
                                                    let col = (t * nc + j) * rows + g * nrows_g;
                                                    for r in 0..nrows_g {
                                                        unsafe {
                                                            out_w.set(col + r, tmp[r * n + j]);
                                                        }
                                                    }
                                                }
                                            }
                                        },
                                    );
                                } else {
                                    // GEMM, not a GEMV per position: the
                                    // batched kernel writes a `[row][batch]`
                                    // span, and the group's weight vectors
                                    // stay in registers across a tile of
                                    // activations. The span is then scattered
                                    // into the [batch][rows] output right
                                    // here, in parallel.
                                    let span = ferrox_quant::Q8_0X4_GEMM_NC;
                                    let n_tiles = batch_size.div_ceil(span);
                                    Self::par_chunked_groups(
                                        n_groups,
                                        nrows_g,
                                        n_tiles,
                                        span,
                                        |g, t0, t1| {
                                            let b0 = t0 * span;
                                            let b1 = (t1 * span).min(batch_size);
                                            let n = b1 - b0;
                                            let mut group = vec![0f32; nrows_g * n];
                                            ferrox_quant::gemm_q4_0x4_group(
                                                &packed,
                                                g,
                                                &acts[b0..b1],
                                                cols,
                                                interleave,
                                                &mut group,
                                            );
                                            for (bi, b) in (b0..b1).enumerate() {
                                                for r in 0..nrows_g {
                                                    unsafe {
                                                        out_w.set(
                                                            b * rows + g * nrows_g + r,
                                                            group[r * n + bi],
                                                        );
                                                    }
                                                }
                                            }
                                        },
                                    );
                                }
                                let data_slice = data.as_slice();
                                let tail = *rows - n_groups * ferrox_quant::Q4_0X4_NROWS;
                                (0..tail)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(tail))
                                    .for_each(|i| {
                                        let r = n_groups * ferrox_quant::Q4_0X4_NROWS + i;
                                        let row = &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                        for (b, act) in acts.iter().enumerate() {
                                            unsafe {
                                                out_w.set(
                                                    b * rows + r,
                                                    ferrox_quant::dot_q4_0_q8(row, act),
                                                );
                                            }
                                        }
                                    });
                            } else {
                                (0..*rows)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(*rows))
                                    .for_each(|r| {
                                        let row =
                                            &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                        for (b, act) in acts.iter().enumerate() {
                                            unsafe {
                                                out_w.set(
                                                    b * rows + r,
                                                    ferrox_quant::dot_q4_0_q8(row, act),
                                                );
                                            }
                                        }
                                    });
                            }
                            return out;
                        }
                        QuantKind::Q4K if cols.is_multiple_of(256) => {
                            let acts_owned: Vec<_>;
                            let acts: &[ferrox_quant::Q8KActivations] = match shared {
                                Some(BatchActs::Q8K(a)) if a.len() == batch_size => a,
                                _ => {
                                    acts_owned = (0..batch_size)
                                        .into_par_iter()
                                        .map(|b| {
                                            ferrox_quant::quantize_activations_q8_k(
                                                &x_batch[b * cols..(b + 1) * cols],
                                            )
                                        })
                                        .collect();
                                    &acts_owned
                                }
                            };
                            let n_groups = *rows / ferrox_quant::Q4_KX8_NROWS;
                            if n_groups > 0 {
                                let interleave = ferrox_quant::q4_kx8_interleave();
                                let packed = get_or_repack_q4k(data.as_slice(), *rows, cols);
                                let nc = ferrox_quant::Q4_KX8_GEMM_NC;
                                // On the i8mm path, interleave each quad of
                                // activations once per matmul (llama.cpp
                                // `ggml_quantize_mat_q8_K_4x8` into `wdata`);
                                // the kernel used to redo it per row-group.
                                let act_tiles: Vec<ferrox_quant::Q8KActsX4> =
                                    if ferrox_quant::q4_kx8_gemm_uses_acts_x4(interleave) {
                                        acts.par_chunks(nc)
                                            .map(|chunk| {
                                                ferrox_quant::prepare_q8_k_acts_x4(chunk, cols)
                                            })
                                            .collect()
                                    } else {
                                        Vec::new()
                                    };
                                let n_tiles = batch_size.div_ceil(nc);
                                Self::par_chunked_groups(
                                    n_groups,
                                    ferrox_quant::Q4_KX8_NROWS,
                                    n_tiles,
                                    nc,
                                    |g, t0, t1| {
                                        let mut tile = [0f32;
                                            ferrox_quant::Q4_KX8_NROWS
                                                * ferrox_quant::Q4_KX8_GEMM_NC];
                                        for t in t0..t1 {
                                            let chunk =
                                                &acts[t * nc..((t + 1) * nc).min(batch_size)];
                                            let n = chunk.len();
                                            let tile = &mut tile[..ferrox_quant::Q4_KX8_NROWS * n];
                                            if act_tiles.is_empty() {
                                                ferrox_quant::gemm_q4_kx8_group(
                                                    &packed, g, chunk, cols, interleave, tile,
                                                );
                                            } else {
                                                ferrox_quant::gemm_q4_kx8_group_x4(
                                                    &packed,
                                                    g,
                                                    &act_tiles[t],
                                                    cols,
                                                    interleave,
                                                    tile,
                                                );
                                            }
                                            for j in 0..n {
                                                let col = (t * nc + j) * rows
                                                    + g * ferrox_quant::Q4_KX8_NROWS;
                                                for r in 0..ferrox_quant::Q4_KX8_NROWS {
                                                    unsafe {
                                                        out_w.set(col + r, tile[r * n + j]);
                                                    }
                                                }
                                            }
                                        }
                                    },
                                );
                                let data_slice = data.as_slice();
                                let tail = *rows - n_groups * ferrox_quant::Q4_KX8_NROWS;
                                (0..tail)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(tail))
                                    .for_each(|i| {
                                        let r = n_groups * ferrox_quant::Q4_KX8_NROWS + i;
                                        let row = &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                        for (b, act) in acts.iter().enumerate() {
                                            unsafe {
                                                out_w.set(
                                                    b * rows + r,
                                                    ferrox_quant::dot_q4_k_q8(row, act),
                                                );
                                            }
                                        }
                                    });
                            } else {
                                (0..*rows)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(*rows))
                                    .for_each(|r| {
                                        let row =
                                            &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                                        for (b, act) in acts.iter().enumerate() {
                                            unsafe {
                                                out_w.set(
                                                    b * rows + r,
                                                    ferrox_quant::dot_q4_k_q8(row, act),
                                                );
                                            }
                                        }
                                    });
                            }
                            return out;
                        }
                        QuantKind::Q5K if cols.is_multiple_of(256) => {
                            let acts_owned: Vec<_>;
                            let acts: &[ferrox_quant::Q8KActivations] = match shared {
                                Some(BatchActs::Q8K(a)) if a.len() == batch_size => a,
                                _ => {
                                    acts_owned = (0..batch_size)
                                        .into_par_iter()
                                        .map(|b| {
                                            ferrox_quant::quantize_activations_q8_k(
                                                &x_batch[b * cols..(b + 1) * cols],
                                            )
                                        })
                                        .collect();
                                    &acts_owned
                                }
                            };
                            // Q5_Kx8 multi-act NEON GEMM amortizes weight unpack.
                            let use_kx8 = cfg!(target_arch = "aarch64");
                            let n_groups = if use_kx8 {
                                *rows / ferrox_quant::Q5_KX8_NROWS
                            } else {
                                0
                            };
                            if n_groups > 0 {
                                let interleave = ferrox_quant::q5_kx8_interleave();
                                let packed = get_or_repack_q5k(data.as_slice(), *rows, cols);
                                let nc = ferrox_quant::Q5_KX8_GEMM_NC;
                                // On the i8mm path, interleave each quad of
                                // activations once per matmul; the kernel
                                // consumes it for every row-group.
                                let act_tiles: Vec<ferrox_quant::Q8KActsX4> =
                                    if ferrox_quant::q5_kx8_gemm_uses_acts_x4(interleave) {
                                        acts.par_chunks(nc)
                                            .map(|chunk| {
                                                ferrox_quant::prepare_q8_k_acts_x4(chunk, cols)
                                            })
                                            .collect()
                                    } else {
                                        Vec::new()
                                    };
                                let n_tiles = batch_size.div_ceil(nc);
                                Self::par_chunked_groups(
                                    n_groups,
                                    ferrox_quant::Q5_KX8_NROWS,
                                    n_tiles,
                                    nc,
                                    |g, t0, t1| {
                                        let mut tile = [0f32;
                                            ferrox_quant::Q5_KX8_NROWS
                                                * ferrox_quant::Q5_KX8_GEMM_NC];
                                        for t in t0..t1 {
                                            let chunk =
                                                &acts[t * nc..((t + 1) * nc).min(batch_size)];
                                            let n = chunk.len();
                                            let tile = &mut tile[..ferrox_quant::Q5_KX8_NROWS * n];
                                            if act_tiles.is_empty() {
                                                ferrox_quant::gemm_q5_kx8_group(
                                                    &packed, g, chunk, cols, interleave, tile,
                                                );
                                            } else {
                                                ferrox_quant::gemm_q5_kx8_group_x4(
                                                    &packed,
                                                    g,
                                                    &act_tiles[t],
                                                    cols,
                                                    interleave,
                                                    tile,
                                                );
                                            }
                                            for j in 0..n {
                                                let col = (t * nc + j) * rows
                                                    + g * ferrox_quant::Q5_KX8_NROWS;
                                                for r in 0..ferrox_quant::Q5_KX8_NROWS {
                                                    unsafe {
                                                        out_w.set(col + r, tile[r * n + j]);
                                                    }
                                                }
                                            }
                                        }
                                    },
                                );
                                let data_slice = data.as_slice();
                                let tail = *rows - n_groups * ferrox_quant::Q5_KX8_NROWS;
                                (0..tail)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(tail))
                                    .for_each(|i| {
                                        let r = n_groups * ferrox_quant::Q5_KX8_NROWS + i;
                                        let row = &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                        for (b, act) in acts.iter().enumerate() {
                                            unsafe {
                                                out_w.set(
                                                    b * rows + r,
                                                    ferrox_quant::dot_q5_k_q8(row, act),
                                                );
                                            }
                                        }
                                    });
                            } else {
                                let data_slice = data.as_slice();
                                (0..*rows)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(*rows))
                                    .for_each(|r| {
                                        let row = &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                        let nc = ferrox_quant::Q5_K_GEMM_NC;
                                        for (t, chunk) in acts.chunks(nc).enumerate() {
                                            let n = chunk.len();
                                            let mut tmp = [0f32; ferrox_quant::Q5_K_GEMM_NC];
                                            ferrox_quant::gemm_q5_k_q8_row(
                                                row,
                                                chunk,
                                                &mut tmp[..n],
                                            );
                                            for (j, v) in tmp[..n].iter().enumerate() {
                                                unsafe {
                                                    out_w.set((t * nc + j) * rows + r, *v);
                                                }
                                            }
                                        }
                                    });
                            }
                            return out;
                        }
                        QuantKind::Q6K if cols.is_multiple_of(256) => {
                            let acts_owned: Vec<_>;
                            let acts: &[ferrox_quant::Q8KActivations] = match shared {
                                Some(BatchActs::Q8K(a)) if a.len() == batch_size => a,
                                _ => {
                                    acts_owned = (0..batch_size)
                                        .into_par_iter()
                                        .map(|b| {
                                            ferrox_quant::quantize_activations_q8_k(
                                                &x_batch[b * cols..(b + 1) * cols],
                                            )
                                        })
                                        .collect();
                                    &acts_owned
                                }
                            };
                            // Kx8 batch path only where the i8mm GEMM
                            // exists (the scalar Kx8 GEMM measured slower
                            // than the per-row NEON dot on Phi ffn_down,
                            // so everything else keeps the row path).
                            let interleave = ferrox_quant::q6_kx8_interleave();
                            let use_kx8 = ferrox_quant::q6_kx8_gemm_uses_acts_x4(interleave);
                            let n_groups = if use_kx8 {
                                *rows / ferrox_quant::Q6_KX8_NROWS
                            } else {
                                0
                            };
                            if n_groups > 0 {
                                let packed = get_or_repack_q6k(data.as_slice(), *rows, cols);
                                // Quads of 4 (the i8mm tile shape), not
                                // [`Q6_KX8_GEMM_NC`].
                                let nc = ferrox_quant::Q8K_ACTS_X4_NC;
                                let act_tiles: Vec<ferrox_quant::Q8KActsX4> = acts
                                    .par_chunks(nc)
                                    .map(|chunk| ferrox_quant::prepare_q8_k_acts_x4(chunk, cols))
                                    .collect();
                                let n_tiles = batch_size.div_ceil(nc);
                                Self::par_chunked_groups(
                                    n_groups,
                                    ferrox_quant::Q6_KX8_NROWS,
                                    n_tiles,
                                    nc,
                                    |g, t0, t1| {
                                        let mut tile = [0f32;
                                            ferrox_quant::Q6_KX8_NROWS
                                                * ferrox_quant::Q8K_ACTS_X4_NC];
                                        for t in t0..t1 {
                                            let chunk =
                                                &acts[t * nc..((t + 1) * nc).min(batch_size)];
                                            let n = chunk.len();
                                            let tile = &mut tile[..ferrox_quant::Q6_KX8_NROWS * n];
                                            ferrox_quant::gemm_q6_kx8_group_x4(
                                                &packed,
                                                g,
                                                &act_tiles[t],
                                                cols,
                                                interleave,
                                                tile,
                                            );
                                            for j in 0..n {
                                                let col = (t * nc + j) * rows
                                                    + g * ferrox_quant::Q6_KX8_NROWS;
                                                for r in 0..ferrox_quant::Q6_KX8_NROWS {
                                                    unsafe {
                                                        out_w.set(col + r, tile[r * n + j]);
                                                    }
                                                }
                                            }
                                        }
                                    },
                                );
                                let data_slice = data.as_slice();
                                let tail = *rows - n_groups * ferrox_quant::Q6_KX8_NROWS;
                                (0..tail)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(tail))
                                    .for_each(|i| {
                                        let r = n_groups * ferrox_quant::Q6_KX8_NROWS + i;
                                        let row = &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                        for (b, act) in acts.iter().enumerate() {
                                            unsafe {
                                                out_w.set(
                                                    b * rows + r,
                                                    ferrox_quant::dot_q6_k_q8(row, act),
                                                );
                                            }
                                        }
                                    });
                            } else {
                                let data_slice = data.as_slice();
                                (0..*rows)
                                    .into_par_iter()
                                    .with_min_len(Self::min_rows_per_task(*rows))
                                    .for_each(|r| {
                                        let row = &data_slice[r * row_bytes..(r + 1) * row_bytes];
                                        let nc = ferrox_quant::Q6_K_GEMM_NC;
                                        for (t, chunk) in acts.chunks(nc).enumerate() {
                                            let mut tmp = [0f32; ferrox_quant::Q6_K_GEMM_NC];
                                            let n = chunk.len();
                                            ferrox_quant::gemm_q6_k_q8_row(
                                                row,
                                                chunk,
                                                &mut tmp[..n],
                                            );
                                            for (j, v) in tmp[..n].iter().enumerate() {
                                                unsafe {
                                                    out_w.set((t * nc + j) * rows + r, *v);
                                                }
                                            }
                                        }
                                    });
                            }
                            return out;
                        }
                        QuantKind::Q5K | QuantKind::Q6K => {}
                        _ => {}
                    }
                }

