rlx_cpu/

thunk.rs

// RLX — versatile ML compiler + runtime.
// Copyright (C) 2026 Eugene Hauptmann, Nataliya Kosmyna.
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, version 3.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <https://www.gnu.org/licenses/>.

//! Thunks — pre-compiled kernel dispatch with zero per-call overhead.
//!
//! At compile time, the graph is lowered into a flat `Vec<Thunk>` where each
//! thunk holds pre-computed arena offsets, dimensions, and kernel type.
//! At runtime, the executor just iterates thunks and calls kernels directly.

// Edition 2024: bodies of `unsafe fn` are safe by default; `sl`/`sl_mut` stay `unsafe fn`.
#![allow(unsafe_op_in_unsafe_fn)]
//! No match dispatch, no HashMap lookup, no dimension computation.

use crate::arena::Arena;
use crate::op_registry::CpuKernel;
use rlx_ir::op::{Activation, BinaryOp, CmpOp, ReduceOp};
use rlx_ir::{Graph, NodeId, Op, Shape};
use std::collections::HashMap;
use std::sync::Arc;

/// Instrumentation: count of `FusedNomicLayer` thunks emitted by the full-layer
/// fusion in this process. The fusion is otherwise invisible through the
/// `Session` API, so out-of-tree model tests (rlx-models' Nomic parity test)
/// load this to confirm the fusion actually fired (non-vacuous parity). Reset
/// by storing 0 before a compile.
pub static FUSED_NOMIC_LAYER_COUNT: std::sync::atomic::AtomicU64 =
    std::sync::atomic::AtomicU64::new(0);

/// A pre-compiled kernel call with all args resolved to arena offsets.
#[derive(Clone)]
pub enum Thunk {
    /// Skip (Input/Param already in arena)
    Nop,
    /// Fused element-wise region: ONE pass applies the whole `chain` of scalar
    /// steps per output element (bias-add → relu → … collapse into a single
    /// kernel, no intermediate tensors). This is RLX's XLA-style auto-fusion
    /// path on CPU — the GPU backends already run the same `ElementwiseRegion`
    /// IR via a generated kernel; this is the scalar interpreter twin so any
    /// fused chain runs without a hand-written per-op kernel.
    /// `dst`/`input_offs` are byte offsets; `input_modulus[i]` is input i's
    /// broadcast period (0 ⇒ read element `gid`; scalar inputs read element 0).
    ElementwiseRegion {
        dst: usize,
        len: u32,
        input_offs: Vec<usize>,
        chain: Vec<rlx_ir::op::ChainStep>,
        scalar_input_mask: u32,
        input_modulus: [u32; 16],
    },
    /// C = A @ B (BLAS sgemm)
    Sgemm {
        a: usize,
        b: usize,
        c: usize,
        m: u32,
        k: u32,
        n: u32,
    },
    /// `C[m,n] = op(A) @ op(B)` with optional transpose of each operand,
    /// done via cblas trans flags (no materialized transpose). Emitted when
    /// the thunk compiler folds a last-two-axis `Op::Transpose` feeding a
    /// matmul (the dominant cost in matmul backprop). `a`/`b` are the
    /// *pre-transpose* operands. lda/ldb are derived from the trans flags.
    SgemmT {
        a: usize,
        b: usize,
        c: usize,
        m: u32,
        k: u32,
        n: u32,
        ta: bool,
        tb: bool,
    },
    /// Fused SGD-with-momentum parameter update, one pass per param.
    /// Computes `v' = mom·v + g ; p' = p − lr·v'` reading `param`/`vel`/
    /// `grad` and writing `p_out`/`v_out`. Emitted when the thunk compiler
    /// folds the in-graph SGD chain `Sub(p, Mul(Add(Mul(v,mom),g), lr))`
    /// (4 `BinaryFull` ops + 2 full-size constants per param) into a single
    /// kernel — the dominant cost of the fully-fused training step. All
    /// fields are arena byte offsets except the scalar hyperparameters.
    SgdMomentum {
        param: usize,
        vel: usize,
        grad: usize,
        p_out: usize,
        v_out: usize,
        lr: f32,
        mom: f32,
        len: u32,
    },
    /// Complex (C64) dense GEMM `C = A·B`. Operands are interleaved
    /// `[re, im]` f32; `a`/`b`/`c` are byte offsets, `m`/`k`/`n` are
    /// complex-element matrix dims (`A` `[m,k]`, `B` `[k,n]`, `C` `[m,n]`).
    CgemmC64 {
        a: usize,
        b: usize,
        c: usize,
        m: u32,
        k: u32,
        n: u32,
    },
    /// f64 dense solve `x = A⁻¹·b` via LAPACK dgesv.
    /// `a`, `b`, `x` are byte-offsets into the arena. `n` is the matrix
    /// dimension; `nrhs` is 1 for a vector RHS or >1 for multi-RHS.
    /// The kernel materializes scratch copies of A and b internally
    /// (LAPACK overwrites both with LU factors and solution).
    DenseSolveF64 {
        a: usize,
        b: usize,
        x: usize,
        n: u32,
        nrhs: u32,
    },
    /// f32 twin of `DenseSolveF64`. Calls LAPACK `sgesv` (or the
    /// no-blas Rust fallback). Same arena byte-offset contract.
    DenseSolveF32 {
        a: usize,
        b: usize,
        x: usize,
        n: u32,
        nrhs: u32,
    },
    /// Batched f64 dense solve. `a`, `b`, `x` are byte-offsets to
    /// the leading slice; `batch` is the number of independent
    /// systems. Per slice the kernel calls `dgesv(A_i, b_i, n, nrhs)`
    /// — LAPACK has no batched dgesv on Accelerate, so we loop.
    BatchedDenseSolveF64 {
        a: usize,
        b: usize,
        x: usize,
        batch: u32,
        n: u32,
        nrhs: u32,
    },
    /// Batched f32 dense solve — loop of `sgesv` per batch slice.
    BatchedDenseSolveF32 {
        a: usize,
        b: usize,
        x: usize,
        batch: u32,
        n: u32,
        nrhs: u32,
    },
    /// Batched f64 matmul. Both inputs and output have a leading
    /// batch axis of size `batch`. Per-batch independent dgemm:
    /// `C[i] = A[i] @ B[i]` for `i in 0..batch`. Used by VJP rules
    /// that emit per-batch outer products (e.g., BatchedDenseSolve
    /// VJP). The unbatched `Dgemm` thunk handles the rank-2 case.
    BatchedDgemmF64 {
        a: usize,
        b: usize,
        c: usize,
        batch: u32,
        m: u32,
        k: u32,
        n: u32,
    },
    /// Batched f32 matmul — same loop-per-batch shape as
    /// `BatchedDgemmF64` but calling `sgemm`. Needed for attention
    /// patterns where both operands carry a batch dim (e.g. q@k^T
    /// and attn@v in decomposed self-attention). The 2-D `Sgemm`
    /// flatten trick is wrong in that case because it treats `b` as
    /// a single shared RHS across every batch.
    BatchedSgemm {
        a: usize,
        b: usize,
        c: usize,
        batch: u32,
        m: u32,
        k: u32,
        n: u32,
    },
    /// C = A @ B via Accelerate cblas_dgemm. Mirror of `Sgemm` at f64.
    Dgemm {
        a: usize,
        b: usize,
        c: usize,
        m: u32,
        k: u32,
        n: u32,
    },
    /// f64 N-D index walk used for both `Op::Transpose` and `Op::Expand`.
    /// `in_strides` carries 0s on broadcast axes (Expand) or permuted
    /// strides (Transpose). Mirror of `Thunk::Transpose` at f64.
    TransposeF64 {
        src: usize,
        dst: usize,
        in_total: u32,
        out_dims: Vec<u32>,
        in_strides: Vec<u32>,
    },
    /// f64 element-wise activation. Single-input, single-output. The
    /// kernel always reads from `src` and writes to `dst`, so it works
    /// whether or not the planner aliased the two slots.
    ActivationF64 {
        src: usize,
        dst: usize,
        len: u32,
        kind: Activation,
    },
    /// Element-wise complex squared-magnitude: `|z|² = re² + im²`.
    /// Reads the C64 input at `src` as `2·len` f32 ([re,im] pairs),
    /// writes `len` f32 to `dst`.
    ComplexNormSqF32 {
        src: usize,
        dst: usize,
        /// Logical element count (number of complex values).
        len: u32,
    },
    /// Wirtinger backward for [`ComplexNormSqF32`]: `dz = g · z` as
    /// C64. Reads `z` at `2·len` f32 + `g` at `len` f32; writes
    /// `2·len` f32 to `dz`.
    ComplexNormSqBackwardF32 {
        z: usize,
        g: usize,
        dz: usize,
        len: u32,
    },
    /// Element-wise C64 conjugate: writes `[re_i, -im_i]` per element.
    /// Layout matches the rest of C64 here ([re,im] interleaved f32).
    ConjugateC64 {
        src: usize,
        dst: usize,
        len: u32,
    },
    /// C64 element-wise activation. Only kinds with well-defined
    /// complex extensions are supported: Neg, Exp, Log, Sqrt.
    /// Everything else (Sigmoid, Tanh, Relu, Abs, Sin/Cos/Tan/Atan,
    /// Round, GeLU family) is rejected at lowering — those don't have
    /// single natural complex definitions. `len` is the **complex
    /// element count** (the f32 buffer holds `2·len` floats).
    ActivationC64 {
        src: usize,
        dst: usize,
        len: u32,
        kind: Activation,
    },
    /// f64 contiguous reduction along a single axis range. Layout
    /// `[outer, reduced, inner]` in memory; output is `[outer, inner]`.
    /// Sum only for now (Mean composes via 1/N multiply post-pass).
    ReduceSumF64 {
        src: usize,
        dst: usize,
        outer: u32,
        reduced: u32,
        inner: u32,
    },
    /// f64 plain copy (Reshape / Cast at the same dtype). Mirrors `Copy`
    /// but at 8 bytes per element.
    CopyF64 {
        src: usize,
        dst: usize,
        len: u32,
    },
    /// i64 element copy (Reshape/Cast on i64 tensors).
    CopyI64 {
        src: usize,
        dst: usize,
        len: u32,
    },
    /// Round f32 → i64 (ONNX Cast on duration scalar).
    CastF32ToI64 {
        src: usize,
        dst: usize,
        len: u32,
    },
    CastF32ToF64 {
        src: usize,
        dst: usize,
        len: u32,
    },
    CastF32ToI32 {
        src: usize,
        dst: usize,
        len: u32,
    },
    /// i64 → f32 (ONNX Cast on shape scalars, e.g. Albert head-dim).
    CastI64ToF32 {
        src: usize,
        dst: usize,
        len: u32,
    },
    /// bool → i32 (BERT attention mask grid).
    CastBoolToI32 {
        src: usize,
        dst: usize,
        len: u32,
    },
    CastBoolToF32 {
        src: usize,
        dst: usize,
        len: u32,
    },
    /// i32 → f32 (BERT attention mask cast before subtract).
    CastI32ToF32 {
        src: usize,
        dst: usize,
        len: u32,
    },
    /// f64 element-wise binary with broadcast. `len`/`lhs_len`/`rhs_len`
    /// are element counts; kernel does `out[i] = lhs[i % lhs_len] OP rhs[i % rhs_len]`.
    /// Mirror of `BinaryFull` at 8 bytes per element.
    BinaryFullF64 {
        lhs: usize,
        rhs: usize,
        dst: usize,
        len: u32,
        lhs_len: u32,
        rhs_len: u32,
        op: BinaryOp,
        /// Output shape dims (row-major). Empty in the fast path. See
        /// `BinaryFull` doc for the broadcast convention.
        out_dims_bcast: Vec<u32>,
        bcast_lhs_strides: Vec<u32>,
        bcast_rhs_strides: Vec<u32>,
    },
    /// f64 concat — byte-for-byte mirror of `Concat` but copies
    /// 8 bytes per element. Element-counted offsets/strides match
    /// the f32 variant; the executor scales by elem_size internally.
    ConcatF64 {
        dst: usize,
        outer: u32,
        inner: u32,
        total_axis: u32,
        inputs: Vec<(usize, u32, u32)>,
    },
    /// C64 element-wise binary with broadcast. Same `len` /
    /// `lhs_len` / `rhs_len` semantics as `BinaryFull` but each
    /// "element" is one complex value (8 bytes = `[re, im]` as two
    /// f32s). The executor reads the underlying f32 buffer at
    /// `2·len` floats and walks element pairs. Supports Add / Sub /
    /// Mul / Div; Max / Min / Pow have no single natural complex
    /// definition and panic at lowering.
    BinaryFullC64 {
        lhs: usize,
        rhs: usize,
        dst: usize,
        /// Complex element count (NOT f32 count). f32 buffer length
        /// is `2·len`.
        len: u32,
        lhs_len: u32,
        rhs_len: u32,
        op: BinaryOp,
        out_dims_bcast: Vec<u32>,
        bcast_lhs_strides: Vec<u32>,
        bcast_rhs_strides: Vec<u32>,
    },
    /// Bounded scan. Holds a recursively-compiled body schedule + a
    /// pre-initialized body arena snapshot (constants filled). Each
    /// outer execution clones the snapshot, copies the carry-in slot
    /// from the outer arena, runs the body schedule `length` times,
    /// then writes the final carry to the outer arena.
    ///
    /// Single-carry MVP — body has exactly one Input and one output,
    /// both same shape and dtype.
    Scan {
        body: Arc<ThunkSchedule>,
        body_init: Arc<Vec<u8>>, // pristine body arena bytes
        body_input_off: usize,   // byte offset of the body's carry-Input slot
        body_output_off: usize,  // byte offset of the body's output slot
        outer_init_off: usize,   // outer-arena offset of the initial carry
        outer_final_off: usize,  // outer-arena offset of the final carry / trajectory base
        length: u32,
        carry_bytes: u32, // carry size in bytes
        /// When true, write each step's carry to the outer arena at
        /// offset `outer_final_off + t * carry_bytes`, producing a
        /// `[length, *carry]` stacked trajectory. When false, only the
        /// final carry lands at `outer_final_off`.
        save_trajectory: bool,
        /// Per-step `xs` inputs. For each: (body_x_input_off,
        /// outer_xs_base_off, per_step_bytes). Per iteration `t`, the
        /// executor copies `outer_xs_base_off + t * per_step_bytes`
        /// into `body_x_input_off`. Empty when the scan has no xs.
        xs_inputs: Arc<Vec<(usize, usize, u32)>>,
        /// Broadcast inputs — values constant across iterations. For
        /// each: (body_bcast_input_off, outer_bcast_off, total_bytes).
        /// Filled into `body_buf` ONCE before the scan loop starts
        /// (xs in contrast are re-filled every iteration). Empty when
        /// the scan has no bcasts.
        bcast_inputs: Arc<Vec<(usize, usize, u32)>>,
        /// Number of trajectory checkpoints (when `save_trajectory`).
        /// `0` or `length` ⇒ save every iteration. Otherwise save only
        /// `K` rows at indices `floor((k+1) * length / K) - 1` for
        /// `k in 0..K`. Last index is always `length-1` so the final
        /// carry is always cached.
        num_checkpoints: u32,
    },

    /// Reverse-mode AD companion to `Thunk::Scan`. Walks `t = length-1
    /// .. 0`, threading `dcarry` through the body's VJP. Per iteration:
    /// writes `carry_t` (from outer init or trajectory), each `xs_i[t]`
    /// slice, and the current `dcarry` into the body_vjp's Input
    /// slots, runs body_vjp, reads new `dcarry` from its single output.
    /// f64 carry only — the upstream-accumulation step in trajectory
    /// mode does an element-wise f64 add.
    ScanBackward {
        body_vjp: Arc<ThunkSchedule>,
        body_init: Arc<Vec<u8>>,
        body_carry_in_off: usize, // body_vjp's mirrored body-carry-input slot
        body_x_offs: Arc<Vec<usize>>, // body_vjp's mirrored x_t_i Input slots, in xs order
        body_d_output_off: usize, // body_vjp's "d_output" Input slot
        body_dcarry_out_off: usize, // body_vjp's gradient output
        outer_init_off: usize,    // original init carry
        outer_traj_off: usize,    // [length-or-K, *carry] trajectory base
        outer_upstream_off: usize, // upstream gradient (carry shape, or [length, *carry])
        /// Per-xs entries: (outer_xs_base_off, per_step_bytes). Read
        /// `xs_i[t]` from `outer_xs_base_off + t * per_step_bytes`.
        outer_xs_offs: Arc<Vec<(usize, u32)>>,
        outer_dinit_off: usize, // output: dinit
        length: u32,
        carry_bytes: u32,
        /// Bytes per element in the carry tensor: 4 for f32, 8 for f64.
        /// Used to dispatch the trajectory-mode upstream accumulation
        /// kernel (the dcarry += upstream\[t\] add must use the right
        /// floating-point type — a hard-coded f64 add silently does
        /// nothing for an f32 carry whose `cb` isn't divisible by 8).
        carry_elem_size: u32,
        save_trajectory: bool, // true → upstream is per-step; false → just final
        /// Recursive checkpointing config. `0` or `length` ⇒ full
        /// trajectory cached, no recompute (existing behavior).
        /// `0 < K < length` ⇒ trajectory has only K rows; the executor
        /// recomputes intermediate carries via `forward_body` between
        /// checkpoints. Memory: O(K · carry_bytes); time: O(length).
        num_checkpoints: u32,
        /// Forward body schedule (same compiled body as the forward
        /// Op::Scan), used for recompute when `num_checkpoints` is
        /// active. `None` for the All strategy.
        forward_body: Option<Arc<ThunkSchedule>>,
        /// Pristine forward body arena bytes (constants filled).
        forward_body_init: Option<Arc<Vec<u8>>>,
        /// Forward body's carry-Input and output slot offsets — needed
        /// to seed/read the body during recompute.
        forward_body_carry_in_off: usize,
        forward_body_output_off: usize,
        /// Forward body's per-step xs Input slots (one per outer xs).
        /// Same indexing convention as `body_x_offs`.
        forward_body_x_offs: Arc<Vec<usize>>,
    },

    /// Companion to `ScanBackward` that materializes one stacked
    /// `dxs_i`. Same backward loop; per iteration, after running
    /// body_vjp, copies its `body_dxs_out_off` slot into the outer
    /// arena at `outer_dxs_off + t * per_step_bytes`. dcarry threading
    /// is identical — we still need it for the body_vjp recurrence
    /// even though we don't write it back to the outer arena.
    ScanBackwardXs {
        body_vjp: Arc<ThunkSchedule>,
        body_init: Arc<Vec<u8>>,
        body_carry_in_off: usize,
        body_x_offs: Arc<Vec<usize>>,
        body_d_output_off: usize,
        body_dcarry_out_off: usize,
        body_dxs_out_off: usize, // the body_vjp output we extract per step
        outer_init_off: usize,
        outer_traj_off: usize,
        outer_upstream_off: usize,
        outer_xs_offs: Arc<Vec<(usize, u32)>>,
        outer_dxs_off: usize, // base of the stacked [length, *per_step] output
        length: u32,
        carry_bytes: u32,
        /// Same role as `Thunk::ScanBackward::carry_elem_size`.
        carry_elem_size: u32,
        per_step_bytes: u32, // bytes per row of the dxs output
        save_trajectory: bool,
        /// Recursive checkpointing config. Same semantics as
        /// `Thunk::ScanBackward::num_checkpoints` — `0` or `length`
        /// means "save every step's carry"; `0 < K < length` means
        /// the trajectory has only K rows and the executor recomputes
        /// intermediate carries via `forward_body` (which must be
        /// `Some`). Implemented via segment-cached recompute,
        /// mirroring the `ScanBackward` path.
        num_checkpoints: u32,
        forward_body: Option<Arc<ThunkSchedule>>,
        forward_body_init: Option<Arc<Vec<u8>>>,
        forward_body_carry_in_off: usize,
        forward_body_output_off: usize,
        forward_body_x_offs: Arc<Vec<usize>>,
    },
    /// User-defined sub-graph (`Op::CustomFn`) — runs `fwd_body` once.
    /// Per execution: clone `body_init`, copy each primal input from the
    /// outer arena into its body Input slot, run the body schedule,
    /// copy the body's single output back to the outer arena.
    CustomFn {
        body: Arc<ThunkSchedule>,
        body_init: Arc<Vec<u8>>,
        /// Per primal input: (body_input_off, outer_input_off, bytes).
        inputs: Arc<Vec<(usize, usize, u32)>>,
        body_output_off: usize,
        outer_output_off: usize,
        out_bytes: u32,
    },
    /// C = A @ B; C += bias; C = act(C)
    FusedMmBiasAct {
        a: usize,
        w: usize,
        bias: usize,
        c: usize,
        m: u32,
        k: u32,
        n: u32,
        act: Option<Activation>,
    },
    /// out = LN(x + residual + bias, gamma, beta)
    FusedResidualLN {
        x: usize,
        res: usize,
        bias: usize,
        g: usize,
        b: usize,
        out: usize,
        rows: u32,
        h: u32,
        eps: f32,
        has_bias: bool,
    },
    /// out = RmsNorm(x + residual + bias, gamma, beta)
    FusedResidualRmsNorm {
        x: usize,
        res: usize,
        bias: usize,
        g: usize,
        b: usize,
        out: usize,
        rows: u32,
        h: u32,
        eps: f32,
        has_bias: bool,
    },
    /// out = bias_add(data, bias, m, n) for Binary::Add with broadcast
    BiasAdd {
        src: usize,
        bias: usize,
        dst: usize,
        m: u32,
        n: u32,
    },
    /// Element-wise binary op with NumPy-style broadcast.
    ///
    /// Fast path (`lhs_len == rhs_len == len`): plain element-wise loop,
    /// SIMD-vectorized on aarch64 for `Add`/`Mul`. `bcast_*` fields
    /// are unused.
    ///
    /// Broadcast path: uses `out_dims_bcast` + `bcast_lhs_strides` +
    /// `bcast_rhs_strides` to compute per-cell indices into each
    /// operand. The strides are precomputed at thunk-construction
    /// time from the operands' true shapes (with stride 0 on any axis
    /// where the operand has size 1). This is the only correct way
    /// to handle bidirectional broadcasts like `[N, 1] op [1, S]
    /// → [N, S]`, which simple `i % lhs_len` modulo indexing maps to
    /// wrong cells.
    BinaryFull {
        lhs: usize,
        rhs: usize,
        dst: usize,
        len: u32,
        lhs_len: u32,
        rhs_len: u32,
        op: BinaryOp,
        /// Output shape dims (row-major). Empty in the fast path.
        out_dims_bcast: Vec<u32>,
        /// Per-dim stride into `lhs` (0 where lhs broadcasts).
        bcast_lhs_strides: Vec<u32>,
        /// Per-dim stride into `rhs`.
        bcast_rhs_strides: Vec<u32>,
        /// Element size (4 = F32, 8 = I64).
        elem_bytes: u8,
    },
    /// Activation in-place
    ActivationInPlace {
        data: usize,
        len: u32,
        act: Activation,
    },
    /// Gather axis=0: table\[idx\] → out
    Gather {
        table: usize,
        table_len: u32,
        idx: usize,
        dst: usize,
        num_idx: u32,
        trailing: u32,
        /// 1 when the index tensor is i64 (ONNX Gather indices).
        idx_i64: u8,
        /// Element size of table/output (4 = f32, 8 = i64).
        table_bytes: u8,
    },
    /// Narrow: copy slice (`elem_bytes` = source element size: 4 for f32, 8 for f64).
    Narrow {
        src: usize,
        dst: usize,
        outer: u32,
        src_stride: u32,
        dst_stride: u32,
        inner: u32,
        elem_bytes: u8,
    },
    /// Reverse (flip) element order along the axes in `rev_mask`. `dims` is the
    /// row-major input shape; output shape is identical. Dtype-agnostic
    /// (byte-copy of `elem_bytes` per element).
    Reverse {
        src: usize,
        dst: usize,
        dims: Vec<u32>,
        rev_mask: Vec<bool>,
        elem_bytes: u8,
    },
    /// Copy (reshape, expand)
    Copy {
        src: usize,
        dst: usize,
        len: u32,
    },
    /// LayerNorm standalone
    LayerNorm {
        src: usize,
        g: usize,
        b: usize,
        dst: usize,
        rows: u32,
        h: u32,
        eps: f32,
    },
    /// GroupNorm on NCHW `[N,C,H,W]`.
    GroupNorm {
        src: usize,
        g: usize,
        b: usize,
        dst: usize,
        n: u32,
        c: u32,
        h: u32,
        w: u32,
        num_groups: u32,
        eps: f32,
    },
    /// BatchNorm inference: frozen mean/var, feature axis last.
    BatchNormInference {
        src: usize,
        g: usize,
        b: usize,
        mean: usize,
        var: usize,
        dst: usize,
        count: u32,
        channels: u32,
        eps: f32,
    },
    BatchNormInferenceBackwardInput {
        x: usize,
        gamma: usize,
        mean: usize,
        var: usize,
        dy: usize,
        dx: usize,
        count: u32,
        channels: u32,
        eps: f32,
    },
    BatchNormInferenceBackwardGamma {
        x: usize,
        mean: usize,
        var: usize,
        dy: usize,
        dgamma: usize,
        count: u32,
        channels: u32,
        eps: f32,
    },
    BatchNormInferenceBackwardBeta {
        dy: usize,
        dbeta: usize,
        count: u32,
        channels: u32,
    },
    /// LayerNorm2d on NCHW (SAM / candle semantics).
    LayerNorm2d {
        src: usize,
        g: usize,
        b: usize,
        dst: usize,
        n: u32,
        c: u32,
        h: u32,
        w: u32,
        eps: f32,
    },
    /// ConvTranspose2d on NCHW.
    ConvTranspose2d {
        src: usize,
        weight: usize,
        dst: usize,
        n: u32,
        c_in: u32,
        h: u32,
        w_in: u32,
        c_out: u32,
        h_out: u32,
        w_out: u32,
        kh: u32,
        kw: u32,
        sh: u32,
        sw: u32,
        ph: u32,
        pw: u32,
        dh: u32,
        dw: u32,
        groups: u32,
    },
    /// Nearest 2× upsample on NCHW (per-batch slice).
    ResizeNearest2x {
        src: usize,
        dst: usize,
        n: u32,
        c: u32,
        h: u32,
        w: u32,
    },
    /// SAM2 axial 2-D RoPE on `[batch, seq, num_heads * head_dim]`.
    AxialRope2d {
        src: usize,
        dst: usize,
        batch: u32,
        seq: u32,
        hidden: u32,
        end_x: u32,
        end_y: u32,
        head_dim: u32,
        num_heads: u32,
        theta: f32,
        repeat_factor: u32,
    },
    /// RMSNorm: out = (x / sqrt(mean(x^2) + eps)) * gamma + beta. No mean
    /// subtraction, hence cheaper than LayerNorm. Used by Llama-class models.
    RmsNorm {
        src: usize,
        g: usize,
        b: usize,
        dst: usize,
        rows: u32,
        h: u32,
        eps: f32,
    },
    /// Softmax
    Softmax {
        data: usize,
        rows: u32,
        cols: u32,
    },
    /// Inclusive (or exclusive) cumulative sum along the last axis
    /// (callers pre-flatten higher-dim cumsums via reshape views).
    Cumsum {
        src: usize,
        dst: usize,
        rows: u32,
        cols: u32,
        exclusive: bool,
    },
    /// Mamba-style selective scan (plan #15).
    /// Inputs: x, delta \[b,s,h\], a \[h,n\], b \[b,s,n\], c \[b,s,n\].
    /// Output: y \[b,s,h\]. State h carries through the seq.
    SelectiveScan {
        x: usize,
        delta: usize,
        a: usize,
        b: usize,
        c: usize,
        dst: usize,
        batch: u32,
        seq: u32,
        hidden: u32,
        state_size: u32,
    },

    /// Gated DeltaNet linear-attention scan (Qwen3.5/3.6 trunk).
    /// Inputs: q, k, v `[b, s, h, n]`; g, beta `[b, s, h]`. Output:
    /// `[b, s, h, n]`. See `Op::GatedDeltaNet` for math.
    GatedDeltaNet {
        q: usize,
        k: usize,
        v: usize,
        g: usize,
        beta: usize,
        /// When non-zero, load initial `[b, h, n, n]` state and write
        /// the final state back in place after the scan.
        state: usize,
        dst: usize,
        batch: u32,
        seq: u32,
        heads: u32,
        state_size: u32,
    },

    /// Multi-layer (optionally bidirectional, optional carry) LSTM with
    /// packed weights. See `Op::Lstm`. `h0`/`c0` are valid only when
    /// `carry`; `dst` is `[b, s, D*h]`.
    Lstm {
        x: usize,
        w_ih: usize,
        w_hh: usize,
        bias: usize,
        h0: usize,
        c0: usize,
        dst: usize,
        batch: u32,
        seq: u32,
        input_size: u32,
        hidden: u32,
        num_layers: u32,
        bidirectional: bool,
        carry: bool,
    },
    /// GRU (gate order r, z, n) with separate `b_ih`/`b_hh`. See `Op::Gru`.
    Gru {
        x: usize,
        w_ih: usize,
        w_hh: usize,
        b_ih: usize,
        b_hh: usize,
        h0: usize,
        dst: usize,
        batch: u32,
        seq: u32,
        input_size: u32,
        hidden: u32,
        num_layers: u32,
        bidirectional: bool,
        carry: bool,
    },
    /// Elman RNN (`tanh`/`relu`), single merged bias. See `Op::Rnn`.
    Rnn {
        x: usize,
        w_ih: usize,
        w_hh: usize,
        bias: usize,
        h0: usize,
        dst: usize,
        batch: u32,
        seq: u32,
        input_size: u32,
        hidden: u32,
        num_layers: u32,
        bidirectional: bool,
        carry: bool,
        relu: bool,
    },
    /// Mamba-2 / SSD scalar-decay SSM scan. See `Op::Mamba2`.
    Mamba2 {
        x: usize,
        dt: usize,
        a: usize,
        b: usize,
        c: usize,
        dst: usize,
        batch: u32,
        seq: u32,
        heads: u32,
        head_dim: u32,
        state_size: u32,
    },

    /// 1×1 conv fast path (plan #26). The general Conv2D thunk
    /// runs the textbook 7-deep loop; a 1×1 stride-1 padding-0
    /// groups-1 conv is mathematically a per-batch matmul, and
    /// dispatching it through BLAS is 3-10× faster than the
    /// scalar nest. Common case: ViT patch-projection follow-on,
    /// transformer "expert" reductions in some MoE designs.
    ///
    /// Per batch: weight `[c_out, c_in]` × input `[c_in, h*w]`
    ///         = output `[c_out, h*w]`.
    Conv2D1x1 {
        src: usize,
        weight: usize,
        dst: usize,
        n: u32,
        c_in: u32,
        c_out: u32,
        hw: u32,
    },

    /// Fused dequant + matmul (plan #5). Today supports
    /// `QuantScheme::Int8Block` (symmetric); other schemes panic
    /// at lowering time with a clear message until kernels are added.
    DequantMatMul {
        x: usize,
        w_q: usize,   // packed i8 bytes for Int8 schemes
        scale: usize, // [k/block, n] f32 scale
        zp: usize,    // [k/block, n] f32 zero-point (0 for sym)
        dst: usize,
        m: u32,
        k: u32,
        n: u32,
        block_size: u32,
        is_asymmetric: bool,
    },

    /// GGUF-format dequant + matmul. Weight is a packed byte tensor
    /// in one of the K-quant super-block layouts (Q4_K, Q5_K, Q6_K,
    /// Q8_K). Scales / mins live inside the packed bytes — no
    /// side-channel scale tensor.
    ///
    /// Today this is a "dequant-to-scratch then sgemm" kernel — it
    /// keeps the *arena* memory footprint down (weights stay packed)
    /// but the dequant itself happens per matmul. A future fully
    /// fused tile-streaming kernel would close the compute gap.
    DequantMatMulGguf {
        x: usize,   // f32 activations [m, k]
        w_q: usize, // packed weight bytes (k*n elements packed)
        dst: usize, // f32 output [m, n]
        m: u32,
        k: u32,
        n: u32,
        scheme: rlx_ir::quant::QuantScheme,
    },

    /// Int4 block dequant + matmul (packed nibbles, side scale/zp).
    DequantMatMulInt4 {
        x: usize,
        w_q: usize,
        scale: usize,
        zp: usize,
        dst: usize,
        m: u32,
        k: u32,
        n: u32,
        block_size: u32,
        is_asymmetric: bool,
    },

    /// FP8 dequant + matmul (per-tensor or per-column scale).
    DequantMatMulFp8 {
        x: usize,
        w_q: usize,
        scale: usize,
        dst: usize,
        m: u32,
        k: u32,
        n: u32,
        e5m2: bool,
    },

    /// NVFP4 (E2M1) block dequant + matmul — 16-wide groups, FP8 scales.
    DequantMatMulNvfp4 {
        x: usize,
        w_q: usize,
        scale: usize,
        global_scale: usize,
        dst: usize,
        m: u32,
        k: u32,
        n: u32,
    },

    /// Native low-precision scaled GEMM (FP8/FP6/FP4) — CPU reference oracle.
    /// TN layout: lhs [m,k], rhs [n,k] codes, out [m,n] f32.
    ScaledMatMul {
        lhs: usize,
        rhs: usize,
        lhs_scale: usize,
        rhs_scale: usize,
        bias: usize, // valid iff has_bias
        dst: usize,
        m: u32,
        k: u32,
        n: u32,
        lhs_fmt: rlx_ir::ScaledFormat,
        rhs_fmt: rlx_ir::ScaledFormat,
        layout: rlx_ir::ScaleLayout,
        has_bias: bool,
    },

    /// Quantize f32 → packed low-precision codes (reads the scale tensor).
    ScaledQuantize {
        x: usize,
        scale: usize,
        dst: usize,
        rows: u32,
        cols: u32,
        fmt: rlx_ir::ScaledFormat,
        layout: rlx_ir::ScaleLayout,
    },

    /// Compute the per-tensor / per-block scale tensor for `Op::ScaledQuantize`.
    ScaledQuantScale {
        x: usize,
        dst: usize,
        rows: u32,
        cols: u32,
        fmt: rlx_ir::ScaledFormat,
        layout: rlx_ir::ScaleLayout,
    },

    /// Reconstruct f32 from packed codes (`Op::ScaledDequantize`).
    ScaledDequantize {
        codes: usize,
        scale: usize,
        dst: usize,
        rows: u32,
        cols: u32,
        fmt: rlx_ir::ScaledFormat,
        layout: rlx_ir::ScaleLayout,
    },

    /// Fused LoRA matmul (plan #9): out = x·W + scale * (x·A)·B.
    /// `r` is the LoRA rank (typically 4-64) — the rank-r
    /// intermediate `x·A` lives in scratch, never on the arena.
    LoraMatMul {
        x: usize,
        w: usize,
        a: usize,
        b: usize,
        dst: usize,
        m: u32,
        k: u32,
        n: u32,
        r: u32,
        scale: f32,
    },
    /// Fused sample: logits [batch, vocab] → token ids \[batch\].
    /// See Op::Sample. Output values are f32-encoded usize indices
    /// (matches the rest of the IR's "ids as f32" convention).
    Sample {
        logits: usize,
        dst: usize,
        batch: u32,
        vocab: u32,
        top_k: u32,       // 0 = disabled
        top_p: f32,       // 1.0 = disabled
        temperature: f32, // 1.0 = neutral
        seed: u64,
    },
    /// ONNX `RandomNormalLike` fill.
    RngNormal {
        dst: usize,
        len: u32,
        mean: f32,
        scale: f32,
        key: u64,
        op_seed: Option<f32>,
    },
    /// ONNX `RandomUniformLike` fill.
    RngUniform {
        dst: usize,
        len: u32,
        low: f32,
        high: f32,
        key: u64,
        op_seed: Option<f32>,
    },
    /// Attention SDPA. `mask` is the offset of the optional mask tensor
    /// (only meaningful when `mask_kind == MaskKind::Custom`); other
    /// kinds synthesize the mask in-kernel.
    ///
    /// Q/K/V each carry a `_row_stride` (elements per source row).
    /// Defaults to `heads * head_dim` — matches the standalone
    /// "Q/K/V are their own contiguous buffers" case. The Narrow→
    /// Attention fusion below rewrites these to the parent QKV stride
    /// (typically `3 * heads * head_dim`) so the kernel reads QKV
    /// directly without materializing the per-head buffers (plan #46).
    Attention {
        q: usize,
        k: usize,
        v: usize,
        mask: usize,
        out: usize,
        batch: u32,
        /// Query sequence length.
        seq: u32,
        /// Key/value sequence length. Differs from `seq` during cached decode.
        kv_seq: u32,
        heads: u32,
        head_dim: u32,
        mask_kind: rlx_ir::op::MaskKind,
        /// Softmax score scale (`Op::Attention::score_scale`). `head_dim^-0.5`
        /// when the op left it unset. Must be honored — Gemma 4 uses `1.0`
        /// (Q/K are per-head RMS-normed, so no `1/sqrt(d)` pre-scale).
        scale: f32,
        q_row_stride: u32,
        k_row_stride: u32,
        v_row_stride: u32,
        /// Memory layout flag. `false` (the historical default) →
        /// `[B, S, H, D]` row-major: per-head offset is
        /// `bi*S*H*D + si*H*D + hi*D`. `true` → `[B, H, S, D]`
        /// (head-major), matching the convention used by rlx-cuda /
        /// rlx-rocm / rlx-tpu: per-head offset is
        /// `bi*H*S*D + hi*S*D + si*D`. Detected at lowering time
        /// from the input shape vs `num_heads` / `head_dim`.
        bhsd: bool,
    },
    /// [`Op::AttentionBackward`] — emits dQ, dK, or dV (see `wrt`).
    AttentionBackward {
        q: usize,
        k: usize,
        v: usize,
        dy: usize,
        mask: usize,
        out: usize,
        batch: u32,
        seq: u32,
        kv_seq: u32,
        heads: u32,
        head_dim: u32,
        mask_kind: rlx_ir::op::MaskKind,
        wrt: rlx_ir::op::AttentionBwdWrt,
        bhsd: bool,
    },
    /// RoPE (rotary position embeddings).
    /// `src_row_stride` is elements per source row (defaults to `hidden`
    /// for the standalone case; set to `qkv_axis * inner` when the
    /// thunk fusion pass below rewires Rope to read directly from the
    /// fused QKV buffer — plan #45).
    Rope {
        src: usize,
        cos: usize,
        sin: usize,
        dst: usize,
        batch: u32,
        seq: u32,
        hidden: u32,
        head_dim: u32,
        n_rot: u32,
        cos_len: u32,
        src_row_stride: u32,
        /// `true` = GPT-J / llama.cpp-NORM interleaved pairs `(2i, 2i+1)`;
        /// `false` = HF / NeoX rotate-half pairs `(i, i+n_rot/2)`.
        interleaved: bool,
    },
    /// Fused attention block: QKV proj → split → \[RoPE\] → SDPA → output proj.
    /// All intermediates stay in L1 cache. Zero arena writes between ops.
    FusedAttnBlock {
        hidden: usize,
        qkv_w: usize,
        out_w: usize,
        mask: usize,
        /// How to mask attention scores. `Custom` reads the per-key `mask`
        /// buffer (BERT-style padding); `Causal` / `SlidingWindow` are
        /// synthesized in-kernel with no buffer; `None` applies no mask.
        /// Mirrors [`Thunk::Attention`]'s `mask_kind` — the attention-block
        /// fusion MUST carry it through, otherwise a causal decoder silently
        /// attends to future tokens (the fused kernel would only ever apply
        /// the per-key padding mask).
        mask_kind: rlx_ir::op::MaskKind,
        out: usize,
        qkv_b: usize,
        out_b: usize, // 0 = no bias
        cos: usize,
        sin: usize,
        cos_len: u32, // 0 = no RoPE
        batch: u32,
        seq: u32,
        hs: u32,
        nh: u32,
        dh: u32,
        has_bias: bool,
        has_rope: bool,
        /// RoPE pairing: `true` = GPT-J interleaved `(2i,2i+1)`, `false` = NeoX
        /// rotate-half `(i,i+d/2)`. Captured from the fused `Op::Rope` so the
        /// inline rope matches the standalone kernel (a GptJ model must not be
        /// silently rotated NeoX-style by the fused path).
        interleaved: bool,
    },
    /// Fused ENTIRE transformer layer: attention + residual + LN + FFN + residual + LN.
    /// Combines ~10 thunks into 1. All intermediates on stack. Zero arena traffic.
    FusedBertLayer {
        // attention
        hidden: usize,
        qkv_w: usize,
        qkv_b: usize,
        out_w: usize,
        out_b: usize,
        mask: usize,
        // LN1
        ln1_g: usize,
        ln1_b: usize,
        eps1: f32,
        // FFN (GELU)
        fc1_w: usize,
        fc1_b: usize,
        fc2_w: usize,
        fc2_b: usize,
        // LN2
        ln2_g: usize,
        ln2_b: usize,
        eps2: f32,
        // output
        out: usize,
        // dims
        batch: u32,
        seq: u32,
        hs: u32,
        nh: u32,
        dh: u32,
        int_dim: u32,
    },
    /// Fused Nomic transformer layer: attention+RoPE + residual + LN + SwiGLU FFN + residual + LN.
    FusedNomicLayer {
        hidden: usize,
        qkv_w: usize,
        out_w: usize,
        mask: usize,
        cos: usize,
        sin: usize,
        cos_len: u32,
        ln1_g: usize,
        ln1_b: usize,
        eps1: f32,
        fc11_w: usize,
        fc12_w: usize,
        fc2_w: usize,
        ln2_g: usize,
        ln2_b: usize,
        eps2: f32,
        out: usize,
        batch: u32,
        seq: u32,
        hs: u32,
        nh: u32,
        dh: u32,
        int_dim: u32,
        /// RoPE pairing, threaded from the consumed `FusedAttnBlock`
        /// (`true` = GPT-J interleaved, `false` = NeoX rotate-half).
        interleaved: bool,
    },
    /// Fused SwiGLU: out\[r,i\] = x\[r,i\] * silu(x[r, n_half+i]).
    /// Input: [outer, 2*n_half] — concatenated up||gate per row.
    /// Output: [outer, n_half].
    FusedSwiGLU {
        src: usize,
        dst: usize,
        n_half: u32,
        total: u32,
        gate_first: bool,
    },
    /// Concat along an axis: output[outer, axis, inner] = inputs concatenated.
    /// Each entry of `inputs` is (src_offset, axis_len_for_that_input) in u32
    /// elements. `outer`, `inner`, and `total_axis_len` are pre-computed
    /// at compile time to avoid per-run shape work.
    Concat {
        dst: usize,
        outer: u32,
        inner: u32,
        total_axis: u32,
        /// `(src_offset, axis_extent, input_numel)` — `input_numel` enables
        /// outer-dim broadcast when rank-deficient inputs are concatenated.
        inputs: Vec<(usize, u32, u32)>,
    },
    /// Element-wise comparison: out = (lhs CMP rhs) ? 1 : 0 (Bool u8 or F32 0/1).
    Compare {
        lhs: usize,
        rhs: usize,
        dst: usize,
        len: u32,
        op: CmpOp,
        /// Nonzero when lhs/rhs are i64 (mask/range ops).
        inputs_i64: u8,
        /// Input element size (1 = Bool, 4 = F32, 8 = I64).
        inputs_elem_bytes: u8,
        /// Output element size (1 = Bool, 4 = F32).
        dst_elem_bytes: u8,
    },
    /// Reduction along a contiguous range of axes. Input layout (after
    /// shape decomposition) is `[outer, reduced, inner]`; output is
    /// `[outer, inner]`. The single-axis cases (axis=0 → outer=1;
    /// axis=last → inner=1) and contiguous multi-axis (e.g. reduce over
    /// [0, 1] of an [N, C, H, W] tensor → outer=1, reduced=N*C, inner=H*W)
    /// all map onto this triplet. Non-contiguous axes are not supported
    /// and bail to Nop in the compile pass.
    Reduce {
        src: usize,
        dst: usize,
        outer: u32,
        reduced: u32,
        inner: u32,
        op: ReduceOp,
    },
    /// Index of the max (`is_max`) or min along the reduced axis; writes the
    /// winning index as an f32 into `dst`.
    ArgReduce {
        src: usize,
        dst: usize,
        outer: u32,
        reduced: u32,
        inner: u32,
        is_max: bool,
    },
    /// Top-K **indices** along the last axis. Input shape `[outer, axis_dim]`,
    /// output `[outer, k]` (f32 or i64 per `indices_i64`). Ties broken by
    /// smaller index. Used by MoE gating + beam search.
    TopK {
        src: usize,
        dst: usize,
        outer: u32,
        axis_dim: u32,
        k: u32,
        indices_i64: u8,
    },
    /// Indexed batched matmul: out\[i\] = input\[i\] @ weight[expert_idx\[i\]].
    /// Naive impl per token; for real MoE workloads, sort-by-expert + run
    /// segmented GEMM would amortize. Done when there's a workload.
    GroupedMatMul {
        input: usize,
        weight: usize,
        expert_idx: usize,
        dst: usize,
        m: u32,
        k_dim: u32,
        n: u32,
        num_experts: u32,
    },
    /// GGUF K-quant packed expert stack + grouped matmul (MoE FFN).
    DequantGroupedMatMulGguf {
        input: usize,
        w_q: usize,
        expert_idx: usize,
        dst: usize,
        m: u32,
        k_dim: u32,
        n: u32,
        num_experts: u32,
        scheme: rlx_ir::quant::QuantScheme,
    },
    /// Materialize packed MoE weights to F32 `[E, K, N]` (autodiff helper).
    DequantMoEWeightsGguf {
        w_q: usize,
        dst: usize,
        k_dim: u32,
        n: u32,
        num_experts: u32,
        scheme: rlx_ir::quant::QuantScheme,
    },
    /// Scatter-add: dst[indices\[i\] * trailing + j] += updates[i * trailing + j].
    /// Output is zeroed first; multiple updates to the same row accumulate.
    ScatterAdd {
        updates: usize,
        indices: usize,
        dst: usize,
        num_updates: u32,
        out_dim: u32,
        trailing: u32,
    },
    /// Ternary select: out = cond != 0 ? on_true : on_false
    Where {
        cond: usize,
        on_true: usize,
        on_false: usize,
        dst: usize,
        len: u32,
        elem_bytes: u8,
        /// Element size for cond (1 = Bool mask, 4 = F32 0/1).
        cond_elem_bytes: u8,
    },
    /// Single-rounded fused multiply-add: `dst = a*b + c` (one rounding —
    /// enables error-free transforms / compensated arithmetic).
    Fma {
        a: usize,
        b: usize,
        c: usize,
        dst: usize,
        len: u32,
        elem_bytes: u8,
    },
    /// General N-D transpose / broadcast. `out_dims[i]` is the output's dim
    /// i length; `in_strides[i]` is the input stride (in elements) used to
    /// index that dim — 0 for broadcast dims (Expand). `in_total` is the
    /// total element count in the source buffer (≤ output total when
    /// broadcasting). Strides are pre-computed at compile time.
    Transpose {
        src: usize,
        dst: usize,
        in_total: u32,
        out_dims: Vec<u32>,
        in_strides: Vec<u32>,
        elem_bytes: u8,
    },
    /// Gather along an arbitrary axis. `outer = product(dims[..axis])`,
    /// `trailing = product(dims[axis+1..])`, `axis_dim` = the dimension
    /// being indexed into. Output: outer × num_idx × trailing.
    /// (axis=0 still routes to the simpler Thunk::Gather fast path.)
    GatherAxis {
        table: usize,
        idx: usize,
        dst: usize,
        outer: u32,
        axis_dim: u32,
        num_idx: u32,
        trailing: u32,
        idx_i64: u8,
        table_bytes: u8,
    },
    /// 2D pooling (Max or Mean). Input layout [N, C, H, W], output
    /// [N, C, H_out, W_out]. Padding is implicit-zero; Mean divides by
    /// the full kernel area (matches torch's `count_include_pad=True`).
    Pool2D {
        src: usize,
        dst: usize,
        n: u32,
        c: u32,
        h: u32,
        w: u32,
        h_out: u32,
        w_out: u32,
        kh: u32,
        kw: u32,
        sh: u32,
        sw: u32,
        ph: u32,
        pw: u32,
        kind: ReduceOp,
    },
    /// 2D convolution. Input [N, C_in, H, W], weight [C_out, C_in_per_group, kH, kW],
    /// output [N, C_out, H_out, W_out]. Bias is a separate Op::Binary::Add
    /// after the conv (matching the IR's input layout — Op::Conv has 2 inputs).
    /// Naive direct convolution; sufficient for correctness, not optimised.
    Conv2D {
        src: usize,
        weight: usize,
        dst: usize,
        n: u32,
        c_in: u32,
        h: u32,
        w: u32,
        c_out: u32,
        h_out: u32,
        w_out: u32,
        kh: u32,
        kw: u32,
        sh: u32,
        sw: u32,
        ph: u32,
        pw: u32,
        dh: u32,
        dw: u32,
        groups: u32,
    },