                (0..*rows)
                    .into_par_iter()
                    .with_min_len(Self::min_rows_per_task(*rows))
                    .for_each(|r| {
                        let row = &data.as_slice()[r * row_bytes..(r + 1) * row_bytes];
                        for b in 0..batch_size {
                            let x = &x_batch[b * cols..(b + 1) * cols];
                            unsafe {
                                out_w.set(b * rows + r, Self::dot(*kind, row, x));
                            }
                        }
                    });
                out
            }
            WeightMatrix::Mxfp4 {
                packed,
                scale,
                rows,
                cols: _,
            } => {
                let packed_row_bytes = cols / 2;
                let scale_row_bytes = cols / ferrox_quant::MXFP4_GROUP_SIZE;
                let mut out = vec![0f32; batch_size * rows];
                let out_w = BatchOut(out.as_mut_ptr());
                (0..*rows)
                    .into_par_iter()
                    .with_min_len(Self::min_rows_per_task(*rows))
                    .for_each(|r| {
                        let prow =
                            &packed.as_slice()[r * packed_row_bytes..(r + 1) * packed_row_bytes];
                        let srow =
                            &scale.as_slice()[r * scale_row_bytes..(r + 1) * scale_row_bytes];
                        for b in 0..batch_size {
                            let x = &x_batch[b * cols..(b + 1) * cols];
                            unsafe {
                                out_w.set(
                                    b * rows + r,
                                    ferrox_quant::dot_mxfp4_row_f32(prow, srow, x),
                                );
                            }
                        }
                    });
                out
            }
        }
    }

    /// Bytes actually resident in memory for this matrix -- the number
    /// that matters for "can this model's weights fit in RAM/VRAM at
    /// all," as opposed to the always-4x-larger f32-expanded size.
    pub fn resident_bytes(&self) -> usize {
        match self {
            WeightMatrix::F32(t) => t.len() * 4,
            WeightMatrix::Quantized { data, .. } => data.len(),
            WeightMatrix::Mxfp4 { packed, scale, .. } => packed.len() + scale.len(),
        }
    }

    /// Dispatches a single matvec through a real GPU kernel when a GPU
    /// feature is compiled in (`cuda` and/or `metal`) and this matrix
    /// is one of the five GPU-accelerated quant kinds (Q8_0, Q4_0,
    /// Q4_K, Q5_K, Q6_K). Returns `None` for every other case (no GPU
    /// feature, `F32`/`Mxfp4`/`Mxfp4Gguf`, or a `Quantized` kind other
    /// than the five below), so the caller falls back to `apply()` on
    /// the CPU -- this is a real dispatch decision
    /// (`ferrox_moe::run_expert_placed` uses it exactly this way), not
    /// a stub. Metal weight buffers are process-resident after the first
    /// upload (`ferrox_metal::gpu` weight cache); activations still
    /// upload per call. When both `cuda` and `metal` are enabled, CUDA
    /// is tried first and Metal is the fallback.
    #[cfg(any(feature = "cuda", feature = "metal"))]
    pub fn apply_gpu(&self, x: &[f32]) -> Option<Vec<f32>> {
        assert_eq!(
            x.len(),
            self.cols(),
            "activation length must match matrix column count"
        );

        // F32 stays on CPU in apply_gpu: a lone small router matvec is
        // faster as host GEMV than a Metal sync. F32 Metal launches are
        // used when fused into MoE resident decode (encode_matvec).
        let WeightMatrix::Quantized {
            data,
            rows,
            cols,
            kind,
        } = self
        else {
            // Deliberate, and recorded rather than hidden: an MoE
            // router is a lone small F32 matvec that costs more to ship
            // to the GPU than to compute on the host.
            let backend = active_backend();
            if backend.is_accelerator() {
                crate::kernel_registry::miss_by_design(
                    crate::kernel_registry::Lookup::new(
                        backend,
                        crate::kernel_registry::op::MATVEC,
                        None,
                    ),
                    "host GEMV",
                );
            }
            return None;
        };
        let row_bytes = self.block_bytes_per_row(*kind, *cols);

        #[cfg(feature = "cuda")]
        {
            let launch: Option<CudaMatvecLaunchFn> = match kind {
                QuantKind::Q8_0 => Some(ferrox_cuda::gpu::launch_q8_0_matvec),
                QuantKind::Q4_0 => Some(ferrox_cuda::gpu::launch_q4_0_matvec),
                QuantKind::Q4K => Some(ferrox_cuda::gpu::launch_q4_k_matvec),
                QuantKind::Q5K => Some(ferrox_cuda::gpu::launch_q5_k_matvec),
                QuantKind::Q6K => Some(ferrox_cuda::gpu::launch_q6_k_matvec),
                _ => None,
            };
            if let Some(launch) = launch {
                let n_blocks_per_row = row_bytes / Self::block_bytes_for_kind(*kind);
                match launch(data.as_slice(), x, *rows, row_bytes, n_blocks_per_row) {
                    Ok(out) => return Some(out),
                    Err(e) => {
                        eprintln!(
                            "ferrox: CUDA matvec dispatch failed, trying next backend / CPU: {e}"
                        );
                    }
                }
            }
        }

        #[cfg(feature = "metal")]
        {
            let launch: Option<MetalMatvecLaunchFn> = match kind {
                QuantKind::Q8_0 => Some(ferrox_metal::gpu::launch_q8_0_matvec),
                QuantKind::Q4_0 => Some(ferrox_metal::gpu::launch_q4_0_matvec),
                QuantKind::Q4K => Some(ferrox_metal::gpu::launch_q4_k_matvec),
                QuantKind::Q5K => Some(ferrox_metal::gpu::launch_q5_k_matvec),
                QuantKind::Q6K => Some(ferrox_metal::gpu::launch_q6_k_matvec),
                QuantKind::IQ4XS => Some(ferrox_metal::gpu::launch_iq4_xs_matvec),
                _ => None,
            };
            // This table and `metal_matvec_kind_name` answer the same
            // question and must never diverge; when they did, IQ4_XS
            // prefill silently moved to the CPU.
            debug_assert_eq!(
                launch.is_some(),
                metal_matvec_kind_name(*kind).is_some(),
                "apply_gpu's Metal launch table disagrees with metal_matvec_kind_name for {:?}",
                kind
            );
            if let Some(launch) = launch {
                match launch(data.as_slice(), x, *rows, row_bytes) {
                    Ok(out) => return Some(out),
                    Err(e) => {
                        eprintln!("ferrox: Metal matvec dispatch failed, falling back to CPU: {e}");
                    }
                }
            }
        }

        // Reached only on a miss or a launch error, i.e. only when the
        // caller is about to run the whole matvec on the host anyway --
        // so recording it here costs nothing measurable and is the only
        // signal that a GPU run is quietly not one.
        let backend = active_backend();
        if backend.is_accelerator() {
            crate::kernel_registry::miss(
                crate::kernel_registry::Lookup::new(
                    backend,
                    crate::kernel_registry::op::MATVEC,
                    Some(*kind),
                ),
                "CPU apply_cpu",
            );
        }
        None
    }

    /// Runs several independent matvecs that share the same activation
    /// `x` in one GPU dispatch (one upload of `x`, one wait). Tries
    /// CUDA first (when `cuda_dense_enabled()`), then Metal (when
    /// `metal_dense_enabled()`). Intended for Q/K/V (and similar)
    /// projections. Returns `None` if no GPU backend is enabled, any
    /// matrix lacks a GPU kernel, or all fused launches fail — caller
    /// should fall back to sequential [`Self::apply`].
    #[cfg(any(feature = "cuda", feature = "metal"))]
    pub fn apply_gpu_multi(mats: &[&WeightMatrix], x: &[f32]) -> Option<Vec<Vec<f32>>> {
        if mats.is_empty() {
            return None;
        }
        assert_eq!(
            x.len(),
            mats[0].cols(),
            "activation length must match matrix column count"
        );

        // Try CUDA first if enabled.
        #[cfg(feature = "cuda")]
        if cuda_dense_enabled() {
            let mut launches = Vec::with_capacity(mats.len());
            for m in mats {
                assert_eq!(m.cols(), mats[0].cols());
                let WeightMatrix::Quantized {
                    data,
                    rows,
                    cols,
                    kind,
                } = m
                else {
                    return None;
                };
                let (kernel_src, module_name, fn_name) = match kind {
                    QuantKind::Q8_0 => (
                        ferrox_cuda::gpu::Q8_0_MATVEC_KERNEL_SRC,
                        "ferrox_q8_0",
                        "q8_0_matvec",
                    ),
                    QuantKind::Q4_0 => (
                        ferrox_cuda::gpu::Q4_0_MATVEC_KERNEL_SRC,
                        "ferrox_q4_0",
                        "q4_0_matvec",
                    ),
                    QuantKind::Q4K => (
                        ferrox_cuda::gpu::Q4_K_MATVEC_KERNEL_SRC,
                        "ferrox_q4_k",
                        "q4_k_matvec",
                    ),
                    QuantKind::Q5K => (
                        ferrox_cuda::gpu::Q5_K_MATVEC_KERNEL_SRC,
                        "ferrox_q5_k",
                        "q5_k_matvec",
                    ),
                    QuantKind::Q6K => (
                        ferrox_cuda::gpu::Q6_K_MATVEC_KERNEL_SRC,
                        "ferrox_q6_k",
                        "q6_k_matvec",
                    ),
                    _ => return None,
                };
                let row_bytes = m.block_bytes_per_row(*kind, *cols);
                let n_blocks_per_row = row_bytes / Self::block_bytes_for_kind(*kind);
                launches.push(ferrox_cuda::gpu::MatvecLaunch {
                    kernel_src,
                    module_name,
                    fn_name,
                    // Borrow mmap/owned storage — never to_vec() (breaks
                    // resident_cuda_weights pointer cache; re-uploads GB).
                    weights: data.as_slice(),
                    rows: *rows,
                    row_bytes,
                    n_blocks_per_row,
                });
            }
            match ferrox_cuda::gpu::launch_matvec_multi(x, &launches) {
                Ok(outs) => return Some(outs),
                Err(e) => {
                    eprintln!("ferrox: CUDA multi-matvec failed, trying next backend: {e}");
                }
            }
        }

        // Try Metal if CUDA didn't return or failed.
        #[cfg(feature = "metal")]
        if metal_dense_enabled() {
            let mut launches = Vec::with_capacity(mats.len());
            let mut held: Vec<(&[u8], usize, usize, &'static str)> = Vec::with_capacity(mats.len());
            for m in mats {
                assert_eq!(m.cols(), mats[0].cols());
                let WeightMatrix::Quantized {
                    data,
                    rows,
                    cols,
                    kind,
                } = m
                else {
                    return None;
                };
                let kind_name = match kind {
                    QuantKind::Q8_0 => "Q8_0",
                    QuantKind::Q4_0 => "Q4_0",
                    QuantKind::Q4K => "Q4_K",
                    QuantKind::Q5K => "Q5_K",
                    QuantKind::Q6K => "Q6_K",
                    QuantKind::IQ4XS => "IQ4_XS",
                    _ => return None,
                };
                let row_bytes = m.block_bytes_per_row(*kind, *cols);
                held.push((data.as_slice(), *rows, row_bytes, kind_name));
            }
            for (weights, rows, row_bytes, kind_name) in &held {
                let (src, fn_name, block_bytes, block_elems, rows_per_tg) =
                    ferrox_metal::gpu::matvec_launch_meta(kind_name)?;
                launches.push(ferrox_metal::gpu::MatvecLaunch {
                    kernel_src: src,
                    fn_name,
                    block_bytes,
                    block_elems,
                    weights,
                    rows: *rows,
                    row_bytes: *row_bytes,
                    rows_per_tg,
                });
            }
            match ferrox_metal::gpu::launch_matvec_fused(x, &launches) {
                Ok(outs) => return Some(outs),
                Err(e) => {
                    eprintln!("ferrox: Metal fused matvec failed, falling back to CPU: {e}");
                }
            }
        }

        None
    }

    /// Dense SwiGLU FFN on GPU with device-resident activations:
    /// one upload of `x`, gate+up+silu×up+down on device, one download.
    /// Tries CUDA first when enabled, then Metal. Returns `None` if
    /// no GPU path applies — caller falls back to [`Self::apply`] /
    /// multi-matvec.
    #[cfg(any(feature = "cuda", feature = "metal"))]
    pub fn apply_gpu_dense_ffn_swiglu(
        gate: &WeightMatrix,
        up: &WeightMatrix,
        down: &WeightMatrix,
        x: &[f32],
    ) -> Option<Vec<f32>> {
        #[cfg(feature = "cuda")]
        {
            if cuda_dense_enabled() {
                fn cuda_launch(m: &WeightMatrix) -> Option<ferrox_cuda::gpu::MatvecLaunch<'_>> {
                    let WeightMatrix::Quantized {
                        data,
                        rows,
                        cols,
                        kind,
                    } = m
                    else {
                        return None;
                    };
                    let (kernel_src, module_name, fn_name) = match kind {
                        QuantKind::Q8_0 => (
                            ferrox_cuda::gpu::Q8_0_MATVEC_KERNEL_SRC,
                            "ferrox_q8_0",
                            "q8_0_matvec",
                        ),
                        QuantKind::Q4_0 => (
                            ferrox_cuda::gpu::Q4_0_MATVEC_KERNEL_SRC,
                            "ferrox_q4_0",
                            "q4_0_matvec",
                        ),
                        QuantKind::Q4K => (
                            ferrox_cuda::gpu::Q4_K_MATVEC_KERNEL_SRC,
                            "ferrox_q4_k",
                            "q4_k_matvec",
                        ),
                        QuantKind::Q5K => (
                            ferrox_cuda::gpu::Q5_K_MATVEC_KERNEL_SRC,
                            "ferrox_q5_k",
                            "q5_k_matvec",
                        ),
                        QuantKind::Q6K => (
                            ferrox_cuda::gpu::Q6_K_MATVEC_KERNEL_SRC,
                            "ferrox_q6_k",
                            "q6_k_matvec",
                        ),
                        _ => return None,
                    };
                    let row_bytes = m.block_bytes_per_row(*kind, *cols);
                    let n_blocks_per_row = row_bytes / WeightMatrix::block_bytes_for_kind(*kind);
                    Some(ferrox_cuda::gpu::MatvecLaunch {
                        kernel_src,
                        module_name,
                        fn_name,
                        weights: data.as_slice(),
                        rows: *rows,
                        row_bytes,
                        n_blocks_per_row,
                    })
                }
                if let (Some(g), Some(u), Some(d)) =
                    (cuda_launch(gate), cuda_launch(up), cuda_launch(down))
                {
                    assert_eq!(gate.cols(), x.len());
                    assert_eq!(up.cols(), x.len());
                    assert_eq!(down.cols(), gate.rows());
                    match ferrox_cuda::gpu::launch_dense_ffn_swiglu(&g, &u, &d, x) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!("ferrox: CUDA dense FFN fuse failed, trying next: {e}");
                        }
                    }
                }
            }
        }
        #[cfg(feature = "metal")]
        {
            if metal_dense_enabled() {
                fn metal_launch(m: &WeightMatrix) -> Option<ferrox_metal::gpu::MatvecLaunch<'_>> {
                    let WeightMatrix::Quantized {
                        data,
                        rows,
                        cols: _,
                        kind,
                    } = m
                    else {
                        return None;
                    };
                    let kind_name = match kind {
                        QuantKind::Q8_0 => "Q8_0",
                        QuantKind::Q4_0 => "Q4_0",
                        QuantKind::Q4K => "Q4_K",
                        QuantKind::Q5K => "Q5_K",
                        QuantKind::Q6K => "Q6_K",
                        QuantKind::IQ4XS => "IQ4_XS",
                        _ => return None,
                    };
                    let (src, fn_name, block_bytes, block_elems, rows_per_tg) =
                        ferrox_metal::gpu::matvec_launch_meta(kind_name)?;
                    // A zero-row matrix has no rows to stride over, so
                    // there is no meaningful row size; `checked_div`
                    // says that once instead of splitting it across a
                    // guard and a bare division.
                    let row_bytes = data.as_slice().len().checked_div(*rows).unwrap_or(0);
                    Some(ferrox_metal::gpu::MatvecLaunch {
                        kernel_src: src,
                        fn_name,
                        block_bytes,
                        block_elems,
                        weights: data.as_slice(),
                        rows: *rows,
                        row_bytes,
                        rows_per_tg,
                    })
                }
                if let (Some(g), Some(u), Some(d)) =
                    (metal_launch(gate), metal_launch(up), metal_launch(down))
                {
                    assert_eq!(gate.cols(), x.len());
                    assert_eq!(up.cols(), x.len());
                    assert_eq!(down.cols(), gate.rows());
                    match ferrox_metal::gpu::launch_dense_ffn_swiglu(&g, &u, &d, x) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!("ferrox: Metal dense FFN fuse failed, falling back: {e}");
                        }
                    }
                }
            }
        }
        None
    }