    // ── Backward / training kernels ─────────────────────────────
    /// Real INT8 matmul with i32 accumulation.
    ///   `out[m, n] = requantize(bias[n] + Σₖ (x[m,k]-x_zp)·(w[k,n]-w_zp), mult, out_zp)`
    /// Reads `x` and `w` as i8, `bias` as i32; writes `out` as i8.
    /// Same kernel shape as `rlx_cortexm::dense::dense_i8` — promoted
    /// to a desktop thunk so a quantized graph compiled here doesn't
    /// have to round-trip through fake-quant.
    QMatMul {
        x: usize,
        w: usize,
        bias: usize,
        out: usize,
        m: u32,
        k: u32,
        n: u32,
        x_zp: i32,
        w_zp: i32,
        out_zp: i32,
        mult: f32,
    },

    /// Real INT8 conv2d, NCHW layout. Same loop shape as `Thunk::Conv2D`
    /// but with i8 reads, i32 accumulation, and per-output requantize
    /// to i8. Bias is i32 in the accumulator scale.
    QConv2d {
        x: usize,
        w: usize,
        bias: usize,
        out: usize,
        n: u32,
        c_in: u32,
        h: u32,
        w_in: u32,
        c_out: u32,
        h_out: u32,
        w_out: u32,
        kh: u32,
        kw: u32,
        sh: u32,
        sw: u32,
        ph: u32,
        pw: u32,
        dh: u32,
        dw: u32,
        groups: u32,
        x_zp: i32,
        w_zp: i32,
        out_zp: i32,
        mult: f32,
    },

    /// INT8 quantize. Reads `x` as f32, writes `q` as i8.
    /// `chan = (i / inner) % chan_dim` selects the per-channel
    /// scale/zp; `chan_axis` is informational only (the kernel uses
    /// `chan_dim` and `inner` directly).
    /// For per-tensor, `chan_dim = 1` and `inner = len` so `chan` is
    /// always 0.
    Quantize {
        x: usize,
        q: usize,
        len: u32,
        chan_axis: u32,
        chan_dim: u32,
        inner: u32,
        scales: Vec<f32>,
        zero_points: Vec<i32>,
    },

    /// INT8 dequantize — inverse of `Thunk::Quantize`.
    Dequantize {
        q: usize,
        x: usize,
        len: u32,
        chan_axis: u32,
        chan_dim: u32,
        inner: u32,
        scales: Vec<f32>,
        zero_points: Vec<i32>,
    },

    /// QAT fake-quantize. Per-channel (or per-tensor) symmetric
    /// quantize-then-dequantize on the fly. Computes
    ///   `s[c] = max(|x[..., c, ...]|) / q_max`
    /// then
    ///   `out[i] = clamp(round(x[i]/s[c]), -q_max, q_max) * s[c]`
    /// with `q_max = {127, 7, 1}` for `bits = {8, 4, 2}`. Same
    /// channel-layout convention as `Thunk::Quantize`: every
    /// element's channel is `(i / inner) % chan_dim`. The kernel
    /// does two passes — one to scan max-abs per channel, one to
    /// quant-dequant per element.
    FakeQuantize {
        x: usize,
        out: usize,
        len: u32,
        chan_axis: u32,
        chan_dim: u32,
        inner: u32,
        bits: u8,
        /// STE variant — informational on the forward side (output is
        /// the same regardless), kernel-relevant in the matching
        /// `FakeQuantizeBackward` thunk.
        ste: rlx_ir::op::SteKind,
        /// Scale-tracking strategy. `PerBatch` recomputes
        /// `max_abs/q_max` every call (the original path). `EMA{decay}`
        /// blends per-batch max-abs into the `state_off` buffer; `Fixed`
        /// reads `state_off` and never updates it.
        scale_mode: rlx_ir::op::ScaleMode,
        /// `Some(off)` for `EMA` and `Fixed`; `None` for `PerBatch`.
        /// Points at a `[chan_dim]` f32 buffer holding the running scale
        /// per channel.
        state_off: Option<usize>,
    },

    /// Backward pass for `Op::FakeQuantize` under one of four STE
    /// variants. Computes `dx[i]` from the f32 forward input `x` and
    /// the upstream gradient `dy`, using the same per-channel scale
    /// scheme as the forward.
    FakeQuantizeBackward {
        x: usize,
        dy: usize,
        dx: usize,
        len: u32,
        chan_axis: u32,
        chan_dim: u32,
        inner: u32,
        bits: u8,
        ste: rlx_ir::op::SteKind,
    },

    /// LSQ forward — same kernel shape as `FakeQuantize` Fixed mode.
    /// Reads scale from `scale_off` (a `[chan_dim]` Param tensor).
    FakeQuantizeLSQ {
        x: usize,
        scale_off: usize,
        out: usize,
        len: u32,
        chan_axis: u32,
        chan_dim: u32,
        inner: u32,
        bits: u8,
    },

    /// LSQ backward, x-gradient. STE-clipped: passes upstream
    /// through inside the quantization range, zeros outside.
    FakeQuantizeLSQBackwardX {
        x: usize,
        scale_off: usize,
        dy: usize,
        dx: usize,
        len: u32,
        chan_axis: u32,
        chan_dim: u32,
        inner: u32,
        bits: u8,
    },

    /// LSQ backward, scale-gradient. Per-channel:
    ///   `dscale[c] = sum_i ψ(x[i]/s[c]) · upstream[i]`
    /// where `ψ(z) = -z + round(z)` if `|z| ≤ q_max` else
    /// `sign(z) · q_max`. Output shape: `[chan_dim]`.
    FakeQuantizeLSQBackwardScale {
        x: usize,
        scale_off: usize,
        dy: usize,
        dscale: usize,
        len: u32,
        chan_axis: u32,
        chan_dim: u32,
        inner: u32,
        bits: u8,
    },

    /// ReLU backward: `dx[i] = dy[i] if x[i] > 0 else 0`.
    ReluBackward {
        x: usize,
        dy: usize,
        dx: usize,
        len: u32,
    },
    /// f64 sibling of `ReluBackward` — same shape as the f32 variant
    /// but reads/writes 8 bytes per element. Required because
    /// `ReluBackward`'s `&[f32]` slot view returns half of every f64
    /// otherwise → backward silently produces 0 gradients on an f64
    /// graph. Mirrors the `ActivationBackwardF64` split.
    ReluBackwardF64 {
        x: usize,
        dy: usize,
        dx: usize,
        len: u32,
    },

    /// Generic element-wise activation backward.
    /// `dx[i] = (d/dx act(x))[i] · dy[i]`. The closure dispatch is
    /// per-element; expensive activations (Gelu) recompute internals
    /// inline rather than threading an extra "saved y" tensor through.
    ActivationBackward {
        x: usize,
        dy: usize,
        dx: usize,
        len: u32,
        kind: Activation,
    },
    /// f64 sibling of `ActivationBackward` — slot offsets, len in
    /// elements; kernel reads/writes 8 bytes per element. Required
    /// because `ActivationBackward`'s `&[f32]` slot view silently
    /// returns garbage on an f64 graph (cb % 4 still works but every
    /// loaded value is half of an f64 → wrong gradient).
    ActivationBackwardF64 {
        x: usize,
        dy: usize,
        dx: usize,
        len: u32,
        kind: Activation,
    },

    /// LayerNorm backward — input gradient. Recomputes mean/var/x̂ from
    /// `x` and emits the closed-form `d_x` per row.
    LayerNormBackwardInput {
        x: usize,
        gamma: usize,
        dy: usize,
        dx: usize,
        rows: u32,
        h: u32,
        eps: f32,
    },

    /// LayerNorm backward — gamma gradient. `d_gamma[d] = Σ_row dy·x̂`.
    LayerNormBackwardGamma {
        x: usize,
        dy: usize,
        dgamma: usize,
        rows: u32,
        h: u32,
        eps: f32,
    },

    RmsNormBackwardInput {
        x: usize,
        gamma: usize,
        beta: usize,
        dy: usize,
        dx: usize,
        rows: u32,
        h: u32,
        eps: f32,
    },
    RmsNormBackwardGamma {
        x: usize,
        gamma: usize,
        beta: usize,
        dy: usize,
        dgamma: usize,
        rows: u32,
        h: u32,
        eps: f32,
    },
    RmsNormBackwardBeta {
        x: usize,
        gamma: usize,
        beta: usize,
        dy: usize,
        dbeta: usize,
        rows: u32,
        h: u32,
        eps: f32,
    },
    RopeBackward {
        dy: usize,
        cos: usize,
        sin: usize,
        dx: usize,
        batch: u32,
        seq: u32,
        hidden: u32,
        head_dim: u32,
        n_rot: u32,
        cos_len: u32,
    },
    CumsumBackward {
        dy: usize,
        dx: usize,
        rows: u32,
        cols: u32,
        exclusive: bool,
    },
    GatherBackward {
        dy: usize,
        indices: usize,
        dst: usize,
        outer: u32,
        axis_dim: u32,
        num_idx: u32,
        trailing: u32,
    },

    GroupNormBackwardInput {
        x: usize,
        gamma: usize,
        beta: usize,
        dy: usize,
        dx: usize,
        n: u32,
        c: u32,
        h: u32,
        w: u32,
        num_groups: u32,
        eps: f32,
    },
    GroupNormBackwardGamma {
        x: usize,
        dy: usize,
        dgamma: usize,
        n: u32,
        c: u32,
        h: u32,
        w: u32,
        num_groups: u32,
        eps: f32,
    },
    GroupNormBackwardBeta {
        dy: usize,
        dbeta: usize,
        n: u32,
        c: u32,
        h: u32,
        w: u32,
    },

    /// 2D max-pool backward (NCHW). Recomputes the argmax position
    /// inside each window and accumulates `dy` into `dx` at that
    /// position. Output is zeroed first; ties resolve to the first
    /// hit (lowest (kh,kw) index), matching what the forward kernel
    /// does with `acc.max(v)`.
    MaxPool2dBackward {
        x: usize,
        dy: usize,
        dx: usize,
        n: u32,
        c: u32,
        h: u32,
        w: u32,
        h_out: u32,
        w_out: u32,
        kh: u32,
        kw: u32,
        sh: u32,
        sw: u32,
        ph: u32,
        pw: u32,
    },

    /// 2D conv backward w.r.t. input (`dx = conv_transpose(dy, w)`).
    /// `dy [N, C_out, H_out, W_out]`, `w [C_out, C_in_per_group, kH, kW]`,
    /// `dx [N, C_in, H, W]`.
    Conv2dBackwardInput {
        dy: usize,
        w: usize,
        dx: usize,
        n: u32,
        c_in: u32,
        h: u32,
        w_in: u32,
        c_out: u32,
        h_out: u32,
        w_out: u32,
        kh: u32,
        kw: u32,
        sh: u32,
        sw: u32,
        ph: u32,
        pw: u32,
        dh: u32,
        dw: u32,
        groups: u32,
    },

    /// 2D conv backward w.r.t. weight. `x [N, C_in, H, W]`,
    /// `dy [N, C_out, H_out, W_out]`, `dw [C_out, C_in_per_group, kH, kW]`.
    /// `dw` is zeroed before accumulation.
    Conv2dBackwardWeight {
        x: usize,
        dy: usize,
        dw: usize,
        n: u32,
        c_in: u32,
        h: u32,
        w: u32,
        c_out: u32,
        h_out: u32,
        w_out: u32,
        kh: u32,
        kw: u32,
        sh: u32,
        sw: u32,
        ph: u32,
        pw: u32,
        dh: u32,
        dw_dil: u32,
        groups: u32,
    },

    /// NCHW im2col for conv backward-weight matmul. Output `[M, C·kH·kW]`
    /// with `M = N · H_out · W_out`. `n == 0` means infer batch from `x`.
    Im2Col {
        x: usize,
        col: usize,
        n: u32,
        c_in: u32,
        h: u32,
        w: u32,
        h_out: u32,
        w_out: u32,
        kh: u32,
        kw: u32,
        sh: u32,
        sw: u32,
        ph: u32,
        pw: u32,
        dh: u32,
        dw_dil: u32,
    },

    /// Fused softmax + cross-entropy loss against a dense target
    /// distribution. `logits [N, C]`, `targets [N, C]`, output `[N]`
    /// per-row loss `lse(logits[n]) - Σ_c targets[n,c]·logits[n,c]`.
    /// Numerically stable (max-subtract before exp).
    SoftmaxCrossEntropyDense {
        logits: usize,
        targets: usize,
        dst: usize,
        n: u32,
        c: u32,
    },

    /// Fused softmax + cross-entropy loss with f32-encoded integer
    /// labels. `logits [N, C]`, `labels [N]`, output `[N]` per-row loss.
    /// Numerically stable (max-subtract before exp).
    SoftmaxCrossEntropy {
        logits: usize,
        labels: usize,
        dst: usize,
        n: u32,
        c: u32,
    },

    /// Backward of the fused loss above.
    /// `dlogits[n, k] = (softmax(logits[n])[k] - one_hot(labels[n])[k]) * d_loss[n]`.
    SoftmaxCrossEntropyBackward {
        logits: usize,
        labels: usize,
        d_loss: usize,
        dlogits: usize,
        n: u32,
        c: u32,
    },

    /// User-registered custom op (CPU side). Lowered from `Op::Custom`.
    /// `kernel` is resolved against the global CPU kernel registry at
    /// compile time and stored as `Arc<dyn CpuKernel>` so execution
    /// avoids per-call lookups. v1: f32 contiguous only — see
    /// `op_registry::CpuKernel::execute_f32`.
    CustomOp {
        kernel: Arc<dyn CpuKernel>,
        inputs: Vec<(usize, u32, Shape)>, // (offset, len_elements, shape)
        output: (usize, u32, Shape),      // (offset, len_elements, shape)
        attrs: Vec<u8>,
    },

    /// 1D FFT along the last axis. Input/output are `[..., 2N]`
    /// real-block layout (first N real, second N imag along the
    /// transformed axis). `outer` is the product of all leading axes;
    /// `n_complex` is N (the number of complex points). Both halves
    /// of the real-block layout are read together by the kernel.
    /// `dtype` selects the f32 or f64 path; the two share structure
    /// but not buffers, so a flag at compile time avoids per-row
    /// dispatch.
    /// CPU reference 3D Gaussian splat render ([`rlx_ir::Op::GaussianSplatRender`]).
    GaussianSplatRender {
        positions_off: usize,
        positions_len: usize,
        scales_off: usize,
        scales_len: usize,
        rotations_off: usize,
        rotations_len: usize,
        opacities_off: usize,
        opacities_len: usize,
        colors_off: usize,
        colors_len: usize,
        sh_coeffs_off: usize,
        sh_coeffs_len: usize,
        meta_off: usize,
        dst_off: usize,
        dst_len: usize,
        width: u32,
        height: u32,
        tile_size: u32,
        radius_scale: f32,
        alpha_cutoff: f32,
        max_splat_steps: u32,
        transmittance_threshold: f32,
        max_list_entries: u32,
    },
    GaussianSplatRenderBackward {
        positions_off: usize,
        positions_len: usize,
        scales_off: usize,
        scales_len: usize,
        rotations_off: usize,
        rotations_len: usize,
        opacities_off: usize,
        opacities_len: usize,
        colors_off: usize,
        colors_len: usize,
        sh_coeffs_off: usize,
        sh_coeffs_len: usize,
        meta_off: usize,
        d_loss_off: usize,
        d_loss_len: usize,
        packed_off: usize,
        packed_len: usize,
        width: u32,
        height: u32,
        tile_size: u32,
        radius_scale: f32,
        alpha_cutoff: f32,
        max_splat_steps: u32,
        transmittance_threshold: f32,
        max_list_entries: u32,
        loss_grad_clip: f32,
        sh_band: u32,
        max_anisotropy: f32,
    },
    /// Strict IR stage 1 — project + bin + sort + rays ([`Op::GaussianSplatPrepare`]).
    GaussianSplatPrepare {
        positions_off: usize,
        positions_len: usize,
        scales_off: usize,
        scales_len: usize,
        rotations_off: usize,
        rotations_len: usize,
        opacities_off: usize,
        opacities_len: usize,
        colors_off: usize,
        colors_len: usize,
        sh_coeffs_off: usize,
        sh_coeffs_len: usize,
        meta_off: usize,
        meta_len: usize,
        prep_off: usize,
        prep_len: usize,
        width: u32,
        height: u32,
        tile_size: u32,
        radius_scale: f32,
        alpha_cutoff: f32,
        max_splat_steps: u32,
        transmittance_threshold: f32,
        max_list_entries: u32,
    },
    /// Strict IR stage 2 — tile raster from prepare buffer ([`Op::GaussianSplatRasterize`]).
    GaussianSplatRasterize {
        prep_off: usize,
        prep_len: usize,
        meta_off: usize,
        meta_len: usize,
        dst_off: usize,
        dst_len: usize,
        count: usize,
        width: u32,
        height: u32,
        tile_size: u32,
        alpha_cutoff: f32,
        max_splat_steps: u32,
        transmittance_threshold: f32,
        max_list_entries: u32,
    },
    Fft1d {
        src: usize,
        dst: usize,
        outer: u32,
        n_complex: u32,
        inverse: bool,
        norm_tag: u32,
        dtype: rlx_ir::DType,
    },
    FftButterflyStage {
        state_src: usize,
        state_dst: usize,
        gate_src: usize,
        rev_src: usize,
        tw_re_src: usize,
        tw_im_src: usize,
        batch: u32,
        n_fft: u32,
        stage: u32,
    },
    LogMel {
        spec: usize,
        filters: usize,
        dst: usize,
        outer: u32,
        n_fft: u32,
        n_bins: u32,
        n_mels: u32,
    },
    LogMelBackward {
        spec: usize,
        filters: usize,
        dy: usize,
        dst: usize,
        outer: u32,
        n_fft: u32,
        n_bins: u32,
        n_mels: u32,
    },
    WelchPeaks {
        spec: usize,
        dst: usize,
        welch_batch: u32,
        n_fft: u32,
        n_segments: u32,
        k: u32,
    },
}

/// Compiled thunk schedule — the runtime hot path.
/// Nop thunks are filtered out at compile time for zero iteration overhead.
#[derive(Clone)]
pub struct ThunkSchedule {
    pub thunks: Vec<Thunk>,
    /// TIDE merged placement mask (union across layers).
    pub moe_resident: Option<std::sync::Arc<[bool]>>,
    /// Per MoE layer placement (`layer[e]`); preferred when set.
    pub moe_resident_layers: Option<std::sync::Arc<Vec<std::sync::Arc<[bool]>>>>,
    /// MoE router TopK capture (per-layer refresh).
    pub moe_topk_capture: Option<std::sync::Arc<crate::moe_topk_capture::MoeTopkCapture>>,
    /// Cached config values.
    pub mask_threshold: f32,
    pub mask_neg_inf: f32,
    pub score_skip: f32,
    /// Pre-compiled closure dispatch (zero match overhead). `Arc` (not
    /// `Box`) so the schedule can be `Clone` — multiple parallel
    /// executors share the same compiled closures (they're read-only
    /// `Fn(*mut u8)` so concurrent dispatch is safe; the arena pointer
    /// they receive is the only mutable state and is per-executor).
    pub compiled_fns: Vec<Arc<dyn Fn(*mut u8) + Send + Sync>>,
    /// Runtime-mutable RNG policy for [`Thunk::RngNormal`] / [`Thunk::RngUniform`].
    pub rng: Arc<std::sync::RwLock<rlx_ir::RngOptions>>,
}

impl ThunkSchedule {
    pub fn strip_nops(&mut self) {
        self.thunks.retain(|t| !matches!(t, Thunk::Nop));
        // compiled_fns must be rebuilt after stripping — caller should
        // call strip_nops() before compile_closures().
        self.compiled_fns.clear();
    }
}

/// Get the arena byte offset for a node.
fn node_offset(arena: &Arena, id: NodeId) -> usize {
    if arena.has_buffer(id) {
        arena.byte_offset(id)
    } else {
        usize::MAX
    }
}

/// Every byte-offset that a thunk reads from. Used by the Narrow→Rope
/// fusion (#45) to verify a Narrow's dst has exactly one consumer
/// before eliding it. Conservative: when in doubt about reads (an op
/// not yet listed here), the fusion will skip — correctness over
/// completeness.
fn thunk_read_offsets(t: &Thunk) -> Vec<usize> {
    match t {
        Thunk::Sgemm { a, b, .. } => vec![*a, *b],
        Thunk::SgemmT { a, b, .. } => vec![*a, *b],
        Thunk::SgdMomentum {
            param, vel, grad, ..
        } => vec![*param, *vel, *grad],
        Thunk::DenseSolveF64 { a, b, .. } => vec![*a, *b],
        Thunk::DenseSolveF32 { a, b, .. } => vec![*a, *b],
        Thunk::BatchedDenseSolveF64 { a, b, .. } => vec![*a, *b],
        Thunk::BatchedDgemmF64 { a, b, .. } => vec![*a, *b],
        Thunk::BatchedSgemm { a, b, .. } => vec![*a, *b],
        Thunk::FusedMmBiasAct { a, w, bias, .. } => vec![*a, *w, *bias],
        Thunk::ElementwiseRegion { input_offs, .. } => input_offs.clone(),
        Thunk::BiasAdd { src, bias, .. } => vec![*src, *bias],
        Thunk::BinaryFull { lhs, rhs, .. } => vec![*lhs, *rhs],
        Thunk::BinaryFullF64 { lhs, rhs, .. } => vec![*lhs, *rhs],
        Thunk::BinaryFullC64 { lhs, rhs, .. } => vec![*lhs, *rhs],
        Thunk::ComplexNormSqF32 { src, .. } => vec![*src],
        Thunk::ComplexNormSqBackwardF32 { z, g, .. } => vec![*z, *g],
        Thunk::ConjugateC64 { src, .. } => vec![*src],
        Thunk::Scan {
            outer_init_off,
            xs_inputs,
            ..
        } => {
            let mut v = vec![*outer_init_off];
            for (_, outer_xs_off, _) in xs_inputs.iter() {
                v.push(*outer_xs_off);
            }
            v
        }
        Thunk::ScanBackward {
            outer_init_off,
            outer_traj_off,
            outer_upstream_off,
            outer_xs_offs,
            ..
        } => {
            let mut v = vec![*outer_init_off, *outer_traj_off, *outer_upstream_off];
            for (off, _) in outer_xs_offs.iter() {
                v.push(*off);
            }
            v
        }
        Thunk::ScanBackwardXs {
            outer_init_off,
            outer_traj_off,
            outer_upstream_off,
            outer_xs_offs,
            ..
        } => {
            let mut v = vec![*outer_init_off, *outer_traj_off, *outer_upstream_off];
            for (off, _) in outer_xs_offs.iter() {
                v.push(*off);
            }
            v
        }
        Thunk::CustomFn { inputs, .. } => {
            inputs.iter().map(|(_, outer_off, _)| *outer_off).collect()
        }
        Thunk::ActivationInPlace { data, .. } => vec![*data],
        Thunk::LayerNorm { src, g, b, .. } | Thunk::GroupNorm { src, g, b, .. } => {
            vec![*src, *g, *b]
        }
        Thunk::BatchNormInference {
            src,
            g,
            b,
            mean,
            var,
            ..
        } => vec![*src, *g, *b, *mean, *var],
        Thunk::ResizeNearest2x { src, .. } => vec![*src],
        Thunk::AxialRope2d { src, .. } => vec![*src],
        Thunk::FusedResidualLN {
            x, res, bias, g, b, ..
        } => vec![*x, *res, *bias, *g, *b],
        Thunk::FusedResidualRmsNorm {
            x, res, bias, g, b, ..
        } => vec![*x, *res, *bias, *g, *b],
        Thunk::RmsNorm { src, g, b, .. } => vec![*src, *g, *b],
        Thunk::Softmax { data, .. } => vec![*data],
        Thunk::Cumsum { src, .. } => vec![*src],
        Thunk::Sample { logits, .. } => vec![*logits],
        Thunk::RngNormal { .. } | Thunk::RngUniform { .. } => vec![],
        Thunk::LoraMatMul { x, w, a, b, .. } => vec![*x, *w, *a, *b],
        Thunk::DequantMatMul {
            x, w_q, scale, zp, ..
        } => vec![*x, *w_q, *scale, *zp],
        Thunk::DequantMatMulGguf { x, w_q, .. } => vec![*x, *w_q],
        Thunk::DequantMatMulInt4 {
            x, w_q, scale, zp, ..
        } => vec![*x, *w_q, *scale, *zp],
        Thunk::DequantMatMulFp8 { x, w_q, scale, .. } => vec![*x, *w_q, *scale],
        Thunk::DequantMatMulNvfp4 {
            x,
            w_q,
            scale,
            global_scale,
            ..
        } => vec![*x, *w_q, *scale, *global_scale],
        Thunk::ScaledMatMul {
            lhs,
            rhs,
            lhs_scale,
            rhs_scale,
            bias,
            has_bias,
            ..
        } => {
            let mut v = vec![*lhs, *rhs, *lhs_scale, *rhs_scale];
            if *has_bias {
                v.push(*bias);
            }
            v
        }
        Thunk::ScaledQuantize { x, scale, .. } => vec![*x, *scale],
        Thunk::ScaledQuantScale { x, .. } => vec![*x],
        Thunk::ScaledDequantize { codes, scale, .. } => vec![*codes, *scale],
        Thunk::Conv2D1x1 { src, weight, .. } => vec![*src, *weight],
        Thunk::SelectiveScan {
            x, delta, a, b, c, ..
        } => vec![*x, *delta, *a, *b, *c],
        Thunk::GatedDeltaNet {
            q,
            k,
            v,
            g,
            beta,
            state,
            ..
        } => {
            let mut v = vec![*q, *k, *v, *g, *beta];
            if *state != 0 {
                v.push(*state);
            }
            v
        }
        Thunk::Attention { q, k, v, mask, .. } => vec![*q, *k, *v, *mask],
        Thunk::AttentionBackward {
            q, k, v, dy, mask, ..
        } => {
            let mut v = vec![*q, *k, *v, *dy];
            if *mask != 0 {
                v.push(*mask);
            }
            v
        }
        Thunk::Rope { src, cos, sin, .. } => vec![*src, *cos, *sin],
        Thunk::FusedAttnBlock {
            hidden,
            qkv_w,
            out_w,
            mask,
            qkv_b,
            out_b,
            cos,
            sin,
            ..
        } => vec![*hidden, *qkv_w, *out_w, *mask, *qkv_b, *out_b, *cos, *sin],
        Thunk::FusedSwiGLU { src, .. } => vec![*src],
        Thunk::Concat { inputs, .. } => inputs.iter().map(|(off, _, _)| *off).collect(),
        Thunk::ConcatF64 { inputs, .. } => inputs.iter().map(|(off, _, _)| *off).collect(),
        Thunk::Narrow { src, .. } => vec![*src],
        Thunk::Copy { src, .. } => vec![*src],
        Thunk::Gather { table, idx, .. } => vec![*table, *idx],
        // Anything not enumerated → return the dst as a "read" too,
        // forcing the fusion to bail (read_count >= 2 → skip). Keeps
        // this list safe to be incomplete.
        _ => vec![],
    }
}

/// Fused dequant + matmul (plan #5). Int8-blockwise weights: each
/// `block_size` consecutive elements of a column share one f32
/// scale (and optionally a zero-point). The dequant happens inside
/// the inner accumulate so the f32 weight is never materialized.
///
/// `w_bytes` is the row-major i8 weight matrix `[k, n]`. `scales`
/// and `zps` are `[k/block, n]`. When `asym=false`, `zps` may be
/// empty.
///
/// Today this is the reference scalar implementation — the win is
/// memory bandwidth, not flops, since LLM weights dominate the
/// working set. A NEON SIMD path that loads 16 i8 → splat-scale →
/// fused-multiply-add is the natural follow-on.
#[allow(clippy::too_many_arguments)]
pub fn dequant_matmul_int8(
    x: &[f32],       // [m, k]
    w_bytes: &[i8],  // [k, n]
    scales: &[f32],  // [k/block, n]
    zps: &[f32],     // [k/block, n] or empty
    out: &mut [f32], // [m, n]
    m: usize,
    k: usize,
    n: usize,
    block_size: usize,
    asym: bool,
) {
    let blocks_per_col = k.div_ceil(block_size);
    for i in 0..m {
        for j in 0..n {
            let mut acc = 0f32;
            for p in 0..k {
                let block = p / block_size;
                let s = scales[block * n + j];
                let z = if asym { zps[block * n + j] } else { 0.0 };
                let q = w_bytes[p * n + j] as f32;
                let dequantized = (q - z) * s;
                acc += x[i * k + p] * dequantized;
            }
            out[i * n + j] = acc;
        }
    }
    let _ = blocks_per_col;
}

#[allow(clippy::too_many_arguments)]
fn dequant_matmul_int4(
    x: &[f32],
    w_bytes: &[u8],
    scales: &[f32],
    zps: &[f32],
    out: &mut [f32],
    m: usize,
    k: usize,
    n: usize,
    block_size: usize,
    asym: bool,
) {
    for i in 0..m {
        for j in 0..n {
            let mut acc = 0f32;
            for p in 0..k {
                let block = p / block_size;
                let s = scales[block * n + j];
                let z = if asym { zps[block * n + j] } else { 0.0 };
                let byte_idx = (p * n + j) / 2;
                let nibble = if (p * n + j) & 1 == 0 {
                    w_bytes[byte_idx] & 0x0F
                } else {
                    w_bytes[byte_idx] >> 4
                };
                let dequantized = (nibble as f32 - z) * s;
                acc += x[i * k + p] * dequantized;
            }
            out[i * n + j] = acc;
        }
    }
}

fn fp8_e4m3_to_f32(b: u8) -> f32 {
    let sign = if b & 0x80 != 0 { -1.0 } else { 1.0 };
    let exp = (b >> 3) & 0x0F;
    let mant = b & 0x07;
    if exp == 0 {
        if mant == 0 {
            return 0.0;
        }
        return sign * (mant as f32) * 2f32.powi(-9);
    }
    if exp == 0x0F {
        return if mant == 0 {
            sign * f32::INFINITY
        } else {
            f32::NAN
        };
    }
    sign * (1.0 + mant as f32 / 8.0) * 2f32.powi(exp as i32 - 7)
}

fn fp8_e5m2_to_f32(b: u8) -> f32 {
    let sign = if b & 0x80 != 0 { -1.0 } else { 1.0 };
    let exp = (b >> 2) & 0x1F;
    let mant = b & 0x03;
    if exp == 0 {
        if mant == 0 {
            return 0.0;
        }
        return sign * (mant as f32) * 2f32.powi(-16);
    }
    if exp == 0x1F {
        return if mant == 0 {
            sign * f32::INFINITY
        } else {
            f32::NAN
        };
    }
    sign * (1.0 + mant as f32 / 4.0) * 2f32.powi(exp as i32 - 15)
}

#[allow(clippy::too_many_arguments)]
fn dequant_matmul_fp8(
    x: &[f32],
    w_bytes: &[u8],
    scales: &[f32],
    out: &mut [f32],
    m: usize,
    k: usize,
    n: usize,
    e5m2: bool,
) {
    let dequant = if e5m2 {
        fp8_e5m2_to_f32
    } else {
        fp8_e4m3_to_f32
    };
    for i in 0..m {
        for j in 0..n {
            let mut acc = 0f32;
            for p in 0..k {
                let w = dequant(w_bytes[p * n + j]);
                let s = scales.get(j).copied().unwrap_or(1.0);
                acc += x[i * k + p] * w * s;
            }
            out[i * n + j] = acc;
        }
    }
}

#[allow(clippy::too_many_arguments)]
pub fn dequant_matmul_nvfp4(
    x: &[f32],
    w_bytes: &[u8],
    scale_bytes: &[u8],
    global_scale: f32,
    out: &mut [f32],
    m: usize,
    k: usize,
    n: usize,
) {
    use rlx_ir::{NVFP4_GROUP_SIZE, fp4_e2m1_to_f32, fp8_e4m3_scale_to_f32};
    let gs = NVFP4_GROUP_SIZE;
    for i in 0..m {
        for j in 0..n {
            let mut acc = 0f32;
            for p in 0..k {
                let byte_idx = (p * n + j) / 2;
                let nibble = if (p * n + j) & 1 == 0 {
                    w_bytes[byte_idx] & 0x0F
                } else {
                    w_bytes[byte_idx] >> 4
                };
                let block = p / gs;
                let scale = fp8_e4m3_scale_to_f32(scale_bytes[block * n + j]);
                let w = fp4_e2m1_to_f32(nibble) * scale * global_scale;
                acc += x[i * k + p] * w;
            }
            out[i * n + j] = acc;
        }
    }
}

// ── Native low-precision (FP8/FP6/FP4) scaled GEMM — CPU reference oracle ──
//
// Decode-and-accumulate *reference* for `Op::ScaledMatMul` + its quantize
// producers. CPUs have no fp8 matrix units; correctness, not speed, is the
// point — every GPU backend's native tensor-core path is checked against these.
// Layout is TN: lhs [m,k], rhs [n,k] (K-last), out = lhs·rhsᵀ. Block scales
// (when any) run along the last/contraction axis of each operand.

/// Blocks along a `len`-element axis (1 for per-tensor).
#[inline]
fn lowp_nblk(len: usize, layout: rlx_ir::ScaleLayout) -> usize {
    match layout {
        rlx_ir::ScaleLayout::PerTensor => 1,
        _ => len.div_ceil(layout.block() as usize),
    }
}

/// Snap a raw f32 scale to the grid storable for `layout`, so quantizer and
/// matmul agree bit-for-bit on the reconstructed value.
#[inline]
fn lowp_snap_scale(layout: rlx_ir::ScaleLayout, s: f32) -> f32 {
    use rlx_ir::lowp_codec;
    match layout {
        rlx_ir::ScaleLayout::PerTensor => s,
        rlx_ir::ScaleLayout::BlockMxE8M0 { .. } => {
            lowp_codec::e8m0_to_f32(lowp_codec::f32_to_e8m0(s))
        }
        rlx_ir::ScaleLayout::Nvfp4 { .. } => lowp_codec::decode(
            rlx_ir::ScaledFormat::F8E4M3,
            lowp_codec::encode(rlx_ir::ScaledFormat::F8E4M3, s),
        ),
    }
}

/// Scale for element (`free`, `contract`) given decoded raw scales.
#[inline]
fn lowp_scale_at(
    layout: rlx_ir::ScaleLayout,
    scales: &[f32],
    free: usize,
    contract: usize,
    nblk: usize,
) -> f32 {
    match layout {
        rlx_ir::ScaleLayout::PerTensor => scales.first().copied().unwrap_or(1.0),
        _ => scales[free * nblk + contract / layout.block() as usize],
    }
}

/// Compute (snapped) raw f32 scales for `x` (`[rows, cols]`, blocks along
/// `cols` = contraction). PerTensor → 1 value; block → `rows*nblk` row-major.
fn lowp_compute_scales(
    x: &[f32],
    fmt: rlx_ir::ScaledFormat,
    layout: rlx_ir::ScaleLayout,
    rows: usize,
    cols: usize,
) -> Vec<f32> {
    let maxf = fmt.max_finite();
    let to_scale = |amax: f32| if amax > 0.0 { amax / maxf } else { 1.0 };
    match layout {
        rlx_ir::ScaleLayout::PerTensor => {
            let amax = x.iter().fold(0.0f32, |a, &v| a.max(v.abs()));
            vec![to_scale(amax)]
        }
        _ => {
            let block = layout.block() as usize;
            let nblk = cols.div_ceil(block);
            let mut out = vec![1.0f32; rows * nblk];
            for r in 0..rows {
                for b in 0..nblk {
                    let lo = b * block;
                    let hi = (lo + block).min(cols);
                    let mut amax = 0.0f32;
                    for c in lo..hi {
                        amax = amax.max(x[r * cols + c].abs());
                    }
                    out[r * nblk + b] = lowp_snap_scale(layout, to_scale(amax));
                }
            }
            out
        }
    }
}

/// Quantize `x` (`[rows, cols]`, blocks along cols) to packed codes using the
/// already-snapped, decoded raw `scales`.
fn lowp_quantize(
    x: &[f32],
    scales: &[f32],
    fmt: rlx_ir::ScaledFormat,
    layout: rlx_ir::ScaleLayout,
    rows: usize,
    cols: usize,
    out: &mut [u8],
) {
    let nblk = lowp_nblk(cols, layout);
    for r in 0..rows {
        for c in 0..cols {
            let s = lowp_scale_at(layout, scales, r, c, nblk);
            let v = if s != 0.0 { x[r * cols + c] / s } else { 0.0 };
            out[r * cols + c] = rlx_ir::lowp_codec::encode(fmt, v);
        }
    }
}

/// TN scaled GEMM: lhs [m,k] codes, rhs [n,k] codes, out [m,n] f32.
#[allow(clippy::too_many_arguments)]
fn lowp_scaled_matmul(
    lhs: &[u8],
    rhs: &[u8],
    lhs_scales: &[f32],
    rhs_scales: &[f32],
    bias: Option<&[f32]>,
    out: &mut [f32],
    m: usize,
    n: usize,
    k: usize,
    layout: rlx_ir::ScaleLayout,
    lhs_fmt: rlx_ir::ScaledFormat,
    rhs_fmt: rlx_ir::ScaledFormat,
) {
    use rlx_ir::lowp_codec::decode;
    let nblk = lowp_nblk(k, layout);
    for i in 0..m {
        for j in 0..n {
            let mut acc = 0f32;
            for p in 0..k {
                let a =
                    decode(lhs_fmt, lhs[i * k + p]) * lowp_scale_at(layout, lhs_scales, i, p, nblk);
                let b =
                    decode(rhs_fmt, rhs[j * k + p]) * lowp_scale_at(layout, rhs_scales, j, p, nblk);
                acc += a * b;
            }
            out[i * n + j] = acc + bias.map_or(0.0, |bb| bb[j]);
        }
    }
}

/// Reconstruct f32 from packed codes: `out[i] = decode(code[i]) · scale(block)`.
/// `[rows, cols]`, blocks along cols. Inverse of [`lowp_quantize`].
fn lowp_dequantize(
    codes: &[u8],
    scales: &[f32],
    fmt: rlx_ir::ScaledFormat,
    layout: rlx_ir::ScaleLayout,
    rows: usize,
    cols: usize,
    out: &mut [f32],
) {
    use rlx_ir::lowp_codec::decode;
    let nblk = lowp_nblk(cols, layout);
    for r in 0..rows {
        for c in 0..cols {
            let s = lowp_scale_at(layout, scales, r, c, nblk);
            out[r * cols + c] = decode(fmt, codes[r * cols + c]) * s;
        }
    }
}

/// Decode a stored scale tensor (f32 for per-tensor, E8M0/E4M3 bytes for block
/// layouts) into raw f32 scales. `n` is the scale-element count.
unsafe fn lowp_read_scales(
    layout: rlx_ir::ScaleLayout,
    base: *mut u8,
    offset: usize,
    n: usize,
) -> Vec<f32> {
    use rlx_ir::lowp_codec;
    match layout {
        rlx_ir::ScaleLayout::PerTensor => {
            unsafe { std::slice::from_raw_parts(base.add(offset) as *const f32, n) }.to_vec()
        }
        rlx_ir::ScaleLayout::BlockMxE8M0 { .. } => {
            let bytes = unsafe { std::slice::from_raw_parts(base.add(offset), n) };
            bytes.iter().map(|&b| lowp_codec::e8m0_to_f32(b)).collect()
        }
        rlx_ir::ScaleLayout::Nvfp4 { .. } => {
            let bytes = unsafe { std::slice::from_raw_parts(base.add(offset), n) };
            bytes
                .iter()
                .map(|&b| lowp_codec::decode(rlx_ir::ScaledFormat::F8E4M3, b))
                .collect()
        }
    }
}

/// Fused sampling step: logits → top-k filter → top-p truncation
/// → softmax → multinomial sample. Operates on one row of length
/// `vocab` and returns the sampled index. Plan #42.
///
/// Internal scratch is on the stack via SmallVec-style fallback —
/// for `vocab > 8192` we heap-allocate a working buffer; below
/// that we keep things in a fixed array. (TODO: thread the
/// scratch through ThunkSchedule like sdpa_scores does.)
fn sample_row(
    logits: &[f32],
    top_k: usize,
    top_p: f32,
    temperature: f32,
    rng: &mut rlx_ir::Philox4x32,
) -> usize {
    let v = logits.len();
    if v == 0 {
        return 0;
    }
    let temp = temperature.max(1e-6);
    // Copy + temperature-scale into a working buffer.
    let mut scaled: Vec<f32> = logits.iter().map(|&x| x / temp).collect();

    // Top-k: zero out everything but the k largest by setting to -inf.
    if top_k > 0 && top_k < v {
        // Partial selection: find k-th largest then mask below.
        let mut indexed: Vec<(usize, f32)> = scaled.iter().copied().enumerate().collect();
        // Sort descending; partial would be O(n log k), full sort is fine
        // for typical vocab sizes (32k-128k) — single-row work.
        indexed.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        let cutoff = indexed[top_k - 1].1;
        for x in scaled.iter_mut() {
            if *x < cutoff {
                *x = f32::NEG_INFINITY;
            }
        }
    }

    // Stable softmax.
    let mut max_l = f32::NEG_INFINITY;
    for &x in &scaled {
        if x > max_l {
            max_l = x;
        }
    }
    let mut sum = 0.0f32;
    for x in scaled.iter_mut() {
        *x = (*x - max_l).exp();
        sum += *x;
    }
    let inv = 1.0 / sum.max(f32::MIN_POSITIVE);
    for x in scaled.iter_mut() {
        *x *= inv;
    }

    // Top-p: keep the smallest set of tokens whose cumulative
    // probability exceeds top_p (after sorting descending).
    if top_p < 1.0 {
        let mut indexed: Vec<(usize, f32)> = scaled.iter().copied().enumerate().collect();
        indexed.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        let mut cum = 0.0f32;
        let mut keep = vec![false; v];
        for (idx, p) in indexed.iter() {
            keep[*idx] = true;
            cum += *p;
            if cum >= top_p {
                break;
            }
        }
        let mut new_sum = 0.0f32;
        for (i, x) in scaled.iter_mut().enumerate() {
            if !keep[i] {
                *x = 0.0;
            }
            new_sum += *x;
        }
        let inv = 1.0 / new_sum.max(f32::MIN_POSITIVE);
        for x in scaled.iter_mut() {
            *x *= inv;
        }
    }

    // Multinomial sample via inverse-CDF.
    let r = rng.next_f32();
    let mut acc = 0.0f32;
    for (i, &p) in scaled.iter().enumerate() {
        acc += p;
        if r <= acc {
            return i;
        }
    }
    v - 1 // floating-point edge case fallback
}

/// Apply a synthetic (kernel-generated) attention mask to a `[q_seq, k_seq]`
/// scores matrix. Custom masks are read from a tensor and not handled here.
/// `None` is a no-op so callers don't need to special-case it.
#[inline]
fn apply_synthetic_mask(
    scores: &mut [f32],
    q_seq: usize,
    k_seq: usize,
    kind: rlx_ir::op::MaskKind,
) {
    let neg = crate::config::RuntimeConfig::global().attn_mask_neg_inf;
    let q_offset = k_seq.saturating_sub(q_seq);
    match kind {
        rlx_ir::op::MaskKind::None | rlx_ir::op::MaskKind::Custom | rlx_ir::op::MaskKind::Bias => {}
        rlx_ir::op::MaskKind::Causal => {
            for qi in 0..q_seq {
                let abs_q = q_offset + qi;
                for ki in (abs_q + 1)..k_seq {
                    scores[qi * k_seq + ki] = neg;
                }
            }
        }
        rlx_ir::op::MaskKind::SlidingWindow(w) => {
            for qi in 0..q_seq {
                let abs_q = q_offset + qi;
                let lo = abs_q.saturating_sub(w);
                for ki in 0..k_seq {
                    if ki < lo || ki > abs_q {
                        scores[qi * k_seq + ki] = neg;
                    }
                }
            }
        }
    }
}

/// NCL `[N,C,L]` or NCHW `[N,C,H,W]` → `(n, c, h, w)` for 2D conv/norm thunks.
fn conv_nchw_dims(shape: &Shape) -> (u32, u32, u32, u32) {
    match shape.rank() {
        3 => (
            shape.dim(0).unwrap_static() as u32,
            shape.dim(1).unwrap_static() as u32,
            1,
            shape.dim(2).unwrap_static() as u32,
        ),
        4 => (
            shape.dim(0).unwrap_static() as u32,
            shape.dim(1).unwrap_static() as u32,
            shape.dim(2).unwrap_static() as u32,
            shape.dim(3).unwrap_static() as u32,
        ),
        r => panic!("conv_nchw_dims: expected rank 3 or 4, got {r}"),
    }
}