    /// Runs one weight matrix against `batch_size` activations in a
    /// single Metal command buffer (shared resident weights, one
    /// upload of `x_batch`, one GPU wait). `x_batch` / return layout
    /// match [`Self::apply_batch`]: `[batch, cols]` → `[batch, rows]`.
    /// Returns `None` if Metal dense is off, the kind lacks a Metal
    /// kernel, or the launch fails.
    ///
    /// Q4_K / Q6_K with `batch_size >= 2` use
    /// [`ferrox_metal::gpu::launch_q4_k_matmul_batch`] /
    /// [`ferrox_metal::gpu::launch_q6_k_matmul_batch`]; other kinds
    /// fall through to [`ferrox_metal::gpu::launch_matvec_batch`].
    #[cfg(feature = "metal")]
    pub fn apply_gpu_batch(&self, x_batch: &[f32], batch_size: usize) -> Option<Vec<f32>> {
        if !metal_dense_enabled() || batch_size == 0 {
            return None;
        }
        let WeightMatrix::Quantized {
            data,
            rows,
            cols,
            kind,
        } = self
        else {
            return None;
        };
        let Some(kind_name) = metal_matvec_kind_name(*kind) else {
            crate::kernel_registry::miss(
                crate::kernel_registry::Lookup::new(
                    crate::kernel_registry::Backend::Metal,
                    crate::kernel_registry::op::GEMM_PREFILL,
                    Some(*kind),
                ),
                "CPU apply_batch",
            );
            return None;
        };
        let (src, fn_name, block_bytes, block_elems, rows_per_tg) =
            ferrox_metal::gpu::matvec_launch_meta(kind_name)?;
        let row_bytes = self.block_bytes_per_row(*kind, *cols);
        // First-cut Q4/Q6 matmul kernels can lose to N× matvec on Host B
        // for typical chat prompts (8B fair: ~17 vs ~21 prompt tok/s).
        // Opt in with FERROX_METAL_MATMUL=1 once tiling improves.
        let use_matmul = batch_size >= 2 && metal_matmul_opt_in();
        // Weight-reuse mul_mm for prefill batch ≥ 4 (Q4_0 / Q4_K / Q6_K).
        // Default **on**; `FERROX_METAL_MUL_MM=0` forces N× matvec batch.
        // Threshold 4 (was 8) covers shorter prompts without changing the
        // decode path (batch_size == 1 still uses matvec).
        let use_mul_mm = batch_size >= 4 && metal_mul_mm_enabled();
        if use_mul_mm {
            // Observation only: a kind with a matvec kernel but no
            // simdgroup GEMM still runs on Metal, as `batch` separate
            // matvecs over the same weights. That is the shape that cost
            // IQ4_XS 13.7x, and it is invisible in the output.
            if !metal_mul_mm_kind_supported(*kind) {
                crate::kernel_registry::miss(
                    crate::kernel_registry::Lookup::new(
                        crate::kernel_registry::Backend::Metal,
                        crate::kernel_registry::op::GEMM_PREFILL,
                        Some(*kind),
                    ),
                    "Metal N x matvec batch",
                );
            }
            match kind {
                QuantKind::Q4_0 => {
                    match ferrox_metal::gpu::launch_q4_0_mul_mm_sg(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal Q4_0 simdgroup mul_mm failed, batched fallback: {e}"
                            );
                        }
                    }
                    match ferrox_metal::gpu::launch_q4_0_mul_mm(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!("ferrox: Metal Q4_0 mul_mm failed, matvec fallback: {e}");
                        }
                    }
                }
                // Q8_0 had no batched GPU kernel at all, so a 512-token
                // prefill ran 512 independent matvecs over the same
                // weights. Those are the 14-30x `pp512` rows.
                QuantKind::Q8_0 => {
                    match ferrox_metal::gpu::launch_q8_0_mul_mm_sg(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal Q8_0 simdgroup mul_mm failed, matvec fallback: {e}"
                            );
                        }
                    }
                }
                QuantKind::Q5K => {
                    match ferrox_metal::gpu::launch_q5_k_mul_mm_sg(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal Q5_K simdgroup mul_mm failed, matvec fallback: {e}"
                            );
                        }
                    }
                }
                QuantKind::IQ4XS => {
                    match ferrox_metal::gpu::launch_iq4_xs_mul_mm_sg(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal IQ4_XS simdgroup mul_mm failed, matvec fallback: {e}"
                            );
                        }
                    }
                }
                QuantKind::Q4K => {
                    // True simdgroup GEMM: each 64x32 output tile reads its
                    // weight slice once into threadgroup memory instead of
                    // once per token. `launch_q4_k_mul_mm` below is the
                    // batched-matvec fallback it replaces -- correct, but it
                    // re-reads the whole matrix for every token, which is why
                    // Metal `pp512` was 14-99x behind llama.cpp.
                    match ferrox_metal::gpu::launch_q4_k_mul_mm_sg(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal Q4_K simdgroup mul_mm failed, batched-matvec fallback: {e}"
                            );
                        }
                    }
                    match ferrox_metal::gpu::launch_q4_k_mul_mm(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal Q4_K mul_mm (MUL_MM path) failed, matvec fallback: {e}"
                            );
                        }
                    }
                }
                QuantKind::Q6K => {
                    // Same simdgroup GEMM as Q4_K. `ffn_down` and `attn_v`
                    // are Q6_K in every Q4_K_M checkpoint, so without this
                    // a third of the FFN stayed on the batched-matvec path
                    // and capped what the Q4_K GEMM could deliver.
                    match ferrox_metal::gpu::launch_q6_k_mul_mm_sg(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal Q6_K simdgroup mul_mm failed, matmul-batch fallback: {e}"
                            );
                        }
                    }
                    match ferrox_metal::gpu::launch_q6_k_matmul_batch(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal Q6_K matmul batch (MUL_MM path) failed, matvec fallback: {e}"
                            );
                        }
                    }
                }
                _ => {}
            }
        }
        if use_matmul {
            match kind {
                QuantKind::Q4K => {
                    match ferrox_metal::gpu::launch_q4_k_matmul_batch(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal Q4_K matmul batch failed, matvec fallback: {e}"
                            );
                        }
                    }
                }
                QuantKind::Q6K => {
                    match ferrox_metal::gpu::launch_q6_k_matmul_batch(
                        data.as_slice(),
                        x_batch,
                        *rows,
                        row_bytes,
                        batch_size,
                    ) {
                        Ok(out) => return Some(out),
                        Err(e) => {
                            eprintln!(
                                "ferrox: Metal Q6_K matmul batch failed, matvec fallback: {e}"
                            );
                        }
                    }
                }
                _ => {}
            }
        }
        let launch = ferrox_metal::gpu::MatvecLaunch {
            kernel_src: src,
            fn_name,
            block_bytes,
            block_elems,
            weights: data.as_slice(),
            rows: *rows,
            row_bytes,
            rows_per_tg,
        };
        match ferrox_metal::gpu::launch_matvec_batch(&launch, x_batch, batch_size) {
            Ok(out) => Some(out),
            Err(e) => {
                eprintln!("ferrox: Metal batch matvec failed, falling back: {e}");
                None
            }
        }
    }

    /// Delegates to [`metal_matvec_kind_name`]. Kept as a method because
    /// the call sites read better, but it must never grow a list of its
    /// own again — a second copy of this list is what sent IQ4_XS
    /// batched prefill to the CPU.
    #[cfg(feature = "metal")]
    fn metal_kind_supported(kind: QuantKind) -> bool {
        metal_matvec_kind_name(kind).is_some()
    }

    /// Eagerly resolve, and record, every kernel lookup this matrix's
    /// dispatch paths will make later, without dispatching anything.
    ///
    /// Call once per weight while the model is being built, with `role`
    /// naming the tensor (`"attn_q"`, `"ffn_down"`, ...). The predicates
    /// consulted here are the *same functions* the hot path consults, so
    /// the recorded prediction cannot drift from the decision. See
    /// [`crate::kernel_registry`] for why this exists and
    /// [`crate::kernel_registry::seal`] for what is done with it.
    ///
    /// Observation only: nothing here influences a later dispatch.
    #[track_caller]
    pub fn probe_kernels(&self, role: &'static str) {
        if !crate::kernel_registry::enabled() {
            return;
        }
        self.probe_kernels_into(
            crate::kernel_registry::global(),
            role,
            std::panic::Location::caller(),
        );
    }

    /// [`Self::probe_kernels`] against an explicit registry and call
    /// site, so tests can probe into an instance of their own instead of
    /// the process-wide one.
    pub fn probe_kernels_into(
        &self,
        reg: &crate::kernel_registry::Registry,
        role: &'static str,
        loc: &'static std::panic::Location<'static>,
    ) {
        self.probe_kernels_for(reg, active_backend(), role, loc)
    }

    /// [`Self::probe_kernels_into`] against an explicit backend rather
    /// than [`active_backend`]. Lets a test on a CPU-only build ask what
    /// a Metal or CUDA run would resolve -- which is the only way the
    /// kernel-coverage tests can run under plain
    /// `cargo test --workspace`, where every GPU feature is off.
    pub fn probe_kernels_for(
        &self,
        reg: &crate::kernel_registry::Registry,
        backend: crate::kernel_registry::Backend,
        role: &'static str,
        loc: &'static std::panic::Location<'static>,
    ) {
        use crate::kernel_registry::{op, Backend, Lookup, Outcome};

        let kind = self.quant_kind();
        let cols = self.cols();
        let look = |op: &'static str| Lookup {
            backend,
            op,
            role,
            kind,
        };

        // Whether the accelerator, if one is selected, can run this
        // matrix at all -- and if so, whether prefill gets a real GEMM
        // or `batch` matvecs over the same weights.
        let (matvec, gemm) = match backend {
            Backend::Metal => (
                kind.is_some_and(|k| metal_matvec_kind_name(k).is_some()),
                kind.is_some_and(metal_mul_mm_kind_supported),
            ),
            // CUDA has real matvec kernels and no batched GEMM: a
            // batched prefill is a per-position matvec loop.
            Backend::Cuda => (kind.is_some_and(cuda_matvec_kind_supported), false),
            Backend::Cpu => (false, false),
        };

        if backend.is_accelerator() {
            reg.record_build_at(
                loc,
                look(op::MATVEC),
                match kind {
                    // An accelerator kernel exists for this format.
                    _ if matvec => Outcome::Hit,
                    // No kernel: the whole matvec runs on the host.
                    Some(_) => Outcome::slow_path("CPU apply_cpu"),
                    // F32 has no quantized kernel by construction, and a
                    // lone small F32 matvec (an MoE router) is host work
                    // on purpose -- see `apply_gpu`.
                    None => Outcome::by_design("host GEMV"),
                },
            );
            reg.record_build_at(
                loc,
                look(op::GEMM_PREFILL),
                match (gemm, backend, matvec, kind) {
                    (true, ..) => Outcome::Hit,
                    // Still on the GPU, but re-reading the whole weight
                    // matrix once per position. This is the 13.7x shape.
                    (false, Backend::Cuda, true, _) => {
                        Outcome::slow_path("CUDA per-position matvec")
                    }
                    (false, _, true, _) => Outcome::slow_path("Metal N x matvec batch"),
                    (false, _, false, Some(_)) => Outcome::slow_path("CPU apply_batch"),
                    (false, _, false, None) => Outcome::by_design("CPU f32 GEMM"),
                },
            );
        }

        // The host path is what every accelerator miss lands on, so
        // record its tier too: integer vec_dot, or the much slower f32
        // dequant-dot.
        if !matvec || !gemm {
            let int_dot =
                cpu_int_dot_enabled() && kind.is_some_and(|k| cpu_int_dot_kind_supported(k, cols));
            reg.record_build_at(
                loc,
                Lookup {
                    backend: Backend::Cpu,
                    op: op::MATVEC,
                    role,
                    kind,
                },
                match kind {
                    _ if int_dot => Outcome::Hit,
                    // A quantized weight with no integer vec_dot kernel
                    // dequantizes to f32 first: a much slower engine,
                    // and invisible in the output.
                    Some(_) => Outcome::slow_path("f32 dequant-dot"),
                    None => Outcome::by_design("f32 GEMM"),
                },
            );
        }
    }

    /// The block size (in bytes) for exactly the quant kinds
    /// `apply_gpu` dispatches to a real kernel for -- a small,
    /// deliberately partial mirror of `block_bytes_per_row`'s per-kind
    /// match (only these five formats have a real GPU kernel today).
    #[cfg(feature = "cuda")]
    fn block_bytes_for_kind(kind: QuantKind) -> usize {
        match kind {
            QuantKind::Q8_0 => ferrox_quant::Q8_0_BLOCK_BYTES,
            QuantKind::Q4_0 => ferrox_quant::Q4_0_BLOCK_BYTES,
            QuantKind::Q4K => ferrox_quant::Q4_K_BLOCK_BYTES,
            QuantKind::Q5K => ferrox_quant::Q5_K_BLOCK_BYTES,
            QuantKind::Q6K => ferrox_quant::Q6_K_BLOCK_BYTES,
            _ => unreachable!("apply_gpu only calls this for the five GPU-dispatchable kinds"),
        }
    }
}
#[cfg(test)]
mod tests {

    /// The four dtypes the drifted copies were missing.
    ///
    /// Three of the six loaders stopped at IQ1_M, so `IQ1_S`,
    /// `IQ2_XXS`, `IQ3_XXS` and `MXFP4` mapped to `None` there -- and a
    /// `None` is `LoadError::UnsupportedDtype`, not a slower path. A
    /// DeepSeek-MLA checkpoint at `IQ2_XXS`, an ordinary quant for a
    /// model that size, was refused outright while the same quant
    /// loaded on the generic path. One table is what stops that
    /// recurring.
    #[test]
    fn the_four_dtypes_the_duplicated_tables_disagreed_about_all_map() {
        assert_eq!(quant_kind_for(GgmlType::IQ1S), Some(QuantKind::IQ1S));
        assert_eq!(quant_kind_for(GgmlType::IQ2XXS), Some(QuantKind::IQ2XXS));
        assert_eq!(quant_kind_for(GgmlType::IQ3XXS), Some(QuantKind::IQ3XXS));
        assert_eq!(quant_kind_for(GgmlType::MXFP4), Some(QuantKind::Mxfp4Gguf));
    }

    /// Every dtype with a CPU dequant kernel must be reachable through
    /// this map, or the kernel exists and no loader can ever hand it a
    /// tensor. Checked against the two backend tables rather than a
    /// hand-written list, so adding a kernel without a mapping fails
    /// here instead of at a user's load.
    #[test]
    fn every_dtype_with_a_gpu_kernel_is_reachable_through_the_map() {
        let mapped: Vec<QuantKind> = [
            GgmlType::Q8_0,
            GgmlType::Q4_0,
            GgmlType::Q4K,
            GgmlType::Q5K,
            GgmlType::Q6K,
            GgmlType::IQ4XS,
        ]
        .into_iter()
        .map(|d| quant_kind_for(d).expect("a dtype with a GPU kernel must map"))
        .collect();
        for kind in mapped {
            assert!(
                metal_mul_mm_kind_supported(kind) || cuda_matvec_kind_supported(kind),
                "{kind:?} was listed as having a GPU kernel"
            );
        }
    }

    /// F32 and F16 are not quantized, so `None` is the right answer and
    /// not a gap: the loader builds a plain `WeightMatrix::F32` for
    /// them rather than reporting an unsupported dtype.
    #[test]
    fn an_unquantized_dtype_maps_to_nothing() {
        assert_eq!(quant_kind_for(GgmlType::F32), None);
        assert_eq!(quant_kind_for(GgmlType::F16), None);
    }
    use super::*;

    /// `dequant_row` must reproduce exactly the values a full-buffer
    /// dequantization of the same row produces, for every storage
    /// variant -- and read only that row's bytes (each row here has
    /// distinct values, so an off-by-one-row slice fails loudly).
    #[test]
    fn dequant_row_matches_full_dequant_per_row() {
        // F32 variant.
        let rows = 3;
        let cols = 64;
        let f32_data: Vec<f32> = (0..rows * cols).map(|i| (i as f32) * 0.1 - 5.0).collect();
        let m = WeightMatrix::F32(Tensor::new(f32_data.clone(), vec![rows, cols]));
        for r in 0..rows {
            assert_eq!(m.dequant_row(r), &f32_data[r * cols..(r + 1) * cols]);
        }

        // Quantized (Q8_0) variant: quantize each row independently and
        // compare dequant_row against dequantizing that row's bytes.
        let mut packed = Vec::new();
        for r in 0..rows {
            packed.extend(make_q8_0_row(&f32_data[r * cols..(r + 1) * cols]));
        }
        let row_bytes = packed.len() / rows;
        let q = WeightMatrix::Quantized {
            data: WeightBytes::Owned(packed.clone()),
            rows,
            cols,
            kind: QuantKind::Q8_0,
        };
        for r in 0..rows {
            let expected =
                ferrox_quant::dequant_q8_0(&packed[r * row_bytes..(r + 1) * row_bytes]).unwrap();
            assert_eq!(q.dequant_row(r), expected, "Q8_0 row {r}");
        }

        // Mxfp4 (two-buffer) variant: arbitrary valid bytes, compare
        // against the row-level reference dequantizer directly.
        let cols = 64;
        let packed: Vec<u8> = pseudo_bytes(7, rows * cols / 2);
        let scales: Vec<u8> = pseudo_bytes(11, rows * cols / 32);
        let m = WeightMatrix::Mxfp4 {
            packed: WeightBytes::Owned(packed.clone()),
            scale: WeightBytes::Owned(scales.clone()),
            rows,
            cols,
        };
        for r in 0..rows {
            let expected = ferrox_quant::dequant_mxfp4_row(
                &packed[r * cols / 2..(r + 1) * cols / 2],
                &scales[r * cols / 32..(r + 1) * cols / 32],
            )
            .unwrap();
            assert_eq!(m.dequant_row(r), expected, "Mxfp4 row {r}");
        }
    }

    /// A quantized matrix used as an embedding table: `dequant_row`
    /// then a dot product must agree with `apply` against a one-hot...
    /// no -- more directly, with the fused `dot` of that row, proving
    /// row lookup and matmul read identical bytes.
    #[test]
    fn dequant_row_agrees_with_fused_dot_on_the_same_row() {
        let rows = 4;
        let cols = 64;
        let f32_data: Vec<f32> = (0..rows * cols)
            .map(|i| ((i as f32) * 0.13).sin())
            .collect();
        let mut packed = Vec::new();
        for r in 0..rows {
            packed.extend(make_q8_0_row(&f32_data[r * cols..(r + 1) * cols]));
        }
        let q = WeightMatrix::Quantized {
            data: WeightBytes::Owned(packed),
            rows,
            cols,
            kind: QuantKind::Q8_0,
        };
        let x: Vec<f32> = (0..cols).map(|i| ((i as f32) * 0.031).cos()).collect();
        let applied = q.apply(&x);
        for (r, &got) in applied.iter().enumerate() {
            let via_row: f32 = q.dequant_row(r).iter().zip(&x).map(|(a, b)| a * b).sum();
            assert!(
                (got - via_row).abs() < 1e-4,
                "row {r}: apply={got} via dequant_row={via_row}"
            );
        }
    }

    fn make_q8_0_row(values: &[f32]) -> Vec<u8> {
        ferrox_quant::quantize_q8_0(values)
    }