/// Compile graph into thunk schedule.
pub fn compile_thunks(graph: &Graph, arena: &Arena) -> ThunkSchedule {
    compile_thunks_with_rng(graph, arena, rlx_ir::RngOptions::default())
}

/// Compile graph into thunk schedule with explicit RNG policy.
pub fn compile_thunks_with_rng(
    graph: &Graph,
    arena: &Arena,
    rng: rlx_ir::RngOptions,
) -> ThunkSchedule {
    let rng_shared = Arc::new(std::sync::RwLock::new(rng));
    let mut thunks = Vec::with_capacity(graph.len());

    // ── Auto-fuse last-two-axis Transpose → MatMul into a trans-Sgemm ──
    // Matmul backprop emits `Transpose(operand) → MatMul` for `dA=g·Bᵀ` and
    // `dB=Aᵀ·g`; the transpose is a full copy (the dominant backward cost).
    // Fold it into cblas trans flags (no copy). Guarded to the safe 2-D F32
    // case where the transpose is used only by this matmul.
    let mut use_counts: std::collections::HashMap<NodeId, usize> = std::collections::HashMap::new();
    for n in graph.nodes() {
        for &i in &n.inputs {
            *use_counts.entry(i).or_insert(0) += 1;
        }
    }
    let is_t2 = |g: &Graph, id: NodeId| -> bool {
        matches!(&g.node(id).op, Op::Transpose { perm } if perm.as_slice() == [1, 0])
            && g.node(id).shape.rank() == 2
    };
    let mut folded_transpose: std::collections::HashSet<NodeId> = std::collections::HashSet::new();
    let mut matmul_fold: std::collections::HashMap<NodeId, (NodeId, bool, NodeId, bool)> =
        std::collections::HashMap::new();
    for n in graph.nodes() {
        if !matches!(n.op, Op::MatMul) {
            continue;
        }
        let (a_id, b_id) = (n.inputs[0], n.inputs[1]);
        if graph.node(a_id).shape.rank() != 2
            || graph.node(b_id).shape.rank() != 2
            || n.shape.dtype() != rlx_ir::DType::F32
        {
            continue;
        }
        let fold_a = is_t2(graph, a_id) && use_counts.get(&a_id) == Some(&1);
        let fold_b = is_t2(graph, b_id) && use_counts.get(&b_id) == Some(&1);
        if !fold_a && !fold_b {
            continue;
        }
        let (asrc, ta) = if fold_a {
            (graph.node(a_id).inputs[0], true)
        } else {
            (a_id, false)
        };
        let (bsrc, tb) = if fold_b {
            (graph.node(b_id).inputs[0], true)
        } else {
            (b_id, false)
        };
        matmul_fold.insert(n.id, (asrc, ta, bsrc, tb));
        if fold_a {
            folded_transpose.insert(a_id);
        }
        if fold_b {
            folded_transpose.insert(b_id);
        }
    }

    // ── Auto-fuse the in-graph SGD-with-momentum update into one kernel ──
    // The fully-fused training step appends, per parameter, the chain
    //   v' = Add(Mul(vel, momᶜ), grad) ;  p' = Sub(param, Mul(v', lrᶜ))
    // where `momᶜ`/`lrᶜ` are full-size constant tensors. That lowers to 4
    // `BinaryFull` ops (+2 constants) per param and dominates the step
    // (~60% of CPU time on the MLP). Collapse the whole chain into a single
    // `SgdMomentum` thunk attached to the `Sub` (p'), which also writes v''s
    // slot. Guarded to exactly this shape: both p' and v' are graph outputs,
    // the inner nodes have a single in-graph use, and the scalars are
    // uniform full-size F32 constants.
    let out_set: std::collections::HashSet<NodeId> = graph.outputs.iter().copied().collect();
    let const_scalar = |g: &Graph, id: NodeId, n: usize| -> Option<f32> {
        if let Op::Constant { data } = &g.node(id).op {
            if data.len() == n * 4 {
                return Some(f32::from_le_bytes([data[0], data[1], data[2], data[3]]));
            }
        }
        None
    };
    // p' node → (param, vel, grad, v' node, lr, mom, len)
    let mut sgd_fold: std::collections::HashMap<
        NodeId,
        (NodeId, NodeId, NodeId, NodeId, f32, f32, usize),
    > = std::collections::HashMap::new();
    let mut sgd_elim: std::collections::HashSet<NodeId> = std::collections::HashSet::new();
    for n in graph.nodes() {
        // p' = Sub(param, lr_v)
        if !matches!(n.op, Op::Binary(BinaryOp::Sub)) || n.shape.dtype() != rlx_ir::DType::F32 {
            continue;
        }
        if !out_set.contains(&n.id) {
            continue;
        }
        let len = match n.shape.num_elements() {
            Some(l) => l,
            None => continue,
        };
        let (param, lr_v) = (n.inputs[0], n.inputs[1]);
        // lr_v = Mul(v', lrᶜ), single in-graph use
        let lrv = graph.node(lr_v);
        if !matches!(lrv.op, Op::Binary(BinaryOp::Mul)) || use_counts.get(&lr_v) != Some(&1) {
            continue;
        }
        let (v_new, lr_c) = (lrv.inputs[0], lrv.inputs[1]);
        let lr = match const_scalar(graph, lr_c, len) {
            Some(v) => v,
            None => continue,
        };
        // v' = Add(v_scaled, grad), graph output, single in-graph use
        let vnew = graph.node(v_new);
        if !matches!(vnew.op, Op::Binary(BinaryOp::Add))
            || use_counts.get(&v_new) != Some(&1)
            || !out_set.contains(&v_new)
        {
            continue;
        }
        let (v_scaled, grad) = (vnew.inputs[0], vnew.inputs[1]);
        // v_scaled = Mul(vel, momᶜ), single in-graph use
        let vs = graph.node(v_scaled);
        if !matches!(vs.op, Op::Binary(BinaryOp::Mul)) || use_counts.get(&v_scaled) != Some(&1) {
            continue;
        }
        let (vel, mom_c) = (vs.inputs[0], vs.inputs[1]);
        let mom = match const_scalar(graph, mom_c, len) {
            Some(v) => v,
            None => continue,
        };
        sgd_fold.insert(n.id, (param, vel, grad, v_new, lr, mom, len));
        sgd_elim.insert(v_scaled);
        sgd_elim.insert(v_new);
        sgd_elim.insert(lr_v);
    }

    for node in graph.nodes() {
        // View ops (Reshape / same-dtype Cast / axis-0 Narrow) are aliased
        // to their parent's slot by the memory planner — no copy needed.
        // Plan #46.
        if rlx_opt::is_pure_view(graph, node) {
            thunks.push(Thunk::Nop);
            continue;
        }
        // Transpose folded into a downstream matmul's trans flag — skip it.
        if folded_transpose.contains(&node.id) {
            thunks.push(Thunk::Nop);
            continue;
        }
        // Inner nodes of an SGD chain folded into one SgdMomentum thunk.
        if sgd_elim.contains(&node.id) {
            thunks.push(Thunk::Nop);
            continue;
        }
        let t = match &node.op {
            Op::Input { .. } | Op::Param { .. } | Op::Constant { .. } => Thunk::Nop,

            Op::FusedMatMulBiasAct { activation } => {
                let shape = &node.shape;
                let n = shape.dim(shape.rank() - 1).unwrap_static();
                let total = shape.num_elements().unwrap();
                let m = total / n;
                let a_len = get_len(graph, node.inputs[0]);
                let k = a_len / m;
                Thunk::FusedMmBiasAct {
                    a: node_offset(arena, node.inputs[0]),
                    w: node_offset(arena, node.inputs[1]),
                    bias: node_offset(arena, node.inputs[2]),
                    c: node_offset(arena, node.id),
                    m: m as u32,
                    k: k as u32,
                    n: n as u32,
                    act: *activation,
                }
            }

            Op::FusedResidualLN { has_bias, eps } => {
                let h = node.shape.dim(node.shape.rank() - 1).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                let rows = total / h;
                let (g_idx, b_idx) = if *has_bias { (3, 4) } else { (2, 3) };
                Thunk::FusedResidualLN {
                    x: node_offset(arena, node.inputs[0]),
                    res: node_offset(arena, node.inputs[1]),
                    bias: if *has_bias {
                        node_offset(arena, node.inputs[2])
                    } else {
                        0
                    },
                    g: node_offset(arena, node.inputs[g_idx]),
                    b: node_offset(arena, node.inputs[b_idx]),
                    out: node_offset(arena, node.id),
                    rows: rows as u32,
                    h: h as u32,
                    eps: *eps,
                    has_bias: *has_bias,
                }
            }

            Op::FusedResidualRmsNorm { has_bias, eps } => {
                let h = node.shape.dim(node.shape.rank() - 1).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                let rows = total / h;
                let (g_idx, b_idx) = if *has_bias { (3, 4) } else { (2, 3) };
                Thunk::FusedResidualRmsNorm {
                    x: node_offset(arena, node.inputs[0]),
                    res: node_offset(arena, node.inputs[1]),
                    bias: if *has_bias {
                        node_offset(arena, node.inputs[2])
                    } else {
                        0
                    },
                    g: node_offset(arena, node.inputs[g_idx]),
                    b: node_offset(arena, node.inputs[b_idx]),
                    out: node_offset(arena, node.id),
                    rows: rows as u32,
                    h: h as u32,
                    eps: *eps,
                    has_bias: *has_bias,
                }
            }

            Op::MatMul => {
                let shape = &node.shape;
                let a_shape = &graph.node(node.inputs[0]).shape;
                let b_shape = &graph.node(node.inputs[1]).shape;
                // Prefer inferred matmul shape from operands — ONNX bundle
                // meta often over-ranks outputs (e.g. [seq, seq, H]).
                let eff =
                    rlx_ir::shape::matmul_shape(a_shape, b_shape).unwrap_or_else(|_| shape.clone());
                let rank = eff.rank().max(2);
                let n = eff.dim(rank - 1).unwrap_static();
                let k_dim = a_shape.dim(a_shape.rank().max(2) - 1).unwrap_static();
                if shape.dtype() == rlx_ir::DType::C64 {
                    // Complex GEMM (interleaved re/im). Handles 2D and
                    // 3D×2D (flatten M); both-operand batched C64 is not
                    // yet wired.
                    let both = a_shape.rank() >= 3 && b_shape.rank() >= 3;
                    assert!(!both, "batched (both-operand) C64 matmul not yet supported");
                    let m: usize = if a_shape.rank() >= 3 {
                        (0..a_shape.rank() - 1)
                            .map(|d| a_shape.dim(d).unwrap_static())
                            .product()
                    } else {
                        a_shape.dim(a_shape.rank() - 2).unwrap_static()
                    };
                    Thunk::CgemmC64 {
                        a: node_offset(arena, node.inputs[0]),
                        b: node_offset(arena, node.inputs[1]),
                        c: node_offset(arena, node.id),
                        m: m as u32,
                        k: k_dim as u32,
                        n: n as u32,
                    }
                } else {
                    // Batched GEMM only when both operands carry batch dimensions.
                    // 3D×2D (activations × shared weight) must flatten to one Sgemm.
                    let both_batched = a_shape.rank() >= 3 && b_shape.rank() >= 3;
                    let batched_3d =
                        rank >= 3 && both_batched && a_shape.rank() + b_shape.rank() > 4;
                    if batched_3d && shape.dtype() == rlx_ir::DType::F64 {
                        let mut batch_prod = 1usize;
                        for d in 0..rank - 2 {
                            batch_prod *= eff.dim(d).unwrap_static();
                        }
                        let m_dim = eff.dim(rank - 2).unwrap_static();
                        Thunk::BatchedDgemmF64 {
                            a: node_offset(arena, node.inputs[0]),
                            b: node_offset(arena, node.inputs[1]),
                            c: node_offset(arena, node.id),
                            batch: batch_prod as u32,
                            m: m_dim as u32,
                            k: k_dim as u32,
                            n: n as u32,
                        }
                    } else if batched_3d && shape.dtype() == rlx_ir::DType::F32 {
                        let mut batch_prod = 1usize;
                        for d in 0..rank - 2 {
                            batch_prod *= eff.dim(d).unwrap_static();
                        }
                        let m_dim = eff.dim(rank - 2).unwrap_static();
                        Thunk::BatchedSgemm {
                            a: node_offset(arena, node.inputs[0]),
                            b: node_offset(arena, node.inputs[1]),
                            c: node_offset(arena, node.id),
                            batch: batch_prod as u32,
                            m: m_dim as u32,
                            k: k_dim as u32,
                            n: n as u32,
                        }
                    } else {
                        let m = if a_shape.rank() >= 3 && b_shape.rank() <= 2 {
                            let mut m_prod = 1usize;
                            for d in 0..a_shape.rank() - 1 {
                                m_prod *= a_shape.dim(d).unwrap_static();
                            }
                            m_prod
                        } else if a_shape.rank() >= 2 {
                            a_shape.dim(a_shape.rank() - 2).unwrap_static()
                        } else {
                            eff.num_elements().unwrap_or(1) / n.max(1)
                        };
                        match shape.dtype() {
                            rlx_ir::DType::F64 => Thunk::Dgemm {
                                a: node_offset(arena, node.inputs[0]),
                                b: node_offset(arena, node.inputs[1]),
                                c: node_offset(arena, node.id),
                                m: m as u32,
                                k: k_dim as u32,
                                n: n as u32,
                            },
                            _ => {
                                if let Some(&(asrc, ta, bsrc, tb)) = matmul_fold.get(&node.id) {
                                    // Folded Transpose→MatMul: read pre-transpose
                                    // operands, do the transpose via cblas flags.
                                    Thunk::SgemmT {
                                        a: node_offset(arena, asrc),
                                        b: node_offset(arena, bsrc),
                                        c: node_offset(arena, node.id),
                                        m: m as u32,
                                        k: k_dim as u32,
                                        n: n as u32,
                                        ta,
                                        tb,
                                    }
                                } else {
                                    Thunk::Sgemm {
                                        a: node_offset(arena, node.inputs[0]),
                                        b: node_offset(arena, node.inputs[1]),
                                        c: node_offset(arena, node.id),
                                        m: m as u32,
                                        k: k_dim as u32,
                                        n: n as u32,
                                    }
                                }
                            }
                        }
                    }
                }
            }

            Op::Binary(op) => {
                if let Some(&(param, vel, grad, v_new, lr, mom, len)) = sgd_fold.get(&node.id) {
                    // Folded SGD-momentum: this `Sub` (p') drives a single
                    // SgdMomentum thunk that also writes v''s slot.
                    thunks.push(Thunk::SgdMomentum {
                        param: node_offset(arena, param),
                        vel: node_offset(arena, vel),
                        grad: node_offset(arena, grad),
                        p_out: node_offset(arena, node.id),
                        v_out: node_offset(arena, v_new),
                        lr,
                        mom,
                        len: len as u32,
                    });
                    continue;
                }
                let lhs_len = get_len(graph, node.inputs[0]);
                let rhs_len = get_len(graph, node.inputs[1]);
                let out_len = node.shape.num_elements().unwrap();
                if node.shape.dtype() == rlx_ir::DType::C64 {
                    // Native C64 element-wise. Add/Sub/Mul/Div lower
                    // to `BinaryFullC64`; the rest don't have a
                    // single natural complex definition.
                    match op {
                        BinaryOp::Add | BinaryOp::Sub | BinaryOp::Mul | BinaryOp::Div => {}
                        BinaryOp::Max | BinaryOp::Min | BinaryOp::Pow => panic!(
                            "Op::Binary({op:?}) on DType::C64: complex \
                             max/min/pow have no single natural definition \
                             — caller should drop to 2N-real-block (see \
                             spike-ac) and pick a convention there"
                        ),
                    }
                }
                // Compute broadcast strides for the slow path. Empty
                // vectors when no broadcast is needed (the fast-path
                // kernel ignores them anyway).
                let (out_dims_bcast, bcast_lhs_strides, bcast_rhs_strides) =
                    if lhs_len == out_len && rhs_len == out_len {
                        (Vec::new(), Vec::new(), Vec::new())
                    } else {
                        let lhs_dims = get_static_dims(graph, node.inputs[0]);
                        let rhs_dims = get_static_dims(graph, node.inputs[1]);
                        let out_dims_v = get_static_dims(graph, node.id);
                        if lhs_dims.is_empty() || rhs_dims.is_empty() || out_dims_v.is_empty() {
                            // Dynamic shape — fall back to the legacy
                            // modulo path (correct for scalar / last-
                            // axis broadcast, which is the only
                            // dynamic case in practice).
                            (Vec::new(), Vec::new(), Vec::new())
                        } else {
                            let ls = broadcast_strides(&lhs_dims, &out_dims_v);
                            let rs = broadcast_strides(&rhs_dims, &out_dims_v);
                            let od: Vec<u32> = out_dims_v.iter().map(|x| *x as u32).collect();
                            (od, ls, rs)
                        }
                    };
                if node.shape.dtype() == rlx_ir::DType::C64 {
                    Thunk::BinaryFullC64 {
                        lhs: node_offset(arena, node.inputs[0]),
                        rhs: node_offset(arena, node.inputs[1]),
                        dst: node_offset(arena, node.id),
                        len: out_len as u32,
                        lhs_len: lhs_len as u32,
                        rhs_len: rhs_len as u32,
                        op: *op,
                        out_dims_bcast,
                        bcast_lhs_strides,
                        bcast_rhs_strides,
                    }
                } else if node.shape.dtype() == rlx_ir::DType::F64 {
                    // f64 path — no BiasAdd fast-path (yet); use the
                    // general binary-with-broadcast kernel.
                    Thunk::BinaryFullF64 {
                        lhs: node_offset(arena, node.inputs[0]),
                        rhs: node_offset(arena, node.inputs[1]),
                        dst: node_offset(arena, node.id),
                        len: out_len as u32,
                        lhs_len: lhs_len as u32,
                        rhs_len: rhs_len as u32,
                        op: *op,
                        out_dims_bcast,
                        bcast_lhs_strides,
                        bcast_rhs_strides,
                    }
                } else if matches!(op, BinaryOp::Add)
                    && rhs_len < out_len
                    && out_len % rhs_len == 0
                    && is_trailing_bias_broadcast(
                        graph.node(node.inputs[1]).shape.dims(),
                        graph.node(node.id).shape.dims(),
                    )
                {
                    // `BiasAdd` is only correct when the bias is a
                    // *trailing* broadcast — rhs dims match the right-
                    // hand side of the output dims (with size-1 only
                    // allowed in left-padded outer positions).
                    // SAM's rel-pos `[bh, h, w, 1, w] + [bh, h, w, h, w]`
                    // has rhs_len divide out_len cleanly but is a
                    // mid-shape singleton, NOT a trailing broadcast.
                    // Routing it through BiasAdd silently treats it as
                    // last-`rhs_len`-cols repeated — wrong values.
                    Thunk::BiasAdd {
                        src: node_offset(arena, node.inputs[0]),
                        bias: node_offset(arena, node.inputs[1]),
                        dst: node_offset(arena, node.id),
                        m: (out_len / rhs_len) as u32,
                        n: rhs_len as u32,
                    }
                } else {
                    let lhs_len = get_len(graph, node.inputs[0]);
                    Thunk::BinaryFull {
                        lhs: node_offset(arena, node.inputs[0]),
                        rhs: node_offset(arena, node.inputs[1]),
                        dst: node_offset(arena, node.id),
                        len: out_len as u32,
                        lhs_len: lhs_len as u32,
                        rhs_len: rhs_len as u32,
                        op: *op,
                        out_dims_bcast,
                        bcast_lhs_strides,
                        bcast_rhs_strides,
                        elem_bytes: node.shape.dtype().size_bytes() as u8,
                    }
                }
            }

            Op::Activation(act) => {
                let len = node.shape.num_elements().unwrap();
                let in_off = node_offset(arena, node.inputs[0]);
                let out_off = node_offset(arena, node.id);
                if node.shape.dtype() == rlx_ir::DType::C64 {
                    // Only Neg/Exp/Log/Sqrt have natural complex
                    // extensions used in signal-processing graphs.
                    // Everything else (Sigmoid, Tanh, Relu, Abs,
                    // Sin/Cos/Tan/Atan, Round, GeLU family) is rejected.
                    match act {
                        Activation::Neg | Activation::Exp | Activation::Log | Activation::Sqrt => {}
                        other => panic!(
                            "Op::Activation({other:?}) on DType::C64: no \
                             natural complex extension — supported on C64: \
                             Neg, Exp, Log, Sqrt"
                        ),
                    }
                    Thunk::ActivationC64 {
                        src: in_off,
                        dst: out_off,
                        len: len as u32,
                        kind: *act,
                    }
                } else if node.shape.dtype() == rlx_ir::DType::F64 {
                    Thunk::ActivationF64 {
                        src: in_off,
                        dst: out_off,
                        len: len as u32,
                        kind: *act,
                    }
                } else if in_off == out_off {
                    // ActivationInPlace operates on a single buffer. When the
                    // planner has assigned input and output the same slot
                    // (typical post-fusion case), we just run on that slot.
                    Thunk::ActivationInPlace {
                        data: out_off,
                        len: len as u32,
                        act: *act,
                    }
                } else {
                    // Two-step: copy input → output, then activate output in place.
                    // The schedule executes them in this order; downstream
                    // thunks see the activated output at out_off.
                    thunks.push(Thunk::Copy {
                        src: in_off,
                        dst: out_off,
                        len: len as u32,
                    });
                    Thunk::ActivationInPlace {
                        data: out_off,
                        len: len as u32,
                        act: *act,
                    }
                }
            }

            Op::Gather { axis } if *axis == 0 => {
                let table_shape = &graph.node(node.inputs[0]).shape;
                let table_total = table_shape.num_elements().unwrap();
                let trailing: usize = (1..table_shape.rank())
                    .map(|i| table_shape.dim(i).unwrap_static())
                    .product();
                let idx_len = get_len(graph, node.inputs[1]);
                let idx_i64 =
                    u8::from(graph.node(node.inputs[1]).shape.dtype() == rlx_ir::DType::I64);
                let table_bytes = graph.node(node.inputs[0]).shape.dtype().size_bytes() as u8;
                Thunk::Gather {
                    table: node_offset(arena, node.inputs[0]),
                    table_len: table_total as u32,
                    idx: node_offset(arena, node.inputs[1]),
                    dst: node_offset(arena, node.id),
                    num_idx: idx_len as u32,
                    trailing: trailing as u32,
                    idx_i64,
                    table_bytes,
                }
            }

            Op::Gather { axis } => {
                // Non-zero axis: outer × num_idx × trailing layout.
                let table_shape = &graph.node(node.inputs[0]).shape;
                let rank = table_shape.rank();
                let outer: usize = (0..*axis)
                    .map(|i| table_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                let trailing: usize = (*axis + 1..rank)
                    .map(|i| table_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                let axis_dim = table_shape.dim(*axis).unwrap_static();
                let idx_len = get_len(graph, node.inputs[1]);
                let idx_i64 =
                    u8::from(graph.node(node.inputs[1]).shape.dtype() == rlx_ir::DType::I64);
                let table_bytes = graph.node(node.inputs[0]).shape.dtype().size_bytes() as u8;
                Thunk::GatherAxis {
                    table: node_offset(arena, node.inputs[0]),
                    idx: node_offset(arena, node.inputs[1]),
                    dst: node_offset(arena, node.id),
                    outer: outer as u32,
                    axis_dim: axis_dim as u32,
                    num_idx: idx_len as u32,
                    trailing: trailing as u32,
                    idx_i64,
                    table_bytes,
                }
            }

            Op::Narrow { axis, start, len } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let elem_bytes = in_shape.dtype().size_bytes() as u8;
                let rank = in_shape.rank();
                let outer: usize = (0..*axis)
                    .map(|i| in_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                let inner: usize = (*axis + 1..rank)
                    .map(|i| in_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                let in_axis = in_shape.dim(*axis).unwrap_static();
                let src_byte_offset =
                    node_offset(arena, node.inputs[0]) + start * inner * elem_bytes as usize;
                Thunk::Narrow {
                    src: src_byte_offset,
                    dst: node_offset(arena, node.id),
                    outer: outer as u32,
                    src_stride: (in_axis * inner) as u32, // elements per outer step in source
                    dst_stride: (*len * inner) as u32,    // elements per outer step in dest
                    inner: (*len * inner) as u32,         // elements to copy per outer step
                    elem_bytes,
                }
            }

            Op::Reverse { axes } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let rank = in_shape.rank();
                let dims: Vec<u32> = (0..rank)
                    .map(|i| in_shape.dim(i).unwrap_static() as u32)
                    .collect();
                let mut rev_mask = vec![false; rank];
                for &a in axes {
                    if a < rank {
                        rev_mask[a] = true;
                    }
                }
                Thunk::Reverse {
                    src: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    dims,
                    rev_mask,
                    elem_bytes: in_shape.dtype().size_bytes() as u8,
                }
            }

            Op::Reshape { .. } | Op::StopGradient => {
                // Pure layout change: same total element count, plain copy.
                let len = node.shape.num_elements().unwrap();
                let src = node_offset(arena, node.inputs[0]);
                let dst = node_offset(arena, node.id);
                match node.shape.dtype() {
                    rlx_ir::DType::F64 => Thunk::CopyF64 {
                        src,
                        dst,
                        len: len as u32,
                    },
                    rlx_ir::DType::I64 => Thunk::CopyI64 {
                        src,
                        dst,
                        len: len as u32,
                    },
                    _ => Thunk::Copy {
                        src,
                        dst,
                        len: len as u32,
                    },
                }
            }

            Op::Cast { to } => {
                let in_node = graph.node(node.inputs[0]);
                let in_dtype = in_node.shape.dtype();
                let out_dtype = *to;
                let len = node.shape.num_elements().unwrap();
                let src = node_offset(arena, node.inputs[0]);
                let dst = node_offset(arena, node.id);
                if in_dtype == rlx_ir::DType::F32 && out_dtype == rlx_ir::DType::I64 {
                    Thunk::CastF32ToI64 {
                        src,
                        dst,
                        len: len as u32,
                    }
                } else if in_dtype == rlx_ir::DType::F32 && out_dtype == rlx_ir::DType::F64 {
                    Thunk::CastF32ToF64 {
                        src,
                        dst,
                        len: len as u32,
                    }
                } else if in_dtype == rlx_ir::DType::F32 && out_dtype == rlx_ir::DType::I32 {
                    Thunk::CastF32ToI32 {
                        src,
                        dst,
                        len: len as u32,
                    }
                } else if in_dtype == rlx_ir::DType::I64 && out_dtype == rlx_ir::DType::F32 {
                    Thunk::CastI64ToF32 {
                        src,
                        dst,
                        len: len as u32,
                    }
                } else if in_dtype == rlx_ir::DType::Bool && out_dtype == rlx_ir::DType::I32 {
                    Thunk::CastBoolToI32 {
                        src,
                        dst,
                        len: len as u32,
                    }
                } else if in_dtype == rlx_ir::DType::Bool && out_dtype == rlx_ir::DType::F32 {
                    // Bool is 1 byte; the generic f32 Copy below would misread it as
                    // 4-byte f32. VITS sequence masks are `Cast(Less(...), f32)`.
                    Thunk::CastBoolToF32 {
                        src,
                        dst,
                        len: len as u32,
                    }
                } else if in_dtype == rlx_ir::DType::I32 && out_dtype == rlx_ir::DType::F32 {
                    Thunk::CastI32ToF32 {
                        src,
                        dst,
                        len: len as u32,
                    }
                } else if in_dtype == out_dtype {
                    match out_dtype {
                        rlx_ir::DType::F64 => Thunk::CopyF64 {
                            src,
                            dst,
                            len: len as u32,
                        },
                        rlx_ir::DType::I64 => Thunk::CopyI64 {
                            src,
                            dst,
                            len: len as u32,
                        },
                        _ => Thunk::Copy {
                            src,
                            dst,
                            len: len as u32,
                        },
                    }
                } else {
                    Thunk::Copy {
                        src,
                        dst,
                        len: len as u32,
                    }
                }
            }

            Op::Quantize {
                axis,
                scales,
                zero_points,
            } => {
                let (chan_axis, chan_dim, inner) = quant_layout(&node.shape, *axis);
                Thunk::Quantize {
                    x: node_offset(arena, node.inputs[0]),
                    q: node_offset(arena, node.id),
                    len: node.shape.num_elements().unwrap() as u32,
                    chan_axis: chan_axis as u32,
                    chan_dim: chan_dim as u32,
                    inner: inner as u32,
                    scales: scales.clone(),
                    zero_points: zero_points.clone(),
                }
            }

            Op::FakeQuantize {
                bits,
                axis,
                ste,
                scale_mode,
            } => {
                let (chan_axis, chan_dim, inner) = quant_layout(&node.shape, *axis);
                let state_off = match scale_mode {
                    rlx_ir::op::ScaleMode::PerBatch => None,
                    rlx_ir::op::ScaleMode::EMA { .. } | rlx_ir::op::ScaleMode::Fixed => {
                        // Second input carries the [chan_dim] scale state.
                        debug_assert_eq!(
                            node.inputs.len(),
                            2,
                            "EMA/Fixed FakeQuantize needs a state input"
                        );
                        Some(node_offset(arena, node.inputs[1]))
                    }
                };
                Thunk::FakeQuantize {
                    x: node_offset(arena, node.inputs[0]),
                    out: node_offset(arena, node.id),
                    len: node.shape.num_elements().unwrap() as u32,
                    chan_axis: chan_axis as u32,
                    chan_dim: chan_dim as u32,
                    inner: inner as u32,
                    bits: *bits,
                    ste: *ste,
                    scale_mode: *scale_mode,
                    state_off,
                }
            }

            Op::FakeQuantizeLSQ { bits, axis } => {
                let (chan_axis, chan_dim, inner) = quant_layout(&node.shape, *axis);
                Thunk::FakeQuantizeLSQ {
                    x: node_offset(arena, node.inputs[0]),
                    scale_off: node_offset(arena, node.inputs[1]),
                    out: node_offset(arena, node.id),
                    len: node.shape.num_elements().unwrap() as u32,
                    chan_axis: chan_axis as u32,
                    chan_dim: chan_dim as u32,
                    inner: inner as u32,
                    bits: *bits,
                }
            }

            Op::FakeQuantizeLSQBackwardX { bits, axis } => {
                let (chan_axis, chan_dim, inner) = quant_layout(&node.shape, *axis);
                Thunk::FakeQuantizeLSQBackwardX {
                    x: node_offset(arena, node.inputs[0]),
                    scale_off: node_offset(arena, node.inputs[1]),
                    dy: node_offset(arena, node.inputs[2]),
                    dx: node_offset(arena, node.id),
                    len: node.shape.num_elements().unwrap() as u32,
                    chan_axis: chan_axis as u32,
                    chan_dim: chan_dim as u32,
                    inner: inner as u32,
                    bits: *bits,
                }
            }

            Op::FakeQuantizeLSQBackwardScale { bits, axis } => {
                // Output shape is [chan_dim] — node.shape doesn't
                // describe the input data layout, but inputs[0] does.
                let in_shape = &graph.node(node.inputs[0]).shape;
                let (chan_axis, chan_dim, inner) = quant_layout(in_shape, *axis);
                Thunk::FakeQuantizeLSQBackwardScale {
                    x: node_offset(arena, node.inputs[0]),
                    scale_off: node_offset(arena, node.inputs[1]),
                    dy: node_offset(arena, node.inputs[2]),
                    dscale: node_offset(arena, node.id),
                    len: in_shape.num_elements().unwrap() as u32,
                    chan_axis: chan_axis as u32,
                    chan_dim: chan_dim as u32,
                    inner: inner as u32,
                    bits: *bits,
                }
            }

            Op::FakeQuantizeBackward { bits, axis, ste } => {
                let (chan_axis, chan_dim, inner) = quant_layout(&node.shape, *axis);
                Thunk::FakeQuantizeBackward {
                    x: node_offset(arena, node.inputs[0]),
                    dy: node_offset(arena, node.inputs[1]),
                    dx: node_offset(arena, node.id),
                    len: node.shape.num_elements().unwrap() as u32,
                    chan_axis: chan_axis as u32,
                    chan_dim: chan_dim as u32,
                    inner: inner as u32,
                    bits: *bits,
                    ste: *ste,
                }
            }

            Op::Dequantize {
                axis,
                scales,
                zero_points,
            } => {
                let (chan_axis, chan_dim, inner) = quant_layout(&node.shape, *axis);
                Thunk::Dequantize {
                    q: node_offset(arena, node.inputs[0]),
                    x: node_offset(arena, node.id),
                    len: node.shape.num_elements().unwrap() as u32,
                    chan_axis: chan_axis as u32,
                    chan_dim: chan_dim as u32,
                    inner: inner as u32,
                    scales: scales.clone(),
                    zero_points: zero_points.clone(),
                }
            }

            Op::Expand { .. } => {
                // Broadcast: build per-output-dim strides where any input dim
                // of size 1 has stride 0 (read the same element repeatedly).
                // Reuses the Thunk::Transpose runtime — N-D walk with strides
                // is identical; only the strides differ.
                let in_shape = &graph.node(node.inputs[0]).shape;
                let out_shape = &node.shape;
                let in_rank = in_shape.rank();
                let out_rank = out_shape.rank();
                // Implicit leading 1s if input has lower rank.
                let pad = out_rank.saturating_sub(in_rank);
                let in_dims: Vec<usize> = (0..out_rank)
                    .map(|i| {
                        if i < pad {
                            1
                        } else {
                            in_shape.dim(i - pad).unwrap_static()
                        }
                    })
                    .collect();
                // Row-major input strides (over the padded shape).
                let mut in_strides_full = vec![1usize; out_rank];
                for d in (0..out_rank.saturating_sub(1)).rev() {
                    in_strides_full[d] = in_strides_full[d + 1] * in_dims[d + 1];
                }
                let out_dims: Vec<u32> = (0..out_rank)
                    .map(|i| out_shape.dim(i).unwrap_static() as u32)
                    .collect();
                // Stride is 0 for broadcast dims (in_dim == 1 && out_dim > 1).
                let in_strides: Vec<u32> = (0..out_rank)
                    .map(|i| {
                        if in_dims[i] == 1 && (out_dims[i] as usize) > 1 {
                            0
                        } else {
                            in_strides_full[i] as u32
                        }
                    })
                    .collect();
                let in_total = in_dims.iter().product::<usize>() as u32;
                let src = node_offset(arena, node.inputs[0]);
                let dst = node_offset(arena, node.id);
                let elem_bytes = node.shape.dtype().size_bytes() as u8;
                match node.shape.dtype() {
                    rlx_ir::DType::F64 => Thunk::TransposeF64 {
                        src,
                        dst,
                        in_total,
                        out_dims,
                        in_strides,
                    },
                    _ => Thunk::Transpose {
                        src,
                        dst,
                        in_total,
                        out_dims,
                        in_strides,
                        elem_bytes,
                    },
                }
            }

            Op::RmsNorm { eps, .. } => {
                let h = node.shape.dim(node.shape.rank() - 1).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                Thunk::RmsNorm {
                    src: node_offset(arena, node.inputs[0]),
                    g: node_offset(arena, node.inputs[1]),
                    b: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    rows: (total / h) as u32,
                    h: h as u32,
                    eps: *eps,
                }
            }

            Op::LayerNorm { eps, .. } => {
                let h = node.shape.dim(node.shape.rank() - 1).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                Thunk::LayerNorm {
                    src: node_offset(arena, node.inputs[0]),
                    g: node_offset(arena, node.inputs[1]),
                    b: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    rows: (total / h) as u32,
                    h: h as u32,
                    eps: *eps,
                }
            }

            Op::GroupNorm { num_groups, eps } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let (n, c, h, w) = conv_nchw_dims(in_shape);
                Thunk::GroupNorm {
                    src: node_offset(arena, node.inputs[0]),
                    g: node_offset(arena, node.inputs[1]),
                    b: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    n,
                    c,
                    h,
                    w,
                    num_groups: *num_groups as u32,
                    eps: *eps,
                }
            }

            Op::BatchNormInference { eps } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let rank = in_shape.rank();
                let channels = in_shape.dim(rank - 1).unwrap_static();
                let total = in_shape.num_elements().unwrap_or(0);
                let count = (total / channels.max(1)) as u32;
                Thunk::BatchNormInference {
                    src: node_offset(arena, node.inputs[0]),
                    g: node_offset(arena, node.inputs[1]),
                    b: node_offset(arena, node.inputs[2]),
                    mean: node_offset(arena, node.inputs[3]),
                    var: node_offset(arena, node.inputs[4]),
                    dst: node_offset(arena, node.id),
                    count,
                    channels: channels as u32,
                    eps: *eps,
                }
            }

            Op::BatchNormInferenceBackwardInput { eps } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let rank = x_shape.rank();
                let channels = x_shape.dim(rank - 1).unwrap_static();
                let total = x_shape.num_elements().unwrap_or(0);
                Thunk::BatchNormInferenceBackwardInput {
                    x: node_offset(arena, node.inputs[0]),
                    gamma: node_offset(arena, node.inputs[1]),
                    mean: node_offset(arena, node.inputs[2]),
                    var: node_offset(arena, node.inputs[3]),
                    dy: node_offset(arena, node.inputs[4]),
                    dx: node_offset(arena, node.id),
                    count: (total / channels.max(1)) as u32,
                    channels: channels as u32,
                    eps: *eps,
                }
            }

            Op::BatchNormInferenceBackwardGamma { eps } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let rank = x_shape.rank();
                let channels = x_shape.dim(rank - 1).unwrap_static();
                let total = x_shape.num_elements().unwrap_or(0);
                let _gamma_shape = &graph.node(node.id).shape;
                Thunk::BatchNormInferenceBackwardGamma {
                    x: node_offset(arena, node.inputs[0]),
                    mean: node_offset(arena, node.inputs[1]),
                    var: node_offset(arena, node.inputs[2]),
                    dy: node_offset(arena, node.inputs[3]),
                    dgamma: node_offset(arena, node.id),
                    count: (total / channels.max(1)) as u32,
                    channels: channels as u32,
                    eps: *eps,
                }
            }

            Op::BatchNormInferenceBackwardBeta => {
                let dy_shape = &graph.node(node.inputs[0]).shape;
                let rank = dy_shape.rank();
                let channels = dy_shape.dim(rank - 1).unwrap_static();
                let total = dy_shape.num_elements().unwrap_or(0);
                Thunk::BatchNormInferenceBackwardBeta {
                    dy: node_offset(arena, node.inputs[0]),
                    dbeta: node_offset(arena, node.id),
                    count: (total / channels.max(1)) as u32,
                    channels: channels as u32,
                }
            }

            Op::LayerNorm2d { eps } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let (n, c, h, w) = conv_nchw_dims(in_shape);
                Thunk::LayerNorm2d {
                    src: node_offset(arena, node.inputs[0]),
                    g: node_offset(arena, node.inputs[1]),
                    b: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    n,
                    c,
                    h,
                    w,
                    eps: *eps,
                }
            }

            Op::ConvTranspose2d {
                kernel_size,
                stride,
                padding,
                dilation,
                output_padding: _,
                groups,
            } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let out_shape = &node.shape;
                let (n, c_in, h, w_in) = conv_nchw_dims(in_shape);
                let (_, c_out, h_out, w_out) = conv_nchw_dims(out_shape);
                Thunk::ConvTranspose2d {
                    src: node_offset(arena, node.inputs[0]),
                    weight: node_offset(arena, node.inputs[1]),
                    dst: node_offset(arena, node.id),
                    n,
                    c_in,
                    h,
                    w_in,
                    c_out,
                    h_out,
                    w_out,
                    kh: kernel_size[0] as u32,
                    kw: kernel_size[1] as u32,
                    sh: stride.first().copied().unwrap_or(1) as u32,
                    sw: stride.get(1).copied().unwrap_or(1) as u32,
                    ph: padding.first().copied().unwrap_or(0) as u32,
                    pw: padding.get(1).copied().unwrap_or(0) as u32,
                    dh: dilation.first().copied().unwrap_or(1) as u32,
                    dw: dilation.get(1).copied().unwrap_or(1) as u32,
                    groups: *groups as u32,
                }
            }

            Op::ResizeNearest2x => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let (n, c, h, w) = conv_nchw_dims(in_shape);
                Thunk::ResizeNearest2x {
                    src: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    n,
                    c,
                    h,
                    w,
                }
            }

            Op::AxialRope2d {
                end_x,
                end_y,
                head_dim,
                num_heads,
                theta,
                repeat_factor,
            } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let batch = in_shape.dim(0).unwrap_static() as u32;
                let seq = in_shape.dim(1).unwrap_static() as u32;
                let hidden = in_shape.dim(2).unwrap_static() as u32;
                Thunk::AxialRope2d {
                    src: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    batch,
                    seq,
                    hidden,
                    end_x: *end_x as u32,
                    end_y: *end_y as u32,
                    head_dim: *head_dim as u32,
                    num_heads: *num_heads as u32,
                    theta: *theta,
                    repeat_factor: *repeat_factor as u32,
                }
            }

            Op::Softmax { axis } => {
                let rank = node.shape.rank();
                let ax = if *axis < 0 {
                    (rank as i32 + axis) as usize
                } else {
                    *axis as usize
                };
                let cols = node.shape.dim(ax).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                let in_off = node_offset(arena, node.inputs[0]);
                let out_off = node_offset(arena, node.id);
                // Softmax kernel runs in-place on its data buffer. If the
                // planner gave input and output separate slots (their live
                // ranges overlap, so no aliasing), the output starts
                // uninitialized — emit a Copy first so the data is there.
                // Same pattern as Op::Activation.
                if in_off != out_off {
                    thunks.push(Thunk::Copy {
                        src: in_off,
                        dst: out_off,
                        len: total as u32,
                    });
                }
                Thunk::Softmax {
                    data: out_off,
                    rows: (total / cols) as u32,
                    cols: cols as u32,
                }
            }

            Op::SelectiveScan { state_size } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let (batch, seq, hidden) = (
                    in_shape.dim(0).unwrap_static(),
                    in_shape.dim(1).unwrap_static(),
                    in_shape.dim(2).unwrap_static(),
                );
                Thunk::SelectiveScan {
                    x: node_offset(arena, node.inputs[0]),
                    delta: node_offset(arena, node.inputs[1]),
                    a: node_offset(arena, node.inputs[2]),
                    b: node_offset(arena, node.inputs[3]),
                    c: node_offset(arena, node.inputs[4]),
                    dst: node_offset(arena, node.id),
                    batch: batch as u32,
                    seq: seq as u32,
                    hidden: hidden as u32,
                    state_size: *state_size as u32,
                }
            }

            Op::GatedDeltaNet {
                state_size,
                carry_state,
            } => {
                let q_shape = &graph.node(node.inputs[0]).shape;
                let (batch, seq, heads) = (
                    q_shape.dim(0).unwrap_static(),
                    q_shape.dim(1).unwrap_static(),
                    q_shape.dim(2).unwrap_static(),
                );
                let state_off = if *carry_state {
                    node_offset(arena, node.inputs[5])
                } else {
                    0
                };
                Thunk::GatedDeltaNet {
                    q: node_offset(arena, node.inputs[0]),
                    k: node_offset(arena, node.inputs[1]),
                    v: node_offset(arena, node.inputs[2]),
                    g: node_offset(arena, node.inputs[3]),
                    beta: node_offset(arena, node.inputs[4]),
                    state: state_off,
                    dst: node_offset(arena, node.id),
                    batch: batch as u32,
                    seq: seq as u32,
                    heads: heads as u32,
                    state_size: *state_size as u32,
                }
            }

            Op::Lstm {
                hidden_size,
                num_layers,
                bidirectional,
                carry,
            } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let (batch, seq, input_size) = (
                    x_shape.dim(0).unwrap_static(),
                    x_shape.dim(1).unwrap_static(),
                    x_shape.dim(2).unwrap_static(),
                );
                let (h0, c0) = if *carry {
                    (
                        node_offset(arena, node.inputs[4]),
                        node_offset(arena, node.inputs[5]),
                    )
                } else {
                    (0, 0)
                };
                Thunk::Lstm {
                    x: node_offset(arena, node.inputs[0]),
                    w_ih: node_offset(arena, node.inputs[1]),
                    w_hh: node_offset(arena, node.inputs[2]),
                    bias: node_offset(arena, node.inputs[3]),
                    h0,
                    c0,
                    dst: node_offset(arena, node.id),
                    batch: batch as u32,
                    seq: seq as u32,
                    input_size: input_size as u32,
                    hidden: *hidden_size as u32,
                    num_layers: *num_layers as u32,
                    bidirectional: *bidirectional,
                    carry: *carry,
                }
            }

            Op::Gru {
                hidden_size,
                num_layers,
                bidirectional,
                carry,
            } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let (batch, seq, input_size) = (
                    x_shape.dim(0).unwrap_static(),
                    x_shape.dim(1).unwrap_static(),
                    x_shape.dim(2).unwrap_static(),
                );
                // Inputs: x, w_ih, w_hh, b_ih, b_hh (+ h0 when carry).
                let h0 = if *carry {
                    node_offset(arena, node.inputs[5])
                } else {
                    0
                };
                Thunk::Gru {
                    x: node_offset(arena, node.inputs[0]),
                    w_ih: node_offset(arena, node.inputs[1]),
                    w_hh: node_offset(arena, node.inputs[2]),
                    b_ih: node_offset(arena, node.inputs[3]),
                    b_hh: node_offset(arena, node.inputs[4]),
                    h0,
                    dst: node_offset(arena, node.id),
                    batch: batch as u32,
                    seq: seq as u32,
                    input_size: input_size as u32,
                    hidden: *hidden_size as u32,
                    num_layers: *num_layers as u32,
                    bidirectional: *bidirectional,
                    carry: *carry,
                }
            }

            Op::Rnn {
                hidden_size,
                num_layers,
                bidirectional,
                carry,
                relu,
            } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let (batch, seq, input_size) = (
                    x_shape.dim(0).unwrap_static(),
                    x_shape.dim(1).unwrap_static(),
                    x_shape.dim(2).unwrap_static(),
                );
                // Inputs: x, w_ih, w_hh, bias (+ h0 when carry).
                let h0 = if *carry {
                    node_offset(arena, node.inputs[4])
                } else {
                    0
                };
                Thunk::Rnn {
                    x: node_offset(arena, node.inputs[0]),
                    w_ih: node_offset(arena, node.inputs[1]),
                    w_hh: node_offset(arena, node.inputs[2]),
                    bias: node_offset(arena, node.inputs[3]),
                    h0,
                    dst: node_offset(arena, node.id),
                    batch: batch as u32,
                    seq: seq as u32,
                    input_size: input_size as u32,
                    hidden: *hidden_size as u32,
                    num_layers: *num_layers as u32,
                    bidirectional: *bidirectional,
                    carry: *carry,
                    relu: *relu,
                }
            }

            Op::Mamba2 {
                head_dim,
                state_size,
            } => {
                // x [B,S,H,P]; dt [B,S,H]; a [H]; b,c [B,S,H,N].
                let x_shape = &graph.node(node.inputs[0]).shape;
                Thunk::Mamba2 {
                    x: node_offset(arena, node.inputs[0]),
                    dt: node_offset(arena, node.inputs[1]),
                    a: node_offset(arena, node.inputs[2]),
                    b: node_offset(arena, node.inputs[3]),
                    c: node_offset(arena, node.inputs[4]),
                    dst: node_offset(arena, node.id),
                    batch: x_shape.dim(0).unwrap_static() as u32,
                    seq: x_shape.dim(1).unwrap_static() as u32,
                    heads: x_shape.dim(2).unwrap_static() as u32,
                    head_dim: *head_dim as u32,
                    state_size: *state_size as u32,
                }
            }

            Op::QMatMul {
                x_zp,
                w_zp,
                out_zp,
                mult,
            } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let w_shape = &graph.node(node.inputs[1]).shape;
                let m = x_shape.dim(0).unwrap_static();
                let k = x_shape.dim(1).unwrap_static();
                let n = w_shape.dim(1).unwrap_static();
                Thunk::QMatMul {
                    x: node_offset(arena, node.inputs[0]),
                    w: node_offset(arena, node.inputs[1]),
                    bias: node_offset(arena, node.inputs[2]),
                    out: node_offset(arena, node.id),
                    m: m as u32,
                    k: k as u32,
                    n: n as u32,
                    x_zp: *x_zp,
                    w_zp: *w_zp,
                    out_zp: *out_zp,
                    mult: *mult,
                }
            }

            Op::QConv2d {
                kernel_size,
                stride,
                padding,
                dilation,
                groups,
                x_zp,
                w_zp,
                out_zp,
                mult,
            } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let w_shape = &graph.node(node.inputs[1]).shape;
                let out_shape = &node.shape;
                if kernel_size.len() == 2
                    && in_shape.rank() == 4
                    && w_shape.rank() == 4
                    && out_shape.rank() == 4
                {
                    Thunk::QConv2d {
                        x: node_offset(arena, node.inputs[0]),
                        w: node_offset(arena, node.inputs[1]),
                        bias: node_offset(arena, node.inputs[2]),
                        out: node_offset(arena, node.id),
                        n: in_shape.dim(0).unwrap_static() as u32,
                        c_in: in_shape.dim(1).unwrap_static() as u32,
                        h: in_shape.dim(2).unwrap_static() as u32,
                        w_in: in_shape.dim(3).unwrap_static() as u32,
                        c_out: out_shape.dim(1).unwrap_static() as u32,
                        h_out: out_shape.dim(2).unwrap_static() as u32,
                        w_out: out_shape.dim(3).unwrap_static() as u32,
                        kh: kernel_size[0] as u32,
                        kw: kernel_size[1] as u32,
                        sh: stride.first().copied().unwrap_or(1) as u32,
                        sw: stride.get(1).copied().unwrap_or(1) as u32,
                        ph: padding.first().copied().unwrap_or(0) as u32,
                        pw: padding.get(1).copied().unwrap_or(0) as u32,
                        dh: dilation.first().copied().unwrap_or(1) as u32,
                        dw: dilation.get(1).copied().unwrap_or(1) as u32,
                        groups: *groups as u32,
                        x_zp: *x_zp,
                        w_zp: *w_zp,
                        out_zp: *out_zp,
                        mult: *mult,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::DequantMatMul { scheme } => {
                use rlx_ir::quant::QuantScheme;
                let n = node.shape.dim(node.shape.rank() - 1).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                let m = total / n.max(1);
                let x_total = graph.node(node.inputs[0]).shape.num_elements().unwrap();
                let k = x_total / m.max(1);
                if scheme.is_gguf() {
                    Thunk::DequantMatMulGguf {
                        x: node_offset(arena, node.inputs[0]),
                        w_q: node_offset(arena, node.inputs[1]),
                        dst: node_offset(arena, node.id),
                        m: m as u32,
                        k: k as u32,
                        n: n as u32,
                        scheme: *scheme,
                    }
                } else {
                    match scheme {
                        QuantScheme::Nvfp4Block => Thunk::DequantMatMulNvfp4 {
                            x: node_offset(arena, node.inputs[0]),
                            w_q: node_offset(arena, node.inputs[1]),
                            scale: node_offset(arena, node.inputs[2]),
                            global_scale: node_offset(arena, node.inputs[3]),
                            dst: node_offset(arena, node.id),
                            m: m as u32,
                            k: k as u32,
                            n: n as u32,
                        },
                        QuantScheme::Int4Block { block_size } => Thunk::DequantMatMulInt4 {
                            x: node_offset(arena, node.inputs[0]),
                            w_q: node_offset(arena, node.inputs[1]),
                            scale: node_offset(arena, node.inputs[2]),
                            zp: node_offset(arena, node.inputs[3]),
                            dst: node_offset(arena, node.id),
                            m: m as u32,
                            k: k as u32,
                            n: n as u32,
                            block_size: *block_size,
                            is_asymmetric: false,
                        },
                        QuantScheme::Fp8E4m3 => Thunk::DequantMatMulFp8 {
                            x: node_offset(arena, node.inputs[0]),
                            w_q: node_offset(arena, node.inputs[1]),
                            scale: node_offset(arena, node.inputs[2]),
                            dst: node_offset(arena, node.id),
                            m: m as u32,
                            k: k as u32,
                            n: n as u32,
                            e5m2: false,
                        },
                        QuantScheme::Fp8E5m2 => Thunk::DequantMatMulFp8 {
                            x: node_offset(arena, node.inputs[0]),
                            w_q: node_offset(arena, node.inputs[1]),
                            scale: node_offset(arena, node.inputs[2]),
                            dst: node_offset(arena, node.id),
                            m: m as u32,
                            k: k as u32,
                            n: n as u32,
                            e5m2: true,
                        },
                        QuantScheme::Int8Block { block_size } => Thunk::DequantMatMul {
                            x: node_offset(arena, node.inputs[0]),
                            w_q: node_offset(arena, node.inputs[1]),
                            scale: node_offset(arena, node.inputs[2]),
                            zp: node_offset(arena, node.inputs[3]),
                            dst: node_offset(arena, node.id),
                            m: m as u32,
                            k: k as u32,
                            n: n as u32,
                            block_size: *block_size,
                            is_asymmetric: false,
                        },
                        QuantScheme::Int8BlockAsym { block_size } => Thunk::DequantMatMul {
                            x: node_offset(arena, node.inputs[0]),
                            w_q: node_offset(arena, node.inputs[1]),
                            scale: node_offset(arena, node.inputs[2]),
                            zp: node_offset(arena, node.inputs[3]),
                            dst: node_offset(arena, node.id),
                            m: m as u32,
                            k: k as u32,
                            n: n as u32,
                            block_size: *block_size,
                            is_asymmetric: true,
                        },
                        other => panic!(
                            "DequantMatMul on CPU supports Int8/Int4/FP8/NVFP4 legacy or GGUF schemes; got {other}"
                        ),
                    }
                }
            }

            Op::ScaledMatMul {
                lhs_format,
                rhs_format,
                scale_layout,
                has_bias,
            } => {
                // TN: lhs [m,k], rhs [n,k], out [m,n].
                let n = node.shape.dim(node.shape.rank() - 1).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                let m = total / n.max(1);
                let lhs_total = graph.node(node.inputs[0]).shape.num_elements().unwrap();
                let k = lhs_total / m.max(1);
                Thunk::ScaledMatMul {
                    lhs: node_offset(arena, node.inputs[0]),
                    rhs: node_offset(arena, node.inputs[1]),
                    lhs_scale: node_offset(arena, node.inputs[2]),
                    rhs_scale: node_offset(arena, node.inputs[3]),
                    bias: if *has_bias {
                        node_offset(arena, node.inputs[4])
                    } else {
                        0
                    },
                    dst: node_offset(arena, node.id),
                    m: m as u32,
                    k: k as u32,
                    n: n as u32,
                    lhs_fmt: *lhs_format,
                    rhs_fmt: *rhs_format,
                    layout: *scale_layout,
                    has_bias: *has_bias,
                }
            }

            Op::ScaledQuantize {
                format,
                scale_layout,
            } => {
                let xs = &graph.node(node.inputs[0]).shape;
                let cols = xs.dim(xs.rank() - 1).unwrap_static();
                let rows = xs.num_elements().unwrap() / cols.max(1);
                Thunk::ScaledQuantize {
                    x: node_offset(arena, node.inputs[0]),
                    scale: node_offset(arena, node.inputs[1]),
                    dst: node_offset(arena, node.id),
                    rows: rows as u32,
                    cols: cols as u32,
                    fmt: *format,
                    layout: *scale_layout,
                }
            }

            Op::ScaledQuantScale {
                format,
                scale_layout,
            } => {
                let xs = &graph.node(node.inputs[0]).shape;
                let cols = xs.dim(xs.rank() - 1).unwrap_static();
                let rows = xs.num_elements().unwrap() / cols.max(1);
                Thunk::ScaledQuantScale {
                    x: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    rows: rows as u32,
                    cols: cols as u32,
                    fmt: *format,
                    layout: *scale_layout,
                }
            }

            Op::ScaledDequantize {
                format,
                scale_layout,
            } => {
                let xs = &graph.node(node.inputs[0]).shape;
                let cols = xs.dim(xs.rank() - 1).unwrap_static();
                let rows = xs.num_elements().unwrap() / cols.max(1);
                Thunk::ScaledDequantize {
                    codes: node_offset(arena, node.inputs[0]),
                    scale: node_offset(arena, node.inputs[1]),
                    dst: node_offset(arena, node.id),
                    rows: rows as u32,
                    cols: cols as u32,
                    fmt: *format,
                    layout: *scale_layout,
                }
            }

            Op::LoraMatMul { scale } => {
                // x [m, k], w [k, n], a [k, r], b [r, n].
                let n = node.shape.dim(node.shape.rank() - 1).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                let m = total / n.max(1);
                let x_total = graph.node(node.inputs[0]).shape.num_elements().unwrap();
                let k = x_total / m.max(1);
                let a_total = graph.node(node.inputs[2]).shape.num_elements().unwrap();
                let r = a_total / k.max(1);
                Thunk::LoraMatMul {
                    x: node_offset(arena, node.inputs[0]),
                    w: node_offset(arena, node.inputs[1]),
                    a: node_offset(arena, node.inputs[2]),
                    b: node_offset(arena, node.inputs[3]),
                    dst: node_offset(arena, node.id),
                    m: m as u32,
                    k: k as u32,
                    n: n as u32,
                    r: r as u32,
                    scale: *scale,
                }
            }

            Op::Sample {
                top_k,
                top_p,
                temperature,
                seed,
            } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                // Logits are [batch, vocab] (or [vocab] → batch=1).
                let (batch, vocab) = if in_shape.rank() >= 2 {
                    (
                        in_shape.dim(0).unwrap_static(),
                        in_shape.dim(in_shape.rank() - 1).unwrap_static(),
                    )
                } else {
                    (1, in_shape.num_elements().unwrap_or(0))
                };
                Thunk::Sample {
                    logits: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    batch: batch as u32,
                    vocab: vocab as u32,
                    top_k: *top_k as u32,
                    top_p: *top_p,
                    temperature: *temperature,
                    seed: *seed,
                }
            }

            Op::RngNormal {
                mean,
                scale,
                key,
                op_seed,
            } => Thunk::RngNormal {
                dst: node_offset(arena, node.id),
                len: node.shape.num_elements().unwrap_or(0) as u32,
                mean: *mean,
                scale: *scale,
                key: *key,
                op_seed: *op_seed,
            },

            Op::RngUniform {
                low,
                high,
                key,
                op_seed,
            } => Thunk::RngUniform {
                dst: node_offset(arena, node.id),
                len: node.shape.num_elements().unwrap_or(0) as u32,
                low: *low,
                high: *high,
                key: *key,
                op_seed: *op_seed,
            },

            Op::Cumsum { axis, exclusive } => {
                // For now CPU only supports last-axis cumsum (the
                // common case for sampling / ragged offsets).
                // Other axes can lower via Transpose → Cumsum →
                // Transpose; not on the hot path today.
                let rank = node.shape.rank();
                let ax = if *axis < 0 {
                    (rank as i32 + axis) as usize
                } else {
                    *axis as usize
                };
                assert_eq!(
                    ax,
                    rank - 1,
                    "Cumsum only supports the last axis on CPU today"
                );
                let cols = node.shape.dim(ax).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                Thunk::Cumsum {
                    src: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    rows: (total / cols) as u32,
                    cols: cols as u32,
                    exclusive: *exclusive,
                }
            }

            Op::Attention {
                num_heads,
                head_dim,
                mask_kind,
                score_scale,
                attn_logit_softcap: _,
            } => {
                // Layout dispatch: rank-4 input could be either
                // `[B, S, H, D]` (CPU's historical convention) or
                // `[B, H, S, D]` (the convention the GPU/TPU backends
                // share). Disambiguate by which axis matches
                // `num_heads`. Rank-3 is always `[B, S, H*D]`.
                let q_shape = &graph.node(node.inputs[0]).shape;
                let k_shape = &graph.node(node.inputs[1]).shape;
                let rank = q_shape.rank();
                let (batch, seq, kv_seq, bhsd) = if rank == 4 {
                    let d1 = q_shape.dim(1).unwrap_static();
                    let d2 = q_shape.dim(2).unwrap_static();
                    if d1 == *num_heads {
                        // [B, H, S, D]
                        (
                            q_shape.dim(0).unwrap_static(),
                            d2,
                            k_shape.dim(2).unwrap_static(),
                            true,
                        )
                    } else {
                        // [B, S, H, D]
                        (
                            q_shape.dim(0).unwrap_static(),
                            d1,
                            k_shape.dim(1).unwrap_static(),
                            false,
                        )
                    }
                } else if rank >= 3 {
                    (
                        q_shape.dim(0).unwrap_static(),
                        q_shape.dim(1).unwrap_static(),
                        k_shape.dim(1).unwrap_static(),
                        false,
                    )
                } else {
                    (
                        1,
                        q_shape.dim(0).unwrap_static(),
                        k_shape.dim(0).unwrap_static(),
                        false,
                    )
                };
                let mask_off = if matches!(
                    mask_kind,
                    rlx_ir::op::MaskKind::Custom | rlx_ir::op::MaskKind::Bias
                ) {
                    node_offset(arena, node.inputs[3])
                } else {
                    0
                };
                let hs = (*num_heads * *head_dim) as u32;
                Thunk::Attention {
                    q: node_offset(arena, node.inputs[0]),
                    k: node_offset(arena, node.inputs[1]),
                    v: node_offset(arena, node.inputs[2]),
                    mask: mask_off,
                    out: node_offset(arena, node.id),
                    batch: batch as u32,
                    seq: seq as u32,
                    kv_seq: kv_seq as u32,
                    heads: *num_heads as u32,
                    head_dim: *head_dim as u32,
                    mask_kind: *mask_kind,
                    scale: score_scale.unwrap_or((*head_dim as f32).powf(-0.5)),
                    // Defaults: each input is its own contiguous buffer
                    // with row stride = hidden. Rewritten by the
                    // Narrow→Attention fusion when applicable.
                    q_row_stride: hs,
                    k_row_stride: hs,
                    v_row_stride: hs,
                    bhsd,
                }
            }

            Op::AttentionBackward {
                num_heads,
                head_dim,
                mask_kind,
                wrt,
            } => {
                let q_shape = &graph.node(node.inputs[0]).shape;
                let k_shape = &graph.node(node.inputs[1]).shape;
                let rank = q_shape.rank();
                let (batch, seq, kv_seq, bhsd) = if rank == 4 {
                    let d1 = q_shape.dim(1).unwrap_static();
                    let d2 = q_shape.dim(2).unwrap_static();
                    if d1 == *num_heads {
                        (
                            q_shape.dim(0).unwrap_static(),
                            d2,
                            k_shape.dim(2).unwrap_static(),
                            true,
                        )
                    } else {
                        (
                            q_shape.dim(0).unwrap_static(),
                            d1,
                            k_shape.dim(1).unwrap_static(),
                            false,
                        )
                    }
                } else if rank >= 3 {
                    (
                        q_shape.dim(0).unwrap_static(),
                        q_shape.dim(1).unwrap_static(),
                        k_shape.dim(1).unwrap_static(),
                        false,
                    )
                } else {
                    (
                        1,
                        q_shape.dim(0).unwrap_static(),
                        k_shape.dim(0).unwrap_static(),
                        false,
                    )
                };
                let mask_off = if matches!(
                    mask_kind,
                    rlx_ir::op::MaskKind::Custom | rlx_ir::op::MaskKind::Bias
                ) {
                    node_offset(arena, node.inputs[4])
                } else {
                    0
                };
                Thunk::AttentionBackward {
                    q: node_offset(arena, node.inputs[0]),
                    k: node_offset(arena, node.inputs[1]),
                    v: node_offset(arena, node.inputs[2]),
                    dy: node_offset(arena, node.inputs[3]),
                    mask: mask_off,
                    out: node_offset(arena, node.id),
                    batch: batch as u32,
                    seq: seq as u32,
                    kv_seq: kv_seq as u32,
                    heads: *num_heads as u32,
                    head_dim: *head_dim as u32,
                    mask_kind: *mask_kind,
                    wrt: *wrt,
                    bhsd,
                }
            }

            Op::FusedAttentionBlock {
                num_heads,
                head_dim,
                has_bias,
                has_rope,
            } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let (batch, seq) = if x_shape.rank() >= 3 {
                    (
                        x_shape.dim(0).unwrap_static(),
                        x_shape.dim(1).unwrap_static(),
                    )
                } else {
                    let total = x_shape.num_elements().unwrap();
                    let s = x_shape.dim(x_shape.rank() - 2).unwrap_static();
                    (total / (s * num_heads * head_dim), s)
                };
                let hs = (*num_heads * *head_dim) as u32;
                // Inputs: hidden, qkv_w, out_w, mask, [qkv_b, out_b], [cos, sin]
                let mut idx = 4;
                let (qkv_b_off, out_b_off) = if *has_bias {
                    let qb = node_offset(arena, node.inputs[idx]);
                    let ob = node_offset(arena, node.inputs[idx + 1]);
                    idx += 2;
                    (qb, ob)
                } else {
                    (0, 0)
                };
                let (cos_off, sin_off, cl) = if *has_rope {
                    let c = node_offset(arena, node.inputs[idx]);
                    let s = node_offset(arena, node.inputs[idx + 1]);
                    let clen = get_len(graph, node.inputs[idx]);
                    (c, s, clen as u32)
                } else {
                    (0, 0, 0)
                };

                Thunk::FusedAttnBlock {
                    hidden: node_offset(arena, node.inputs[0]),
                    qkv_w: node_offset(arena, node.inputs[1]),
                    out_w: node_offset(arena, node.inputs[2]),
                    mask: node_offset(arena, node.inputs[3]),
                    // The MIR `Op::FusedAttentionBlock` is emitted only for the
                    // BERT-style per-key padding mask (the MIR fusion pass is
                    // `Custom`-only), so the buffer mask is authoritative here.
                    mask_kind: rlx_ir::op::MaskKind::Custom,
                    out: node_offset(arena, node.id),
                    qkv_b: qkv_b_off,
                    out_b: out_b_off,
                    cos: cos_off,
                    sin: sin_off,
                    cos_len: cl,
                    batch: batch as u32,
                    seq: seq as u32,
                    hs,
                    nh: *num_heads as u32,
                    dh: *head_dim as u32,
                    has_bias: *has_bias,
                    has_rope: *has_rope,
                    // The MIR `Op::FusedAttentionBlock` is BERT-only (NeoX rope).
                    interleaved: false,
                }
            }

            Op::Rope {
                head_dim,
                n_rot,
                style,
            } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let (batch, seq, hidden) = if x_shape.rank() >= 3 {
                    (
                        x_shape.dim(0).unwrap_static(),
                        x_shape.dim(1).unwrap_static(),
                        x_shape.dim(2).unwrap_static(),
                    )
                } else {
                    let total = x_shape.num_elements().unwrap();
                    (
                        1,
                        x_shape.dim(0).unwrap_static(),
                        total / x_shape.dim(0).unwrap_static(),
                    )
                };
                let cos_len = get_len(graph, node.inputs[1]);
                Thunk::Rope {
                    src: node_offset(arena, node.inputs[0]),
                    cos: node_offset(arena, node.inputs[1]),
                    sin: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    batch: batch as u32,
                    seq: seq as u32,
                    hidden: hidden as u32,
                    head_dim: *head_dim as u32,
                    n_rot: *n_rot as u32,
                    cos_len: cos_len as u32,
                    // Default: source rows are tightly packed (rewritten
                    // by the Narrow→Rope fusion pass below if Rope ends
                    // up reading from a wider parent like QKV).
                    src_row_stride: hidden as u32,
                    interleaved: matches!(style, rlx_ir::op::RopeStyle::GptJ),
                }
            }

            Op::FusedSwiGLU {
                cast_to: _,
                gate_first,
            } => {
                let n_half = node.shape.dim(node.shape.rank() - 1).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                Thunk::FusedSwiGLU {
                    src: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    n_half: n_half as u32,
                    total: total as u32,
                    gate_first: *gate_first,
                }
            }

            Op::Conv {
                kernel_size,
                stride,
                padding,
                dilation,
                groups,
            } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let w_shape = &graph.node(node.inputs[1]).shape;
                let out_shape = &node.shape;
                // 1×1 fast path (plan #26): kH=kW=1, stride=1,
                // padding=0, dilation=1, groups=1. Emits a single
                // Conv2D1x1 thunk that BLAS-dispatches per batch.
                let is_1x1_simple = kernel_size.len() == 2
                    && kernel_size[0] == 1
                    && kernel_size[1] == 1
                    && stride.iter().all(|&s| s == 1)
                    && padding.iter().all(|&p| p == 0)
                    && dilation.iter().all(|&d| d == 1)
                    && *groups == 1;
                if is_1x1_simple
                    && in_shape.rank() >= 3
                    && out_shape.rank() >= 3
                    && w_shape.rank() >= 2
                {
                    let (n, c_in, h, w) = conv_nchw_dims(in_shape);
                    let (_, c_out, _, _) = conv_nchw_dims(out_shape);
                    Thunk::Conv2D1x1 {
                        src: node_offset(arena, node.inputs[0]),
                        weight: node_offset(arena, node.inputs[1]),
                        dst: node_offset(arena, node.id),
                        n,
                        c_in,
                        c_out,
                        hw: h.saturating_mul(w),
                    }
                } else if kernel_size.len() == 2
                    && in_shape.rank() >= 3
                    && w_shape.rank() >= 2
                    && out_shape.rank() >= 3
                {
                    let (n, c_in, h, w_in) = conv_nchw_dims(in_shape);
                    let (_, c_out, h_out, w_out) = conv_nchw_dims(out_shape);
                    // rlx lowers ONNX 1D convs as 2D NCHW with a unit H axis and the
                    // length in W (`[N,C,1,L]`), but keeps the length kernel/stride/pad/
                    // dilation at index 0 (`kernel=[k,1]`). A literal 2D conv would run
                    // the k-tap kernel over the singleton H axis and ignore the length.
                    // Since `[N,C,1,L]` and `[N,C,L,1]` share the same row-major layout,
                    // relabel the length onto the H axis (no data copy) so the kernel
                    // convolves it — matching the MLX 1D path and onnxruntime.
                    let one_d_w = h == 1
                        && w_in > 1
                        && kernel_size[0] > 1
                        && kernel_size.get(1).copied().unwrap_or(1) == 1;
                    let (h, w_in, h_out, w_out, kh, kw, sh, sw, ph, pw, dh, dw) = if one_d_w {
                        (
                            w_in,
                            1,
                            w_out,
                            1,
                            kernel_size[0] as u32,
                            1,
                            stride.first().copied().unwrap_or(1) as u32,
                            1,
                            padding.first().copied().unwrap_or(0) as u32,
                            0,
                            dilation.first().copied().unwrap_or(1) as u32,
                            1,
                        )
                    } else {
                        (
                            h,
                            w_in,
                            h_out,
                            w_out,
                            kernel_size[0] as u32,
                            kernel_size[1] as u32,
                            stride.first().copied().unwrap_or(1) as u32,
                            stride.get(1).copied().unwrap_or(1) as u32,
                            padding.first().copied().unwrap_or(0) as u32,
                            padding.get(1).copied().unwrap_or(0) as u32,
                            dilation.first().copied().unwrap_or(1) as u32,
                            dilation.get(1).copied().unwrap_or(1) as u32,
                        )
                    };
                    Thunk::Conv2D {
                        src: node_offset(arena, node.inputs[0]),
                        weight: node_offset(arena, node.inputs[1]),
                        dst: node_offset(arena, node.id),
                        n,
                        c_in,
                        h,
                        w: w_in,
                        c_out,
                        h_out,
                        w_out,
                        kh,
                        kw,
                        sh,
                        sw,
                        ph,
                        pw,
                        dh,
                        dw,
                        groups: *groups as u32,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::Pool {
                kind,
                kernel_size,
                stride,
                padding,
            } => {
                // Currently support 2D pooling on rank-4 NCHW tensors.
                let in_shape = &graph.node(node.inputs[0]).shape;
                let out_shape = &node.shape;
                if kernel_size.len() == 2 && in_shape.rank() == 4 && out_shape.rank() == 4 {
                    Thunk::Pool2D {
                        src: node_offset(arena, node.inputs[0]),
                        dst: node_offset(arena, node.id),
                        n: in_shape.dim(0).unwrap_static() as u32,
                        c: in_shape.dim(1).unwrap_static() as u32,
                        h: in_shape.dim(2).unwrap_static() as u32,
                        w: in_shape.dim(3).unwrap_static() as u32,
                        h_out: out_shape.dim(2).unwrap_static() as u32,
                        w_out: out_shape.dim(3).unwrap_static() as u32,
                        kh: kernel_size[0] as u32,
                        kw: kernel_size[1] as u32,
                        sh: stride.first().copied().unwrap_or(1) as u32,
                        sw: stride.get(1).copied().unwrap_or(1) as u32,
                        ph: padding.first().copied().unwrap_or(0) as u32,
                        pw: padding.get(1).copied().unwrap_or(0) as u32,
                        kind: *kind,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::Transpose { perm } => {
                // Pre-compute (out_dims, in_strides_for_each_out_dim) so the
                // runtime loop is just an N-D index walk + scatter.
                let in_shape = &graph.node(node.inputs[0]).shape;
                let in_rank = in_shape.rank();
                if perm.iter().any(|&p| p >= in_rank) {
                    Thunk::Nop
                } else {
                    let in_dims: Vec<usize> = (0..in_rank)
                        .map(|i| in_shape.dim(i).unwrap_static())
                        .collect();
                    // Row-major input strides: stride[d] = product of dims[d+1..].
                    let mut in_strides_full = vec![1usize; in_rank];
                    for d in (0..in_rank.saturating_sub(1)).rev() {
                        in_strides_full[d] = in_strides_full[d + 1] * in_dims[d + 1];
                    }
                    let out_dims: Vec<u32> = perm.iter().map(|&p| in_dims[p] as u32).collect();
                    let in_strides: Vec<u32> =
                        perm.iter().map(|&p| in_strides_full[p] as u32).collect();
                    let in_total = in_dims.iter().product::<usize>() as u32;
                    let src = node_offset(arena, node.inputs[0]);
                    let dst = node_offset(arena, node.id);
                    let elem_bytes = node.shape.dtype().size_bytes() as u8;
                    match node.shape.dtype() {
                        rlx_ir::DType::F64 => Thunk::TransposeF64 {
                            src,
                            dst,
                            in_total,
                            out_dims,
                            in_strides,
                        },
                        _ => Thunk::Transpose {
                            src,
                            dst,
                            in_total,
                            out_dims,
                            in_strides,
                            elem_bytes,
                        },
                    }
                }
            }

            Op::ScatterAdd => {
                // updates: [num_updates, ...trailing], indices: [num_updates],
                // output: [out_dim, ...trailing]
                let upd_shape = &graph.node(node.inputs[0]).shape;
                let out_shape = &node.shape;
                let num_updates = upd_shape.dim(0).unwrap_static();
                let out_dim = out_shape.dim(0).unwrap_static();
                let trailing: usize = (1..out_shape.rank())
                    .map(|i| out_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                Thunk::ScatterAdd {
                    updates: node_offset(arena, node.inputs[0]),
                    indices: node_offset(arena, node.inputs[1]),
                    dst: node_offset(arena, node.id),
                    num_updates: num_updates as u32,
                    out_dim: out_dim as u32,
                    trailing: trailing as u32,
                }
            }

            Op::GroupedMatMul => {
                // Inputs: [input(M, K), weight(E, K, N), expert_idx(M)]
                let in_shape = &graph.node(node.inputs[0]).shape;
                let w_shape = &graph.node(node.inputs[1]).shape;
                let m = in_shape.dim(in_shape.rank() - 2).unwrap_static();
                let k_dim = in_shape.dim(in_shape.rank() - 1).unwrap_static();
                let num_experts = w_shape.dim(0).unwrap_static();
                let n = w_shape.dim(2).unwrap_static();
                Thunk::GroupedMatMul {
                    input: node_offset(arena, node.inputs[0]),
                    weight: node_offset(arena, node.inputs[1]),
                    expert_idx: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    m: m as u32,
                    k_dim: k_dim as u32,
                    n: n as u32,
                    num_experts: num_experts as u32,
                }
            }

            Op::DequantGroupedMatMul { scheme } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let w_shape = &graph.node(node.inputs[1]).shape;
                let m = in_shape.dim(in_shape.rank() - 2).unwrap_static();
                let k_dim = in_shape.dim(in_shape.rank() - 1).unwrap_static();
                let out_shape = &node.shape;
                let n = out_shape.dim(out_shape.rank() - 1).unwrap_static();
                let block_elems = scheme.gguf_block_size() as usize;
                let block_bytes = scheme.gguf_block_bytes() as usize;
                let slab_bytes = (k_dim * n) / block_elems * block_bytes;
                let total_bytes = w_shape.num_elements().unwrap();
                let num_experts = total_bytes / slab_bytes.max(1);
                Thunk::DequantGroupedMatMulGguf {
                    input: node_offset(arena, node.inputs[0]),
                    w_q: node_offset(arena, node.inputs[1]),
                    expert_idx: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    m: m as u32,
                    k_dim: k_dim as u32,
                    n: n as u32,
                    num_experts: num_experts as u32,
                    scheme: *scheme,
                }
            }

            Op::DequantMoEWeights { scheme } => {
                let w_shape = &graph.node(node.inputs[0]).shape;
                let out_shape = &node.shape;
                let num_experts = out_shape.dim(0).unwrap_static();
                let k_dim = out_shape.dim(1).unwrap_static();
                let n = out_shape.dim(2).unwrap_static();
                let block_elems = scheme.gguf_block_size() as usize;
                let block_bytes = scheme.gguf_block_bytes() as usize;
                let slab_bytes = (k_dim * n) / block_elems * block_bytes;
                let total_bytes = w_shape.num_elements().unwrap();
                assert_eq!(
                    total_bytes,
                    num_experts * slab_bytes,
                    "DequantMoEWeights packed bytes mismatch"
                );
                Thunk::DequantMoEWeightsGguf {
                    w_q: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    k_dim: k_dim as u32,
                    n: n as u32,
                    num_experts: num_experts as u32,
                    scheme: *scheme,
                }
            }

            Op::TopK { k } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let rank = in_shape.rank();
                let axis_dim = in_shape.dim(rank - 1).unwrap_static();
                let outer = in_shape.num_elements().unwrap() / axis_dim;
                let indices_i64 = u8::from(graph.node(node.id).shape.dtype() == rlx_ir::DType::I64);
                Thunk::TopK {
                    src: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    outer: outer as u32,
                    axis_dim: axis_dim as u32,
                    k: *k as u32,
                    indices_i64,
                }
            }

            Op::Reduce {
                op,
                axes,
                keep_dim: _,
            } => {
                // Decompose the input shape into [outer, reduced, inner]
                // around the reduced axis range. Non-contiguous reduced
                // axes aren't supported here — caller must transpose them
                // contiguous first (the coverage tool would surface the
                // gap if a model needs it).
                let in_shape = &graph.node(node.inputs[0]).shape;
                let rank = in_shape.rank();
                let mut sorted = axes.clone();
                sorted.sort();
                sorted.dedup();
                let contiguous = sorted.windows(2).all(|w| w[1] == w[0] + 1)
                    && !sorted.is_empty()
                    && *sorted.last().unwrap() < rank;
                if !contiguous {
                    Thunk::Nop
                } else {
                    let first = sorted[0];
                    let last = *sorted.last().unwrap();
                    let outer: usize = (0..first)
                        .map(|i| in_shape.dim(i).unwrap_static())
                        .product::<usize>()
                        .max(1);
                    let reduced: usize = (first..=last)
                        .map(|i| in_shape.dim(i).unwrap_static())
                        .product();
                    let inner: usize = (last + 1..rank)
                        .map(|i| in_shape.dim(i).unwrap_static())
                        .product::<usize>()
                        .max(1);
                    let src = node_offset(arena, node.inputs[0]);
                    let dst = node_offset(arena, node.id);
                    if node.shape.dtype() == rlx_ir::DType::F64 && matches!(op, ReduceOp::Sum) {
                        Thunk::ReduceSumF64 {
                            src,
                            dst,
                            outer: outer as u32,
                            reduced: reduced as u32,
                            inner: inner as u32,
                        }
                    } else {
                        Thunk::Reduce {
                            src,
                            dst,
                            outer: outer as u32,
                            reduced: reduced as u32,
                            inner: inner as u32,
                            op: *op,
                        }
                    }
                }
            }

            Op::ArgMax { axis, keep_dim: _ } | Op::ArgMin { axis, keep_dim: _ } => {
                let in_shape = &graph.node(node.inputs[0]).shape;
                let rank = in_shape.rank();
                let outer: usize = (0..*axis)
                    .map(|i| in_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                let reduced = in_shape.dim(*axis).unwrap_static();
                let inner: usize = (*axis + 1..rank)
                    .map(|i| in_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                Thunk::ArgReduce {
                    src: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    outer: outer as u32,
                    reduced: reduced as u32,
                    inner: inner as u32,
                    is_max: matches!(node.op, Op::ArgMax { .. }),
                }
            }

            Op::Compare(cmp) => {
                let len = node.shape.num_elements().unwrap();
                let in_dtype = graph.node(node.inputs[0]).shape.dtype();
                let inputs_i64 = u8::from(in_dtype == rlx_ir::DType::I64);
                Thunk::Compare {
                    lhs: node_offset(arena, node.inputs[0]),
                    rhs: node_offset(arena, node.inputs[1]),
                    dst: node_offset(arena, node.id),
                    len: len as u32,
                    op: *cmp,
                    inputs_i64,
                    inputs_elem_bytes: in_dtype.size_bytes() as u8,
                    dst_elem_bytes: node.shape.dtype().size_bytes() as u8,
                }
            }

            Op::Where => {
                let len = node.shape.num_elements().unwrap();
                let elem_bytes = node.shape.dtype().size_bytes() as u8;
                let cond_elem_bytes = graph.node(node.inputs[0]).shape.dtype().size_bytes() as u8;
                Thunk::Where {
                    cond: node_offset(arena, node.inputs[0]),
                    on_true: node_offset(arena, node.inputs[1]),
                    on_false: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    len: len as u32,
                    elem_bytes,
                    cond_elem_bytes,
                }
            }

            Op::Fma => {
                let len = node.shape.num_elements().unwrap();
                Thunk::Fma {
                    a: node_offset(arena, node.inputs[0]),
                    b: node_offset(arena, node.inputs[1]),
                    c: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    len: len as u32,
                    elem_bytes: node.shape.dtype().size_bytes() as u8,
                }
            }

            Op::ReluBackward => {
                let len: usize = (0..node.shape.rank())
                    .map(|i| node.shape.dim(i).unwrap_static())
                    .product();
                let x = node_offset(arena, node.inputs[0]);
                let dy = node_offset(arena, node.inputs[1]);
                let dx = node_offset(arena, node.id);
                match node.shape.dtype() {
                    rlx_ir::DType::F64 => Thunk::ReluBackwardF64 {
                        x,
                        dy,
                        dx,
                        len: len as u32,
                    },
                    _ => Thunk::ReluBackward {
                        x,
                        dy,
                        dx,
                        len: len as u32,
                    },
                }
            }

            Op::ComplexNormSq => {
                let len: usize = (0..node.shape.rank())
                    .map(|i| node.shape.dim(i).unwrap_static())
                    .product();
                let src = node_offset(arena, node.inputs[0]);
                let dst = node_offset(arena, node.id);
                Thunk::ComplexNormSqF32 {
                    src,
                    dst,
                    len: len as u32,
                }
            }

            Op::ComplexNormSqBackward => {
                let len: usize = (0..node.shape.rank())
                    .map(|i| node.shape.dim(i).unwrap_static())
                    .product();
                let z = node_offset(arena, node.inputs[0]);
                let g = node_offset(arena, node.inputs[1]);
                let dz = node_offset(arena, node.id);
                Thunk::ComplexNormSqBackwardF32 {
                    z,
                    g,
                    dz,
                    len: len as u32,
                }
            }

            Op::Conjugate => {
                let len: usize = (0..node.shape.rank())
                    .map(|i| node.shape.dim(i).unwrap_static())
                    .product();
                Thunk::ConjugateC64 {
                    src: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    len: len as u32,
                }
            }

            Op::ActivationBackward { kind } => {
                let len: usize = (0..node.shape.rank())
                    .map(|i| node.shape.dim(i).unwrap_static())
                    .product();
                let x = node_offset(arena, node.inputs[0]);
                let dy = node_offset(arena, node.inputs[1]);
                let dx = node_offset(arena, node.id);
                match node.shape.dtype() {
                    rlx_ir::DType::F64 => Thunk::ActivationBackwardF64 {
                        x,
                        dy,
                        dx,
                        len: len as u32,
                        kind: *kind,
                    },
                    _ => Thunk::ActivationBackward {
                        x,
                        dy,
                        dx,
                        len: len as u32,
                        kind: *kind,
                    },
                }
            }

            Op::LayerNormBackwardInput { eps, .. } => {
                // axis = -1 only (matches forward LayerNorm thunk).
                let h = node.shape.dim(node.shape.rank() - 1).unwrap_static();
                let total = node.shape.num_elements().unwrap();
                Thunk::LayerNormBackwardInput {
                    x: node_offset(arena, node.inputs[0]),
                    gamma: node_offset(arena, node.inputs[1]),
                    dy: node_offset(arena, node.inputs[2]),
                    dx: node_offset(arena, node.id),
                    rows: (total / h) as u32,
                    h: h as u32,
                    eps: *eps,
                }
            }

            Op::LayerNormBackwardGamma { eps, .. } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let h = x_shape.dim(x_shape.rank() - 1).unwrap_static();
                let x_total = x_shape.num_elements().unwrap();
                Thunk::LayerNormBackwardGamma {
                    x: node_offset(arena, node.inputs[0]),
                    dy: node_offset(arena, node.inputs[1]),
                    dgamma: node_offset(arena, node.id),
                    rows: (x_total / h) as u32,
                    h: h as u32,
                    eps: *eps,
                }
            }

            Op::RmsNormBackwardInput { eps, .. }
            | Op::RmsNormBackwardGamma { eps, .. }
            | Op::RmsNormBackwardBeta { eps, .. } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let h = x_shape.dim(x_shape.rank() - 1).unwrap_static();
                let rows = (x_shape.num_elements().unwrap() / h) as u32;
                let off = |i: usize| node_offset(arena, node.inputs[i]);
                let common = (off(0), off(1), off(2), off(3), rows, h as u32, *eps);
                match &node.op {
                    Op::RmsNormBackwardInput { .. } => Thunk::RmsNormBackwardInput {
                        x: common.0,
                        gamma: common.1,
                        beta: common.2,
                        dy: common.3,
                        dx: node_offset(arena, node.id),
                        rows: common.4,
                        h: common.5,
                        eps: common.6,
                    },
                    Op::RmsNormBackwardGamma { .. } => Thunk::RmsNormBackwardGamma {
                        x: common.0,
                        gamma: common.1,
                        beta: common.2,
                        dy: common.3,
                        dgamma: node_offset(arena, node.id),
                        rows: common.4,
                        h: common.5,
                        eps: common.6,
                    },
                    Op::RmsNormBackwardBeta { .. } => Thunk::RmsNormBackwardBeta {
                        x: common.0,
                        gamma: common.1,
                        beta: common.2,
                        dy: common.3,
                        dbeta: node_offset(arena, node.id),
                        rows: common.4,
                        h: common.5,
                        eps: common.6,
                    },
                    _ => unreachable!(),
                }
            }

            Op::RopeBackward { head_dim, n_rot } => {
                let dy_shape = &graph.node(node.inputs[0]).shape;
                let (batch, seq, hidden) = if dy_shape.rank() >= 3 {
                    (
                        dy_shape.dim(0).unwrap_static(),
                        dy_shape.dim(1).unwrap_static(),
                        dy_shape.dim(2).unwrap_static(),
                    )
                } else {
                    (
                        1,
                        dy_shape.dim(0).unwrap_static(),
                        dy_shape.dim(1).unwrap_static(),
                    )
                };
                let cos_shape = &graph.node(node.inputs[1]).shape;
                let cos_len = cos_shape.num_elements().unwrap();
                Thunk::RopeBackward {
                    dy: node_offset(arena, node.inputs[0]),
                    cos: node_offset(arena, node.inputs[1]),
                    sin: node_offset(arena, node.inputs[2]),
                    dx: node_offset(arena, node.id),
                    batch: batch as u32,
                    seq: seq as u32,
                    hidden: hidden as u32,
                    head_dim: *head_dim as u32,
                    n_rot: *n_rot as u32,
                    cos_len: cos_len as u32,
                }
            }

            Op::CumsumBackward { exclusive, .. } => {
                let dy_shape = &graph.node(node.inputs[0]).shape;
                let rank = dy_shape.rank();
                let cols = dy_shape.dim(rank - 1).unwrap_static();
                let rows = dy_shape.num_elements().unwrap() / cols;
                Thunk::CumsumBackward {
                    dy: node_offset(arena, node.inputs[0]),
                    dx: node_offset(arena, node.id),
                    rows: rows as u32,
                    cols: cols as u32,
                    exclusive: *exclusive,
                }
            }

            Op::GatherBackward { .. } => {
                let dy_shape = &graph.node(node.inputs[0]).shape;
                let idx_shape = &graph.node(node.inputs[1]).shape;
                let out_shape = &node.shape;
                let rank = out_shape.rank();
                let axis = match &node.op {
                    Op::GatherBackward { axis } => *axis,
                    _ => 0,
                };
                let axis_u = if axis < 0 {
                    (rank as i32 + axis) as usize
                } else {
                    axis as usize
                };
                let outer: usize = (0..axis_u)
                    .map(|i| dy_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                let num_idx = idx_shape.dim(axis_u).unwrap_static();
                let trailing: usize = (axis_u + 1..dy_shape.rank())
                    .map(|i| dy_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                let axis_dim = out_shape.dim(axis_u).unwrap_static();
                Thunk::GatherBackward {
                    dy: node_offset(arena, node.inputs[0]),
                    indices: node_offset(arena, node.inputs[1]),
                    dst: node_offset(arena, node.id),
                    outer: outer as u32,
                    axis_dim: axis_dim as u32,
                    num_idx: num_idx as u32,
                    trailing: trailing as u32,
                }
            }

            Op::GroupNormBackwardInput { num_groups, eps }
            | Op::GroupNormBackwardGamma { num_groups, eps }
            | Op::GroupNormBackwardBeta { num_groups, eps } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let n = x_shape.dim(0).unwrap_static() as u32;
                let c = x_shape.dim(1).unwrap_static() as u32;
                let h = x_shape.dim(2).unwrap_static() as u32;
                let w = x_shape.dim(3).unwrap_static() as u32;
                match &node.op {
                    Op::GroupNormBackwardInput { .. } => Thunk::GroupNormBackwardInput {
                        x: node_offset(arena, node.inputs[0]),
                        gamma: node_offset(arena, node.inputs[1]),
                        beta: node_offset(arena, node.inputs[2]),
                        dy: node_offset(arena, node.inputs[3]),
                        dx: node_offset(arena, node.id),
                        n,
                        c,
                        h,
                        w,
                        num_groups: *num_groups as u32,
                        eps: *eps,
                    },
                    Op::GroupNormBackwardGamma { .. } => Thunk::GroupNormBackwardGamma {
                        x: node_offset(arena, node.inputs[0]),
                        dy: node_offset(arena, node.inputs[1]),
                        dgamma: node_offset(arena, node.id),
                        n,
                        c,
                        h,
                        w,
                        num_groups: *num_groups as u32,
                        eps: *eps,
                    },
                    Op::GroupNormBackwardBeta { .. } => Thunk::GroupNormBackwardBeta {
                        dy: node_offset(arena, node.inputs[1]),
                        dbeta: node_offset(arena, node.id),
                        n,
                        c,
                        h,
                        w,
                    },
                    _ => unreachable!(),
                }
            }