    /// Deterministic byte generator for MXFP4 test fixtures (no
    /// quantizer exists in `ferrox_quant` -- MXFP4 is only ever a
    /// real, already-quantized checkpoint format, never produced by
    /// ferrox -- so tests build arbitrary-but-valid-shaped bytes
    /// directly, same convention as `ferrox-models::kimi_loader`'s
    /// tests).
    fn pseudo_bytes(seed: u32, len: usize) -> Vec<u8> {
        let mut state = seed.wrapping_mul(2654435761).wrapping_add(1);
        (0..len)
            .map(|_| {
                state = state.wrapping_mul(1103515245).wrapping_add(12345);
                (state >> 16) as u8
            })
            .collect()
    }

    /// Clamped to a realistic E8M0 scale range -- see
    /// `ferrox-models::kimi_loader`'s identical helper for why (byte
    /// 255 is OCP-spec-reserved for NaN, and bytes above ~252 can
    /// legitimately overflow f32::MAX when combined with E2M1's max
    /// magnitude; neither is representative of a real trained weight).
    fn pseudo_mxfp4_scale_bytes(seed: u32, len: usize) -> Vec<u8> {
        pseudo_bytes(seed, len)
            .into_iter()
            .map(|b| b % 180)
            .collect()
    }

    #[test]
    fn f32_and_mxfp4_paths_agree() {
        let rows = 2;
        let cols = 64; // 2 MXFP4 groups of 32 per row
        let packed = pseudo_bytes(1, rows * (cols / 2));
        let scale = pseudo_mxfp4_scale_bytes(2, rows * (cols / ferrox_quant::MXFP4_GROUP_SIZE));
        let x: Vec<f32> = (0..cols).map(|i| (i as f32) * 0.01 - 0.3).collect();

        // Independent reference: dequantize each row to plain f32 (the
        // already-tested `dequant_mxfp4_row`), then use the ordinary
        // F32 matmul path.
        let mut f32_weights = Vec::with_capacity(rows * cols);
        for r in 0..rows {
            let prow = &packed[r * (cols / 2)..(r + 1) * (cols / 2)];
            let srow = &scale[r * (cols / ferrox_quant::MXFP4_GROUP_SIZE)
                ..(r + 1) * (cols / ferrox_quant::MXFP4_GROUP_SIZE)];
            f32_weights.extend(ferrox_quant::dequant_mxfp4_row(prow, srow).unwrap());
        }
        let f32_matrix = WeightMatrix::F32(Tensor::new(f32_weights, vec![rows, cols]));
        let f32_out = f32_matrix.apply(&x);

        let mxfp4_matrix = WeightMatrix::Mxfp4 {
            packed: WeightBytes::Owned(packed),
            scale: WeightBytes::Owned(scale),
            rows,
            cols,
        };
        let mxfp4_out = mxfp4_matrix.apply(&x);

        assert_eq!(f32_out.len(), rows);
        assert_eq!(mxfp4_out.len(), rows);
        for (f, m) in f32_out.iter().zip(mxfp4_out.iter()) {
            assert!((f - m).abs() < 1e-3, "f32={f} mxfp4={m}");
        }
    }

    #[test]
    fn mxfp4_apply_batch_matches_sequential_apply_calls() {
        let rows = 3;
        let cols = 64;
        let packed = pseudo_bytes(3, rows * (cols / 2));
        let scale = pseudo_mxfp4_scale_bytes(4, rows * (cols / ferrox_quant::MXFP4_GROUP_SIZE));
        let matrix = WeightMatrix::Mxfp4 {
            packed: WeightBytes::Owned(packed),
            scale: WeightBytes::Owned(scale),
            rows,
            cols,
        };

        let batch_size = 4;
        let x_batch: Vec<f32> = (0..batch_size * cols)
            .map(|i| ((i % 13) as f32) * 0.02 - 0.15)
            .collect();

        let batched = matrix.apply_batch(&x_batch, batch_size);
        assert_eq!(batched.len(), batch_size * rows);

        for b in 0..batch_size {
            let x = &x_batch[b * cols..(b + 1) * cols];
            let sequential = matrix.apply(x);
            let from_batch = &batched[b * rows..(b + 1) * rows];
            assert_eq!(
                sequential, from_batch,
                "batch row {b} disagrees with sequential apply()"
            );
        }
    }

    #[test]
    fn mxfp4_resident_bytes_matches_the_packed_plus_scale_byte_count_not_eager_f32() {
        let rows = 2;
        let cols = 64;
        let packed = pseudo_bytes(5, rows * (cols / 2));
        let scale = pseudo_mxfp4_scale_bytes(6, rows * (cols / ferrox_quant::MXFP4_GROUP_SIZE));
        let packed_len = packed.len();
        let scale_len = scale.len();
        let matrix = WeightMatrix::Mxfp4 {
            packed: WeightBytes::Owned(packed),
            scale: WeightBytes::Owned(scale),
            rows,
            cols,
        };

        assert_eq!(matrix.resident_bytes(), packed_len + scale_len);
        // Real MXFP4 packs 2 values/byte plus 1 scale byte per 32
        // values -- resident_bytes should be far below the 4-bytes-
        // per-value eager-f32 footprint.
        let eager_f32_bytes = rows * cols * 4;
        assert!(
            matrix.resident_bytes() * 4 < eager_f32_bytes,
            "expected MXFP4 resident bytes well under 1/4 of eager f32: got {} vs {}",
            matrix.resident_bytes(),
            eager_f32_bytes
        );
    }

    #[test]
    fn f32_and_quantized_paths_agree_within_quant_error() {
        // 1 row, 32 cols, values chosen to keep Q8_0 error small.
        let weights: Vec<f32> = (0..32).map(|i| ((i as f32) - 16.0) * 0.2).collect();
        let x: Vec<f32> = (0..32).map(|i| (i as f32) * 0.05 - 0.8).collect();

        let f32_matrix = WeightMatrix::F32(Tensor::new(weights.clone(), vec![1, 32]));
        let f32_out = f32_matrix.apply(&x);

        let packed = make_q8_0_row(&weights);
        let quant_matrix = WeightMatrix::Quantized {
            data: WeightBytes::Owned(packed),
            rows: 1,
            cols: 32,
            kind: QuantKind::Q8_0,
        };
        let quant_out = quant_matrix.apply(&x);

        assert_eq!(f32_out.len(), 1);
        assert_eq!(quant_out.len(), 1);
        assert!(
            (f32_out[0] - quant_out[0]).abs() < 0.05,
            "f32={} quant={}",
            f32_out[0],
            quant_out[0]
        );
    }

    #[test]
    fn quantized_resident_bytes_is_smaller_than_f32() {
        let weights = vec![0.1f32; 64]; // 2 rows x 32 cols
        let f32_matrix = WeightMatrix::F32(Tensor::new(weights.clone(), vec![2, 32]));

        let mut packed = Vec::new();
        for chunk in weights.chunks(32) {
            packed.extend(ferrox_quant::quantize_q8_0(chunk));
        }
        let quant_matrix = WeightMatrix::Quantized {
            data: WeightBytes::Owned(packed),
            rows: 2,
            cols: 32,
            kind: QuantKind::Q8_0,
        };

        assert_eq!(f32_matrix.resident_bytes(), 64 * 4); // 256 bytes
        assert_eq!(quant_matrix.resident_bytes(), 2 * 34); // 68 bytes
        assert!(quant_matrix.resident_bytes() < f32_matrix.resident_bytes());
        // Q8_0 should be close to the theoretical ~4x reduction vs f32.
        let ratio = f32_matrix.resident_bytes() as f32 / quant_matrix.resident_bytes() as f32;
        assert!(ratio > 3.5, "expected ~4x reduction, got {ratio}x");
    }

    #[test]
    fn rows_and_cols_report_correctly_for_both_variants() {
        let f32_matrix = WeightMatrix::F32(Tensor::new(vec![0.0; 6], vec![2, 3]));
        assert_eq!(f32_matrix.rows(), 2);
        assert_eq!(f32_matrix.cols(), 3);

        let quant_matrix = WeightMatrix::Quantized {
            data: WeightBytes::Owned(vec![0u8; 34]),
            rows: 1,
            cols: 32,
            kind: QuantKind::Q8_0,
        };
        assert_eq!(quant_matrix.rows(), 1);
        assert_eq!(quant_matrix.cols(), 32);
    }

    #[test]
    #[should_panic]
    fn apply_panics_on_activation_length_mismatch() {
        let f32_matrix = WeightMatrix::F32(Tensor::new(vec![0.0; 6], vec![2, 3]));
        f32_matrix.apply(&[1.0, 2.0]); // wrong length (needs 3)
    }

    #[test]
    fn apply_batch_with_batch_size_one_matches_apply() {
        let weights: Vec<f32> = (0..32).map(|i| (i as f32 - 16.0) * 0.13).collect();
        let x: Vec<f32> = (0..32).map(|i| (i as f32) * 0.02 - 0.3).collect();

        let f32_matrix = WeightMatrix::F32(Tensor::new(weights.clone(), vec![1, 32]));
        let single = f32_matrix.apply(&x);
        let batched = f32_matrix.apply_batch(&x, 1);
        assert_eq!(single, batched);

        let packed = ferrox_quant::quantize_q8_0(&weights);
        let quant_matrix = WeightMatrix::Quantized {
            data: WeightBytes::Owned(packed),
            rows: 1,
            cols: 32,
            kind: QuantKind::Q8_0,
        };
        let single_q = quant_matrix.apply(&x);
        let batched_q = quant_matrix.apply_batch(&x, 1);
        assert_eq!(single_q, batched_q);
    }

    #[test]
    fn apply_batch_matches_sequential_apply_calls_for_each_row_f32() {
        let rows = 3;
        let cols = 32;
        let weights: Vec<f32> = (0..rows * cols)
            .map(|i| ((i % 17) as f32 - 8.0) * 0.05)
            .collect();
        let matrix = WeightMatrix::F32(Tensor::new(weights, vec![rows, cols]));

        let batch_size = 4;
        let x_batch: Vec<f32> = (0..batch_size * cols)
            .map(|i| ((i % 13) as f32) * 0.03 - 0.2)
            .collect();

        let batched = matrix.apply_batch(&x_batch, batch_size);
        assert_eq!(batched.len(), batch_size * rows);

        for b in 0..batch_size {
            let x = &x_batch[b * cols..(b + 1) * cols];
            let sequential = matrix.apply(x);
            let from_batch = &batched[b * rows..(b + 1) * rows];
            assert_eq!(
                sequential, from_batch,
                "batch row {b} disagrees with sequential apply()"
            );
        }
    }