            Op::MaxPool2dBackward {
                kernel_size,
                stride,
                padding,
            } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let dy_shape = &graph.node(node.inputs[1]).shape;
                if kernel_size.len() == 2 && x_shape.rank() == 4 && dy_shape.rank() == 4 {
                    Thunk::MaxPool2dBackward {
                        x: node_offset(arena, node.inputs[0]),
                        dy: node_offset(arena, node.inputs[1]),
                        dx: node_offset(arena, node.id),
                        n: x_shape.dim(0).unwrap_static() as u32,
                        c: x_shape.dim(1).unwrap_static() as u32,
                        h: x_shape.dim(2).unwrap_static() as u32,
                        w: x_shape.dim(3).unwrap_static() as u32,
                        h_out: dy_shape.dim(2).unwrap_static() as u32,
                        w_out: dy_shape.dim(3).unwrap_static() as u32,
                        kh: kernel_size[0] as u32,
                        kw: kernel_size[1] as u32,
                        sh: stride.first().copied().unwrap_or(1) as u32,
                        sw: stride.get(1).copied().unwrap_or(1) as u32,
                        ph: padding.first().copied().unwrap_or(0) as u32,
                        pw: padding.get(1).copied().unwrap_or(0) as u32,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::Conv2dBackwardInput {
                kernel_size,
                stride,
                padding,
                dilation,
                groups,
            } => {
                let dy_shape = &graph.node(node.inputs[0]).shape;
                let w_shape = &graph.node(node.inputs[1]).shape;
                let out_shape = &node.shape;
                if kernel_size.len() == 2
                    && dy_shape.rank() == 4
                    && w_shape.rank() == 4
                    && out_shape.rank() == 4
                {
                    Thunk::Conv2dBackwardInput {
                        dy: node_offset(arena, node.inputs[0]),
                        w: node_offset(arena, node.inputs[1]),
                        dx: node_offset(arena, node.id),
                        n: out_shape.dim(0).unwrap_static() as u32,
                        c_in: out_shape.dim(1).unwrap_static() as u32,
                        h: out_shape.dim(2).unwrap_static() as u32,
                        w_in: out_shape.dim(3).unwrap_static() as u32,
                        c_out: dy_shape.dim(1).unwrap_static() as u32,
                        h_out: dy_shape.dim(2).unwrap_static() as u32,
                        w_out: dy_shape.dim(3).unwrap_static() as u32,
                        kh: kernel_size[0] as u32,
                        kw: kernel_size[1] as u32,
                        sh: stride.first().copied().unwrap_or(1) as u32,
                        sw: stride.get(1).copied().unwrap_or(1) as u32,
                        ph: padding.first().copied().unwrap_or(0) as u32,
                        pw: padding.get(1).copied().unwrap_or(0) as u32,
                        dh: dilation.first().copied().unwrap_or(1) as u32,
                        dw: dilation.get(1).copied().unwrap_or(1) as u32,
                        groups: *groups as u32,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::Conv2dBackwardWeight {
                kernel_size,
                stride,
                padding,
                dilation,
                groups,
            } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let dy_shape = &graph.node(node.inputs[1]).shape;
                let dw_shape = &node.shape;
                if kernel_size.len() == 2
                    && x_shape.rank() == 4
                    && dy_shape.rank() == 4
                    && dw_shape.rank() == 4
                {
                    Thunk::Conv2dBackwardWeight {
                        x: node_offset(arena, node.inputs[0]),
                        dy: node_offset(arena, node.inputs[1]),
                        dw: node_offset(arena, node.id),
                        n: x_shape.dim(0).unwrap_static() as u32,
                        c_in: x_shape.dim(1).unwrap_static() as u32,
                        h: x_shape.dim(2).unwrap_static() as u32,
                        w: x_shape.dim(3).unwrap_static() as u32,
                        c_out: dy_shape.dim(1).unwrap_static() as u32,
                        h_out: dy_shape.dim(2).unwrap_static() as u32,
                        w_out: dy_shape.dim(3).unwrap_static() as u32,
                        kh: kernel_size[0] as u32,
                        kw: kernel_size[1] as u32,
                        sh: stride.first().copied().unwrap_or(1) as u32,
                        sw: stride.get(1).copied().unwrap_or(1) as u32,
                        ph: padding.first().copied().unwrap_or(0) as u32,
                        pw: padding.get(1).copied().unwrap_or(0) as u32,
                        dh: dilation.first().copied().unwrap_or(1) as u32,
                        dw_dil: dilation.get(1).copied().unwrap_or(1) as u32,
                        groups: *groups as u32,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::Im2Col {
                kernel_size,
                stride,
                padding,
                dilation,
            } => {
                let x_shape = &graph.node(node.inputs[0]).shape;
                let out_shape = &node.shape;
                if kernel_size.len() == 2 && x_shape.rank() == 4 && out_shape.rank() == 2 {
                    let n = match x_shape.dim(0) {
                        rlx_ir::shape::Dim::Static(v) => v as u32,
                        _ => 0,
                    };
                    let c_in = x_shape.dim(1).unwrap_static() as u32;
                    let h = x_shape.dim(2).unwrap_static() as u32;
                    let w = x_shape.dim(3).unwrap_static() as u32;
                    let kh = kernel_size[0] as u32;
                    let kw = kernel_size[1] as u32;
                    let sh = stride.first().copied().unwrap_or(1) as u32;
                    let sw = stride.get(1).copied().unwrap_or(1) as u32;
                    let ph = padding.first().copied().unwrap_or(0) as u32;
                    let pw = padding.get(1).copied().unwrap_or(0) as u32;
                    let dh = dilation.first().copied().unwrap_or(1) as u32;
                    let dw_dil = dilation.get(1).copied().unwrap_or(1) as u32;
                    let h_out = rlx_ir::shape::conv2d_spatial_output(
                        h as usize,
                        kh as usize,
                        sh as usize,
                        ph as usize,
                        dh as usize,
                    ) as u32;
                    let w_out = rlx_ir::shape::conv2d_spatial_output(
                        w as usize,
                        kw as usize,
                        sw as usize,
                        pw as usize,
                        dw_dil as usize,
                    ) as u32;
                    Thunk::Im2Col {
                        x: node_offset(arena, node.inputs[0]),
                        col: node_offset(arena, node.id),
                        n,
                        c_in,
                        h,
                        w,
                        h_out,
                        w_out,
                        kh,
                        kw,
                        sh,
                        sw,
                        ph,
                        pw,
                        dh,
                        dw_dil,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::SoftmaxCrossEntropy => {
                let logits_shape = &graph.node(node.inputs[0]).shape;
                if logits_shape.rank() == 2 {
                    Thunk::SoftmaxCrossEntropyDense {
                        logits: node_offset(arena, node.inputs[0]),
                        targets: node_offset(arena, node.inputs[1]),
                        dst: node_offset(arena, node.id),
                        n: logits_shape.dim(0).unwrap_static() as u32,
                        c: logits_shape.dim(1).unwrap_static() as u32,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::SoftmaxCrossEntropyWithLogits => {
                let logits_shape = &graph.node(node.inputs[0]).shape;
                if logits_shape.rank() == 2 {
                    Thunk::SoftmaxCrossEntropy {
                        logits: node_offset(arena, node.inputs[0]),
                        labels: node_offset(arena, node.inputs[1]),
                        dst: node_offset(arena, node.id),
                        n: logits_shape.dim(0).unwrap_static() as u32,
                        c: logits_shape.dim(1).unwrap_static() as u32,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::SoftmaxCrossEntropyBackward => {
                let logits_shape = &graph.node(node.inputs[0]).shape;
                if logits_shape.rank() == 2 {
                    Thunk::SoftmaxCrossEntropyBackward {
                        logits: node_offset(arena, node.inputs[0]),
                        labels: node_offset(arena, node.inputs[1]),
                        d_loss: node_offset(arena, node.inputs[2]),
                        dlogits: node_offset(arena, node.id),
                        n: logits_shape.dim(0).unwrap_static() as u32,
                        c: logits_shape.dim(1).unwrap_static() as u32,
                    }
                } else {
                    Thunk::Nop
                }
            }

            Op::DenseSolve => {
                // A: [n, n], b: [n] or [n, nrhs]. Output matches b.
                let a_shape = &graph.node(node.inputs[0]).shape;
                let n = a_shape.dim(0).unwrap_static();
                debug_assert_eq!(
                    n,
                    a_shape.dim(1).unwrap_static(),
                    "DenseSolve: A must be square"
                );
                let b_elems = node.shape.num_elements().unwrap();
                let nrhs = b_elems / n;
                match node.shape.dtype() {
                    rlx_ir::DType::F64 => Thunk::DenseSolveF64 {
                        a: node_offset(arena, node.inputs[0]),
                        b: node_offset(arena, node.inputs[1]),
                        x: node_offset(arena, node.id),
                        n: n as u32,
                        nrhs: nrhs as u32,
                    },
                    rlx_ir::DType::F32 => Thunk::DenseSolveF32 {
                        a: node_offset(arena, node.inputs[0]),
                        b: node_offset(arena, node.inputs[1]),
                        x: node_offset(arena, node.id),
                        n: n as u32,
                        nrhs: nrhs as u32,
                    },
                    other => panic!(
                        "DenseSolve: F32 + F64 lowered; got {other:?}. \
                         Add another variant when needed."
                    ),
                }
            }

            Op::BatchedDenseSolve => {
                // A: [B, N, N], b: [B, N] or [B, N, K]. Output matches b.
                let a_shape = &graph.node(node.inputs[0]).shape;
                assert_eq!(a_shape.rank(), 3, "BatchedDenseSolve: A rank must be 3");
                let batch = a_shape.dim(0).unwrap_static();
                let n = a_shape.dim(1).unwrap_static();
                debug_assert_eq!(
                    n,
                    a_shape.dim(2).unwrap_static(),
                    "BatchedDenseSolve: A's last two dims must match"
                );
                let total = node.shape.num_elements().unwrap();
                let nrhs = total / (batch * n);
                match node.shape.dtype() {
                    rlx_ir::DType::F32 => Thunk::BatchedDenseSolveF32 {
                        a: node_offset(arena, node.inputs[0]),
                        b: node_offset(arena, node.inputs[1]),
                        x: node_offset(arena, node.id),
                        batch: batch as u32,
                        n: n as u32,
                        nrhs: nrhs as u32,
                    },
                    rlx_ir::DType::F64 => Thunk::BatchedDenseSolveF64 {
                        a: node_offset(arena, node.inputs[0]),
                        b: node_offset(arena, node.inputs[1]),
                        x: node_offset(arena, node.id),
                        batch: batch as u32,
                        n: n as u32,
                        nrhs: nrhs as u32,
                    },
                    other => panic!("BatchedDenseSolve: F32 + F64 only, got {other:?}"),
                }
            }

            Op::Scan {
                body,
                length,
                save_trajectory,
                num_bcast,
                num_xs,
                num_checkpoints,
            } => {
                assert!(
                    *num_checkpoints == 0 || *num_checkpoints <= *length,
                    "Op::Scan: num_checkpoints={} must be 0 or ≤ length={}",
                    *num_checkpoints,
                    *length
                );
                if *num_checkpoints != 0 && *num_checkpoints != *length {
                    assert!(
                        *save_trajectory,
                        "Op::Scan: num_checkpoints<length only meaningful when save_trajectory=true"
                    );
                }
                // Plan + compile the body sub-graph standalone. The body
                // gets its own Arena; per execution we clone its
                // pristine bytes, copy the outer carry (and per-step xs
                // slices, if any) into the body's Input slots, run the
                // body schedule N times, then copy the body's output
                // back to the outer arena.
                //
                // Body invariants: 1 + num_xs Op::Inputs in NodeId order
                // — first declared is the carry, rest are x_t_i. Single
                // graph output (the next carry), same shape as carry.
                let body_plan = rlx_opt::memory::plan_memory(body);
                let _body_arena_size = body_plan.arena_size;
                // Snapshot per-input byte offsets before plan_memory
                // moves into the Arena below.
                let body_offsets: HashMap<NodeId, usize> = body_plan
                    .assignments
                    .iter()
                    .map(|(id, slot)| (*id, slot.offset))
                    .collect();

                // Collect body Input nodes in NodeId order; first is
                // carry, rest are per-step xs in matching order.
                let mut body_inputs: Vec<NodeId> = body
                    .nodes()
                    .iter()
                    .filter(|n| matches!(n.op, Op::Input { .. }))
                    .map(|n| n.id)
                    .collect();
                body_inputs.sort();
                let n_body_inputs = body_inputs.len();
                let expected = 1 + *num_bcast as usize + *num_xs as usize;
                if n_body_inputs != expected {
                    let names: Vec<String> = body
                        .nodes()
                        .iter()
                        .filter_map(|n| match &n.op {
                            Op::Input { name } => Some(format!("{}={}", n.id, name)),
                            _ => None,
                        })
                        .collect();
                    panic!(
                        "Op::Scan body has {} Op::Input nodes; expected {} \
                            (1 carry + {} bcast + {} xs). Inputs by NodeId: [{}]",
                        n_body_inputs,
                        expected,
                        *num_bcast,
                        *num_xs,
                        names.join(", ")
                    );
                }

                let body_input_id = body_inputs[0];
                let body_input_off = body_offsets[&body_input_id];
                let body_output_id = body
                    .outputs
                    .first()
                    .copied()
                    .expect("Op::Scan body must declare one output");
                let body_output_off = body_offsets[&body_output_id];

                let mut body_arena = crate::arena::Arena::from_plan(body_plan);
                // Fill body Constant nodes — mirror the outer-graph logic
                // in rlx-runtime/src/backend.rs (dtype-aware).
                for n in body.nodes() {
                    if let Op::Constant { data } = &n.op
                        && body_arena.has_buffer(n.id)
                        && !data.is_empty()
                    {
                        match n.shape.dtype() {
                            rlx_ir::DType::F64 => {
                                let off = body_arena.byte_offset(n.id);
                                let buf = body_arena.raw_buf_mut();
                                let nbytes = (buf.len() - off).min(data.len());
                                buf[off..off + nbytes].copy_from_slice(&data[..nbytes]);
                            }
                            _ => {
                                let buf = body_arena.slice_mut(n.id);
                                let n_floats = data.len() / 4;
                                let n_lim = buf.len().min(n_floats);
                                for i in 0..n_lim {
                                    let bytes = [
                                        data[i * 4],
                                        data[i * 4 + 1],
                                        data[i * 4 + 2],
                                        data[i * 4 + 3],
                                    ];
                                    buf[i] = f32::from_le_bytes(bytes);
                                }
                            }
                        }
                    }
                }
                let body_init = body_arena.raw_buf().to_vec();
                let body_schedule = compile_thunks_with_rng(body, &body_arena, rng);

                // Carry bytes — for trajectory mode, the outer node's
                // shape is [length, *carry_shape], so dividing by length
                // gives one row's bytes; the body's input slot still
                // holds carry_shape bytes.
                let carry_bytes = if *save_trajectory {
                    let total = node
                        .shape
                        .size_bytes()
                        .expect("Op::Scan trajectory output must have static shape");
                    total / *length as usize
                } else {
                    node.shape
                        .size_bytes()
                        .expect("Op::Scan carry must have static shape")
                };

                // Bcast inputs occupy body_inputs[1..1+num_bcast] and
                // outer node.inputs[1..1+num_bcast]. They keep their
                // natural shape (no [length, ...] prefix) and are
                // copied into body_buf ONCE before the scan loop.
                let mut bcast_inputs: Vec<(usize, usize, u32)> =
                    Vec::with_capacity(*num_bcast as usize);
                for i in 0..*num_bcast as usize {
                    let body_b_id = body_inputs[1 + i];
                    let body_b_off = body_offsets[&body_b_id];
                    let outer_b_id = node.inputs[1 + i];
                    let outer_b_off = node_offset(arena, outer_b_id);
                    let outer_b_shape = &graph.node(outer_b_id).shape;
                    let total = outer_b_shape
                        .size_bytes()
                        .expect("Op::Scan bcast must have static shape");
                    bcast_inputs.push((body_b_off, outer_b_off, total as u32));
                }

                // xs occupy body_inputs[1+num_bcast..] and node.inputs
                // [1+num_bcast..]. Each has shape [length, *per_step];
                // per-step bytes = total / length.
                let mut xs_inputs: Vec<(usize, usize, u32)> = Vec::with_capacity(*num_xs as usize);
                let xs_base = 1 + *num_bcast as usize;
                for i in 0..*num_xs as usize {
                    let body_x_id = body_inputs[xs_base + i];
                    let body_x_off = body_offsets[&body_x_id];
                    let outer_xs_id = node.inputs[xs_base + i];
                    let outer_xs_off = node_offset(arena, outer_xs_id);
                    let outer_xs_shape = &graph.node(outer_xs_id).shape;
                    let total = outer_xs_shape
                        .size_bytes()
                        .expect("Op::Scan xs must have static shape");
                    let per_step = total / *length as usize;
                    xs_inputs.push((body_x_off, outer_xs_off, per_step as u32));
                }

                Thunk::Scan {
                    body: Arc::new(body_schedule),
                    body_init: Arc::new(body_init),
                    body_input_off,
                    body_output_off,
                    outer_init_off: node_offset(arena, node.inputs[0]),
                    outer_final_off: node_offset(arena, node.id),
                    length: *length,
                    carry_bytes: carry_bytes as u32,
                    save_trajectory: *save_trajectory,
                    xs_inputs: Arc::new(xs_inputs),
                    bcast_inputs: Arc::new(bcast_inputs),
                    num_checkpoints: *num_checkpoints,
                }
            }

            Op::ScanBackward {
                body_vjp,
                length,
                save_trajectory,
                num_xs,
                num_checkpoints,
                forward_body,
            } => {
                let is_recursive = *num_checkpoints != 0 && *num_checkpoints != *length;
                if is_recursive {
                    assert!(
                        forward_body.is_some(),
                        "Op::ScanBackward with num_checkpoints<length requires forward_body"
                    );
                }
                // body_vjp has signature
                //   (carry, x_t_0, ..., x_t_{num_xs-1}, d_output) → dcarry
                // Identify slots:
                //   * "d_output" by exact name (AD-introduced seed Input).
                //   * Remaining Inputs sorted by NodeId — first is the
                //     carry mirror, rest are x_t_i mirrors in body's
                //     original Op::Input declaration order.
                let body_plan = rlx_opt::memory::plan_memory(body_vjp);
                let body_offsets: HashMap<NodeId, usize> = body_plan
                    .assignments
                    .iter()
                    .map(|(id, slot)| (*id, slot.offset))
                    .collect();
                let mut body_d_output_off: Option<usize> = None;
                let mut body_other_inputs: Vec<(NodeId, usize)> = Vec::new();
                for n in body_vjp.nodes() {
                    if let Op::Input { name } = &n.op {
                        let off = body_offsets[&n.id];
                        if name == "d_output" {
                            body_d_output_off = Some(off);
                        } else {
                            body_other_inputs.push((n.id, off));
                        }
                    }
                }
                body_other_inputs.sort_by_key(|(id, _)| *id);
                let body_d_output_off =
                    body_d_output_off.expect("ScanBackward body_vjp missing 'd_output' Input");
                let expected_others = 1 + *num_xs as usize;
                assert_eq!(
                    body_other_inputs.len(),
                    expected_others,
                    "ScanBackward body_vjp has {} non-d_output Inputs; \
                     expected {} (1 carry + {} xs)",
                    body_other_inputs.len(),
                    expected_others,
                    num_xs
                );
                let body_carry_in_off = body_other_inputs[0].1;
                let body_x_offs: Vec<usize> = body_other_inputs
                    .iter()
                    .skip(1)
                    .map(|(_, off)| *off)
                    .collect();
                let body_dcarry_out_off = body_offsets[&body_vjp.outputs[0]];

                let mut body_arena = crate::arena::Arena::from_plan(body_plan);
                // Fill body_vjp's Constants (mirrors the Scan lowering).
                for n in body_vjp.nodes() {
                    if let Op::Constant { data } = &n.op
                        && body_arena.has_buffer(n.id)
                        && !data.is_empty()
                    {
                        match n.shape.dtype() {
                            rlx_ir::DType::F64 => {
                                let off = body_arena.byte_offset(n.id);
                                let buf = body_arena.raw_buf_mut();
                                let nb = (buf.len() - off).min(data.len());
                                buf[off..off + nb].copy_from_slice(&data[..nb]);
                            }
                            _ => {
                                let buf = body_arena.slice_mut(n.id);
                                let nf = data.len() / 4;
                                let nl = buf.len().min(nf);
                                for i in 0..nl {
                                    let bytes = [
                                        data[i * 4],
                                        data[i * 4 + 1],
                                        data[i * 4 + 2],
                                        data[i * 4 + 3],
                                    ];
                                    buf[i] = f32::from_le_bytes(bytes);
                                }
                            }
                        }
                    }
                }
                let body_init = body_arena.raw_buf().to_vec();
                let body_schedule = compile_thunks_with_rng(body_vjp, &body_arena, rng);

                // Carry bytes from the dcarry output node (== carry shape).
                let carry_bytes = body_vjp
                    .node(body_vjp.outputs[0])
                    .shape
                    .size_bytes()
                    .expect("ScanBackward dcarry must be statically shaped");
                let carry_elem_size = body_vjp
                    .node(body_vjp.outputs[0])
                    .shape
                    .dtype()
                    .size_bytes() as u32;

                // For each xs input on the outer node:
                // (outer_xs_base, per_step_bytes).
                let mut outer_xs_offs: Vec<(usize, u32)> = Vec::with_capacity(*num_xs as usize);
                for i in 0..*num_xs as usize {
                    let outer_xs_id = node.inputs[3 + i];
                    let outer_xs_off = node_offset(arena, outer_xs_id);
                    let outer_xs_shape = &graph.node(outer_xs_id).shape;
                    let total = outer_xs_shape
                        .size_bytes()
                        .expect("ScanBackward xs must have static shape");
                    let per_step = total / *length as usize;
                    outer_xs_offs.push((outer_xs_off, per_step as u32));
                }

                // If recursive checkpointing is active, we also compile
                // the forward body so the executor can recompute
                // intermediate carries. The forward body is supplied
                // by the AD pass via `forward_body: Some(_)`.
                let (fb_schedule, fb_init, fb_carry_in_off, fb_output_off, fb_x_offs) =
                    if is_recursive {
                        let fb = forward_body.as_ref().unwrap();
                        let fb_plan = rlx_opt::memory::plan_memory(fb);
                        let fb_offsets: HashMap<NodeId, usize> = fb_plan
                            .assignments
                            .iter()
                            .map(|(id, slot)| (*id, slot.offset))
                            .collect();
                        let mut fb_inputs: Vec<NodeId> = fb
                            .nodes()
                            .iter()
                            .filter(|n| matches!(n.op, Op::Input { .. }))
                            .map(|n| n.id)
                            .collect();
                        fb_inputs.sort();
                        let fb_carry = fb_offsets[&fb_inputs[0]];
                        let fb_xs: Vec<usize> = (1..fb_inputs.len())
                            .map(|i| fb_offsets[&fb_inputs[i]])
                            .collect();
                        let fb_out = fb_offsets[&fb.outputs[0]];
                        let mut fb_arena = crate::arena::Arena::from_plan(fb_plan);
                        for n in fb.nodes() {
                            if let Op::Constant { data } = &n.op
                                && fb_arena.has_buffer(n.id)
                                && !data.is_empty()
                            {
                                // Byte-copy works for any
                                // numeric dtype as long as the
                                // arena slot is sized to hold
                                // it — the Constant's `data`
                                // already encodes the right
                                // bytes per element.
                                let off = fb_arena.byte_offset(n.id);
                                let buf = fb_arena.raw_buf_mut();
                                let nb = (buf.len() - off).min(data.len());
                                buf[off..off + nb].copy_from_slice(&data[..nb]);
                            }
                        }
                        let fb_init_bytes = fb_arena.raw_buf().to_vec();
                        let fb_sched = compile_thunks_with_rng(fb, &fb_arena, rng);
                        (
                            Some(Arc::new(fb_sched)),
                            Some(Arc::new(fb_init_bytes)),
                            fb_carry,
                            fb_out,
                            fb_xs,
                        )
                    } else {
                        (None, None, 0, 0, Vec::new())
                    };

                Thunk::ScanBackward {
                    body_vjp: Arc::new(body_schedule),
                    body_init: Arc::new(body_init),
                    body_carry_in_off,
                    body_x_offs: Arc::new(body_x_offs),
                    body_d_output_off,
                    body_dcarry_out_off,
                    outer_init_off: node_offset(arena, node.inputs[0]),
                    outer_traj_off: node_offset(arena, node.inputs[1]),
                    outer_upstream_off: node_offset(arena, node.inputs[2]),
                    outer_xs_offs: Arc::new(outer_xs_offs),
                    outer_dinit_off: node_offset(arena, node.id),
                    length: *length,
                    carry_bytes: carry_bytes as u32,
                    carry_elem_size,
                    save_trajectory: *save_trajectory,
                    num_checkpoints: *num_checkpoints,
                    forward_body: fb_schedule,
                    forward_body_init: fb_init,
                    forward_body_carry_in_off: fb_carry_in_off,
                    forward_body_output_off: fb_output_off,
                    forward_body_x_offs: Arc::new(fb_x_offs),
                }
            }

            Op::ScanBackwardXs {
                body_vjp,
                length,
                save_trajectory,
                num_xs,
                xs_idx,
                num_checkpoints,
                forward_body,
            } => {
                assert!(
                    *num_checkpoints == 0 || *num_checkpoints <= *length,
                    "Op::ScanBackwardXs: num_checkpoints={} must be 0 or ≤ length={}",
                    *num_checkpoints,
                    *length
                );
                let is_recursive = *num_checkpoints != 0 && *num_checkpoints != *length;
                if is_recursive {
                    assert!(
                        forward_body.is_some(),
                        "Op::ScanBackwardXs with num_checkpoints<length \
                         requires forward_body"
                    );
                }
                // Mirror ScanBackward's body_vjp slot identification +
                // arena prep, then add: per-iteration extraction of the
                // body_vjp output that corresponds to the chosen xs.
                //
                // body_vjp's outputs (from `grad(body, [carry, xs_0, ..., xs_{num_xs-1}])`):
                //   outputs[0]      = dcarry
                //   outputs[1 + i]  = dx_t_i
                let body_plan = rlx_opt::memory::plan_memory(body_vjp);
                let body_offsets: HashMap<NodeId, usize> = body_plan
                    .assignments
                    .iter()
                    .map(|(id, slot)| (*id, slot.offset))
                    .collect();
                let mut body_d_output_off: Option<usize> = None;
                let mut body_other_inputs: Vec<(NodeId, usize)> = Vec::new();
                for n in body_vjp.nodes() {
                    if let Op::Input { name } = &n.op {
                        let off = body_offsets[&n.id];
                        if name == "d_output" {
                            body_d_output_off = Some(off);
                        } else {
                            body_other_inputs.push((n.id, off));
                        }
                    }
                }
                body_other_inputs.sort_by_key(|(id, _)| *id);
                let body_d_output_off =
                    body_d_output_off.expect("ScanBackwardXs body_vjp missing 'd_output' Input");
                let expected_others = 1 + *num_xs as usize;
                assert_eq!(
                    body_other_inputs.len(),
                    expected_others,
                    "ScanBackwardXs body_vjp has {} non-d_output Inputs; expected {}",
                    body_other_inputs.len(),
                    expected_others
                );
                let body_carry_in_off = body_other_inputs[0].1;
                let body_x_offs: Vec<usize> = body_other_inputs
                    .iter()
                    .skip(1)
                    .map(|(_, off)| *off)
                    .collect();
                let body_dcarry_out_off = body_offsets[&body_vjp.outputs[0]];
                let dxs_out_node = body_vjp.outputs[1 + *xs_idx as usize];
                let body_dxs_out_off = body_offsets[&dxs_out_node];

                let mut body_arena = crate::arena::Arena::from_plan(body_plan);
                for n in body_vjp.nodes() {
                    if let Op::Constant { data } = &n.op
                        && body_arena.has_buffer(n.id)
                        && !data.is_empty()
                    {
                        match n.shape.dtype() {
                            rlx_ir::DType::F64 => {
                                let off = body_arena.byte_offset(n.id);
                                let buf = body_arena.raw_buf_mut();
                                let nb = (buf.len() - off).min(data.len());
                                buf[off..off + nb].copy_from_slice(&data[..nb]);
                            }
                            _ => {
                                let buf = body_arena.slice_mut(n.id);
                                let nf = data.len() / 4;
                                let nl = buf.len().min(nf);
                                for i in 0..nl {
                                    let bytes = [
                                        data[i * 4],
                                        data[i * 4 + 1],
                                        data[i * 4 + 2],
                                        data[i * 4 + 3],
                                    ];
                                    buf[i] = f32::from_le_bytes(bytes);
                                }
                            }
                        }
                    }
                }
                let body_init = body_arena.raw_buf().to_vec();
                let body_schedule = compile_thunks_with_rng(body_vjp, &body_arena, rng);

                let carry_bytes = body_vjp
                    .node(body_vjp.outputs[0])
                    .shape
                    .size_bytes()
                    .expect("ScanBackwardXs dcarry must be statically shaped");
                let carry_elem_size = body_vjp
                    .node(body_vjp.outputs[0])
                    .shape
                    .dtype()
                    .size_bytes() as u32;
                let per_step_bytes = body_vjp
                    .node(dxs_out_node)
                    .shape
                    .size_bytes()
                    .expect("ScanBackwardXs dxs body output must be statically shaped");

                let mut outer_xs_offs: Vec<(usize, u32)> = Vec::with_capacity(*num_xs as usize);
                for i in 0..*num_xs as usize {
                    let outer_xs_id = node.inputs[3 + i];
                    let outer_xs_off = node_offset(arena, outer_xs_id);
                    let outer_xs_shape = &graph.node(outer_xs_id).shape;
                    let total = outer_xs_shape
                        .size_bytes()
                        .expect("ScanBackwardXs xs must have static shape");
                    let per_step = total / *length as usize;
                    outer_xs_offs.push((outer_xs_off, per_step as u32));
                }

                // Compile forward_body for recompute when checkpointed.
                // Mirrors the same code path in the ScanBackward arm.
                let (fb_schedule, fb_init, fb_carry_in_off, fb_output_off, fb_x_offs) =
                    if is_recursive {
                        let fb = forward_body.as_ref().unwrap();
                        let fb_plan = rlx_opt::memory::plan_memory(fb);
                        let fb_offsets: HashMap<NodeId, usize> = fb_plan
                            .assignments
                            .iter()
                            .map(|(id, slot)| (*id, slot.offset))
                            .collect();
                        let mut fb_inputs: Vec<NodeId> = fb
                            .nodes()
                            .iter()
                            .filter(|n| matches!(n.op, Op::Input { .. }))
                            .map(|n| n.id)
                            .collect();
                        fb_inputs.sort();
                        let fb_carry = fb_offsets[&fb_inputs[0]];
                        let fb_xs: Vec<usize> = (1..fb_inputs.len())
                            .map(|i| fb_offsets[&fb_inputs[i]])
                            .collect();
                        let fb_out = fb_offsets[&fb.outputs[0]];
                        let mut fb_arena = crate::arena::Arena::from_plan(fb_plan);
                        for n in fb.nodes() {
                            if let Op::Constant { data } = &n.op
                                && fb_arena.has_buffer(n.id)
                                && !data.is_empty()
                            {
                                // Byte-copy works for any
                                // numeric dtype as long as the
                                // arena slot is sized to hold
                                // it — the Constant's `data`
                                // already encodes the right
                                // bytes per element.
                                let off = fb_arena.byte_offset(n.id);
                                let buf = fb_arena.raw_buf_mut();
                                let nb = (buf.len() - off).min(data.len());
                                buf[off..off + nb].copy_from_slice(&data[..nb]);
                            }
                        }
                        let fb_init_bytes = fb_arena.raw_buf().to_vec();
                        let fb_sched = compile_thunks_with_rng(fb, &fb_arena, rng);
                        (
                            Some(Arc::new(fb_sched)),
                            Some(Arc::new(fb_init_bytes)),
                            fb_carry,
                            fb_out,
                            fb_xs,
                        )
                    } else {
                        (None, None, 0, 0, Vec::new())
                    };

                Thunk::ScanBackwardXs {
                    body_vjp: Arc::new(body_schedule),
                    body_init: Arc::new(body_init),
                    body_carry_in_off,
                    body_x_offs: Arc::new(body_x_offs),
                    body_d_output_off,
                    body_dcarry_out_off,
                    body_dxs_out_off,
                    outer_init_off: node_offset(arena, node.inputs[0]),
                    outer_traj_off: node_offset(arena, node.inputs[1]),
                    outer_upstream_off: node_offset(arena, node.inputs[2]),
                    outer_xs_offs: Arc::new(outer_xs_offs),
                    outer_dxs_off: node_offset(arena, node.id),
                    length: *length,
                    carry_bytes: carry_bytes as u32,
                    carry_elem_size,
                    per_step_bytes: per_step_bytes as u32,
                    save_trajectory: *save_trajectory,
                    num_checkpoints: *num_checkpoints,
                    forward_body: fb_schedule,
                    forward_body_init: fb_init,
                    forward_body_carry_in_off: fb_carry_in_off,
                    forward_body_output_off: fb_output_off,
                    forward_body_x_offs: Arc::new(fb_x_offs),
                }
            }

            Op::Concat { axis } => {
                // Compute outer/inner from the OUTPUT shape: all inputs share
                // the same shape except along `axis`. The output's leading
                // and trailing dims match.
                let out_shape = &node.shape;
                let rank = out_shape.rank();
                let outer: usize = (0..*axis)
                    .map(|i| out_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                let inner: usize = (*axis + 1..rank)
                    .map(|i| out_shape.dim(i).unwrap_static())
                    .product::<usize>()
                    .max(1);
                let total_axis = out_shape.dim(*axis).unwrap_static();
                let inputs: Vec<(usize, u32, u32)> = node
                    .inputs
                    .iter()
                    .map(|&in_id| {
                        let in_shape = &graph.node(in_id).shape;
                        let in_axis = concat_axis_extent(in_shape, *axis, rank);
                        let in_numel = in_shape.num_elements().unwrap_or(0) as u32;
                        (node_offset(arena, in_id), in_axis as u32, in_numel)
                    })
                    .collect();
                let dst = node_offset(arena, node.id);
                match out_shape.dtype() {
                    rlx_ir::DType::F64 => Thunk::ConcatF64 {
                        dst,
                        outer: outer as u32,
                        inner: inner as u32,
                        total_axis: total_axis as u32,
                        inputs,
                    },
                    _ => Thunk::Concat {
                        dst,
                        outer: outer as u32,
                        inner: inner as u32,
                        total_axis: total_axis as u32,
                        inputs,
                    },
                }
            }

            Op::GaussianSplatRender {
                width,
                height,
                tile_size,
                radius_scale,
                alpha_cutoff,
                max_splat_steps,
                transmittance_threshold,
                max_list_entries,
            } => {
                let elem_len =
                    |id: NodeId| -> usize { graph.node(id).shape.num_elements().unwrap_or(0) };
                Thunk::GaussianSplatRender {
                    positions_off: node_offset(arena, node.inputs[0]),
                    positions_len: elem_len(node.inputs[0]),
                    scales_off: node_offset(arena, node.inputs[1]),
                    scales_len: elem_len(node.inputs[1]),
                    rotations_off: node_offset(arena, node.inputs[2]),
                    rotations_len: elem_len(node.inputs[2]),
                    opacities_off: node_offset(arena, node.inputs[3]),
                    opacities_len: elem_len(node.inputs[3]),
                    colors_off: node_offset(arena, node.inputs[4]),
                    colors_len: elem_len(node.inputs[4]),
                    sh_coeffs_off: node_offset(arena, node.inputs[5]),
                    sh_coeffs_len: elem_len(node.inputs[5]),
                    meta_off: node_offset(arena, node.inputs[6]),
                    dst_off: node_offset(arena, node.id),
                    dst_len: node.shape.num_elements().unwrap_or(0),
                    width: *width,
                    height: *height,
                    tile_size: *tile_size,
                    radius_scale: *radius_scale,
                    alpha_cutoff: *alpha_cutoff,
                    max_splat_steps: *max_splat_steps,
                    transmittance_threshold: *transmittance_threshold,
                    max_list_entries: *max_list_entries,
                }
            }

            Op::GaussianSplatRenderBackward {
                width,
                height,
                tile_size,
                radius_scale,
                alpha_cutoff,
                max_splat_steps,
                transmittance_threshold,
                max_list_entries,
                loss_grad_clip,
                sh_band,
                max_anisotropy,
            } => {
                let elem_len =
                    |id: NodeId| -> usize { graph.node(id).shape.num_elements().unwrap_or(0) };
                Thunk::GaussianSplatRenderBackward {
                    positions_off: node_offset(arena, node.inputs[0]),
                    positions_len: elem_len(node.inputs[0]),
                    scales_off: node_offset(arena, node.inputs[1]),
                    scales_len: elem_len(node.inputs[1]),
                    rotations_off: node_offset(arena, node.inputs[2]),
                    rotations_len: elem_len(node.inputs[2]),
                    opacities_off: node_offset(arena, node.inputs[3]),
                    opacities_len: elem_len(node.inputs[3]),
                    colors_off: node_offset(arena, node.inputs[4]),
                    colors_len: elem_len(node.inputs[4]),
                    sh_coeffs_off: node_offset(arena, node.inputs[5]),
                    sh_coeffs_len: elem_len(node.inputs[5]),
                    meta_off: node_offset(arena, node.inputs[6]),
                    d_loss_off: node_offset(arena, node.inputs[7]),
                    d_loss_len: elem_len(node.inputs[7]),
                    packed_off: node_offset(arena, node.id),
                    packed_len: node.shape.num_elements().unwrap_or(0),
                    width: *width,
                    height: *height,
                    tile_size: *tile_size,
                    radius_scale: *radius_scale,
                    alpha_cutoff: *alpha_cutoff,
                    max_splat_steps: *max_splat_steps,
                    transmittance_threshold: *transmittance_threshold,
                    max_list_entries: *max_list_entries,
                    loss_grad_clip: *loss_grad_clip,
                    sh_band: *sh_band,
                    max_anisotropy: *max_anisotropy,
                }
            }

            Op::GaussianSplatPrepare {
                width,
                height,
                tile_size,
                radius_scale,
                alpha_cutoff,
                max_splat_steps,
                transmittance_threshold,
                max_list_entries,
            } => {
                let elem_len =
                    |id: NodeId| -> usize { graph.node(id).shape.num_elements().unwrap_or(0) };
                Thunk::GaussianSplatPrepare {
                    positions_off: node_offset(arena, node.inputs[0]),
                    positions_len: elem_len(node.inputs[0]),
                    scales_off: node_offset(arena, node.inputs[1]),
                    scales_len: elem_len(node.inputs[1]),
                    rotations_off: node_offset(arena, node.inputs[2]),
                    rotations_len: elem_len(node.inputs[2]),
                    opacities_off: node_offset(arena, node.inputs[3]),
                    opacities_len: elem_len(node.inputs[3]),
                    colors_off: node_offset(arena, node.inputs[4]),
                    colors_len: elem_len(node.inputs[4]),
                    sh_coeffs_off: node_offset(arena, node.inputs[5]),
                    sh_coeffs_len: elem_len(node.inputs[5]),
                    meta_off: node_offset(arena, node.inputs[6]),
                    meta_len: elem_len(node.inputs[6]),
                    prep_off: node_offset(arena, node.id),
                    prep_len: node.shape.num_elements().unwrap_or(0),
                    width: *width,
                    height: *height,
                    tile_size: *tile_size,
                    radius_scale: *radius_scale,
                    alpha_cutoff: *alpha_cutoff,
                    max_splat_steps: *max_splat_steps,
                    transmittance_threshold: *transmittance_threshold,
                    max_list_entries: *max_list_entries,
                }
            }

            Op::GaussianSplatRasterize {
                width,
                height,
                tile_size,
                alpha_cutoff,
                max_splat_steps,
                transmittance_threshold,
                max_list_entries,
            } => {
                let elem_len =
                    |id: NodeId| -> usize { graph.node(id).shape.num_elements().unwrap_or(0) };
                let prep_id = node.inputs[0];
                let count = match &graph.node(prep_id).op {
                    rlx_ir::Op::GaussianSplatPrepare { .. } => {
                        elem_len(graph.node(prep_id).inputs[0]) / 3
                    }
                    _ => 1,
                };
                Thunk::GaussianSplatRasterize {
                    prep_off: node_offset(arena, prep_id),
                    prep_len: elem_len(prep_id),
                    meta_off: node_offset(arena, node.inputs[1]),
                    meta_len: elem_len(node.inputs[1]),
                    dst_off: node_offset(arena, node.id),
                    dst_len: node.shape.num_elements().unwrap_or(0),
                    count,
                    width: *width,
                    height: *height,
                    tile_size: *tile_size,
                    alpha_cutoff: *alpha_cutoff,
                    max_splat_steps: *max_splat_steps,
                    transmittance_threshold: *transmittance_threshold,
                    max_list_entries: *max_list_entries,
                }
            }

            Op::Custom { name, attrs, .. } => {
                let kernel = crate::op_registry::lookup_cpu_kernel(name).unwrap_or_else(|| {
                    panic!(
                        "compile_thunks: no CPU kernel registered for \
                         Op::Custom('{name}'). Register one via \
                         rlx_cpu::op_registry::register_cpu_kernel \
                         before compiling on the CPU backend."
                    )
                });
                let inputs_v: Vec<(usize, u32, Shape)> = node
                    .inputs
                    .iter()
                    .map(|&in_id| {
                        let s = graph.node(in_id).shape.clone();
                        let len = s.num_elements().unwrap_or(0) as u32;
                        (node_offset(arena, in_id), len, s)
                    })
                    .collect();
                let out_len = node.shape.num_elements().unwrap_or(0) as u32;
                Thunk::CustomOp {
                    kernel,
                    inputs: inputs_v,
                    output: (node_offset(arena, node.id), out_len, node.shape.clone()),
                    attrs: attrs.clone(),
                }
            }

            Op::Fft { inverse, norm } => {
                let shape = &node.shape;
                let meta = rlx_ir::fft::fft_meta(shape);
                let dtype = shape.dtype();
                assert!(
                    matches!(
                        dtype,
                        rlx_ir::DType::F32 | rlx_ir::DType::F64 | rlx_ir::DType::C64
                    ),
                    "Op::Fft on CPU requires F32, F64, or C64, got {dtype:?}"
                );
                Thunk::Fft1d {
                    src: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    outer: meta.outer as u32,
                    n_complex: meta.n_complex as u32,
                    inverse: *inverse,
                    norm_tag: norm.tag(),
                    dtype,
                }
            }

            Op::FftButterflyStage { stage, n_fft } => {
                let state_shape = graph.node(node.inputs[0]).shape.clone();
                assert_eq!(
                    state_shape.dtype(),
                    rlx_ir::DType::F32,
                    "Op::FftButterflyStage requires F32 state"
                );
                let batch = state_shape.dim(0).unwrap_static() as u32;
                Thunk::FftButterflyStage {
                    state_src: node_offset(arena, node.inputs[0]),
                    state_dst: node_offset(arena, node.id),
                    gate_src: node_offset(arena, node.inputs[1]),
                    rev_src: node_offset(arena, node.inputs[2]),
                    tw_re_src: node_offset(arena, node.inputs[3]),
                    tw_im_src: node_offset(arena, node.inputs[4]),
                    batch,
                    n_fft: *n_fft,
                    stage: *stage,
                }
            }

            Op::LogMel => {
                let spec_shape = graph.node(node.inputs[0]).shape.clone();
                let filt_shape = graph.node(node.inputs[1]).shape.clone();
                let meta = rlx_ir::audio::log_mel_meta(&spec_shape, &filt_shape)
                    .unwrap_or_else(|e| panic!("Op::LogMel: {e}"));
                Thunk::LogMel {
                    spec: node_offset(arena, node.inputs[0]),
                    filters: node_offset(arena, node.inputs[1]),
                    dst: node_offset(arena, node.id),
                    outer: meta.outer as u32,
                    n_fft: meta.n_fft as u32,
                    n_bins: meta.n_bins as u32,
                    n_mels: meta.n_mels as u32,
                }
            }

            Op::LogMelBackward => {
                let spec_shape = graph.node(node.inputs[0]).shape.clone();
                let filt_shape = graph.node(node.inputs[1]).shape.clone();
                let meta = rlx_ir::audio::log_mel_meta(&spec_shape, &filt_shape)
                    .unwrap_or_else(|e| panic!("Op::LogMelBackward: {e}"));
                Thunk::LogMelBackward {
                    spec: node_offset(arena, node.inputs[0]),
                    filters: node_offset(arena, node.inputs[1]),
                    dy: node_offset(arena, node.inputs[2]),
                    dst: node_offset(arena, node.id),
                    outer: meta.outer as u32,
                    n_fft: meta.n_fft as u32,
                    n_bins: meta.n_bins as u32,
                    n_mels: meta.n_mels as u32,
                }
            }

            Op::WelchPeaks { k, n_segments } => {
                let spec_shape = graph.node(node.inputs[0]).shape.clone();
                let meta = rlx_ir::audio::welch_peaks_meta(&spec_shape, *k, *n_segments)
                    .unwrap_or_else(|e| panic!("Op::WelchPeaks: {e}"));
                Thunk::WelchPeaks {
                    spec: node_offset(arena, node.inputs[0]),
                    dst: node_offset(arena, node.id),
                    welch_batch: meta.welch_batch as u32,
                    n_fft: meta.n_fft as u32,
                    n_segments: meta.n_segments as u32,
                    k: meta.k as u32,
                }
            }

            Op::CustomFn {
                fwd_body,
                num_inputs,
                ..
            } => {
                // Plan + compile the body sub-graph standalone, fill its
                // Constants (mirrors the Op::Scan body lowering), then
                // capture per-input copy specs and the output spec.
                // Body Inputs in NodeId order match the outer node's
                // operand vector by position.
                let body_plan = rlx_opt::memory::plan_memory(fwd_body);
                let body_offsets: HashMap<NodeId, usize> = body_plan
                    .assignments
                    .iter()
                    .map(|(id, slot)| (*id, slot.offset))
                    .collect();

                let mut body_input_ids: Vec<NodeId> = fwd_body
                    .nodes()
                    .iter()
                    .filter(|n| matches!(n.op, Op::Input { .. }))
                    .map(|n| n.id)
                    .collect();
                body_input_ids.sort();
                assert_eq!(
                    body_input_ids.len(),
                    *num_inputs as usize,
                    "Op::CustomFn fwd_body has {} Op::Input(s); declared num_inputs={}",
                    body_input_ids.len(),
                    *num_inputs,
                );

                let mut body_arena = crate::arena::Arena::from_plan(body_plan);
                for n in fwd_body.nodes() {
                    if let Op::Constant { data } = &n.op
                        && body_arena.has_buffer(n.id)
                        && !data.is_empty()
                    {
                        match n.shape.dtype() {
                            rlx_ir::DType::F64 => {
                                let off = body_arena.byte_offset(n.id);
                                let buf = body_arena.raw_buf_mut();
                                let nb = (buf.len() - off).min(data.len());
                                buf[off..off + nb].copy_from_slice(&data[..nb]);
                            }
                            _ => {
                                let buf = body_arena.slice_mut(n.id);
                                let nf = data.len() / 4;
                                let nl = buf.len().min(nf);
                                for i in 0..nl {
                                    let bytes = [
                                        data[i * 4],
                                        data[i * 4 + 1],
                                        data[i * 4 + 2],
                                        data[i * 4 + 3],
                                    ];
                                    buf[i] = f32::from_le_bytes(bytes);
                                }
                            }
                        }
                    }
                }
                let body_init = body_arena.raw_buf().to_vec();
                let body_schedule = compile_thunks_with_rng(fwd_body, &body_arena, rng);

                // Per primal input: (body_input_off, outer_input_off, bytes).
                let inputs_v: Vec<(usize, usize, u32)> = (0..*num_inputs as usize)
                    .map(|i| {
                        let body_in = body_input_ids[i];
                        let body_off = body_offsets[&body_in];
                        let outer_in = node.inputs[i];
                        let outer_off = node_offset(arena, outer_in);
                        let bytes = graph
                            .node(outer_in)
                            .shape
                            .size_bytes()
                            .expect("Op::CustomFn primal input must have static shape");
                        (body_off, outer_off, bytes as u32)
                    })
                    .collect();

                let body_output_id = fwd_body
                    .outputs
                    .first()
                    .copied()
                    .expect("Op::CustomFn fwd_body must declare exactly one output");
                let body_output_off = body_offsets[&body_output_id];
                let out_bytes = node
                    .shape
                    .size_bytes()
                    .expect("Op::CustomFn output must have static shape");