    #[test]
    fn apply_batch_matches_sequential_apply_calls_for_each_row_quantized() {
        let rows = 3;
        let cols = 32;
        let weights: Vec<f32> = (0..rows * cols)
            .map(|i| ((i % 19) as f32 - 9.0) * 0.07)
            .collect();
        let mut packed = Vec::new();
        for row in weights.chunks(cols) {
            packed.extend(ferrox_quant::quantize_q8_0(row));
        }
        let matrix = WeightMatrix::Quantized {
            data: WeightBytes::Owned(packed),
            rows,
            cols,
            kind: QuantKind::Q8_0,
        };

        let batch_size = 5;
        let x_batch: Vec<f32> = (0..batch_size * cols)
            .map(|i| ((i % 11) as f32) * 0.04 - 0.25)
            .collect();

        let batched = matrix.apply_batch(&x_batch, batch_size);
        assert_eq!(batched.len(), batch_size * rows);

        for b in 0..batch_size {
            let x = &x_batch[b * cols..(b + 1) * cols];
            let sequential = matrix.apply(x);
            let from_batch = &batched[b * rows..(b + 1) * rows];
            assert_batch_row_matches(QuantKind::Q8_0, b, &sequential, from_batch);
        }
    }

    /// Minimal f16 encode for small positive normals (test fixtures only).
    fn f16_le(x: f32) -> [u8; 2] {
        let bits = x.to_bits();
        let exp = ((bits >> 23) & 0xff) as i32 - 127 + 15;
        let mant = (bits >> 13) & 0x3ff;
        (((exp as u16) << 10) | mant as u16).to_le_bytes()
    }

    /// Deterministic pseudo-random quantized matrix: every byte pattern is
    /// a valid weight block, only the f16 scale fields need sane values.
    /// Compare one row of `apply_batch` against `apply`, scaled by the
    /// magnitude of the row rather than of each element.
    ///
    /// The element-wise denominator (`err / s.abs().max(1.0)`) is wrong
    /// for a dot product over random data: the sums cancel, so a result
    /// that lands near zero turns a normal rounding difference into a
    /// relative error of 30%. Measured on Metal, the divergence is a
    /// uniform 5.5e-4 of the row's own scale across every quant kind
    /// and batch index, and up to 2.9e-1 of the individual result. The
    /// first number describes the arithmetic; the second describes
    /// which results happened to cancel.
    ///
    /// This matters because `apply_batch` is not `apply` on a GPU
    /// build: `apply_batch` dispatches to Metal while `apply` stays on
    /// the CPU, so this compares two backends. The bound stays tight on
    /// CPU, where both sides are the same code and must agree closely.
    fn assert_batch_row_matches(kind: QuantKind, b: usize, sequential: &[f32], from_batch: &[f32]) {
        let scale = sequential
            .iter()
            .fold(0.0f32, |a, v| a.max(v.abs()))
            .max(1.0);
        // A GPU build compares Metal against the CPU; a CPU build
        // compares the CPU against itself.
        let bound = if cfg!(any(feature = "metal", feature = "cuda")) {
            5e-3
        } else {
            1e-4
        };
        for (r, (s, got)) in sequential.iter().zip(from_batch.iter()).enumerate() {
            let err = (s - got).abs() / scale;
            assert!(
                err < bound,
                "{kind:?} batch {b} row {r}: apply()={s} apply_batch={got} \
                 (err {err:e} of row scale {scale}, bound {bound:e})"
            );
        }
    }

    fn synth_quant_matrix(kind: QuantKind, rows: usize, cols: usize) -> WeightMatrix {
        let mut state = 0x1234_5678u32;
        let mut next = move || {
            state = state.wrapping_mul(1_664_525).wrapping_add(1_013_904_223);
            (state >> 24) as u8
        };
        let mut data = Vec::new();
        match kind {
            QuantKind::Q8_0 | QuantKind::Q4_0 => {
                let qs = if kind == QuantKind::Q8_0 { 32 } else { 16 };
                for _ in 0..rows * (cols / 32) {
                    data.extend_from_slice(&f16_le(0.02 + f32::from(next()) * 0.0004));
                    for _ in 0..qs {
                        data.push(next());
                    }
                }
            }
            QuantKind::Q4K | QuantKind::Q5K => {
                let body = if kind == QuantKind::Q4K {
                    12 + 128
                } else {
                    12 + 32 + 128
                };
                for _ in 0..rows * (cols / 256) {
                    data.extend_from_slice(&f16_le(0.01 + f32::from(next()) * 0.0002));
                    data.extend_from_slice(&f16_le(0.005 + f32::from(next()) * 0.0001));
                    for _ in 0..body {
                        data.push(next());
                    }
                }
            }
            QuantKind::Q6K => {
                for _ in 0..rows * (cols / 256) {
                    for _ in 0..128 + 64 + 16 {
                        data.push(next());
                    }
                    data.extend_from_slice(&f16_le(0.01 + f32::from(next()) * 0.0002));
                }
            }
            _ => unreachable!("synth_quant_matrix: unsupported kind"),
        }
        WeightMatrix::Quantized {
            data: WeightBytes::Owned(data),
            rows,
            cols,
            kind,
        }
    }

    /// `apply_batch` writes straight into the `[batch][rows]` output from
    /// parallel tasks (no staging transpose); the shapes here force every
    /// write pattern: full row-groups, a tail of leftover rows, and both
    /// full and partial activation tiles. Runs against whatever path
    /// `FERROX_CPU_INT_DOT` selects, so exercise it both ways.
    #[test]
    fn apply_batch_matches_apply_across_kinds_with_groups_and_tail() {
        let rows = 19; // 2x8-row groups + 3 tail (4x4-row groups + 3 for Q8_0/Q4_0)
        let cols = 512;
        let batch_size = 6; // one full 4-activation tile + a partial one
        let x_batch: Vec<f32> = (0..batch_size * cols)
            .map(|i| (((i * 31 + 7) % 97) as f32) * 0.021 - 1.0)
            .collect();
        for kind in [
            QuantKind::Q8_0,
            QuantKind::Q4_0,
            QuantKind::Q4K,
            QuantKind::Q5K,
            QuantKind::Q6K,
        ] {
            let matrix = synth_quant_matrix(kind, rows, cols);
            let batched = matrix.apply_batch(&x_batch, batch_size);
            assert_eq!(batched.len(), batch_size * rows);
            for b in 0..batch_size {
                let x = &x_batch[b * cols..(b + 1) * cols];
                let sequential = matrix.apply(x);
                let from_batch = &batched[b * rows..(b + 1) * rows];
                assert_batch_row_matches(kind, b, &sequential, from_batch);
            }
        }
    }

    /// Large enough that `par_chunked_groups` builds a real 2D chunk grid
    /// (32 row-groups × 17 activation tiles) instead of falling back to
    /// one-chunk-per-thread — every (group, tile-range) seam in the
    /// chunked scatter is crossed. The smaller cross-kind test above
    /// covers the fallback path.
    #[test]
    fn apply_batch_chunked_grid_matches_apply() {
        let rows = 259; // 32 groups of 8 + 3 tail (64 of 4 + 3 for Q8_0/Q4_0)
        let cols = 512;
        let batch_size = 66; // 16 full 4-activation tiles + a partial one
        let x_batch: Vec<f32> = (0..batch_size * cols)
            .map(|i| (((i * 37 + 5) % 101) as f32) * 0.019 - 0.95)
            .collect();
        for kind in [
            QuantKind::Q8_0,
            QuantKind::Q4_0,
            QuantKind::Q4K,
            QuantKind::Q5K,
            QuantKind::Q6K,
        ] {
            let matrix = synth_quant_matrix(kind, rows, cols);
            let batched = matrix.apply_batch(&x_batch, batch_size);
            assert_eq!(batched.len(), batch_size * rows);
            for b in [0, 1, 31, 32, 64, 65] {
                let x = &x_batch[b * cols..(b + 1) * cols];
                let sequential = matrix.apply(x);
                let from_batch = &batched[b * rows..(b + 1) * rows];
                assert_batch_row_matches(kind, b, &sequential, from_batch);
            }
        }
    }

    /// Sharing one quantized activation batch across projections must be
    /// invisible in the results: a matching `BatchActs` produces exactly
    /// what `apply_batch` produces (same quantization, same kernels), and
    /// a mismatched variant is ignored rather than misused.
    #[test]
    fn apply_batch_with_shared_acts_matches_apply_batch() {
        let rows = 19;
        let cols = 512;
        let batch_size = 6;
        let x_batch: Vec<f32> = (0..batch_size * cols)
            .map(|i| (((i * 29 + 11) % 89) as f32) * 0.023 - 1.0)
            .collect();
        for kind in [
            QuantKind::Q8_0,
            QuantKind::Q4_0,
            QuantKind::Q4K,
            QuantKind::Q6K,
        ] {
            let matrix = synth_quant_matrix(kind, rows, cols);
            let baseline = matrix.apply_batch(&x_batch, batch_size);

            let shared = matrix.quantize_batch_acts(&x_batch, batch_size);
            let with_shared = matrix.apply_batch_with_acts(&x_batch, batch_size, shared.as_ref());
            assert_eq!(
                baseline, with_shared,
                "{kind:?}: shared acts changed the result"
            );

            let wrong = match kind {
                QuantKind::Q8_0 | QuantKind::Q4_0 => BatchActs::Q8K(Vec::new()),
                _ => BatchActs::Q8(Vec::new()),
            };
            let with_wrong = matrix.apply_batch_with_acts(&x_batch, batch_size, Some(&wrong));
            assert_eq!(
                baseline, with_wrong,
                "{kind:?}: mismatched shared acts were not ignored"
            );
        }
    }

    #[test]
    fn apply_batch_with_zero_batch_size_returns_empty() {
        let matrix = WeightMatrix::F32(Tensor::new(vec![0.0; 6], vec![2, 3]));
        let out = matrix.apply_batch(&[], 0);
        assert!(out.is_empty());
    }

    #[cfg(any(feature = "cuda", feature = "metal"))]
    mod gpu_dispatch {
        use super::*;