                Thunk::CustomFn {
                    body: Arc::new(body_schedule),
                    body_init: Arc::new(body_init),
                    inputs: Arc::new(inputs_v),
                    body_output_off,
                    outer_output_off: node_offset(arena, node.id),
                    out_bytes: out_bytes as u32,
                }
            }

            Op::ElementwiseRegion {
                chain,
                scalar_input_mask,
                input_modulus,
                prologue,
                ..
            } => {
                // The scalar interpreter handles plain chains; prologue (resize)
                // regions are GPU-only and never reach here on the CPU path
                // (the graphfused fusion options disable prologue/FK fusion).
                if *prologue != rlx_ir::op::RegionPrologue::None {
                    Thunk::Nop
                } else {
                    let input_offs: Vec<usize> = node
                        .inputs
                        .iter()
                        .map(|&id| node_offset(arena, id))
                        .collect();
                    Thunk::ElementwiseRegion {
                        dst: node_offset(arena, node.id),
                        len: node.shape.num_elements().unwrap_or(0) as u32,
                        input_offs,
                        chain: chain.clone(),
                        scalar_input_mask: *scalar_input_mask,
                        input_modulus: *input_modulus,
                    }
                }
            }
            _ => Thunk::Nop,
        };
        thunks.push(t);
    }

    let cfg = crate::config::RuntimeConfig::global();
    let mask_thr = cfg.mask_binary_threshold;
    let mask_neg = cfg.attn_mask_neg_inf;
    let score_skip = cfg.score_skip_threshold;

    // Pre-compile closures (skip Nops — they're filtered out)
    let compiled_fns: Vec<Arc<dyn Fn(*mut u8) + Send + Sync>> = thunks
        .iter()
        .filter(|t| !matches!(t, Thunk::Nop))
        .map(|thunk| {
            match thunk.clone() {
                Thunk::Nop => Arc::new(|_: *mut u8| {}) as Arc<dyn Fn(*mut u8) + Send + Sync>,

                Thunk::Sgemm { a, b, c, m, k, n } => {
                    let (m, k, n) = (m as usize, k as usize, n as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        crate::blas::sgemm(
                            sl(a, base, m * k),
                            sl(b, base, k * n),
                            sl_mut(c, base, m * n),
                            m,
                            k,
                            n,
                        );
                    })
                }

                Thunk::CgemmC64 { a, b, c, m, k, n } => {
                    let (m, k, n) = (m as usize, k as usize, n as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        cgemm_c64(a, b, c, m, k, n, base);
                    })
                }

                Thunk::DenseSolveF64 { a, b, x, n, nrhs } => {
                    let (n_, nrhs_) = (n as usize, nrhs as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        let a_src = sl_f64(a, base, n_ * n_);
                        let b_src = sl_f64(b, base, n_ * nrhs_);
                        let mut a_scratch: Vec<f64> = a_src.to_vec();
                        let mut x_buf: Vec<f64> = b_src.to_vec();
                        let info = crate::blas::dgesv(&mut a_scratch, &mut x_buf, n_, nrhs_);
                        if info != 0 {
                            panic!("DenseSolveF64: singular (info={info})");
                        }
                        sl_mut_f64(x, base, n_ * nrhs_).copy_from_slice(&x_buf);
                    })
                }

                Thunk::DenseSolveF32 { a, b, x, n, nrhs } => {
                    let (n_, nrhs_) = (n as usize, nrhs as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        let a_src = sl(a, base, n_ * n_);
                        let b_src = sl(b, base, n_ * nrhs_);
                        let mut a_scratch: Vec<f32> = a_src.to_vec();
                        let mut x_buf: Vec<f32> = b_src.to_vec();
                        let info = crate::blas::sgesv(&mut a_scratch, &mut x_buf, n_, nrhs_);
                        if info != 0 {
                            panic!("DenseSolveF32: singular (info={info})");
                        }
                        sl_mut(x, base, n_ * nrhs_).copy_from_slice(&x_buf);
                    })
                }

                Thunk::FusedMmBiasAct {
                    a,
                    w,
                    bias,
                    c,
                    m,
                    k,
                    n,
                    act,
                } => {
                    let (m, k, n) = (m as usize, k as usize, n as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        let out = sl_mut(c, base, m * n);
                        crate::blas::sgemm(sl(a, base, m * k), sl(w, base, k * n), out, m, k, n);
                        // Bias + activation epilogue. Gelu uses the fused
                        // `par_bias_gelu` kernel (bias add + Gelu in one
                        // pass). For everything else, do the bias add first
                        // and then apply the activation per-element. The
                        // pre-fix code dispatched `_ => bias_add` and dropped
                        // the activation entirely — silent correctness bug
                        // for Silu/Relu/Sigmoid/etc.
                        match act {
                            Some(Activation::Gelu) => {
                                crate::kernels::par_bias_gelu(out, sl(bias, base, n), m, n)
                            }
                            Some(other) => {
                                crate::blas::bias_add(out, sl(bias, base, n), m, n);
                                apply_activation_inplace(out, other);
                            }
                            None => crate::blas::bias_add(out, sl(bias, base, n), m, n),
                        }
                    })
                }

                Thunk::FusedResidualLN {
                    x,
                    res,
                    bias,
                    g,
                    b,
                    out,
                    rows,
                    h,
                    eps,
                    has_bias,
                } => {
                    let (rows, h) = (rows as usize, h as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        let zero = vec![0f32; h]; // closure only — not hot path
                        let bi = if has_bias { sl(bias, base, h) } else { &zero };
                        let xp = sl(x, base, rows * h).as_ptr() as usize;
                        let rp = sl(res, base, rows * h).as_ptr() as usize;
                        let op = sl_mut(out, base, rows * h).as_mut_ptr() as usize;
                        let bp = bi.as_ptr() as usize;
                        let gp = sl(g, base, h).as_ptr() as usize;
                        let bbp = sl(b, base, h).as_ptr() as usize;
                        crate::pool::par_for(rows, 4, &|off, cnt| {
                            let xs = std::slice::from_raw_parts(
                                (xp as *const f32).add(off * h),
                                cnt * h,
                            );
                            let rs = std::slice::from_raw_parts(
                                (rp as *const f32).add(off * h),
                                cnt * h,
                            );
                            let os = std::slice::from_raw_parts_mut(
                                (op as *mut f32).add(off * h),
                                cnt * h,
                            );
                            let bi = std::slice::from_raw_parts(bp as *const f32, h);
                            let g = std::slice::from_raw_parts(gp as *const f32, h);
                            let b = std::slice::from_raw_parts(bbp as *const f32, h);
                            crate::kernels::residual_bias_layer_norm(
                                xs, rs, bi, g, b, os, cnt, h, eps,
                            );
                        });
                    })
                }

                Thunk::BiasAdd {
                    src,
                    bias,
                    dst,
                    m,
                    n,
                } => {
                    let (m, n) = (m as usize, n as usize);
                    let len = m * n;
                    Arc::new(move |base: *mut u8| unsafe {
                        let out = sl_mut(dst, base, len);
                        if src != dst {
                            let src_ptr = base.add(src) as *const f32;
                            let dst_ptr = base.add(dst) as *mut f32;
                            if src_ptr != dst_ptr {
                                std::ptr::copy_nonoverlapping(src_ptr, dst_ptr, len);
                            }
                        }
                        crate::blas::bias_add(out, sl(bias, base, n), m, n);
                    })
                }

                Thunk::Gather {
                    table,
                    table_len,
                    idx,
                    dst,
                    num_idx,
                    trailing,
                    idx_i64,
                    table_bytes,
                } => {
                    let (ni, tr, tl) = (num_idx as usize, trailing as usize, table_len as usize);
                    let rows = tl / tr.max(1);
                    let (idx_i64, table_bytes) = (idx_i64, table_bytes);
                    Arc::new(move |base: *mut u8| unsafe {
                        if table_bytes == 8 {
                            let tab = sl_i64(table, base, tl);
                            let out = sl_mut_i64(dst, base, ni * tr);
                            if idx_i64 != 0 {
                                let ids = sl_i64(idx, base, ni);
                                for i in 0..ni {
                                    let row = ids[i].max(0) as usize;
                                    if row < rows {
                                        out[i * tr..(i + 1) * tr]
                                            .copy_from_slice(&tab[row * tr..(row + 1) * tr]);
                                    }
                                }
                            } else {
                                let ids = sl(idx, base, ni);
                                for i in 0..ni {
                                    let row = ids[i] as usize;
                                    if row < rows {
                                        out[i * tr..(i + 1) * tr]
                                            .copy_from_slice(&tab[row * tr..(row + 1) * tr]);
                                    }
                                }
                            }
                        } else {
                            let tab = sl(table, base, tl);
                            let out = sl_mut(dst, base, ni * tr);
                            if idx_i64 != 0 {
                                let ids = sl_i64(idx, base, ni);
                                for i in 0..ni {
                                    let row = ids[i].max(0) as usize;
                                    if row < rows {
                                        out[i * tr..(i + 1) * tr]
                                            .copy_from_slice(&tab[row * tr..(row + 1) * tr]);
                                    }
                                }
                            } else {
                                let ids = sl(idx, base, ni);
                                for i in 0..ni {
                                    let row = ids[i] as usize;
                                    if row < rows {
                                        out[i * tr..(i + 1) * tr]
                                            .copy_from_slice(&tab[row * tr..(row + 1) * tr]);
                                    }
                                }
                            }
                        }
                    })
                }

                Thunk::Narrow {
                    src,
                    dst,
                    outer,
                    src_stride,
                    dst_stride,
                    inner,
                    elem_bytes,
                } => {
                    narrow_thunk_closure(src, dst, outer, src_stride, dst_stride, inner, elem_bytes)
                }

                Thunk::Reverse {
                    src,
                    dst,
                    dims,
                    rev_mask,
                    elem_bytes,
                } => {
                    let eb = elem_bytes as usize;
                    let rank = dims.len();
                    let total: usize = dims.iter().map(|&d| d as usize).product::<usize>().max(1);
                    // Row-major element strides.
                    let mut strides = vec![1usize; rank];
                    for i in (0..rank.saturating_sub(1)).rev() {
                        strides[i] = strides[i + 1] * dims[i + 1] as usize;
                    }
                    let dims_u: Vec<usize> = dims.iter().map(|&d| d as usize).collect();
                    Arc::new(move |base: *mut u8| unsafe {
                        let src_base = base.add(src);
                        let dst_base = base.add(dst);
                        for o in 0..total {
                            // Output flat index → multi-index → (axis-reversed)
                            // input flat index.
                            let mut rem = o;
                            let mut in_flat = 0usize;
                            for ax in 0..rank {
                                let idx = rem / strides[ax];
                                rem %= strides[ax];
                                let in_idx = if rev_mask[ax] {
                                    dims_u[ax] - 1 - idx
                                } else {
                                    idx
                                };
                                in_flat += in_idx * strides[ax];
                            }
                            std::ptr::copy_nonoverlapping(
                                src_base.add(in_flat * eb),
                                dst_base.add(o * eb),
                                eb,
                            );
                        }
                    })
                }

                Thunk::Copy { src, dst, len } => {
                    let len = len as usize;
                    Arc::new(move |base: *mut u8| unsafe {
                        if src == dst || len == 0 {
                            return;
                        }
                        let src_ptr = base.add(src) as *const f32;
                        let dst_ptr = base.add(dst) as *mut f32;
                        if src_ptr == dst_ptr {
                            return;
                        }
                        std::ptr::copy_nonoverlapping(src_ptr, dst_ptr, len);
                    })
                }

                Thunk::Softmax { data, rows, cols } => {
                    let (rows, cols) = (rows as usize, cols as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        crate::naive::softmax(sl_mut(data, base, rows * cols), rows, cols);
                    })
                }

                Thunk::Cumsum {
                    src,
                    dst,
                    rows,
                    cols,
                    exclusive,
                } => {
                    let (rows, cols) = (rows as usize, cols as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        let s = sl(src, base, rows * cols);
                        let d = sl_mut(dst, base, rows * cols);
                        if exclusive {
                            for r in 0..rows {
                                let mut acc = 0.0f32;
                                for c in 0..cols {
                                    d[r * cols + c] = acc;
                                    acc += s[r * cols + c];
                                }
                            }
                        } else {
                            for r in 0..rows {
                                let mut acc = 0.0f32;
                                for c in 0..cols {
                                    acc += s[r * cols + c];
                                    d[r * cols + c] = acc;
                                }
                            }
                        }
                    })
                }

                Thunk::Sample {
                    logits,
                    dst,
                    batch,
                    vocab,
                    top_k,
                    top_p,
                    temperature,
                    seed,
                } => {
                    let (b, v) = (batch as usize, vocab as usize);
                    let k = (top_k as usize).min(v);
                    Arc::new(move |base: *mut u8| unsafe {
                        let lg = sl(logits, base, b * v);
                        let out = sl_mut(dst, base, b);
                        let mut rng =
                            rlx_ir::Philox4x32::new(if seed == 0 { 0xDEADBEEF } else { seed });
                        for bi in 0..b {
                            let row = &lg[bi * v..(bi + 1) * v];
                            out[bi] = sample_row(row, k, top_p, temperature, &mut rng) as f32;
                        }
                    })
                }

                Thunk::RngNormal {
                    dst,
                    len,
                    mean,
                    scale,
                    key,
                    op_seed,
                } => {
                    let n = len as usize;
                    let rng = rng_shared.clone();
                    Arc::new(move |base: *mut u8| unsafe {
                        let out = sl_mut(dst, base, n);
                        let opts = *rng.read().unwrap();
                        rlx_ir::fill_normal_like(out, mean, scale, opts, key, op_seed);
                    })
                }

                Thunk::RngUniform {
                    dst,
                    len,
                    low,
                    high,
                    key,
                    op_seed,
                } => {
                    let n = len as usize;
                    let rng = rng_shared.clone();
                    Arc::new(move |base: *mut u8| unsafe {
                        let out = sl_mut(dst, base, n);
                        let opts = *rng.read().unwrap();
                        rlx_ir::fill_uniform_like(out, low, high, opts, key, op_seed);
                    })
                }

                Thunk::DequantMatMul {
                    x,
                    w_q,
                    scale,
                    zp,
                    dst,
                    m,
                    k,
                    n,
                    block_size,
                    is_asymmetric,
                } => {
                    let (m, k, n, bs) = (m as usize, k as usize, n as usize, block_size as usize);
                    let n_blocks_per_col = k.div_ceil(bs);
                    Arc::new(move |base: *mut u8| unsafe {
                        let xs = sl(x, base, m * k);
                        // w_q is packed i8 — use raw byte slice + reinterpret.
                        let raw = base.add(w_q);
                        let w_bytes = std::slice::from_raw_parts(raw as *const i8, k * n);
                        let scales = sl(scale, base, n_blocks_per_col * n);
                        let zps = if is_asymmetric {
                            sl(zp, base, n_blocks_per_col * n)
                        } else {
                            &[][..]
                        };
                        let out = sl_mut(dst, base, m * n);
                        dequant_matmul_int8(
                            xs,
                            w_bytes,
                            scales,
                            zps,
                            out,
                            m,
                            k,
                            n,
                            bs,
                            is_asymmetric,
                        );
                    })
                }

                Thunk::DequantMatMulGguf {
                    x,
                    w_q,
                    dst,
                    m,
                    k,
                    n,
                    scheme,
                } => {
                    let (m, k, n) = (m as usize, k as usize, n as usize);
                    let block_bytes = scheme.gguf_block_bytes() as usize;
                    let block_elems = scheme.gguf_block_size() as usize;
                    let total_bytes = (k * n) / block_elems * block_bytes;
                    Arc::new(move |base: *mut u8| unsafe {
                        let xs = sl(x, base, m * k);
                        let w_bytes =
                            std::slice::from_raw_parts(base.add(w_q) as *const u8, total_bytes);
                        let out = sl_mut(dst, base, m * n);
                        crate::gguf_matmul::gguf_matmul_bt(xs, w_bytes, out, m, k, n, scheme);
                    })
                }

                Thunk::DequantMatMulInt4 {
                    x,
                    w_q,
                    scale,
                    zp,
                    dst,
                    m,
                    k,
                    n,
                    block_size,
                    is_asymmetric,
                } => {
                    let (m, k, n, bs) = (m as usize, k as usize, n as usize, block_size as usize);
                    let n_blocks = k.div_ceil(bs);
                    Arc::new(move |base: *mut u8| unsafe {
                        let xs = sl(x, base, m * k);
                        let w_bytes = std::slice::from_raw_parts(
                            base.add(w_q) as *const u8,
                            (k * n).div_ceil(2),
                        );
                        let scales = sl(scale, base, n_blocks * n);
                        let zps = if is_asymmetric {
                            sl(zp, base, n_blocks * n)
                        } else {
                            &[][..]
                        };
                        let out = sl_mut(dst, base, m * n);
                        dequant_matmul_int4(
                            xs,
                            w_bytes,
                            scales,
                            zps,
                            out,
                            m,
                            k,
                            n,
                            bs,
                            is_asymmetric,
                        );
                    })
                }

                Thunk::DequantMatMulFp8 {
                    x,
                    w_q,
                    scale,
                    dst,
                    m,
                    k,
                    n,
                    e5m2,
                } => {
                    let (m, k, n) = (m as usize, k as usize, n as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        let xs = sl(x, base, m * k);
                        let w_bytes = std::slice::from_raw_parts(base.add(w_q) as *const u8, k * n);
                        let scales = sl(scale, base, n);
                        let out = sl_mut(dst, base, m * n);
                        dequant_matmul_fp8(xs, w_bytes, scales, out, m, k, n, e5m2);
                    })
                }

                Thunk::DequantMatMulNvfp4 {
                    x,
                    w_q,
                    scale,
                    global_scale,
                    dst,
                    m,
                    k,
                    n,
                } => {
                    let (m, k, n) = (m as usize, k as usize, n as usize);
                    let n_scale = k.div_ceil(rlx_ir::NVFP4_GROUP_SIZE) * n;
                    Arc::new(move |base: *mut u8| unsafe {
                        let xs = sl(x, base, m * k);
                        let w_bytes = std::slice::from_raw_parts(
                            base.add(w_q) as *const u8,
                            (k * n).div_ceil(2),
                        );
                        let scale_bytes =
                            std::slice::from_raw_parts(base.add(scale) as *const u8, n_scale);
                        let gs = sl(global_scale, base, 1)[0];
                        let out = sl_mut(dst, base, m * n);
                        dequant_matmul_nvfp4(xs, w_bytes, scale_bytes, gs, out, m, k, n);
                    })
                }

                Thunk::ScaledMatMul {
                    lhs,
                    rhs,
                    lhs_scale,
                    rhs_scale,
                    bias,
                    dst,
                    m,
                    k,
                    n,
                    lhs_fmt,
                    rhs_fmt,
                    layout,
                    has_bias,
                } => {
                    let (m, k, n) = (m as usize, k as usize, n as usize);
                    let nblk = lowp_nblk(k, layout);
                    let per_tensor = matches!(layout, rlx_ir::ScaleLayout::PerTensor);
                    let n_lscale = if per_tensor { 1 } else { m * nblk };
                    let n_rscale = if per_tensor { 1 } else { n * nblk };
                    Arc::new(move |base: *mut u8| unsafe {
                        let lhs_b = std::slice::from_raw_parts(base.add(lhs) as *const u8, m * k);
                        let rhs_b = std::slice::from_raw_parts(base.add(rhs) as *const u8, n * k);
                        let ls = lowp_read_scales(layout, base, lhs_scale, n_lscale);
                        let rs = lowp_read_scales(layout, base, rhs_scale, n_rscale);
                        let bias_s = if has_bias { Some(sl(bias, base, n)) } else { None };
                        let out = sl_mut(dst, base, m * n);
                        lowp_scaled_matmul(
                            lhs_b, rhs_b, &ls, &rs, bias_s, out, m, n, k, layout, lhs_fmt, rhs_fmt,
                        );
                    })
                }

                Thunk::ScaledQuantize {
                    x,
                    scale,
                    dst,
                    rows,
                    cols,
                    fmt,
                    layout,
                } => {
                    let (rows, cols) = (rows as usize, cols as usize);
                    let nblk = lowp_nblk(cols, layout);
                    let n_scale = if matches!(layout, rlx_ir::ScaleLayout::PerTensor) {
                        1
                    } else {
                        rows * nblk
                    };
                    Arc::new(move |base: *mut u8| unsafe {
                        let xs = sl(x, base, rows * cols);
                        let scales = lowp_read_scales(layout, base, scale, n_scale);
                        let out =
                            std::slice::from_raw_parts_mut(base.add(dst), rows * cols);
                        lowp_quantize(xs, &scales, fmt, layout, rows, cols, out);
                    })
                }

                Thunk::ScaledQuantScale {
                    x,
                    dst,
                    rows,
                    cols,
                    fmt,
                    layout,
                } => {
                    let (rows, cols) = (rows as usize, cols as usize);
                    let nblk = lowp_nblk(cols, layout);
                    Arc::new(move |base: *mut u8| unsafe {
                        let xs = sl(x, base, rows * cols);
                        let scales = lowp_compute_scales(xs, fmt, layout, rows, cols);
                        match layout {
                            rlx_ir::ScaleLayout::PerTensor => {
                                sl_mut(dst, base, 1)[0] = scales[0];
                            }
                            rlx_ir::ScaleLayout::BlockMxE8M0 { .. } => {
                                let out = std::slice::from_raw_parts_mut(
                                    base.add(dst),
                                    rows * nblk,
                                );
                                for (o, &s) in out.iter_mut().zip(&scales) {
                                    *o = rlx_ir::lowp_codec::f32_to_e8m0(s);
                                }
                            }
                            rlx_ir::ScaleLayout::Nvfp4 { .. } => {
                                let out = std::slice::from_raw_parts_mut(
                                    base.add(dst),
                                    rows * nblk,
                                );
                                for (o, &s) in out.iter_mut().zip(&scales) {
                                    *o = rlx_ir::lowp_codec::encode(rlx_ir::ScaledFormat::F8E4M3, s);
                                }
                            }
                        }
                    })
                }

                Thunk::ScaledDequantize {
                    codes,
                    scale,
                    dst,
                    rows,
                    cols,
                    fmt,
                    layout,
                } => {
                    let (rows, cols) = (rows as usize, cols as usize);
                    let nblk = lowp_nblk(cols, layout);
                    let n_scale = if matches!(layout, rlx_ir::ScaleLayout::PerTensor) {
                        1
                    } else {
                        rows * nblk
                    };
                    Arc::new(move |base: *mut u8| unsafe {
                        let cs = std::slice::from_raw_parts(base.add(codes) as *const u8, rows * cols);
                        let scales = lowp_read_scales(layout, base, scale, n_scale);
                        let out = sl_mut(dst, base, rows * cols);
                        lowp_dequantize(cs, &scales, fmt, layout, rows, cols, out);
                    })
                }

                Thunk::LoraMatMul {
                    x,
                    w,
                    a,
                    b,
                    dst,
                    m,
                    k,
                    n,
                    r,
                    scale,
                } => {
                    let (m, k, n, r) = (m as usize, k as usize, n as usize, r as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        let xs = sl(x, base, m * k);
                        let ws = sl(w, base, k * n);
                        let a_s = sl(a, base, k * r);
                        let bs = sl(b, base, r * n);
                        let out = sl_mut(dst, base, m * n);
                        // Step 1: out = x · W.
                        crate::blas::sgemm(xs, ws, out, m, k, n);
                        // Step 2: tmp = x · A (rank-r intermediate; tiny).
                        let mut tmp = vec![0f32; m * r];
                        crate::blas::sgemm(xs, a_s, &mut tmp, m, k, r);
                        // Step 3: out += scale * (tmp · B).
                        // sgemm_accumulate uses alpha=1.0 internally, so
                        // scale tmp first.
                        if scale != 1.0 {
                            for v in tmp.iter_mut() {
                                *v *= scale;
                            }
                        }
                        crate::blas::sgemm_accumulate(&tmp, bs, out, m, r, n);
                    })
                }

                Thunk::LayerNorm {
                    src,
                    g,
                    b,
                    dst,
                    rows,
                    h,
                    eps,
                } => {
                    let (rows, h) = (rows as usize, h as usize);
                    Arc::new(move |base: *mut u8| unsafe {
                        let inp = sl(src, base, rows * h);
                        let gamma = sl(g, base, h);
                        let beta = sl(b, base, h);
                        let out = sl_mut(dst, base, rows * h);
                        for row in 0..rows {
                            crate::kernels::layer_norm_row(
                                &inp[row * h..(row + 1) * h],
                                gamma,
                                beta,
                                &mut out[row * h..(row + 1) * h],
                                h,
                                eps,
                            );
                        }
                    })
                }

                Thunk::BatchNormInference {
                    src,
                    g,
                    b,
                    mean,
                    var,
                    dst,
                    count,
                    channels,
                    eps,
                } => {
                    let count = count as usize;
                    let c = channels as usize;
                    let n = count * c;
                    let (src, g, b, mean, var, dst) = (src, g, b, mean, var, dst);
                    Arc::new(move |base: *mut u8| unsafe {
                        crate::kernels::batch_norm_inference(
                            sl(src, base, n),
                            sl(g, base, c),
                            sl(b, base, c),
                            sl(mean, base, c),
                            sl(var, base, c),
                            sl_mut(dst, base, n),
                            c,
                            eps,
                        );
                    })
                }

                Thunk::Attention {
                    q,
                    k,
                    v,
                    mask,
                    out,
                    batch,
                    seq,
                    kv_seq,
                    heads,
                    head_dim,
                    mask_kind,
                    scale,
                    q_row_stride,
                    k_row_stride,
                    v_row_stride,
                    bhsd,
                } => {
                    if std::env::var("RLX_ATTN_DEBUG").is_ok() {
                        eprintln!("[attn-compile] batch={batch} seq={seq} kv_seq={kv_seq} heads={heads} bhsd={bhsd}");
                    }
                    // Q seq length (`q_s`) and K/V seq length (`k_s`) differ
                    // during cached decode (`q_s=1`, `k_s=past_seq+1`). The
                    // earlier version of this kernel destructured
                    // `kv_seq: _` and used a single `s = seq` for both axes,
                    // so cached decode only scored 1×1 instead of 1×k_s —
                    // attention couldn't see the past K cache and decode
                    // collapsed into repetitive fragments
                    // (`Self-based on [1\nAnswer: Self-based on [1…`).
                    let (b, q_s, k_s, nh, dh) = (
                        batch as usize,
                        seq as usize,
                        kv_seq as usize,
                        heads as usize,
                        head_dim as usize,
                    );
                    let hs = nh * dh;
                    let qrs = q_row_stride as usize;
                    let krs = k_row_stride as usize;
                    let vrs = v_row_stride as usize;
                    // honor Op::Attention::score_scale (e.g. Gemma 4 = 1.0)
                    Arc::new(move |base: *mut u8| unsafe {
                        if std::env::var("RLX_ATTN_DEBUG").is_ok() {
                            eprintln!("[attn] b={b} q_s={q_s} k_s={k_s} nh={nh} dh={dh} bhsd={bhsd} mask_kind={:?}", mask_kind);
                        }
                        // Slice lengths use the source's row stride so the
                        // compiler-emitted bounds checks cover the whole
                        // strided span (the kernel walks with q/k/v_rs).
                        // For [B, H, S, D] the buffer is dense B*H*S*D.
                        let (q_len, k_len, v_len, o_len) = if bhsd {
                            let qn = b * nh * q_s * dh;
                            let kn = b * nh * k_s * dh;
                            (qn, kn, kn, qn)
                        } else {
                            (b * q_s * qrs, b * k_s * krs, b * k_s * vrs, b * q_s * hs)
                        };
                        let q_d = sl(q, base, q_len);
                        let k_d = sl(k, base, k_len);
                        let v_d = sl(v, base, v_len);
                        let m_d: &[f32] = match mask_kind {
                            rlx_ir::op::MaskKind::Custom => sl(mask, base, b * k_s),
                            rlx_ir::op::MaskKind::Bias => sl(mask, base, b * nh * q_s * k_s),
                            _ => &[],
                        };
                        let o_d = sl_mut(out, base, o_len);
                        let mut qh = vec![0f32; q_s * dh];
                        let mut kh = vec![0f32; k_s * dh];
                        let mut vh = vec![0f32; k_s * dh];
                        let mut sc = vec![0f32; q_s * k_s];
                        let mut oh = vec![0f32; q_s * dh];
                        for bi in 0..b {
                            for hi in 0..nh {
                                // Gather per-head Q.
                                for si in 0..q_s {
                                    let q_off = if bhsd {
                                        bi * nh * q_s * dh + hi * q_s * dh + si * dh
                                    } else {
                                        bi * q_s * qrs + si * qrs + hi * dh
                                    };
                                    qh[si * dh..(si + 1) * dh]
                                        .copy_from_slice(&q_d[q_off..q_off + dh]);
                                }
                                // Gather per-head K, V.
                                for si in 0..k_s {
                                    let (k_off, v_off) = if bhsd {
                                        (
                                            bi * nh * k_s * dh + hi * k_s * dh + si * dh,
                                            bi * nh * k_s * dh + hi * k_s * dh + si * dh,
                                        )
                                    } else {
                                        (
                                            bi * k_s * krs + si * krs + hi * dh,
                                            bi * k_s * vrs + si * vrs + hi * dh,
                                        )
                                    };
                                    kh[si * dh..(si + 1) * dh]
                                        .copy_from_slice(&k_d[k_off..k_off + dh]);
                                    vh[si * dh..(si + 1) * dh]
                                        .copy_from_slice(&v_d[v_off..v_off + dh]);
                                }
                                for qi in 0..q_s {
                                    for ki in 0..k_s {
                                        let mut dot = 0f32;
                                        for d in 0..dh {
                                            dot += qh[qi * dh + d] * kh[ki * dh + d];
                                        }
                                        sc[qi * k_s + ki] = dot * scale;
                                    }
                                }
                                // Apply mask. Causal/SlidingWindow use absolute
                                // positions so they handle Lq != Lk (decode mode
                                // with cached K/V): q_offset = k_s - q_s.
                                let q_offset = k_s.saturating_sub(q_s);
                                match mask_kind {
                                    rlx_ir::op::MaskKind::None => {}
                                    rlx_ir::op::MaskKind::Causal => {
                                        for qi in 0..q_s {
                                            let abs_q = q_offset + qi;
                                            for ki in (abs_q + 1)..k_s {
                                                sc[qi * k_s + ki] = mask_neg;
                                            }
                                        }
                                    }
                                    rlx_ir::op::MaskKind::SlidingWindow(w) => {
                                        for qi in 0..q_s {
                                            let abs_q = q_offset + qi;
                                            let lo = abs_q.saturating_sub(w);
                                            for ki in 0..k_s {
                                                if ki < lo || ki > abs_q {
                                                    sc[qi * k_s + ki] = mask_neg;
                                                }
                                            }
                                        }
                                    }
                                    rlx_ir::op::MaskKind::Custom => {
                                        for qi in 0..q_s {
                                            for ki in 0..k_s {
                                                if m_d[bi * k_s + ki] < mask_thr {
                                                    sc[qi * k_s + ki] = mask_neg;
                                                }
                                            }
                                        }
                                    }
                                    rlx_ir::op::MaskKind::Bias => {
                                        let per_bh = q_s * k_s;
                                        let off = (bi * nh + hi) * per_bh;
                                        for i in 0..per_bh {
                                            sc[i] += m_d[off + i];
                                        }
                                    }
                                }
                                crate::naive::softmax(&mut sc, q_s, k_s);
                                oh.fill(0.0);
                                for qi in 0..q_s {
                                    for ki in 0..k_s {
                                        let w = sc[qi * k_s + ki];
                                        if w > score_skip {
                                            for d in 0..dh {
                                                oh[qi * dh + d] += w * vh[ki * dh + d];
                                            }
                                        }
                                    }
                                }
                                for si in 0..q_s {
                                    let off = if bhsd {
                                        bi * nh * q_s * dh + hi * q_s * dh + si * dh
                                    } else {
                                        bi * q_s * hs + si * hs + hi * dh
                                    };
                                    o_d[off..off + dh].copy_from_slice(&oh[si * dh..(si + 1) * dh]);
                                }
                            }
                        }
                    })
                }

                Thunk::FusedSwiGLU {
                    src,
                    dst,
                    n_half,
                    total,
                    gate_first,
                } => {
                    let n = n_half as usize;
                    let t = total as usize;
                    let outer = t / n;
                    let in_total = outer * 2 * n;
                    Arc::new(move |base: *mut u8| unsafe {
                        let inp = sl(src, base, in_total);
                        let out = sl_mut(dst, base, t);
                        for o in 0..outer {
                            let in_row = &inp[o * 2 * n..(o + 1) * 2 * n];
                            let out_row = &mut out[o * n..(o + 1) * n];
                            for i in 0..n {
                                let (up, gate) = if gate_first {
                                    (in_row[n + i], in_row[i])
                                } else {
                                    (in_row[i], in_row[n + i])
                                };
                                out_row[i] = up * (gate / (1.0 + (-gate).exp()));
                            }
                        }
                    })
                }

                Thunk::Concat {
                    dst,
                    outer,
                    inner,
                    total_axis,
                    inputs,
                } => {
                    let outer = outer as usize;
                    let inner = inner as usize;
                    let total_axis = total_axis as usize;
                    let out_total = outer * total_axis * inner;
                    let mut layout: Vec<(usize, usize, usize, usize)> =
                        Vec::with_capacity(inputs.len());
                    let mut cum: usize = 0;
                    for (src_off, in_axis, in_numel) in &inputs {
                        let in_axis = *in_axis as usize;
                        layout.push((*src_off, cum * inner, in_axis * inner, *in_numel as usize));
                        cum += in_axis;
                    }
                    Arc::new(move |base: *mut u8| unsafe {
                        let out = sl_mut(dst, base, out_total);
                        let row_stride = total_axis * inner;
                        for (src_off, dst_col_off, copy_per_row, in_numel) in &layout {
                            let inp = sl(*src_off, base, (*in_numel).max(1));
                            concat_copy_rows_f32(
                                out,
                                inp,
                                outer,
                                *copy_per_row,
                                row_stride,
                                *dst_col_off,
                                *in_numel,
                            );
                        }
                    })
                }

                Thunk::CustomOp {
                    kernel,
                    inputs,
                    output,
                    attrs,
                } => {
                    // Capture-by-move: clone the Arc and Vecs once into the
                    // closure. Dispatch by output dtype each call (the
                    // dtype is fixed at compile time but it's cheaper to
                    // branch once per execution than to monomorphize a
                    // dozen closure variants).
                    let kernel = kernel.clone();
                    let attrs = attrs.clone();
                    let inputs = inputs.clone();
                    let (out_off, out_len, out_shape) = output.clone();
                    Arc::new(move |base: *mut u8| unsafe {
                        dispatch_custom_op(
                            &*kernel, &inputs, out_off, out_len, &out_shape, &attrs, base,
                        );
                    })
                }

                Thunk::GaussianSplatRender {
                    positions_off,
                    positions_len,
                    scales_off,
                    scales_len,
                    rotations_off,
                    rotations_len,
                    opacities_off,
                    opacities_len,
                    colors_off,
                    colors_len,
                    sh_coeffs_off,
                    sh_coeffs_len,
                    meta_off,
                    dst_off,
                    dst_len,
                    width,
                    height,
                    tile_size,
                    radius_scale,
                    alpha_cutoff,
                    max_splat_steps,
                    transmittance_threshold,
                    max_list_entries,
                } => Arc::new(move |base: *mut u8| unsafe {
                    crate::splat::execute_gaussian_splat_render(
                        positions_off,
                        positions_len,
                        scales_off,
                        scales_len,
                        rotations_off,
                        rotations_len,
                        opacities_off,
                        opacities_len,
                        colors_off,
                        colors_len,
                        sh_coeffs_off,
                        sh_coeffs_len,
                        meta_off,
                        dst_off,
                        dst_len,
                        width,
                        height,
                        tile_size,
                        radius_scale,
                        alpha_cutoff,
                        max_splat_steps,
                        transmittance_threshold,
                        max_list_entries,
                        base,
                    );
                }),

                Thunk::GaussianSplatRenderBackward {
                    positions_off,
                    positions_len,
                    scales_off,
                    scales_len,
                    rotations_off,
                    rotations_len,
                    opacities_off,
                    opacities_len,
                    colors_off,
                    colors_len,
                    sh_coeffs_off,
                    sh_coeffs_len,
                    meta_off,
                    d_loss_off,
                    d_loss_len,
                    packed_off,
                    packed_len,
                    width,
                    height,
                    tile_size,
                    radius_scale,
                    alpha_cutoff,
                    max_splat_steps,
                    transmittance_threshold,
                    max_list_entries,
                    loss_grad_clip,
                    sh_band,
                    max_anisotropy,
                } => Arc::new(move |base: *mut u8| unsafe {
                    crate::splat::execute_gaussian_splat_render_backward(
                        positions_off,
                        positions_len,
                        scales_off,
                        scales_len,
                        rotations_off,
                        rotations_len,
                        opacities_off,
                        opacities_len,
                        colors_off,
                        colors_len,
                        sh_coeffs_off,
                        sh_coeffs_len,
                        meta_off,
                        d_loss_off,
                        d_loss_len,
                        packed_off,
                        packed_len,
                        width,
                        height,
                        tile_size,
                        radius_scale,
                        alpha_cutoff,
                        max_splat_steps,
                        transmittance_threshold,
                        max_list_entries,
                        loss_grad_clip,
                        sh_band,
                        max_anisotropy,
                        base,
                    );
                }),

                Thunk::GaussianSplatPrepare {
                    positions_off,
                    positions_len,
                    scales_off,
                    scales_len,
                    rotations_off,
                    rotations_len,
                    opacities_off,
                    opacities_len,
                    colors_off,
                    colors_len,
                    sh_coeffs_off,
                    sh_coeffs_len,
                    meta_off,
                    meta_len,
                    prep_off,
                    prep_len,
                    width,
                    height,
                    tile_size,
                    radius_scale,
                    alpha_cutoff,
                    max_splat_steps,
                    transmittance_threshold,
                    max_list_entries,
                } => Arc::new(move |base: *mut u8| unsafe {
                    crate::splat::execute_gaussian_splat_prepare(
                        positions_off,
                        positions_len,
                        scales_off,
                        scales_len,
                        rotations_off,
                        rotations_len,
                        opacities_off,
                        opacities_len,
                        colors_off,
                        colors_len,
                        sh_coeffs_off,
                        sh_coeffs_len,
                        meta_off,
                        meta_len,
                        prep_off,
                        prep_len,
                        width,
                        height,
                        tile_size,
                        radius_scale,
                        alpha_cutoff,
                        max_splat_steps,
                        transmittance_threshold,
                        max_list_entries,
                        base,
                    );
                }),

                Thunk::GaussianSplatRasterize {
                    prep_off,
                    prep_len,
                    meta_off,
                    meta_len,
                    dst_off,
                    dst_len,
                    count,
                    width,
                    height,
                    tile_size,
                    alpha_cutoff,
                    max_splat_steps,
                    transmittance_threshold,
                    max_list_entries,
                } => Arc::new(move |base: *mut u8| unsafe {
                    crate::splat::execute_gaussian_splat_rasterize(
                        prep_off,
                        prep_len,
                        meta_off,
                        meta_len,
                        dst_off,
                        dst_len,
                        count,
                        width,
                        height,
                        tile_size,
                        alpha_cutoff,
                        max_splat_steps,
                        transmittance_threshold,
                        max_list_entries,
                        base,
                    );
                }),

                Thunk::Fft1d {
                    src,
                    dst,
                    outer,
                    n_complex,
                    inverse,
                    norm_tag,
                    dtype,
                } => {
                    let f: Arc<dyn Fn(*mut u8) + Send + Sync> = match dtype {
                        rlx_ir::DType::F64 => Arc::new(move |base: *mut u8| unsafe {
                            execute_fft1d_f64(
                                src,
                                dst,
                                outer as usize,
                                n_complex as usize,
                                inverse,
                                norm_tag,
                                base,
                            );
                        }),
                        rlx_ir::DType::F32 => Arc::new(move |base: *mut u8| unsafe {
                            execute_fft1d_f32(
                                src,
                                dst,
                                outer as usize,
                                n_complex as usize,
                                inverse,
                                norm_tag,
                                base,
                            );
                        }),
                        rlx_ir::DType::C64 => Arc::new(move |base: *mut u8| unsafe {
                            execute_fft1d_c64(
                                src,
                                dst,
                                outer as usize,
                                n_complex as usize,
                                inverse,
                                norm_tag,
                                base,
                            );
                        }),
                        other => panic!("Op::Fft on CPU requires F32/F64/C64, got {other:?}"),
                    };
                    f
                }

                Thunk::FftButterflyStage {
                    state_src,
                    state_dst,
                    gate_src,
                    rev_src,
                    tw_re_src,
                    tw_im_src,
                    batch,
                    n_fft,
                    stage,
                } => Arc::new(move |base: *mut u8| unsafe {
                    execute_fft_butterfly_stage_f32(
                        state_src,
                        state_dst,
                        gate_src,
                        rev_src,
                        tw_re_src,
                        tw_im_src,
                        batch as usize,
                        n_fft as usize,
                        stage as usize,
                        base,
                    );
                }),

                Thunk::LogMel {
                    spec,
                    filters,
                    dst,
                    outer,
                    n_fft,
                    n_bins,
                    n_mels,
                } => Arc::new(move |base: *mut u8| unsafe {
                    execute_log_mel_f32(
                        spec,
                        filters,
                        dst,
                        outer as usize,
                        n_fft as usize,
                        n_bins as usize,
                        n_mels as usize,
                        base,
                    );
                }),

                Thunk::LogMelBackward {
                    spec,
                    filters,
                    dy,
                    dst,
                    outer,
                    n_fft,
                    n_bins,
                    n_mels,
                } => Arc::new(move |base: *mut u8| unsafe {
                    execute_log_mel_backward_f32(
                        spec,
                        filters,
                        dy,
                        dst,
                        outer as usize,
                        n_fft as usize,
                        n_bins as usize,
                        n_mels as usize,
                        base,
                    );
                }),

                Thunk::WelchPeaks {
                    spec,
                    dst,
                    welch_batch,
                    n_fft,
                    n_segments,
                    k,
                } => Arc::new(move |base: *mut u8| unsafe {
                    execute_welch_peaks_f32(
                        spec,
                        dst,
                        welch_batch as usize,
                        n_fft as usize,
                        n_segments as usize,
                        k as usize,
                        base,
                    );
                }),

                Thunk::SgdMomentum { param, vel, grad, p_out, v_out, lr, mom, len } => {
                    let len = len as usize;
                    Arc::new(move |base: *mut u8| unsafe {
                        let p = sl(param, base, len);
                        let v = sl(vel, base, len);
                        let g = sl(grad, base, len);
                        let po = sl_mut(p_out, base, len);
                        let vo = sl_mut(v_out, base, len);
                        for i in 0..len {
                            let vn = mom * v[i] + g[i];
                            vo[i] = vn;
                            po[i] = p[i] - lr * vn;
                        }
                    })
                }

                _ => Arc::new(|_: *mut u8| {}),
            }
        })
        .collect();

    // ── Thunk-level attention fusion ──────────────────────
    // For small batch*seq, fuse QKV→Narrow×3→[Rope×2]→Attention→OutProj
    // into a single FusedAttnBlock. Auto-detects from Attention thunks.
    let fuse_threshold: usize = rlx_ir::env::var("RLX_FUSE_ATTN_THRESHOLD")
        .and_then(|v| v.parse().ok())
        .unwrap_or(64);
    let should_fuse = thunks.iter().any(|t| match t {
        Thunk::Attention { batch, seq, .. } => {
            (*batch as usize) * (*seq as usize) <= fuse_threshold
        }
        _ => false,
    });

    if should_fuse {
        // Build non-Nop index for pattern matching across Nop gaps
        let active: Vec<usize> = thunks
            .iter()
            .enumerate()
            .filter(|(_, t)| !matches!(t, Thunk::Nop))
            .map(|(i, _)| i)
            .collect();

        let mut kill = vec![false; thunks.len()]; // mark thunks to remove
        let mut insertions: Vec<(usize, Thunk)> = Vec::new(); // (position, replacement)

        let mut ai = 0;
        while ai < active.len() {
            // Helper: get active thunk at offset from current
            let a = |off: usize| -> Option<(usize, &Thunk)> {
                active.get(ai + off).map(|&idx| (idx, &thunks[idx]))
            };

            // Try BERT pattern: FusedMmBiasAct(QKV) → Narrow×3 → Attention → FusedMmBiasAct(out)
            let matched = (|| {
                let (_i0, t0) = a(0)?;
                let (_, t1) = a(1)?;
                let (_, t2) = a(2)?;
                let (_, t3) = a(3)?;

                // a[0] must be FusedMmBiasAct or Sgemm (QKV projection)
                let (hidden, qkv_w, qkv_b, has_b) = match t0 {
                    Thunk::FusedMmBiasAct {
                        a,
                        w,
                        bias,
                        n: _,
                        act: None,
                        ..
                    } => (*a, *w, *bias, true),
                    Thunk::Sgemm { a, b, n: _, .. } => (*a, *b, 0, false),
                    _ => return None,
                };

                // a[1..3] must be Narrows
                if !matches!(t1, Thunk::Narrow { .. }) {
                    return None;
                }
                if !matches!(t2, Thunk::Narrow { .. }) {
                    return None;
                }
                if !matches!(t3, Thunk::Narrow { .. }) {
                    return None;
                }

                // Look for optional Rope×2 then Attention. Capture the rope
                // pairing (`interleaved` = GPT-J) from the q-rope thunk and
                // require the k-rope to agree — the fused kernel applies one
                // pairing to both.
                let (has_rope, attn_ai, cos_off, sin_off, cl, rope_interleaved) = if let Some((
                    _,
                    Thunk::Rope {
                        cos,
                        sin,
                        cos_len,
                        interleaved,
                        ..
                    },
                )) = a(4)
                {
                    let q_il = *interleaved;
                    match a(5).map(|x| x.1) {
                        Some(Thunk::Rope {
                            interleaved: k_il, ..
                        }) if *k_il == q_il => {
                            if matches!(a(6).map(|x| x.1), Some(Thunk::Attention { .. })) {
                                (true, 6, *cos, *sin, *cos_len, q_il)
                            } else {
                                return None;
                            }
                        }
                        _ => return None,
                    }
                } else if matches!(a(4).map(|x| x.1), Some(Thunk::Attention { .. })) {
                    (false, 4, 0, 0, 0, false)
                } else {
                    return None;
                };

                let (_attn_real_idx, attn_t) = a(attn_ai)?;
                let (batch, seq, heads, head_dim, mask, mask_kind, kv_seq) = match attn_t {
                    Thunk::Attention {
                        batch,
                        seq,
                        heads,
                        head_dim,
                        mask,
                        mask_kind,
                        kv_seq,
                        ..
                    } => (*batch, *seq, *heads, *head_dim, *mask, *mask_kind, *kv_seq),
                    _ => return None,
                };
                // The fused kernel synthesizes Causal / SlidingWindow in-kernel
                // and reads the buffer for Custom; it has no additive-`Bias`
                // path, and its in-kernel position math assumes prefill
                // (`q_seq == kv_seq`, i.e. no KV cache). Fall back to the
                // unfused Attention thunk for anything else.
                if matches!(mask_kind, rlx_ir::op::MaskKind::Bias) || kv_seq != seq {
                    return None;
                }

                // Next active must be out projection (FusedMmBiasAct or Sgemm)
                let (_out_real_idx, out_t) = a(attn_ai + 1)?;
                let (out_w, out_b, out_dst) = match out_t {
                    Thunk::FusedMmBiasAct {
                        w,
                        bias,
                        c,
                        act: None,
                        ..
                    } => (*w, *bias, *c),
                    Thunk::Sgemm { b: w, c, .. } => (*w, 0, *c),
                    _ => return None,
                };

                let hs = heads * head_dim;
                let total_active = attn_ai + 2; // number of active thunks consumed

                Some((
                    total_active,
                    Thunk::FusedAttnBlock {
                        hidden,
                        qkv_w,
                        out_w,
                        mask,
                        mask_kind,
                        out: out_dst,
                        qkv_b: if has_b { qkv_b } else { 0 },
                        out_b: if has_b { out_b } else { 0 },
                        cos: cos_off,
                        sin: sin_off,
                        cos_len: cl,
                        batch,
                        seq,
                        hs,
                        nh: heads,
                        dh: head_dim,
                        has_bias: has_b,
                        has_rope,
                        interleaved: rope_interleaved,
                    },
                ))
            })();

            if let Some((count, fused_thunk)) = matched {
                // Mark consumed thunks for removal
                for off in 0..count {
                    if let Some(&idx) = active.get(ai + off) {
                        kill[idx] = true;
                    }
                }
                // Insert replacement at position of the QKV thunk
                insertions.push((active[ai], fused_thunk));
                ai += count;
            } else {
                ai += 1;
            }
        }

        // Rebuild thunk list: keep non-killed, insert fused at right positions
        if !insertions.is_empty() {
            let mut new_thunks = Vec::with_capacity(thunks.len());
            let mut insert_idx = 0;
            for (i, t) in thunks.into_iter().enumerate() {
                if insert_idx < insertions.len() && insertions[insert_idx].0 == i {
                    new_thunks.push(insertions[insert_idx].1.clone());
                    insert_idx += 1;
                }
                if !kill[i] {
                    new_thunks.push(t);
                }
            }
            if cfg.verbose >= 1 {
                eprintln!(
                    "[rlx] fused_attention: {} attention blocks fused",
                    insertions.len()
                );
            }
            thunks = new_thunks;
        }
    }

    // ── Full layer fusion ──────────────────────────────────
    // After attention blocks are fused, scan for full layer patterns:
    // BERT:  FusedAttnBlock → FusedResidualLN → FusedMmBiasAct(gelu) → Sgemm → BiasAdd → FusedResidualLN
    // Nomic: FusedAttnBlock → BinaryFull(add) → LayerNorm → Sgemm → [Narrow×2 → Silu → BinaryFull(mul)] → Sgemm → BinaryFull(add) → LayerNorm
    if should_fuse {
        let active: Vec<usize> = thunks
            .iter()
            .enumerate()
            .filter(|(_, t)| !matches!(t, Thunk::Nop))
            .map(|(i, _)| i)
            .collect();

        let mut kill = vec![false; thunks.len()];
        let mut insertions: Vec<(usize, Thunk)> = Vec::new();

        let a = |ai: usize| -> Option<&Thunk> { active.get(ai).map(|&i| &thunks[i]) };

        let mut ai = 0;
        while ai < active.len() {
            // BERT pattern: FusedAttnBlock → FusedResidualLN → FusedMmBiasAct(gelu) → FusedMmBiasAct(none) → FusedResidualLN
            let bert_match = (|| -> Option<usize> {
                let fab = a(ai)?;
                let rln1 = a(ai + 1)?;
                let ffn1 = a(ai + 2)?;
                let ffn2 = a(ai + 3)?;
                let rln2 = a(ai + 4)?;

                let (hidden, qkv_w, qkv_b, out_w, out_b, mask, batch, seq, hs, nh, dh) = match fab {
                    Thunk::FusedAttnBlock {
                        hidden,
                        qkv_w,
                        qkv_b,
                        out_w,
                        out_b,
                        mask,
                        // FusedBertLayer applies only the per-key padding mask;
                        // it has no synthesized causal/sliding path, so it must
                        // not swallow a non-`Custom` attention block.
                        mask_kind: rlx_ir::op::MaskKind::Custom,
                        batch,
                        seq,
                        hs,
                        nh,
                        dh,
                        has_bias: true,
                        has_rope: false,
                        ..
                    } => (
                        *hidden, *qkv_w, *qkv_b, *out_w, *out_b, *mask, *batch, *seq, *hs, *nh, *dh,
                    ),
                    _ => return None,
                };
                let (ln1_g, ln1_b, eps1) = match rln1 {
                    Thunk::FusedResidualLN { g, b, eps, .. } => (*g, *b, *eps),
                    _ => return None,
                };
                let (fc1_w, fc1_b, int_dim) = match ffn1 {
                    Thunk::FusedMmBiasAct {
                        w,
                        bias,
                        n,
                        act: Some(Activation::Gelu),
                        ..
                    } => (*w, *bias, *n),
                    _ => return None,
                };
                let (fc2_w, fc2_b) = match ffn2 {
                    Thunk::FusedMmBiasAct {
                        w, bias, act: None, ..
                    } => (*w, *bias),
                    _ => return None,
                };
                let (ln2_g, ln2_b, eps2, out) = match rln2 {
                    Thunk::FusedResidualLN { g, b, eps, out, .. } => (*g, *b, *eps, *out),
                    _ => return None,
                };

                for off in 0..5 {
                    kill[active[ai + off]] = true;
                }
                insertions.push((
                    active[ai],
                    Thunk::FusedBertLayer {
                        hidden,
                        qkv_w,
                        qkv_b,
                        out_w,
                        out_b,
                        mask,
                        ln1_g,
                        ln1_b,
                        eps1,
                        fc1_w,
                        fc1_b,
                        fc2_w,
                        fc2_b,
                        ln2_g,
                        ln2_b,
                        eps2,
                        out,
                        batch,
                        seq,
                        hs,
                        nh,
                        dh,
                        int_dim,
                    },
                ));
                Some(5)
            })();
            if let Some(n) = bert_match {
                ai += n;
                continue;
            }

            // Nomic full-layer fusion — DISABLED. The matcher below targets a
            // stale pipeline shape (`FusedAttnBlock → FusedResidualLN → Sgemm →
            // Narrow×2 → SiLU → Mul → Sgemm → FusedResidualLN`). The current CPU
            // pipeline collapses the SwiGLU itself (`FuseSwiGLU` → one
            // `Op::FusedSwiGLU`/`Thunk::FusedSwiGLU`) and emits a runtime weight
            // `Concat` (runtime weight concat) before the fused fc matmul. So the
            // current post-fusion shape is:
            //   FusedAttnBlock(rope,no-bias) → FusedResidualLN(LN1) → [Concat] →
            //   Sgemm(fc11‖fc12) → FusedSwiGLU → Sgemm(fc2) → FusedResidualLN(LN2)
            // which this matches (the weight `Concat`s are kept — they produce the
            // fused fc weight the kernel reads). The `FusedNomicLayer` exec only
            // implements the up‖gate SwiGLU (`gate_first == false`, what real
            // Nomic emits: `up=fc11`, `gate=silu(fc12)`) and a per-key (`Custom`)
            // mask, so the match bails otherwise. Escape hatch:
            // `RLX_DISABLE_NOMIC_FUSION`. Validated against the real model in
            // `../rlx-models/crates/rlx-nomic` (CPU fused-vs-unfused parity).
            let nomic_match = (|| -> Option<usize> {
                if rlx_ir::env::flag("RLX_DISABLE_NOMIC_FUSION") {
                    return None;
                }
                let (
                    hidden,
                    qkv_w,
                    out_w,
                    mask,
                    cos,
                    sin,
                    cos_len,
                    batch,
                    seq,
                    hs,
                    nh,
                    dh,
                    interleaved,
                ) = match a(ai)? {
                    Thunk::FusedAttnBlock {
                        hidden,
                        qkv_w,
                        out_w,
                        mask,
                        cos,
                        sin,
                        cos_len,
                        batch,
                        seq,
                        hs,
                        nh,
                        dh,
                        has_bias: false,
                        has_rope: true,
                        mask_kind: rlx_ir::op::MaskKind::Custom,
                        interleaved,
                        ..
                    } => (
                        *hidden,
                        *qkv_w,
                        *out_w,
                        *mask,
                        *cos,
                        *sin,
                        *cos_len,
                        *batch,
                        *seq,
                        *hs,
                        *nh,
                        *dh,
                        *interleaved,
                    ),
                    _ => return None,
                };
                // FusedResidualLN for LN1
                let (ln1_g, ln1_b, eps1) = match a(ai + 1)? {
                    Thunk::FusedResidualLN { g, b, eps, .. } => (*g, *b, *eps),
                    _ => return None,
                };
                // Consumed active thunks to remove (the kept weight `Concat`s are
                // NOT added here — the fused kernel still reads their output).
                let mut kills: Vec<usize> = vec![ai, ai + 1];
                // Optional runtime weight Concat (fc11‖fc12), then Sgemm.
                let mut o = 2;
                if matches!(a(ai + o)?, Thunk::Concat { .. }) {
                    o += 1;
                }
                let fused_fc_w = match a(ai + o)? {
                    Thunk::Sgemm { b: w, .. } => *w,
                    _ => return None,
                };
                kills.push(ai + o);
                o += 1;
                // FusedSwiGLU — int_dim is its half width; the exec only handles
                // up‖gate (gate is the SECOND half), so require !gate_first.
                let int_dim = match a(ai + o)? {
                    Thunk::FusedSwiGLU {
                        n_half,
                        gate_first: false,
                        ..
                    } => *n_half,
                    _ => return None,
                };
                kills.push(ai + o);
                o += 1;
                // Sgemm (fc2)
                let fc2_w = match a(ai + o)? {
                    Thunk::Sgemm { b: w, .. } => *w,
                    _ => return None,
                };
                kills.push(ai + o);
                o += 1;
                // FusedResidualLN for LN2
                let (ln2_g, ln2_b, eps2, out) = match a(ai + o)? {
                    Thunk::FusedResidualLN { g, b, eps, out, .. } => (*g, *b, *eps, *out),
                    _ => return None,
                };
                kills.push(ai + o);
                let consumed = o + 1;

                for ki in kills {
                    kill[active[ki]] = true;
                }
                FUSED_NOMIC_LAYER_COUNT.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
                // Insert at the LAST consumed position (LN2), NOT the first
                // (FusedAttnBlock): the fused kernel reads the runtime weight
                // `Concat` (fc11‖fc12, and qkv for split-qkv models) that sits
                // between them, so it must execute AFTER those concats run.
                insertions.push((
                    active[ai + o],
                    Thunk::FusedNomicLayer {
                        hidden,
                        qkv_w,
                        out_w,
                        mask,
                        cos,
                        sin,
                        cos_len,
                        ln1_g,
                        ln1_b,
                        eps1,
                        fc11_w: fused_fc_w,
                        fc12_w: 0,
                        fc2_w,
                        ln2_g,
                        ln2_b,
                        eps2,
                        out,
                        batch,
                        seq,
                        hs,
                        nh,
                        dh,
                        int_dim,
                        interleaved,
                    },
                ));
                Some(consumed)
            })();
            if let Some(n) = nomic_match {
                ai += n;
                continue;
            }

            ai += 1;
        }

        if !insertions.is_empty() {
            let mut new_thunks = Vec::with_capacity(thunks.len());
            let mut ins_idx = 0;
            for (i, t) in thunks.into_iter().enumerate() {
                if ins_idx < insertions.len() && insertions[ins_idx].0 == i {
                    new_thunks.push(insertions[ins_idx].1.clone());
                    ins_idx += 1;
                }
                if !kill[i] {
                    new_thunks.push(t);
                }
            }
            if cfg.verbose >= 1 {
                eprintln!(
                    "[rlx] fused_layer: {} full transformer layers fused",
                    insertions.len()
                );
            }
            thunks = new_thunks;
        }
    }

    // ── Narrow → Rope thunk fusion (plan #45) ──────────────
    // Runs *after* FusedAttnBlock fusion so it only catches the medium-
    // batch path (batch*seq > 64) where the bigger fusion didn't fire.
    // Pattern: a Rope thunk whose `src` is the dst of an immediately-
    // preceding Narrow whose dst has no other consumer in this schedule.
    // Rewrite Rope to read directly from the parent buffer with the
    // parent's row stride; the Narrow becomes a Nop.
    //
    // Skipping the Narrow's write saves one full pass over Q/K (B*S*hs
    // f32) per Rope. For Nomic h=768 / batch=8 / seq=15 / 12 layers
    // that's 2 ropes/layer × 369 KB = ~8.9 MB of write traffic gone.
    {
        // Collect every byte-offset that's read as a thunk's `src` so
        // we know whether a Narrow's dst has consumers other than Rope.
        let mut read_offsets: HashMap<usize, usize> = HashMap::new();
        for t in &thunks {
            for off in thunk_read_offsets(t) {
                *read_offsets.entry(off).or_insert(0) += 1;
            }
        }

        let mut fused_count = 0usize;
        for i in 0..thunks.len().saturating_sub(1) {
            // Look for Rope at i+1 reading from Narrow at i (skip Nops
            // between them since the planner left them in place).
            let narrow = match &thunks[i] {
                Thunk::Narrow { .. } => i,
                _ => continue,
            };
            // Find the next non-Nop thunk
            let mut j = narrow + 1;
            while j < thunks.len() && matches!(thunks[j], Thunk::Nop) {
                j += 1;
            }
            if j >= thunks.len() {
                continue;
            }
            // Must be Rope reading Narrow's dst
            let (n_src, n_dst, n_src_stride) = match &thunks[narrow] {
                Thunk::Narrow {
                    src,
                    dst,
                    src_stride,
                    ..
                } => (*src, *dst, *src_stride),
                _ => continue,
            };
            let rope_reads_narrow = matches!(&thunks[j],
                Thunk::Rope { src, .. } if *src == n_dst);
            if !rope_reads_narrow {
                continue;
            }
            // Conservatively require that the Narrow's dst has exactly
            // one reader (the Rope). Anything else and rewriting would
            // skip a needed write.
            if read_offsets.get(&n_dst).copied().unwrap_or(0) != 1 {
                continue;
            }

            // Rewire: Rope reads from Narrow's adjusted source with the
            // parent buffer's row stride.
            if let Thunk::Rope {
                src,
                src_row_stride,
                ..
            } = &mut thunks[j]
            {
                *src = n_src;
                *src_row_stride = n_src_stride;
            }
            thunks[narrow] = Thunk::Nop;
            fused_count += 1;
        }

        if fused_count > 0 && cfg.verbose >= 1 {
            eprintln!(
                "[rlx] fused_qk_rope: {} Narrow→Rope pairs collapsed",
                fused_count
            );
        }
    }

    // ── Narrow×3 → Attention thunk fusion (plan #46 deep) ────
    // For each Attention thunk in the schedule, look up the producers
    // of its q/k/v inputs. If each is a Narrow whose dst has exactly
    // one consumer (the Attention), rewire Attention to read directly
    // from the parent buffer with the parent's row stride. The three
    // Narrows become Nops.
    //
    // This catches the BERT/Nomic QKV split path that FusedAttnBlock
    // misses (batch*seq > 64) — eliminates Q/K/V copies entirely.
    // For minilm6 batch=32 seq=16 hs=384: 3 × 32*16*384*4 = 2.3 MB
    // per layer × 6 layers = ~14 MB of write traffic gone.
    {
        let mut read_counts: HashMap<usize, usize> = HashMap::new();
        for t in &thunks {
            for off in thunk_read_offsets(t) {
                *read_counts.entry(off).or_insert(0) += 1;
            }
        }
        // Build dst→index map for fast producer lookup.
        let mut dst_to_idx: HashMap<usize, usize> = HashMap::new();
        for (i, t) in thunks.iter().enumerate() {
            if let Thunk::Narrow { dst, .. } = t {
                dst_to_idx.insert(*dst, i);
            }
        }

        let mut fused_count = 0usize;
        for i in 0..thunks.len() {
            let (q_off, k_off, v_off) = match &thunks[i] {
                Thunk::Attention { q, k, v, .. } => (*q, *k, *v),
                _ => continue,
            };
            // All three inputs must come from Narrows.
            let q_n = match dst_to_idx.get(&q_off).copied() {
                Some(x) => x,
                None => continue,
            };
            let k_n = match dst_to_idx.get(&k_off).copied() {
                Some(x) => x,
                None => continue,
            };
            let v_n = match dst_to_idx.get(&v_off).copied() {
                Some(x) => x,
                None => continue,
            };
            // Each Narrow's dst must have exactly one reader (this Attn).
            if read_counts.get(&q_off).copied().unwrap_or(0) != 1 {
                continue;
            }
            if read_counts.get(&k_off).copied().unwrap_or(0) != 1 {
                continue;
            }
            if read_counts.get(&v_off).copied().unwrap_or(0) != 1 {
                continue;
            }

            let (q_src, q_stride) = match &thunks[q_n] {
                Thunk::Narrow {
                    src, src_stride, ..
                } => (*src, *src_stride),
                _ => continue,
            };
            let (k_src, k_stride) = match &thunks[k_n] {
                Thunk::Narrow {
                    src, src_stride, ..
                } => (*src, *src_stride),
                _ => continue,
            };
            let (v_src, v_stride) = match &thunks[v_n] {
                Thunk::Narrow {
                    src, src_stride, ..
                } => (*src, *src_stride),
                _ => continue,
            };

            if let Thunk::Attention {
                q,
                k,
                v,
                q_row_stride,
                k_row_stride,
                v_row_stride,
                ..
            } = &mut thunks[i]
            {
                *q = q_src;
                *k = k_src;
                *v = v_src;
                *q_row_stride = q_stride;
                *k_row_stride = k_stride;
                *v_row_stride = v_stride;
            }
            thunks[q_n] = Thunk::Nop;
            thunks[k_n] = Thunk::Nop;
            thunks[v_n] = Thunk::Nop;
            fused_count += 1;
        }

        if fused_count > 0 && cfg.verbose >= 1 {
            eprintln!(
                "[rlx] fused_strided_attn: {} Narrow×3→Attention rewrites",
                fused_count
            );
        }
    }

    ThunkSchedule {
        thunks,
        moe_resident: None,
        moe_resident_layers: None,
        moe_topk_capture: None,
        mask_threshold: cfg.mask_binary_threshold,
        mask_neg_inf: cfg.attn_mask_neg_inf,
        score_skip: cfg.score_skip_threshold,
        compiled_fns,
        rng: rng_shared,
    }
}

fn get_len(graph: &Graph, id: NodeId) -> usize {
    graph.node(id).shape.num_elements().unwrap_or(0)
}

/// Static `usize` dims of a node's shape, or empty if any dim is dynamic.
fn get_static_dims(graph: &Graph, id: NodeId) -> Vec<usize> {
    let dims = graph.node(id).shape.dims();
    let mut out = Vec::with_capacity(dims.len());
    for d in dims {
        if let Some(s) = match d {
            rlx_ir::Dim::Static(s) => Some(*s),
            _ => None,
        } {
            out.push(s);
        } else {
            return Vec::new();
        }
    }
    out
}

/// Extent along `axis` for a concat input, treating leading implicit 1s when
/// `input.rank() < output.rank()` (ONNX / numpy concat broadcast rules).
fn concat_axis_extent(input: &rlx_ir::Shape, axis: usize, out_rank: usize) -> usize {
    let in_rank = input.rank();
    if axis >= out_rank {
        return 1;
    }
    if axis < in_rank {
        input.dim(axis).unwrap_static()
    } else {
        1
    }
}

fn broadcast_src_index(src_idx: usize, in_len: usize) -> usize {
    if in_len == 0 { 0 } else { src_idx % in_len }
}

fn concat_copy_rows_f32(
    out: &mut [f32],
    inp: &[f32],
    outer: usize,
    copy_per_row: usize,
    row_stride: usize,
    dst_col_off: usize,
    in_numel: usize,
) {
    let need = outer.saturating_mul(copy_per_row.max(1));
    let broadcast_outer = in_numel < need;
    for o in 0..outer {
        let dst_row_start = o * row_stride + dst_col_off;
        if broadcast_outer {
            if in_numel == 1 {
                if copy_per_row == 1 {
                    out[dst_row_start] = inp[0];
                } else {
                    out[dst_row_start..dst_row_start + copy_per_row].fill(inp[0]);
                }
            } else if copy_per_row <= inp.len() {
                out[dst_row_start..dst_row_start + copy_per_row]
                    .copy_from_slice(&inp[..copy_per_row]);
            } else if !inp.is_empty() {
                out[dst_row_start..dst_row_start + copy_per_row].fill(inp[0]);
            }
        } else {
            let src_row_start = o * copy_per_row;
            out[dst_row_start..dst_row_start + copy_per_row]
                .copy_from_slice(&inp[src_row_start..src_row_start + copy_per_row]);
        }
    }
}

fn concat_copy_rows_f64(
    out: &mut [f64],
    inp: &[f64],
    outer: usize,
    copy_per_row: usize,
    row_stride: usize,
    dst_col_off: usize,
    in_numel: usize,
) {
    let need = outer.saturating_mul(copy_per_row.max(1));
    let broadcast_outer = in_numel < need;
    for o in 0..outer {
        let dst_row_start = o * row_stride + dst_col_off;
        if broadcast_outer {
            if in_numel == 1 {
                if copy_per_row == 1 {
                    out[dst_row_start] = inp[0];
                } else {
                    out[dst_row_start..dst_row_start + copy_per_row].fill(inp[0]);
                }
            } else if copy_per_row <= inp.len() {
                out[dst_row_start..dst_row_start + copy_per_row]
                    .copy_from_slice(&inp[..copy_per_row]);
            } else if !inp.is_empty() {
                out[dst_row_start..dst_row_start + copy_per_row].fill(inp[0]);
            }
        } else {
            let src_row_start = o * copy_per_row;
            out[dst_row_start..dst_row_start + copy_per_row]
                .copy_from_slice(&inp[src_row_start..src_row_start + copy_per_row]);
        }
    }
}

/// NumPy-style broadcast strides for one operand into the flat output
/// buffer. Returns a length-`out_dims.len()` `Vec<u32>` where entry
/// `d` is `0` if the input is size-1 (broadcast) at output dim `d`
/// (after left-padding with size-1 to match ranks), otherwise the
/// natural row-major stride into the *input* buffer.
///
/// Caller iterates output flat index `i` → output coords (row-major)
/// → input flat index = dot(coords, strides). The result is correct
/// for any broadcast pattern (scalar, last-axis, middle-axis,
/// bidirectional).
/// True when `rhs_dims` describes a *trailing* broadcast of `out_dims`
/// — i.e. every rhs dim either equals the corresponding output dim
/// (counting from the right) or rhs is shorter (left-padded with 1s).
/// Mid-shape singletons (e.g. rhs `[a, b, 1, d]` into out `[a, b, c, d]`
/// where `c > 1`) are NOT trailing broadcasts and require the
/// shape-aware `BinaryFull` slow path — `BiasAdd`'s linear bias-replicated
/// kernel silently miscomputes them.
fn is_trailing_bias_broadcast(rhs_dims: &[rlx_ir::Dim], out_dims: &[rlx_ir::Dim]) -> bool {
    if rhs_dims.len() > out_dims.len() {
        return false;
    }
    let off = out_dims.len() - rhs_dims.len();
    for i in 0..rhs_dims.len() {
        let r = match rhs_dims[i] {
            rlx_ir::Dim::Static(n) => n,
            _ => return false,
        };
        let o = match out_dims[off + i] {
            rlx_ir::Dim::Static(n) => n,
            _ => return false,
        };
        if r != o {
            return false;
        }
    }
    true
}

fn broadcast_strides(in_dims: &[usize], out_dims: &[usize]) -> Vec<u32> {
    let r_out = out_dims.len();
    let r_in = in_dims.len();
    assert!(
        r_in <= r_out,
        "broadcast: input rank {r_in} > output rank {r_out}"
    );
    let pad = r_out - r_in;
    let mut strides = vec![0u32; r_out];
    let mut acc: usize = 1;
    for d in (0..r_out).rev() {
        let in_size = if d < pad { 1 } else { in_dims[d - pad] };
        if in_size == 1 {
            strides[d] = 0;
        } else {
            assert_eq!(
                in_size, out_dims[d],
                "broadcast: input dim {in_size} doesn't match output dim {} at axis {d}",
                out_dims[d]
            );
            strides[d] = acc as u32;
            acc *= in_size;
        }
    }
    strides
}

/// Execute a thunk schedule on a raw arena buffer.
/// Fastest executor: call pre-compiled closures sequentially.
/// Zero match dispatch — each closure is a direct kernel call.
pub fn execute_compiled(schedule: &ThunkSchedule, arena_buf: &mut [u8]) {
    let base = arena_buf.as_mut_ptr();
    for f in &schedule.compiled_fns {
        f(base);
    }
}

/// Active-extent execution stub. The runtime calls this when it has an
/// active-extent hint set. CPU doesn't implement per-thunk active-extent
/// scaling yet — return false so the caller falls back to the full
/// `execute_thunks` path.
pub fn execute_thunks_active(
    schedule: &ThunkSchedule,
    _arena_buf: &mut [u8],
    _actual: usize,
    _upper: usize,
) -> bool {
    let _ = schedule;
    false
}

/// Match-based executor (fallback, used by tests).
struct MoeResidencyGuard;
impl Drop for MoeResidencyGuard {
    fn drop(&mut self) {
        if let Some(stats) = crate::moe_residency::take_stats() {
            crate::moe_residency::stash_last_forward_stats(stats);
        } else {
            crate::moe_residency::clear_mask();
        }
    }
}

fn thunk_kind_name(t: &Thunk) -> &'static str {
    match t {
        Thunk::Nop => "Nop",
        Thunk::Gather { .. } => "Gather",
        Thunk::GatherAxis { .. } => "GatherAxis",
        Thunk::TopK { .. } => "TopK",
        Thunk::Copy { .. } => "Copy",
        Thunk::CopyF64 { .. } => "CopyF64",
        Thunk::CopyI64 { .. } => "CopyI64",
        Thunk::CastF32ToI64 { .. } => "CastF32ToI64",
        Thunk::CastI64ToF32 { .. } => "CastI64ToF32",
        Thunk::CastBoolToI32 { .. } => "CastBoolToI32",
        Thunk::CastBoolToF32 { .. } => "CastBoolToF32",
        Thunk::CastI32ToF32 { .. } => "CastI32ToF32",
        Thunk::Transpose { .. } => "Transpose",
        Thunk::TransposeF64 { .. } => "TransposeF64",
        Thunk::Where { .. } => "Where",
        Thunk::Fma { .. } => "Fma",
        Thunk::Compare { .. } => "Compare",
        Thunk::BinaryFull { .. } => "BinaryFull",
        Thunk::BinaryFullF64 { .. } => "BinaryFullF64",
        Thunk::Sgemm { .. } => "Sgemm",
        Thunk::SgemmT { .. } => "SgemmT",
        Thunk::SgdMomentum { .. } => "SgdMomentum",
        Thunk::Dgemm { .. } => "Dgemm",
        Thunk::FusedMmBiasAct { .. } => "FusedMmBiasAct",
        Thunk::BiasAdd { .. } => "BiasAdd",
        Thunk::LayerNorm { .. } => "LayerNorm",
        Thunk::Softmax { .. } => "Softmax",
        Thunk::Conv2D { .. } => "Conv2D",
        Thunk::Conv2D1x1 { .. } => "Conv2D1x1",
        Thunk::CustomOp { .. } => "CustomOp",
        Thunk::ActivationInPlace { .. } => "ActivationInPlace",
        Thunk::Narrow { .. } => "Narrow",
        Thunk::Cumsum { .. } => "Cumsum",
        Thunk::Reduce { .. } => "Reduce",
        Thunk::BatchedSgemm { .. } => "BatchedSgemm",
        Thunk::DequantMatMul { .. } => "DequantMatMul",
        Thunk::Quantize { .. } => "Quantize",
        Thunk::Dequantize { .. } => "Dequantize",
        Thunk::ConvTranspose2d { .. } => "ConvTranspose2d",
        Thunk::ResizeNearest2x { .. } => "ResizeNearest2x",
        Thunk::ElementwiseRegion { .. } => "ElementwiseRegion",
        Thunk::Conv2dBackwardInput { .. } => "Conv2dBackwardInput",
        Thunk::Conv2dBackwardWeight { .. } => "Conv2dBackwardWeight",
        Thunk::Pool2D { .. } => "Pool2D",
        Thunk::MaxPool2dBackward { .. } => "MaxPool2dBackward",
        Thunk::ReluBackward { .. } => "ReluBackward",
        Thunk::ActivationBackward { .. } => "ActivationBackward",
        Thunk::Im2Col { .. } => "Im2Col",
        Thunk::SoftmaxCrossEntropyDense { .. } => "SoftmaxCrossEntropyDense",
        Thunk::SoftmaxCrossEntropy { .. } => "SoftmaxCrossEntropy",
        Thunk::SoftmaxCrossEntropyBackward { .. } => "SoftmaxCrossEntropyBackward",
        _ => "Other",
    }
}

/// Per-thunk-kind wall-time accumulator, populated only when the env var
/// `RLX_PROFILE_THUNKS` is set. Used to see which ops dominate a step so the
/// optimizer/kernels can target the real hotspots rather than guesses.
static THUNK_PROFILE: std::sync::Mutex<
    Option<std::collections::BTreeMap<&'static str, (u128, u64)>>,
> = std::sync::Mutex::new(None);

#[inline]
fn profile_record(name: &'static str, d: std::time::Duration) {
    let mut g = THUNK_PROFILE.lock().unwrap();
    let map = g.get_or_insert_with(std::collections::BTreeMap::new);
    let e = map.entry(name).or_insert((0, 0));
    e.0 += d.as_nanos();
    e.1 += 1;
}

/// Print and clear the per-thunk-kind time profile gathered under
/// `RLX_PROFILE_THUNKS`. Call after a run to see where the time went.
pub fn dump_thunk_profile() {
    let mut g = THUNK_PROFILE.lock().unwrap();
    if let Some(map) = g.take() {
        let mut v: Vec<_> = map.into_iter().collect();
        v.sort_by_key(|b| std::cmp::Reverse(b.1.0));
        let total: u128 = v.iter().map(|(_, (ns, _))| *ns).sum();
        eprintln!(
            "[thunk-profile] total {:.1}ms across kinds:",
            total as f64 / 1e6
        );
        for (name, (ns, c)) in v.iter().take(25) {
            eprintln!("  {name:<28} {:>8.1}ms  ({c} calls)", *ns as f64 / 1e6);
        }
    }
}

pub fn execute_thunks(schedule: &ThunkSchedule, arena_buf: &mut [u8]) {
    crate::moe_residency::reset_gmm_counters();
    if let Some(layers) = schedule.moe_resident_layers.clone() {
        crate::moe_residency::set_per_layer_masks(Some(layers));
    } else {
        crate::moe_residency::set_mask(schedule.moe_resident.clone());
    }
    if let Some(cap) = schedule.moe_topk_capture.as_ref() {
        cap.clear();
    }
    let _moe_guard = MoeResidencyGuard;
    let base = arena_buf.as_mut_ptr();
    let mask_thr = schedule.mask_threshold;
    let mask_neg = schedule.mask_neg_inf;
    let score_thr = schedule.score_skip;
    let thunks = &schedule.thunks;
    let len = thunks.len();

    // Pre-allocate ALL reusable buffers once (zero per-call allocation)
    let max_h = thunks
        .iter()
        .filter_map(|t| match t {
            Thunk::FusedResidualLN { h, .. }
            | Thunk::FusedResidualRmsNorm { h, .. }
            | Thunk::LayerNorm { h, .. } => Some(*h as usize),
            _ => None,
        })
        .max()
        .unwrap_or(0);
    let zero_bias = vec![0f32; max_h];

    // Pre-allocate per-(batch,head) score buffers for parallel SDPA.
    // Q/K/V/out are accessed via strided BLAS — no deinterleave copy needed.
    let max_sdpa = thunks
        .iter()
        .filter_map(|t| match t {
            Thunk::Attention {
                batch,
                seq,
                kv_seq,
                heads,
                head_dim,
                ..
            } => Some((
                *batch as usize,
                (*seq as usize).max(*kv_seq as usize),
                *heads as usize,
                *head_dim as usize,
            )),
            _ => None,
        })
        .fold((0, 0, 0, 0), |(mb, ms, mh, md), (b, s, h, d)| {
            (mb.max(b), ms.max(s), mh.max(h), md.max(d))
        });
    let (max_batch, max_seq, max_heads, _max_dh) = max_sdpa;
    let max_units = max_batch * max_heads;
    let mut sdpa_scores = vec![0f32; max_units * max_seq * max_seq];

    // Pre-allocate fused layer buffers (reused across all 12+ layers — zero malloc per layer)
    let fl = thunks
        .iter()
        .filter_map(|t| match t {
            Thunk::FusedBertLayer {
                batch,
                seq,
                hs,
                int_dim,
                ..
            } => {
                let m = (*batch as usize) * (*seq as usize);
                let h = *hs as usize;
                let id = *int_dim as usize;
                Some((m, h, id, m * (*seq as usize)))
            }
            Thunk::FusedNomicLayer {
                batch,
                seq,
                hs,
                int_dim,
                ..
            } => {
                let m = (*batch as usize) * (*seq as usize);
                let h = *hs as usize;
                let id = *int_dim as usize;
                Some((m, h, id, m * (*seq as usize)))
            }
            _ => None,
        })
        .fold((0, 0, 0, 0), |(mm, mh, mi, ms), (m, h, id, ss)| {
            (mm.max(m), mh.max(h), mi.max(id), ms.max(ss))
        });
    let (fl_m, fl_h, fl_int, fl_ss) = fl;
    let mut fl_qkv = vec![0f32; fl_m * 3 * fl_h];
    let mut fl_attn = vec![0f32; fl_m * fl_h];
    let mut fl_res = vec![0f32; fl_m * fl_h];
    let mut fl_normed = vec![0f32; fl_m * fl_h];
    let mut fl_ffn = vec![0f32; fl_m * fl_int.max(2 * fl_int)]; // Nomic needs 2×int for fused fc11+fc12
    let mut fl_sc = vec![0f32; fl_ss.max(1)];

    let trace_thunks = std::env::var_os("RLX_TRACE_THUNK").is_some();
    if trace_thunks {
        eprintln!(
            "[thunk] prealloc max_h={max_h} sdpa={} fl_m={fl_m} fl_h={fl_h} fl_int={fl_int}",
            max_units * max_seq * max_seq
        );
    }
    let profile = std::env::var_os("RLX_PROFILE_THUNKS").is_some();
    // Time the previous thunk at the top of each iteration (avoids touching the
    // giant match's many arms). The last thunk's tail is folded into the next
    // step's first sample — negligible over a training run.
    let mut prof_prev: Option<(&'static str, std::time::Instant)> = None;
    for i in 0..len {
        if profile {
            if let Some((pn, pt)) = prof_prev.take() {
                profile_record(pn, pt.elapsed());
            }
        }
        let thunk = unsafe { thunks.get_unchecked(i) };
        if trace_thunks && (i < 120 || i % 200 == 0 || i + 1 == len) {
            eprintln!("[thunk {i}/{len}] {}", thunk_kind_name(thunk));
        }
        let trace_done = trace_thunks && i < 120;
        if profile {
            prof_prev = Some((thunk_kind_name(thunk), std::time::Instant::now()));
        }
        match thunk {
            Thunk::Nop => {}

            Thunk::ElementwiseRegion {
                dst,
                len,
                input_offs,
                chain,
                scalar_input_mask,
                input_modulus,
            } => {
                let len = *len as usize;
                if !chain.is_empty() && len > 0 {
                    let base_addr = base as usize;
                    let dst = *dst;
                    let scalar_mask = *scalar_input_mask;
                    // Each output element is independent → fan over the range.
                    let eval = |gid: usize| {
                        let v = region_eval_elem(
                            gid,
                            base_addr as *const u8,
                            input_offs,
                            chain,
                            scalar_mask,
                            input_modulus,
                        );
                        unsafe {
                            *((base_addr as *mut u8).add(dst) as *mut f32).add(gid) = v;
                        }
                    };
                    if fast_conv_enabled() && crate::pool::should_parallelize(len) {
                        crate::pool::par_for(len, crate::pool::chunk_floor(len), &|off, cnt| {
                            for gid in off..off + cnt {
                                eval(gid);
                            }
                        });
                    } else {
                        for gid in 0..len {
                            eval(gid);
                        }
                    }
                }
            }

            Thunk::GaussianSplatRender {
                positions_off,
                positions_len,
                scales_off,
                scales_len,
                rotations_off,
                rotations_len,
                opacities_off,
                opacities_len,
                colors_off,
                colors_len,
                sh_coeffs_off,
                sh_coeffs_len,
                meta_off,
                dst_off,
                dst_len,
                width,
                height,
                tile_size,
                radius_scale,
                alpha_cutoff,
                max_splat_steps,
                transmittance_threshold,
                max_list_entries,
            } => unsafe {
                crate::splat::execute_gaussian_splat_render(
                    *positions_off,
                    *positions_len,
                    *scales_off,
                    *scales_len,
                    *rotations_off,
                    *rotations_len,
                    *opacities_off,
                    *opacities_len,
                    *colors_off,
                    *colors_len,
                    *sh_coeffs_off,
                    *sh_coeffs_len,
                    *meta_off,
                    *dst_off,
                    *dst_len,
                    *width,
                    *height,
                    *tile_size,
                    *radius_scale,
                    *alpha_cutoff,
                    *max_splat_steps,
                    *transmittance_threshold,
                    *max_list_entries,
                    base,
                );
            },

            Thunk::GaussianSplatRenderBackward {
                positions_off,
                positions_len,
                scales_off,
                scales_len,
                rotations_off,
                rotations_len,
                opacities_off,
                opacities_len,
                colors_off,
                colors_len,
                sh_coeffs_off,
                sh_coeffs_len,
                meta_off,
                d_loss_off,
                d_loss_len,
                packed_off,
                packed_len,
                width,
                height,
                tile_size,
                radius_scale,
                alpha_cutoff,
                max_splat_steps,
                transmittance_threshold,
                max_list_entries,
                loss_grad_clip,
                sh_band,
                max_anisotropy,
            } => unsafe {
                crate::splat::execute_gaussian_splat_render_backward(
                    *positions_off,
                    *positions_len,
                    *scales_off,
                    *scales_len,
                    *rotations_off,
                    *rotations_len,
                    *opacities_off,
                    *opacities_len,
                    *colors_off,
                    *colors_len,
                    *sh_coeffs_off,
                    *sh_coeffs_len,
                    *meta_off,
                    *d_loss_off,
                    *d_loss_len,
                    *packed_off,
                    *packed_len,
                    *width,
                    *height,
                    *tile_size,
                    *radius_scale,
                    *alpha_cutoff,
                    *max_splat_steps,
                    *transmittance_threshold,
                    *max_list_entries,
                    *loss_grad_clip,
                    *sh_band,
                    *max_anisotropy,
                    base,
                );
            },

            Thunk::GaussianSplatPrepare {
                positions_off,
                positions_len,
                scales_off,
                scales_len,
                rotations_off,
                rotations_len,
                opacities_off,
                opacities_len,
                colors_off,
                colors_len,
                sh_coeffs_off,
                sh_coeffs_len,
                meta_off,
                meta_len,
                prep_off,
                prep_len,
                width,
                height,
                tile_size,
                radius_scale,
                alpha_cutoff,
                max_splat_steps,
                transmittance_threshold,
                max_list_entries,
            } => unsafe {
                crate::splat::execute_gaussian_splat_prepare(
                    *positions_off,
                    *positions_len,
                    *scales_off,
                    *scales_len,
                    *rotations_off,
                    *rotations_len,
                    *opacities_off,
                    *opacities_len,
                    *colors_off,
                    *colors_len,
                    *sh_coeffs_off,
                    *sh_coeffs_len,
                    *meta_off,
                    *meta_len,
                    *prep_off,
                    *prep_len,
                    *width,
                    *height,
                    *tile_size,
                    *radius_scale,
                    *alpha_cutoff,
                    *max_splat_steps,
                    *transmittance_threshold,
                    *max_list_entries,
                    base,
                );
            },

            Thunk::GaussianSplatRasterize {
                prep_off,
                prep_len,
                meta_off,
                meta_len,
                dst_off,
                dst_len,
                count,
                width,
                height,
                tile_size,
                alpha_cutoff,
                max_splat_steps,
                transmittance_threshold,
                max_list_entries,
            } => unsafe {
                crate::splat::execute_gaussian_splat_rasterize(
                    *prep_off,
                    *prep_len,
                    *meta_off,
                    *meta_len,
                    *dst_off,
                    *dst_len,
                    *count,
                    *width,
                    *height,
                    *tile_size,
                    *alpha_cutoff,
                    *max_splat_steps,
                    *transmittance_threshold,
                    *max_list_entries,
                    base,
                );
            },

            Thunk::Fft1d {
                src,
                dst,
                outer,
                n_complex,
                inverse,
                norm_tag,
                dtype,
            } => unsafe {
                match dtype {
                    rlx_ir::DType::F64 => execute_fft1d_f64(
                        *src,
                        *dst,
                        *outer as usize,
                        *n_complex as usize,
                        *inverse,
                        *norm_tag,
                        base,
                    ),
                    rlx_ir::DType::F32 => execute_fft1d_f32(
                        *src,
                        *dst,
                        *outer as usize,
                        *n_complex as usize,
                        *inverse,
                        *norm_tag,
                        base,
                    ),
                    rlx_ir::DType::C64 => execute_fft1d_c64(
                        *src,
                        *dst,
                        *outer as usize,
                        *n_complex as usize,
                        *inverse,
                        *norm_tag,
                        base,
                    ),
                    other => panic!("Op::Fft on CPU requires F32/F64/C64, got {other:?}"),
                }
            },

            Thunk::FftButterflyStage {
                state_src,
                state_dst,
                gate_src,
                rev_src,
                tw_re_src,
                tw_im_src,
                batch,
                n_fft,
                stage,
            } => unsafe {
                execute_fft_butterfly_stage_f32(
                    *state_src,
                    *state_dst,
                    *gate_src,
                    *rev_src,
                    *tw_re_src,
                    *tw_im_src,
                    *batch as usize,
                    *n_fft as usize,
                    *stage as usize,
                    base,
                );
            },

            Thunk::LogMel {
                spec,
                filters,
                dst,
                outer,
                n_fft,
                n_bins,
                n_mels,
            } => unsafe {
                execute_log_mel_f32(
                    *spec,
                    *filters,
                    *dst,
                    *outer as usize,
                    *n_fft as usize,
                    *n_bins as usize,
                    *n_mels as usize,
                    base,
                );
            },

            Thunk::LogMelBackward {
                spec,
                filters,
                dy,
                dst,
                outer,
                n_fft,
                n_bins,
                n_mels,
            } => unsafe {
                execute_log_mel_backward_f32(
                    *spec,
                    *filters,
                    *dy,
                    *dst,
                    *outer as usize,
                    *n_fft as usize,
                    *n_bins as usize,
                    *n_mels as usize,
                    base,
                );
            },

            Thunk::WelchPeaks {
                spec,
                dst,
                welch_batch,
                n_fft,
                n_segments,
                k,
            } => unsafe {
                execute_welch_peaks_f32(
                    *spec,
                    *dst,
                    *welch_batch as usize,
                    *n_fft as usize,
                    *n_segments as usize,
                    *k as usize,
                    base,
                );
            },

            // CustomFn dispatch (interpreted path). Mirrors the
            // pre-compiled-closure variant elsewhere in this file.
            // Patched by rlx-eda.
            Thunk::CustomFn {
                body,
                body_init,
                inputs,
                body_output_off,
                outer_output_off,
                out_bytes,
            } => {
                let mut body_buf: Vec<u8> = (**body_init).clone();
                unsafe {
                    for (body_in_off, outer_in_off, n_bytes) in inputs.iter() {
                        let src = (base as *const u8).add(*outer_in_off);
                        let dst = body_buf.as_mut_ptr().add(*body_in_off);
                        std::ptr::copy_nonoverlapping(src, dst, *n_bytes as usize);
                    }
                }
                execute_thunks(body, &mut body_buf);
                unsafe {
                    let src = body_buf.as_ptr().add(*body_output_off);
                    let dst = base.add(*outer_output_off);
                    std::ptr::copy_nonoverlapping(src, dst, *out_bytes as usize);
                }
            }

            Thunk::Sgemm { a, b, c, m, k, n } => {
                let (m, k, n) = (*m as usize, *k as usize, *n as usize);
                if trace_thunks {
                    eprintln!("[sgemm] m={m} k={k} n={n} a={} b={} c={}", *a, *b, *c);
                }
                let c_len = m.saturating_mul(n);
                let a_len = m.saturating_mul(k);
                let b_len = k.saturating_mul(n);
                let arena_len = arena_buf.len();
                let max_a = (arena_len.saturating_sub(*a)) / 4;
                let max_b = (arena_len.saturating_sub(*b)) / 4;
                let max_c = (arena_len.saturating_sub(*c)) / 4;
                let a_len = a_len.min(max_a);
                let b_len = b_len.min(max_b);
                let c_len = c_len.min(max_c);
                unsafe {
                    let a_sl = sl(*a, base, a_len);
                    let b_sl = sl(*b, base, b_len);
                    let c_sl = sl_mut(*c, base, c_len);
                    if std::ptr::eq(a_sl.as_ptr(), c_sl.as_ptr())
                        || std::ptr::eq(b_sl.as_ptr(), c_sl.as_ptr())
                    {
                        let mut tmp = vec![0.0f32; c_len];
                        crate::blas::sgemm_auto(a_sl, b_sl, &mut tmp, m, k, n);
                        c_sl.copy_from_slice(&tmp);
                    } else {
                        crate::blas::sgemm_auto(a_sl, b_sl, c_sl, m, k, n);
                    }
                }
            }