        /// `apply_gpu` must return `None` for `F32` -- and, crucially,
        /// without ever touching the CUDA driver at all (this runs on
        /// every CI machine, none of which have a GPU): the `let ...
        /// else { return None }` pattern match happens before any
        /// `ferrox_cuda` call, so this is a real, meaningful assertion
        /// about dispatch behavior, not a stub.
        #[test]
        fn apply_gpu_returns_none_for_f32() {
            let matrix = WeightMatrix::F32(Tensor::new(vec![0.0; 6], vec![2, 3]));
            assert!(matrix.apply_gpu(&[0.0, 0.0, 0.0]).is_none());
        }

        #[test]
        fn apply_gpu_returns_none_for_mxfp4() {
            let matrix = WeightMatrix::Mxfp4 {
                packed: WeightBytes::Owned(vec![0u8; 32]),
                scale: WeightBytes::Owned(vec![0u8; 2]),
                rows: 1,
                cols: 64,
            };
            assert!(matrix.apply_gpu(&vec![0.0; 64]).is_none());
        }

        /// A `Quantized` matrix whose `kind` has no real CUDA kernel
        /// (only Q8_0/Q4_0/Q4_K/Q5_K/Q6_K do) must also fall back to
        /// `None`, not panic on the `unreachable!()` in
        /// `block_bytes_for_kind` -- proving the two match arms
        /// (`apply_gpu`'s early match, `block_bytes_for_kind`'s
        /// exhaustive one) stay in sync.
        #[test]
        fn apply_gpu_returns_none_for_an_unsupported_quant_kind() {
            let matrix = WeightMatrix::Quantized {
                data: WeightBytes::Owned(vec![0u8; ferrox_quant::Q2_K_BLOCK_BYTES]),
                rows: 1,
                cols: ferrox_quant::Q2_K_BLOCK_ELEMS,
                kind: QuantKind::Q2K,
            };
            assert!(matrix
                .apply_gpu(&vec![0.0; ferrox_quant::Q2_K_BLOCK_ELEMS])
                .is_none());
        }

        #[test]
        #[ignore = "requires real GPU hardware (CUDA or Metal) -- run with --ignored"]
        fn apply_gpu_matches_apply_for_q8_0_on_real_hardware() {
            let weights: Vec<f32> = (0..64).map(|i| ((i as f32) - 32.0) * 0.05).collect();
            let x: Vec<f32> = (0..64).map(|i| (i as f32) * 0.01 - 0.3).collect();
            let packed = ferrox_quant::quantize_q8_0(&weights);
            let matrix = WeightMatrix::Quantized {
                data: WeightBytes::Owned(packed),
                rows: 1,
                cols: 64,
                kind: QuantKind::Q8_0,
            };

            let cpu = matrix.apply_cpu(&x);
            let gpu = matrix
                .apply_gpu(&x)
                .expect("Q8_0 must dispatch to a real GPU kernel");
            assert_eq!(cpu.len(), gpu.len());
            for (c, g) in cpu.iter().zip(gpu.iter()) {
                assert!((c - g).abs() < 1e-2, "cpu={c} gpu={g}");
            }
        }
    }

    // ---- kernel-lookup registry coverage -------------------------------
    //
    // These are the tests that would have caught the IQ4_XS silent CPU
    // prefill at `cargo test` time instead of via a 13.7x benchmark row.

    /// A quantized matrix of `kind` with `cols` columns, filled with
    /// arbitrary bytes -- the probe reads only shape and kind, never the
    /// weights, so the contents are irrelevant.
    fn shaped(kind: QuantKind, rows: usize, cols: usize) -> WeightMatrix {
        let per_row = match kind {
            QuantKind::Q8_0 => cols / 32 * 34,
            _ => cols,
        };
        WeightMatrix::Quantized {
            data: WeightBytes::Owned(vec![0u8; rows * per_row.max(1)]),
            rows,
            cols,
            kind,
        }
    }

    /// `QuantKind::ALL` must actually list every variant. `name()` is
    /// exhaustive by the compiler, so distinct names prove distinct
    /// variants; the count pins that none was dropped from the list.
    #[test]
    fn quant_kind_all_lists_every_variant_exactly_once() {
        let mut names: Vec<&str> = QuantKind::ALL.iter().map(|k| k.name()).collect();
        let total = names.len();
        names.sort_unstable();
        names.dedup();
        assert_eq!(names.len(), total, "QuantKind::ALL has a duplicate");
        assert_eq!(
            total, 21,
            "a QuantKind variant was added without updating ALL"
        );
    }

    /// The invariant that keeps prefill honest: every kind with a Metal
    /// matvec also has a Metal batched GEMM. Break it and the kind still
    /// "runs on Metal" -- as `batch` separate matvecs over the same
    /// weights, which is exactly the shape that put IQ4_XS 13.7x behind
    /// with no symptom other than a slow benchmark.
    #[test]
    fn every_metal_matvec_kind_also_has_a_metal_gemm() {
        for &k in QuantKind::ALL {
            assert_eq!(
                metal_matvec_kind_name(k).is_some(),
                metal_mul_mm_kind_supported(k),
                "{}: matvec and mul_mm kernel tables disagree -- one of the two \
                 is a silent slow path",
                k.name()
            );
        }
    }

    /// The kind tables are pure lookups over the name, so a kind that
    /// claims a kernel must name itself the way the Metal launch meta
    /// table is keyed.
    #[test]
    fn metal_kind_names_match_the_quant_kind_names() {
        for &k in QuantKind::ALL {
            if let Some(name) = metal_matvec_kind_name(k) {
                assert_eq!(name, k.name());
            }
        }
    }

    /// THE registry test: a kind with no accelerator kernel, probed
    /// while the model is built, must be recorded as a miss and must be
    /// a seal-time violation -- not silently absorbed by a fallback.
    ///
    /// Runs on any build: the backend is passed explicitly, so it does
    /// not need `--features metal` to ask what Metal would resolve.
    #[test]
    fn a_deliberately_unsupported_kind_trips_the_registry() {
        use crate::kernel_registry::{Backend, Outcome};

        let reg = crate::kernel_registry::Registry::new();
        let loc = std::panic::Location::caller();

        // Supported: Q4_K has both a Metal matvec and a Metal GEMM.
        shaped(QuantKind::Q4K, 64, 256).probe_kernels_for(&reg, Backend::Metal, "ffn_down", loc);
        // Unsupported: no Metal kernel of any kind for IQ2_XXS.
        shaped(QuantKind::IQ2XXS, 64, 256).probe_kernels_for(&reg, Backend::Metal, "ffn_up", loc);

        let report = reg.seal();
        let violations = &report.violations;
        assert_eq!(
            violations.len(),
            2,
            "expected matvec + gemm misses for IQ2_XXS only, got: {:?}",
            report
                .entries
                .iter()
                .map(|e| e.to_string())
                .collect::<Vec<_>>()
        );
        assert!(
            violations
                .iter()
                .all(|v| v.key.kind == Some(QuantKind::IQ2XXS)),
            "Q4_K must not be flagged"
        );
        assert!(
            violations.iter().any(|v| matches!(
                v.outcome,
                Outcome::Miss { fallback, .. } if fallback == "CPU apply_batch"
            )),
            "the report must name the fallback that will actually run"
        );
        let rendered = report.render_violations();
        assert!(rendered.contains("IQ2_XXS"), "{rendered}");
        assert!(rendered.contains("weight_matrix.rs"), "{rendered}");

        // And the host tier it lands on is recorded too: IQ2_XXS has no
        // integer vec_dot either, so it is f32 dequant-dot.
        assert!(
            report.entries.iter().any(|e| e.key.backend == Backend::Cpu
                && e.key.kind == Some(QuantKind::IQ2XXS)
                && matches!(e.outcome, Outcome::Miss { fallback, .. } if fallback == "f32 dequant-dot")),
            "{:?}",
            report.entries.iter().map(|e| e.to_string()).collect::<Vec<_>>()
        );
    }

    /// A supported kind on a selected accelerator produces no violation
    /// at all -- otherwise the signal is noise and gets ignored.
    #[test]
    fn a_fully_supported_model_seals_clean() {
        use crate::kernel_registry::Backend;

        let reg = crate::kernel_registry::Registry::new();
        let loc = std::panic::Location::caller();
        for kind in [QuantKind::Q4K, QuantKind::Q6K, QuantKind::Q8_0] {
            shaped(kind, 64, 256).probe_kernels_for(&reg, Backend::Metal, "ffn_down", loc);
        }
        let report = reg.seal();
        assert!(report.violations.is_empty(), "{}", report.render());
    }

    /// CUDA has matvec kernels and no batched GEMM, so a CUDA prefill is
    /// a per-position matvec loop. That is a real, known slow path and
    /// the registry must say so by name rather than leave it to a
    /// comment in `apply_batch_with_acts`.
    #[test]
    fn cuda_prefill_is_recorded_as_a_per_position_matvec_loop() {
        use crate::kernel_registry::{op, Backend, Outcome};

        let reg = crate::kernel_registry::Registry::new();
        let loc = std::panic::Location::caller();
        shaped(QuantKind::Q4K, 64, 256).probe_kernels_for(&reg, Backend::Cuda, "ffn_down", loc);
        let report = reg.seal();
        assert!(report.entries.iter().any(|e| e.key.backend == Backend::Cuda
            && e.key.op == op::MATVEC
            && e.outcome == Outcome::Hit));
        assert!(
            report.entries.iter().any(|e| e.key.op == op::GEMM_PREFILL
                && matches!(
                    e.outcome,
                    Outcome::Miss { fallback, .. } if fallback == "CUDA per-position matvec"
                )),
            "{}",
            report.render()
        );
    }

    /// An F32 weight has no quantized kernel by construction; the probe
    /// records the host GEMV but must not call it a violation, or every
    /// MoE router would fail a strict run.
    #[test]
    fn an_f32_weight_is_recorded_without_being_a_violation() {
        use crate::kernel_registry::Backend;

        let reg = crate::kernel_registry::Registry::new();
        let m = WeightMatrix::F32(Tensor::new(vec![0.0; 64 * 32], vec![64, 32]));
        m.probe_kernels_for(
            &reg,
            Backend::Metal,
            "moe_router",
            std::panic::Location::caller(),
        );
        let report = reg.seal();
        assert!(!report.misses.is_empty());
        assert!(report.violations.is_empty(), "{}", report.render());
    }
}