            Thunk::SgemmT {
                a,
                b,
                c,
                m,
                k,
                n,
                ta,
                tb,
            } => {
                // C[m,n] = op(A) @ op(B). RowMajor cblas: lda/ldb = stored
                // row-length of each operand → m if A is transposed else k;
                // k if B is transposed else n. Element counts are m*k / k*n
                // regardless of layout.
                let (m, k, n) = (*m as usize, *k as usize, *n as usize);
                let lda = if *ta { m } else { k };
                let ldb = if *tb { k } else { n };
                let arena_len = arena_buf.len();
                let a_len = (m * k).min((arena_len.saturating_sub(*a)) / 4);
                let b_len = (k * n).min((arena_len.saturating_sub(*b)) / 4);
                let c_len = (m * n).min((arena_len.saturating_sub(*c)) / 4);
                unsafe {
                    let a_sl = sl(*a, base, a_len);
                    let b_sl = sl(*b, base, b_len);
                    let c_sl = sl_mut(*c, base, c_len);
                    let (ap, bp) = (a_sl.as_ptr(), b_sl.as_ptr());
                    if std::ptr::eq(ap, c_sl.as_ptr()) || std::ptr::eq(bp, c_sl.as_ptr()) {
                        let mut tmp = vec![0.0f32; c_len];
                        crate::blas::sgemm_general(
                            ap,
                            bp,
                            tmp.as_mut_ptr(),
                            m,
                            n,
                            k,
                            1.0,
                            0.0,
                            lda,
                            ldb,
                            n,
                            *ta,
                            *tb,
                        );
                        c_sl.copy_from_slice(&tmp);
                    } else {
                        crate::blas::sgemm_general(
                            ap,
                            bp,
                            c_sl.as_mut_ptr(),
                            m,
                            n,
                            k,
                            1.0,
                            0.0,
                            lda,
                            ldb,
                            n,
                            *ta,
                            *tb,
                        );
                    }
                }
            }

            Thunk::SgdMomentum {
                param,
                vel,
                grad,
                p_out,
                v_out,
                lr,
                mom,
                len,
            } => {
                // v' = mom·v + g ;  p' = p − lr·v'  — one fused pass per param,
                // replacing the 4 BinaryFull ops the in-graph SGD chain lowers
                // to. Element-wise, so in-place aliasing (p_out==param etc.) is
                // safe: read index i, write index i.
                let len = *len as usize;
                let (lr, mom) = (*lr, *mom);
                unsafe {
                    let p = sl(*param, base, len);
                    let v = sl(*vel, base, len);
                    let g = sl(*grad, base, len);
                    let po = sl_mut(*p_out, base, len);
                    let vo = sl_mut(*v_out, base, len);
                    if fast_conv_enabled() && crate::pool::should_parallelize(len) {
                        let poa = po.as_mut_ptr() as usize;
                        let voa = vo.as_mut_ptr() as usize;
                        crate::pool::par_for(len, crate::pool::chunk_floor(len), &|off, cnt| {
                            for i in off..off + cnt {
                                let vn = mom * v[i] + g[i];
                                *((voa as *mut f32).add(i)) = vn;
                                *((poa as *mut f32).add(i)) = p[i] - lr * vn;
                            }
                        });
                    } else {
                        for i in 0..len {
                            let vn = mom * v[i] + g[i];
                            vo[i] = vn;
                            po[i] = p[i] - lr * vn;
                        }
                    }
                }
            }

            Thunk::CgemmC64 { a, b, c, m, k, n } => unsafe {
                cgemm_c64(*a, *b, *c, *m as usize, *k as usize, *n as usize, base);
            },

            Thunk::DenseSolveF64 { a, b, x, n, nrhs } => {
                let (n_, nrhs_) = (*n as usize, *nrhs as usize);
                // LAPACK overwrites both A and B; clone into scratch
                // each call. Caller's A and b must be preserved for
                // VJP recompute. (Eventually: swap to a factor-once /
                // solve-many scheme; that's the symbolic-reuse story
                // and lives with the sparse path.)
                unsafe {
                    let a_src = sl_f64(*a, base, n_ * n_);
                    let b_src = sl_f64(*b, base, n_ * nrhs_);
                    let mut a_scratch: Vec<f64> = a_src.to_vec();
                    let mut x_buf: Vec<f64> = b_src.to_vec();
                    let info = crate::blas::dgesv(&mut a_scratch, &mut x_buf, n_, nrhs_);
                    if info != 0 {
                        panic!(
                            "DenseSolveF64: dgesv reported singular matrix \
                                (info={info}, n={n_}, nrhs={nrhs_})"
                        );
                    }
                    let dst = sl_mut_f64(*x, base, n_ * nrhs_);
                    dst.copy_from_slice(&x_buf);
                }
            }

            Thunk::DenseSolveF32 { a, b, x, n, nrhs } => {
                let (n_, nrhs_) = (*n as usize, *nrhs as usize);
                unsafe {
                    let a_src = sl(*a, base, n_ * n_);
                    let b_src = sl(*b, base, n_ * nrhs_);
                    let mut a_scratch: Vec<f32> = a_src.to_vec();
                    let mut x_buf: Vec<f32> = b_src.to_vec();
                    let info = crate::blas::sgesv(&mut a_scratch, &mut x_buf, n_, nrhs_);
                    if info != 0 {
                        panic!(
                            "DenseSolveF32: sgesv reported singular matrix \
                             (info={info}, n={n_}, nrhs={nrhs_})"
                        );
                    }
                    let dst = sl_mut(*x, base, n_ * nrhs_);
                    dst.copy_from_slice(&x_buf);
                }
            }

            Thunk::BatchedDenseSolveF64 {
                a,
                b,
                x,
                batch,
                n,
                nrhs,
            } => {
                // Per slice: extract A_i and b_i, dgesv, write x_i.
                // LAPACK has no batched dgesv on Accelerate, so this
                // is a serial loop over the batch axis. cuSOLVER /
                // hipSOLVER expose `getrfBatched` / `getrsBatched` for
                // the GPU path — we'll wire that in rlx-cuda when
                // someone needs Linux+CUDA.
                let (b_, n_, nrhs_) = (*batch as usize, *n as usize, *nrhs as usize);
                let a_stride = n_ * n_;
                let b_stride = n_ * nrhs_;
                unsafe {
                    let a_full = sl_f64(*a, base, b_ * a_stride);
                    let b_full = sl_f64(*b, base, b_ * b_stride);
                    let x_full = sl_mut_f64(*x, base, b_ * b_stride);
                    for bi in 0..b_ {
                        let mut a_scratch: Vec<f64> =
                            a_full[bi * a_stride..(bi + 1) * a_stride].to_vec();
                        let mut x_buf: Vec<f64> =
                            b_full[bi * b_stride..(bi + 1) * b_stride].to_vec();
                        let info = crate::blas::dgesv(&mut a_scratch, &mut x_buf, n_, nrhs_);
                        if info != 0 {
                            panic!(
                                "BatchedDenseSolveF64: slice {bi} \
                                    singular (info={info}, n={n_}, nrhs={nrhs_})"
                            );
                        }
                        x_full[bi * b_stride..(bi + 1) * b_stride].copy_from_slice(&x_buf);
                    }
                }
            }

            Thunk::BatchedDenseSolveF32 {
                a,
                b,
                x,
                batch,
                n,
                nrhs,
            } => {
                let (b_, n_, nrhs_) = (*batch as usize, *n as usize, *nrhs as usize);
                let a_stride = n_ * n_;
                let b_stride = n_ * nrhs_;
                unsafe {
                    let a_full = sl(*a, base, b_ * a_stride);
                    let b_full = sl(*b, base, b_ * b_stride);
                    let x_full = sl_mut(*x, base, b_ * b_stride);
                    for bi in 0..b_ {
                        let mut a_scratch = a_full[bi * a_stride..(bi + 1) * a_stride].to_vec();
                        let mut x_buf = b_full[bi * b_stride..(bi + 1) * b_stride].to_vec();
                        let info = crate::blas::sgesv(&mut a_scratch, &mut x_buf, n_, nrhs_);
                        if info != 0 {
                            panic!("BatchedDenseSolveF32: slice {bi} singular (info={info})");
                        }
                        x_full[bi * b_stride..(bi + 1) * b_stride].copy_from_slice(&x_buf);
                    }
                }
            }

            Thunk::BatchedDgemmF64 {
                a,
                b,
                c,
                batch,
                m,
                k,
                n,
            } => {
                let (b_, m_, k_, n_) = (*batch as usize, *m as usize, *k as usize, *n as usize);
                let a_stride = m_ * k_;
                let b_stride = k_ * n_;
                let c_stride = m_ * n_;
                unsafe {
                    let a_full = sl_f64(*a, base, b_ * a_stride);
                    let b_full = sl_f64(*b, base, b_ * b_stride);
                    let c_full = sl_mut_f64(*c, base, b_ * c_stride);
                    for bi in 0..b_ {
                        let a_slice = &a_full[bi * a_stride..(bi + 1) * a_stride];
                        let b_slice = &b_full[bi * b_stride..(bi + 1) * b_stride];
                        let c_slice = &mut c_full[bi * c_stride..(bi + 1) * c_stride];
                        crate::blas::dgemm(a_slice, b_slice, c_slice, m_, k_, n_);
                    }
                }
            }

            Thunk::BatchedSgemm {
                a,
                b,
                c,
                batch,
                m,
                k,
                n,
            } => {
                let (b_, m_, k_, n_) = (*batch as usize, *m as usize, *k as usize, *n as usize);
                if trace_thunks {
                    eprintln!(
                        "[batched-sgemm] batch={b_} m={m_} k={k_} n={n_} a={} b={} c={}",
                        *a, *b, *c
                    );
                }
                let a_stride = m_.saturating_mul(k_);
                let b_stride = k_.saturating_mul(n_);
                let c_stride = m_.saturating_mul(n_);
                let arena_len = arena_buf.len();
                let a_cap = (arena_len.saturating_sub(*a)) / 4;
                let b_cap = (arena_len.saturating_sub(*b)) / 4;
                let c_cap = (arena_len.saturating_sub(*c)) / 4;
                let a_elems = (b_ * a_stride).min(a_cap);
                let b_elems = (b_ * b_stride).min(b_cap);
                let c_elems = (b_ * c_stride).min(c_cap);
                let b_eff = b_
                    .min(a_elems.checked_div(a_stride).unwrap_or(0))
                    .min(b_elems.checked_div(b_stride).unwrap_or(0))
                    .min(c_elems.checked_div(c_stride).unwrap_or(0));
                unsafe {
                    let a_full = sl(*a, base, a_elems);
                    let b_full = sl(*b, base, b_elems);
                    let c_full = sl_mut(*c, base, c_elems);
                    for bi in 0..b_eff {
                        let a0 = bi * a_stride;
                        let b0 = bi * b_stride;
                        let c0 = bi * c_stride;
                        if a0 + a_stride > a_full.len()
                            || b0 + b_stride > b_full.len()
                            || c0 + c_stride > c_full.len()
                        {
                            break;
                        }
                        let a_slice = &a_full[a0..a0 + a_stride];
                        let b_slice = &b_full[b0..b0 + b_stride];
                        let c_slice = &mut c_full[c0..c0 + c_stride];
                        if std::ptr::eq(a_slice.as_ptr(), c_slice.as_mut_ptr())
                            || std::ptr::eq(b_slice.as_ptr(), c_slice.as_mut_ptr())
                        {
                            let mut tmp = vec![0.0f32; c_stride];
                            crate::blas::sgemm_auto(a_slice, b_slice, &mut tmp, m_, k_, n_);
                            c_slice.copy_from_slice(&tmp);
                        } else {
                            crate::blas::sgemm_auto(a_slice, b_slice, c_slice, m_, k_, n_);
                        }
                    }
                }
            }

            Thunk::Dgemm { a, b, c, m, k, n } => {
                let (m, k, n) = (*m as usize, *k as usize, *n as usize);
                unsafe {
                    crate::blas::dgemm(
                        sl_f64(*a, base, m * k),
                        sl_f64(*b, base, k * n),
                        sl_mut_f64(*c, base, m * n),
                        m,
                        k,
                        n,
                    );
                }
            }

            Thunk::TransposeF64 {
                src,
                dst,
                in_total,
                out_dims,
                in_strides,
            } => unsafe {
                let inp = sl_f64(*src, base, *in_total as usize);
                let out_total: usize = out_dims.iter().map(|d| *d as usize).product();
                let out = sl_mut_f64(*dst, base, out_total);
                transpose_walk_f64(inp, out, out_dims, in_strides);
            },

            Thunk::ActivationF64 {
                src,
                dst,
                len,
                kind,
            } => {
                let len = *len as usize;
                unsafe {
                    let inp = sl_f64(*src, base, len);
                    let out = sl_mut_f64(*dst, base, len);
                    apply_activation_f64(inp, out, *kind);
                }
            }

            Thunk::ReduceSumF64 {
                src,
                dst,
                outer,
                reduced,
                inner,
            } => {
                let (o, r, n) = (*outer as usize, *reduced as usize, *inner as usize);
                unsafe {
                    let inp = sl_f64(*src, base, o * r * n);
                    let out = sl_mut_f64(*dst, base, o * n);
                    reduce_sum_f64(inp, out, o, r, n);
                }
            }

            Thunk::CopyF64 { src, dst, len } => {
                let mut len = *len as usize;
                if *src == *dst || len == 0 {
                    continue;
                }
                let arena_len = arena_buf.len();
                let max_from_src = (arena_len.saturating_sub(*src)) / 8;
                let max_from_dst = (arena_len.saturating_sub(*dst)) / 8;
                len = len.min(max_from_src).min(max_from_dst);
                if len == 0 {
                    continue;
                }
                let byte_len = len.saturating_mul(8);
                unsafe {
                    std::ptr::copy(base.add(*src), base.add(*dst), byte_len);
                }
            }

            Thunk::CopyI64 { src, dst, len } => {
                let mut len = *len as usize;
                if *src == *dst || len == 0 {
                    continue;
                }
                let arena_len = arena_buf.len();
                let max_from_src = (arena_len.saturating_sub(*src)) / 8;
                let max_from_dst = (arena_len.saturating_sub(*dst)) / 8;
                len = len.min(max_from_src).min(max_from_dst);
                if len == 0 {
                    continue;
                }
                let byte_len = len.saturating_mul(8);
                unsafe {
                    std::ptr::copy(base.add(*src), base.add(*dst), byte_len);
                }
            }

            Thunk::CastF32ToI64 { src, dst, len } => {
                let len = *len as usize;
                if len == 0 {
                    continue;
                }
                unsafe {
                    let inp = sl(*src, base, len);
                    let out = sl_mut_i64(*dst, base, len);
                    for i in 0..len {
                        out[i] = inp[i].round() as i64;
                    }
                }
            }

            Thunk::CastF32ToF64 { src, dst, len } => {
                let len = *len as usize;
                if len == 0 {
                    continue;
                }
                unsafe {
                    let inp = sl(*src, base, len);
                    let out = sl_mut_f64(*dst, base, len);
                    for i in 0..len {
                        out[i] = inp[i] as f64;
                    }
                }
            }

            Thunk::CastF32ToI32 { src, dst, len } => {
                let len = *len as usize;
                if len == 0 {
                    continue;
                }
                unsafe {
                    let inp = sl(*src, base, len);
                    let out = sl_mut_i32(*dst, base, len);
                    for i in 0..len {
                        out[i] = inp[i].round() as i32;
                    }
                }
            }

            Thunk::CastI64ToF32 { src, dst, len } => {
                let len = *len as usize;
                if len == 0 {
                    continue;
                }
                unsafe {
                    let inp = sl_i64(*src, base, len);
                    let out = sl_mut(*dst, base, len);
                    for i in 0..len {
                        out[i] = inp[i] as f32;
                    }
                }
            }

            Thunk::CastBoolToI32 { src, dst, len } => {
                let len = *len as usize;
                if len == 0 {
                    continue;
                }
                unsafe {
                    let inp = &arena_buf[*src..*src + len];
                    let out = sl_mut_i32(*dst, base, len);
                    for i in 0..len {
                        out[i] = i32::from(inp[i] != 0);
                    }
                }
            }

            Thunk::CastI32ToF32 { src, dst, len } => {
                let len = *len as usize;
                if len == 0 {
                    continue;
                }
                unsafe {
                    let inp = sl_i32(*src, base, len);
                    let out = sl_mut(*dst, base, len);
                    for i in 0..len {
                        out[i] = inp[i] as f32;
                    }
                }
            }

            Thunk::CastBoolToF32 { src, dst, len } => {
                let len = *len as usize;
                if len == 0 {
                    continue;
                }
                unsafe {
                    let inp = &arena_buf[*src..*src + len];
                    let out = sl_mut(*dst, base, len);
                    for i in 0..len {
                        out[i] = if inp[i] != 0 { 1.0 } else { 0.0 };
                    }
                }
            }

            Thunk::BinaryFullF64 {
                lhs,
                rhs,
                dst,
                len,
                lhs_len,
                rhs_len,
                op,
                out_dims_bcast,
                bcast_lhs_strides,
                bcast_rhs_strides,
            } => {
                let len = *len as usize;
                let lhs_len = *lhs_len as usize;
                let rhs_len = *rhs_len as usize;
                unsafe {
                    let l = sl_f64(*lhs, base, lhs_len);
                    let r = sl_f64(*rhs, base, rhs_len);
                    let d = sl_mut_f64(*dst, base, len);
                    if lhs_len == len && rhs_len == len {
                        for i in 0..len {
                            d[i] = binary_op_f64(*op, l[i], r[i]);
                        }
                    } else if !out_dims_bcast.is_empty() {
                        // Shape-aware broadcast path: correct for
                        // arbitrary NumPy-style broadcasts including
                        // bidirectional `[N,1] op [1,S]`.
                        let rank = out_dims_bcast.len();
                        let mut coords = vec![0u32; rank];
                        for i in 0..len {
                            let mut rem = i;
                            for ax in (0..rank).rev() {
                                let sz = out_dims_bcast[ax] as usize;
                                coords[ax] = (rem % sz) as u32;
                                rem /= sz;
                            }
                            let mut li: usize = 0;
                            let mut ri: usize = 0;
                            for ax in 0..rank {
                                li += coords[ax] as usize * bcast_lhs_strides[ax] as usize;
                                ri += coords[ax] as usize * bcast_rhs_strides[ax] as usize;
                            }
                            d[i] = binary_op_f64(*op, l[li], r[ri]);
                        }
                    } else {
                        // Fallback: legacy modulo path (preserved for
                        // dynamic-shape graphs where strides can't be
                        // precomputed). Only correct for scalar /
                        // last-axis broadcast.
                        for i in 0..len {
                            d[i] = binary_op_f64(*op, l[i % lhs_len], r[i % rhs_len]);
                        }
                    }
                }
            }

            Thunk::BinaryFullC64 {
                lhs,
                rhs,
                dst,
                len,
                lhs_len,
                rhs_len,
                op,
                out_dims_bcast,
                bcast_lhs_strides,
                bcast_rhs_strides,
            } => {
                // Complex element layout: [re_0, im_0, re_1, im_1, ...]
                // Underlying f32 buffer length is 2·N (N = complex
                // element count). All offsets are byte offsets; the
                // `sl` helper reads as f32 starting at the byte
                // offset, so f32-length = 2·complex-len.
                let n_out = *len as usize;
                let n_l = *lhs_len as usize;
                let n_r = *rhs_len as usize;
                unsafe {
                    let l = sl(*lhs, base, 2 * n_l);
                    let r = sl(*rhs, base, 2 * n_r);
                    let d = sl_mut(*dst, base, 2 * n_out);
                    let do_c64 = |a_re: f32, a_im: f32, b_re: f32, b_im: f32| -> (f32, f32) {
                        match op {
                            BinaryOp::Add => (a_re + b_re, a_im + b_im),
                            BinaryOp::Sub => (a_re - b_re, a_im - b_im),
                            BinaryOp::Mul => (a_re * b_re - a_im * b_im, a_re * b_im + a_im * b_re),
                            BinaryOp::Div => {
                                let denom = b_re * b_re + b_im * b_im;
                                (
                                    (a_re * b_re + a_im * b_im) / denom,
                                    (a_im * b_re - a_re * b_im) / denom,
                                )
                            }
                            BinaryOp::Max | BinaryOp::Min | BinaryOp::Pow => {
                                unreachable!("C64 max/min/pow rejected at lowering")
                            }
                        }
                    };
                    if n_l == n_out && n_r == n_out {
                        for i in 0..n_out {
                            let (re, im) = do_c64(l[2 * i], l[2 * i + 1], r[2 * i], r[2 * i + 1]);
                            d[2 * i] = re;
                            d[2 * i + 1] = im;
                        }
                    } else if !out_dims_bcast.is_empty() {
                        // Strided complex broadcast: strides are in
                        // *complex element* units; multiply by 2 when
                        // indexing into the f32 buffer.
                        let rank = out_dims_bcast.len();
                        let mut coords = vec![0u32; rank];
                        for i in 0..n_out {
                            let mut rem = i;
                            for ax in (0..rank).rev() {
                                let sz = out_dims_bcast[ax] as usize;
                                coords[ax] = (rem % sz) as u32;
                                rem /= sz;
                            }
                            let mut li: usize = 0;
                            let mut ri: usize = 0;
                            for ax in 0..rank {
                                li += coords[ax] as usize * bcast_lhs_strides[ax] as usize;
                                ri += coords[ax] as usize * bcast_rhs_strides[ax] as usize;
                            }
                            let (re, im) =
                                do_c64(l[2 * li], l[2 * li + 1], r[2 * ri], r[2 * ri + 1]);
                            d[2 * i] = re;
                            d[2 * i + 1] = im;
                        }
                    } else {
                        // Modulo fallback (scalar / last-axis broadcast).
                        for i in 0..n_out {
                            let li = if n_l == 1 { 0 } else { i % n_l };
                            let ri = if n_r == 1 { 0 } else { i % n_r };
                            let (re, im) =
                                do_c64(l[2 * li], l[2 * li + 1], r[2 * ri], r[2 * ri + 1]);
                            d[2 * i] = re;
                            d[2 * i + 1] = im;
                        }
                    }
                }
            }

            Thunk::ComplexNormSqF32 { src, dst, len } => {
                let n = *len as usize;
                unsafe {
                    let s = sl(*src, base, 2 * n);
                    let d = sl_mut(*dst, base, n);
                    for i in 0..n {
                        let re = s[2 * i];
                        let im = s[2 * i + 1];
                        d[i] = re * re + im * im;
                    }
                }
            }

            Thunk::ComplexNormSqBackwardF32 { z, g, dz, len } => {
                // Wirtinger: dz = g · z, element-wise complex
                // (g is real, z is complex).
                let n = *len as usize;
                unsafe {
                    let zb = sl(*z, base, 2 * n);
                    let gb = sl(*g, base, n);
                    let db = sl_mut(*dz, base, 2 * n);
                    for i in 0..n {
                        let re = zb[2 * i];
                        let im = zb[2 * i + 1];
                        let gv = gb[i];
                        db[2 * i] = gv * re;
                        db[2 * i + 1] = gv * im;
                    }
                }
            }

            Thunk::ConjugateC64 { src, dst, len } => {
                let n = *len as usize;
                unsafe {
                    let s = sl(*src, base, 2 * n);
                    let d = sl_mut(*dst, base, 2 * n);
                    for i in 0..n {
                        d[2 * i] = s[2 * i];
                        d[2 * i + 1] = -s[2 * i + 1];
                    }
                }
            }

            Thunk::ActivationC64 {
                src,
                dst,
                len,
                kind,
            } => {
                let n = *len as usize;
                unsafe {
                    let s = sl(*src, base, 2 * n);
                    let d = sl_mut(*dst, base, 2 * n);
                    for i in 0..n {
                        let a = s[2 * i];
                        let b = s[2 * i + 1];
                        let (re, im) = match kind {
                            Activation::Neg => (-a, -b),
                            Activation::Exp => {
                                // exp(a + bi) = e^a · (cos b + i·sin b)
                                let ea = a.exp();
                                (ea * b.cos(), ea * b.sin())
                            }
                            Activation::Log => {
                                // log(z) = log|z| + i·arg(z), principal branch
                                let r = (a * a + b * b).sqrt();
                                (r.ln(), b.atan2(a))
                            }
                            Activation::Sqrt => {
                                // sqrt(a+bi) = sqrt((|z|+a)/2) + sign(b)·i·sqrt((|z|-a)/2)
                                // Principal branch; for b == 0 and a < 0 returns +i·sqrt(|a|).
                                let r = (a * a + b * b).sqrt();
                                let re = ((r + a) * 0.5).max(0.0).sqrt();
                                let im_mag = ((r - a) * 0.5).max(0.0).sqrt();
                                let im = if b >= 0.0 { im_mag } else { -im_mag };
                                (re, im)
                            }
                            _ => unreachable!("non-C64 activation kind survived lowering"),
                        };
                        d[2 * i] = re;
                        d[2 * i + 1] = im;
                    }
                }
            }

            Thunk::Scan {
                body,
                body_init,
                body_input_off,
                body_output_off,
                outer_init_off,
                outer_final_off,
                length,
                carry_bytes,
                save_trajectory,
                xs_inputs,
                bcast_inputs,
                num_checkpoints,
            } => {
                let cb = *carry_bytes as usize;
                let n_steps = *length as usize;
                // Checkpoint mode: when 0 < K < length, save trajectory[k]
                // only when t == c_k = floor((k+1) * length / K) - 1.
                // The last index c_{K-1} = length - 1 always.
                let k_total = if *num_checkpoints == 0 || *num_checkpoints == *length {
                    n_steps // save every step
                } else {
                    *num_checkpoints as usize
                };
                let checkpoint_t_for_k = |k: usize| -> usize {
                    if k_total == n_steps {
                        k
                    } else {
                        ((k + 1) * n_steps)
                            .div_ceil(k_total)
                            .saturating_sub(1)
                            .min(n_steps - 1)
                    }
                };
                let mut next_k = 0usize;

                let mut body_buf: Vec<u8> = (**body_init).clone();
                unsafe {
                    std::ptr::copy_nonoverlapping(
                        base.add(*outer_init_off),
                        body_buf.as_mut_ptr().add(*body_input_off),
                        cb,
                    );
                    // Broadcast inputs: copy each one into the body's
                    // input slot ONCE. They aren't touched in the
                    // iteration loop below (in contrast to xs).
                    for (body_b_off, outer_b_off, total_bytes) in bcast_inputs.iter() {
                        std::ptr::copy_nonoverlapping(
                            base.add(*outer_b_off),
                            body_buf.as_mut_ptr().add(*body_b_off),
                            *total_bytes as usize,
                        );
                    }
                }
                for t in 0..n_steps {
                    for (body_x_off, outer_xs_off, per_step_bytes) in xs_inputs.iter() {
                        let psb = *per_step_bytes as usize;
                        unsafe {
                            std::ptr::copy_nonoverlapping(
                                base.add(*outer_xs_off + t * psb),
                                body_buf.as_mut_ptr().add(*body_x_off),
                                psb,
                            );
                        }
                    }

                    execute_thunks(body, &mut body_buf);

                    if *save_trajectory && next_k < k_total && t == checkpoint_t_for_k(next_k) {
                        unsafe {
                            std::ptr::copy_nonoverlapping(
                                body_buf.as_ptr().add(*body_output_off),
                                base.add(*outer_final_off + next_k * cb),
                                cb,
                            );
                        }
                        next_k += 1;
                    }

                    if *body_output_off != *body_input_off {
                        body_buf
                            .copy_within(*body_output_off..*body_output_off + cb, *body_input_off);
                    }
                }

                if !*save_trajectory {
                    // Single final-carry write.
                    unsafe {
                        std::ptr::copy_nonoverlapping(
                            body_buf.as_ptr().add(*body_output_off),
                            base.add(*outer_final_off),
                            cb,
                        );
                    }
                }
            }

            Thunk::ScanBackward {
                body_vjp,
                body_init,
                body_carry_in_off,
                body_x_offs,
                body_d_output_off,
                body_dcarry_out_off,
                outer_init_off,
                outer_traj_off,
                outer_upstream_off,
                outer_xs_offs,
                outer_dinit_off,
                length,
                carry_bytes,
                save_trajectory,
                num_checkpoints,
                forward_body,
                forward_body_init,
                forward_body_carry_in_off,
                forward_body_output_off,
                forward_body_x_offs,
                carry_elem_size,
            } => {
                // Two backward paths share the same per-iteration body
                // (body_vjp run + dcarry threading). The "All" path
                // reads the carry directly from the saved trajectory
                // each step. The "Recursive checkpointing" path stores
                // only K saved checkpoints and reconstructs intermediate
                // carries via Griewank-style recursive subdivision —
                // see [`griewank_process_segment`]. Auxiliary memory
                // is `O(log(segment_size) · carry_bytes)` for the
                // recursion stack, vs the old segment-cache scheme's
                // `O(segment_size · carry_bytes)`. Total recompute work
                // grows from `O(length)` to `O(length · log)`, which
                // is the canonical Griewank trade.
                let cb = *carry_bytes as usize;
                let n_steps = *length as usize;
                let k_total = *num_checkpoints as usize;
                let is_recursive = k_total != 0 && k_total != n_steps;
                let checkpoint_t_for_k = |k: usize| -> usize {
                    ((k + 1) * n_steps)
                        .div_ceil(k_total)
                        .saturating_sub(1)
                        .min(n_steps - 1)
                };

                let mut fwd_buf: Vec<u8> = if is_recursive {
                    (**forward_body_init.as_ref().unwrap()).clone()
                } else {
                    Vec::new()
                };

                let mut dcarry: Vec<u8> = vec![0u8; cb];
                if !*save_trajectory {
                    unsafe {
                        std::ptr::copy_nonoverlapping(
                            base.add(*outer_upstream_off),
                            dcarry.as_mut_ptr(),
                            cb,
                        );
                    }
                }

                let mut body_buf: Vec<u8> = (**body_init).clone();

                // Per-iteration backward action — shared between the
                // direct-trajectory (All) and Griewank (Recursive) paths.
                // Both feed the same body_vjp run with carry-at-t,
                // x_t_i, and d_output, then thread dcarry backward.
                let process_iter =
                    |t: usize, carry_in: &[u8], dcarry: &mut Vec<u8>, body_buf: &mut Vec<u8>| {
                        if *save_trajectory {
                            unsafe {
                                let up_off = *outer_upstream_off + t * cb;
                                match *carry_elem_size {
                                    4 => {
                                        let up_ptr = base.add(up_off) as *const f32;
                                        let dc_ptr = dcarry.as_mut_ptr() as *mut f32;
                                        let n_elems = cb / 4;
                                        for i in 0..n_elems {
                                            *dc_ptr.add(i) += *up_ptr.add(i);
                                        }
                                    }
                                    8 => {
                                        let up_ptr = base.add(up_off) as *const f64;
                                        let dc_ptr = dcarry.as_mut_ptr() as *mut f64;
                                        let n_elems = cb / 8;
                                        for i in 0..n_elems {
                                            *dc_ptr.add(i) += *up_ptr.add(i);
                                        }
                                    }
                                    other => panic!(
                                        "ScanBackward: unsupported carry elem size {other} \
                                     (only f32/f64 carries are supported today)"
                                    ),
                                }
                            }
                        }
                        body_buf[*body_carry_in_off..*body_carry_in_off + cb]
                            .copy_from_slice(carry_in);
                        unsafe {
                            for (i, body_x_off) in body_x_offs.iter().enumerate() {
                                let (outer_xs_off, per_step_bytes) = outer_xs_offs[i];
                                let psb = per_step_bytes as usize;
                                std::ptr::copy_nonoverlapping(
                                    base.add(outer_xs_off + t * psb),
                                    body_buf.as_mut_ptr().add(*body_x_off),
                                    psb,
                                );
                            }
                            std::ptr::copy_nonoverlapping(
                                dcarry.as_ptr(),
                                body_buf.as_mut_ptr().add(*body_d_output_off),
                                cb,
                            );
                        }
                        execute_thunks(body_vjp, body_buf);
                        unsafe {
                            std::ptr::copy_nonoverlapping(
                                body_buf.as_ptr().add(*body_dcarry_out_off),
                                dcarry.as_mut_ptr(),
                                cb,
                            );
                        }
                    };

                if is_recursive {
                    // Griewank treeverse path. Process saved-checkpoint
                    // segments from highest-t to lowest-t; within each,
                    // recursive binary subdivision via
                    // `griewank_process_segment`. Auxiliary memory:
                    // O(log(seg_size) · cb) for the recursion stack
                    // (vs O(seg_size · cb) for the older segment-cache
                    // scheme); recompute work: O(seg_size · log).
                    let leaf_threshold = 4usize;
                    let fb_sched = forward_body.as_ref().unwrap();
                    let fb_init = forward_body_init.as_ref().unwrap().as_slice();
                    let mut segment_end = n_steps - 1;
                    for seg_k in (0..k_total).rev() {
                        let segment_start = if seg_k == 0 {
                            0
                        } else {
                            checkpoint_t_for_k(seg_k - 1) + 1
                        };
                        let mut anchor: Vec<u8> = vec![0u8; cb];
                        unsafe {
                            let src = if seg_k == 0 {
                                base.add(*outer_init_off)
                            } else {
                                base.add(*outer_traj_off + (seg_k - 1) * cb)
                            };
                            std::ptr::copy_nonoverlapping(src, anchor.as_mut_ptr(), cb);
                        }
                        // Closure adapter for the helper's signature
                        // (mutably re-borrows dcarry / body_buf each call).
                        let mut leaf_action = |t: usize, carry_in: &[u8]| {
                            process_iter(t, carry_in, &mut dcarry, &mut body_buf);
                        };
                        unsafe {
                            griewank_process_segment(
                                segment_start,
                                segment_end,
                                &anchor,
                                cb,
                                fb_sched,
                                fb_init,
                                *forward_body_carry_in_off,
                                *forward_body_output_off,
                                forward_body_x_offs,
                                base,
                                outer_xs_offs,
                                &mut fwd_buf,
                                leaf_threshold,
                                &mut leaf_action,
                            );
                        }
                        if seg_k == 0 {
                            break;
                        }
                        segment_end = segment_start - 1;
                    }
                } else {
                    // All-trajectory path: read each carry directly
                    // from the saved trajectory buffer.
                    let mut carry_buf: Vec<u8> = vec![0u8; cb];
                    for t in (0..n_steps).rev() {
                        unsafe {
                            let src = if t == 0 {
                                base.add(*outer_init_off)
                            } else {
                                base.add(*outer_traj_off + (t - 1) * cb)
                            };
                            std::ptr::copy_nonoverlapping(src, carry_buf.as_mut_ptr(), cb);
                        }
                        process_iter(t, &carry_buf, &mut dcarry, &mut body_buf);
                    }
                }

                unsafe {
                    std::ptr::copy_nonoverlapping(dcarry.as_ptr(), base.add(*outer_dinit_off), cb);
                }
            }

            Thunk::ScanBackwardXs {
                body_vjp,
                body_init,
                body_carry_in_off,
                body_x_offs,
                body_d_output_off,
                body_dcarry_out_off,
                body_dxs_out_off,
                outer_init_off,
                outer_traj_off,
                outer_upstream_off,
                outer_xs_offs,
                outer_dxs_off,
                length,
                carry_bytes,
                carry_elem_size,
                per_step_bytes,
                save_trajectory,
                num_checkpoints,
                forward_body,
                forward_body_init,
                forward_body_carry_in_off,
                forward_body_output_off,
                forward_body_x_offs,
            } => {
                let cb = *carry_bytes as usize;
                let psb = *per_step_bytes as usize;
                let n_steps = *length as usize;
                let k_total = *num_checkpoints as usize;
                let is_recursive = k_total != 0 && k_total != n_steps;
                let checkpoint_t_for_k = |k: usize| -> usize {
                    ((k + 1) * n_steps)
                        .div_ceil(k_total)
                        .saturating_sub(1)
                        .min(n_steps - 1)
                };

                // Forward-body recompute scratch + segment cache —
                // exact mirror of the ScanBackward path. With ≈√length
                // checkpoints, total recompute work is O(length).
                let mut fwd_buf: Vec<u8> = if is_recursive {
                    (**forward_body_init.as_ref().unwrap()).clone()
                } else {
                    Vec::new()
                };
                let mut seg_cache: Vec<u8> = Vec::new();
                let mut seg_start_t: usize = usize::MAX;
                let mut seg_count: usize = 0;
                let recompute_carry_t =
                    |t: usize,
                     dst: &mut [u8],
                     fwd_buf: &mut Vec<u8>,
                     seg_cache: &mut Vec<u8>,
                     seg_start_t: &mut usize,
                     seg_count: &mut usize| {
                        if !is_recursive {
                            unsafe {
                                let src = if t == 0 {
                                    base.add(*outer_init_off)
                                } else {
                                    base.add(*outer_traj_off + (t - 1) * cb)
                                };
                                std::ptr::copy_nonoverlapping(src, dst.as_mut_ptr(), cb);
                            }
                            return;
                        }
                        if *seg_start_t != usize::MAX
                            && t >= *seg_start_t
                            && t < *seg_start_t + *seg_count
                        {
                            let off = (t - *seg_start_t) * cb;
                            dst.copy_from_slice(&seg_cache[off..off + cb]);
                            return;
                        }
                        let seg_k = (0..k_total)
                            .find(|&k| t <= checkpoint_t_for_k(k))
                            .unwrap_or(k_total - 1);
                        let (anchor_t, anchor_ptr): (usize, *const u8) = if seg_k == 0 {
                            (0, unsafe { base.add(*outer_init_off) as *const u8 })
                        } else {
                            let prev_ck = checkpoint_t_for_k(seg_k - 1);
                            (prev_ck + 1, unsafe {
                                base.add(*outer_traj_off + (seg_k - 1) * cb) as *const u8
                            })
                        };
                        let seg_end_t = checkpoint_t_for_k(seg_k);
                        let seg_size = seg_end_t - anchor_t + 1;

                        fwd_buf.copy_from_slice(forward_body_init.as_ref().unwrap());
                        unsafe {
                            std::ptr::copy_nonoverlapping(
                                anchor_ptr,
                                fwd_buf.as_mut_ptr().add(*forward_body_carry_in_off),
                                cb,
                            );
                        }
                        seg_cache.resize(seg_size * cb, 0u8);
                        seg_cache[0..cb].copy_from_slice(
                            &fwd_buf[*forward_body_carry_in_off..*forward_body_carry_in_off + cb],
                        );
                        let fb_sched = forward_body.as_ref().unwrap();
                        for i in 1..seg_size {
                            let cur_iter = anchor_t + i - 1;
                            for (idx, fb_x_off) in forward_body_x_offs.iter().enumerate() {
                                let (outer_xs_off, x_psb) = outer_xs_offs[idx];
                                let xb = x_psb as usize;
                                unsafe {
                                    std::ptr::copy_nonoverlapping(
                                        base.add(outer_xs_off + cur_iter * xb),
                                        fwd_buf.as_mut_ptr().add(*fb_x_off),
                                        xb,
                                    );
                                }
                            }
                            execute_thunks(fb_sched, fwd_buf);
                            if *forward_body_output_off != *forward_body_carry_in_off {
                                fwd_buf.copy_within(
                                    *forward_body_output_off..*forward_body_output_off + cb,
                                    *forward_body_carry_in_off,
                                );
                            }
                            let cache_off = i * cb;
                            seg_cache[cache_off..cache_off + cb].copy_from_slice(
                                &fwd_buf
                                    [*forward_body_carry_in_off..*forward_body_carry_in_off + cb],
                            );
                        }
                        *seg_start_t = anchor_t;
                        *seg_count = seg_size;

                        let off = (t - anchor_t) * cb;
                        dst.copy_from_slice(&seg_cache[off..off + cb]);
                    };

                let mut dcarry: Vec<u8> = vec![0u8; cb];
                if !*save_trajectory {
                    unsafe {
                        std::ptr::copy_nonoverlapping(
                            base.add(*outer_upstream_off),
                            dcarry.as_mut_ptr(),
                            cb,
                        );
                    }
                }

                let mut body_buf: Vec<u8> = (**body_init).clone();

                for t in (0..n_steps).rev() {
                    if *save_trajectory {
                        unsafe {
                            let up_off = *outer_upstream_off + t * cb;
                            match *carry_elem_size {
                                4 => {
                                    let up_ptr = base.add(up_off) as *const f32;
                                    let dc_ptr = dcarry.as_mut_ptr() as *mut f32;
                                    let n_elems = cb / 4;
                                    for i in 0..n_elems {
                                        *dc_ptr.add(i) += *up_ptr.add(i);
                                    }
                                }
                                8 => {
                                    let up_ptr = base.add(up_off) as *const f64;
                                    let dc_ptr = dcarry.as_mut_ptr() as *mut f64;
                                    let n_elems = cb / 8;
                                    for i in 0..n_elems {
                                        *dc_ptr.add(i) += *up_ptr.add(i);
                                    }
                                }
                                other => panic!(
                                    "ScanBackwardXs: unsupported carry elem size {other} \
                                     (only f32/f64 carries are supported today)"
                                ),
                            }
                        }
                    }

                    // Seed body_vjp's carry input via the recompute
                    // helper (works for both All and Recursive modes),
                    // then x_t_i + d_output.
                    let carry_dst_start = *body_carry_in_off;
                    {
                        let carry_slice = &mut body_buf[carry_dst_start..carry_dst_start + cb];
                        recompute_carry_t(
                            t,
                            carry_slice,
                            &mut fwd_buf,
                            &mut seg_cache,
                            &mut seg_start_t,
                            &mut seg_count,
                        );
                    }
                    unsafe {
                        for (i, body_x_off) in body_x_offs.iter().enumerate() {
                            let (outer_xs_off, x_psb) = outer_xs_offs[i];
                            let xb = x_psb as usize;
                            std::ptr::copy_nonoverlapping(
                                base.add(outer_xs_off + t * xb),
                                body_buf.as_mut_ptr().add(*body_x_off),
                                xb,
                            );
                        }
                        std::ptr::copy_nonoverlapping(
                            dcarry.as_ptr(),
                            body_buf.as_mut_ptr().add(*body_d_output_off),
                            cb,
                        );
                    }

                    execute_thunks(body_vjp, &mut body_buf);

                    // Stash this step's dxs into row `t` of the outer
                    // [length, *per_step_xs] output.
                    unsafe {
                        std::ptr::copy_nonoverlapping(
                            body_buf.as_ptr().add(*body_dxs_out_off),
                            base.add(*outer_dxs_off + t * psb),
                            psb,
                        );
                    }

                    // Update dcarry for next backward iteration.
                    unsafe {
                        std::ptr::copy_nonoverlapping(
                            body_buf.as_ptr().add(*body_dcarry_out_off),
                            dcarry.as_mut_ptr(),
                            cb,
                        );
                    }
                }
            }

            Thunk::FusedMmBiasAct {
                a,
                w,
                bias,
                c,
                m,
                k,
                n,
                act,
            } => {
                let (m, k, n) = (*m as usize, *k as usize, *n as usize);
                unsafe {
                    let out = sl_mut(*c, base, m * n);
                    crate::blas::sgemm_auto(sl(*a, base, m * k), sl(*w, base, k * n), out, m, k, n);
                    match act {
                        Some(Activation::Gelu) => {
                            crate::kernels::par_bias_gelu(out, sl(*bias, base, n), m, n)
                        }
                        Some(other) => {
                            crate::blas::bias_add(out, sl(*bias, base, n), m, n);
                            apply_activation_inplace(out, *other);
                        }
                        None => crate::blas::bias_add(out, sl(*bias, base, n), m, n),
                    }
                }
            }

            Thunk::FusedResidualLN {
                x,
                res,
                bias,
                g,
                b,
                out,
                rows,
                h,
                eps,
                has_bias,
            } => {
                let (rows, h) = (*rows as usize, *h as usize);
                unsafe {
                    let zero = &zero_bias[..h];
                    let bi = if *has_bias { sl(*bias, base, h) } else { zero };
                    let x_ptr = sl(*x, base, rows * h).as_ptr() as usize;
                    let r_ptr = sl(*res, base, rows * h).as_ptr() as usize;
                    let o_ptr = sl_mut(*out, base, rows * h).as_mut_ptr() as usize;
                    let bi_ptr = bi.as_ptr() as usize;
                    let g_ptr = sl(*g, base, h).as_ptr() as usize;
                    let b_ptr = sl(*b, base, h).as_ptr() as usize;
                    let e = *eps;
                    crate::pool::par_for(rows, 4, &|off, cnt| {
                        let xs =
                            std::slice::from_raw_parts((x_ptr as *const f32).add(off * h), cnt * h);
                        let rs =
                            std::slice::from_raw_parts((r_ptr as *const f32).add(off * h), cnt * h);
                        let os = std::slice::from_raw_parts_mut(
                            (o_ptr as *mut f32).add(off * h),
                            cnt * h,
                        );
                        let bi = std::slice::from_raw_parts(bi_ptr as *const f32, h);
                        let g = std::slice::from_raw_parts(g_ptr as *const f32, h);
                        let b = std::slice::from_raw_parts(b_ptr as *const f32, h);
                        crate::kernels::residual_bias_layer_norm(xs, rs, bi, g, b, os, cnt, h, e);
                    });
                }
            }

            Thunk::FusedResidualRmsNorm {
                x,
                res,
                bias,
                g,
                b,
                out,
                rows,
                h,
                eps,
                has_bias,
            } => {
                let (rows, h) = (*rows as usize, *h as usize);
                unsafe {
                    let zero = &zero_bias[..h];
                    let bi = if *has_bias { sl(*bias, base, h) } else { zero };
                    let x_ptr = sl(*x, base, rows * h).as_ptr() as usize;
                    let r_ptr = sl(*res, base, rows * h).as_ptr() as usize;
                    let o_ptr = sl_mut(*out, base, rows * h).as_mut_ptr() as usize;
                    let bi_ptr = bi.as_ptr() as usize;
                    let g_ptr = sl(*g, base, h).as_ptr() as usize;
                    let b_ptr = sl(*b, base, h).as_ptr() as usize;
                    let e = *eps;
                    crate::pool::par_for(rows, 4, &|off, cnt| {
                        let xs =
                            std::slice::from_raw_parts((x_ptr as *const f32).add(off * h), cnt * h);
                        let rs =
                            std::slice::from_raw_parts((r_ptr as *const f32).add(off * h), cnt * h);
                        let os = std::slice::from_raw_parts_mut(
                            (o_ptr as *mut f32).add(off * h),
                            cnt * h,
                        );
                        let bi = std::slice::from_raw_parts(bi_ptr as *const f32, h);
                        let g = std::slice::from_raw_parts(g_ptr as *const f32, h);
                        let b = std::slice::from_raw_parts(b_ptr as *const f32, h);
                        crate::kernels::residual_bias_rms_norm(xs, rs, bi, g, b, os, cnt, h, e);
                    });
                }
            }

            Thunk::BiasAdd {
                src,
                bias,
                dst,
                m,
                n,
            } => {
                let (m, n) = (*m as usize, *n as usize);
                let len = m * n;
                unsafe {
                    let out = sl_mut(*dst, base, len);
                    if *src != *dst {
                        let src_ptr = base.add(*src) as *const f32;
                        let dst_ptr = base.add(*dst) as *mut f32;
                        if src_ptr != dst_ptr {
                            std::ptr::copy_nonoverlapping(src_ptr, dst_ptr, len);
                        }
                    }
                    crate::blas::bias_add(out, sl(*bias, base, n), m, n);
                }
            }

            Thunk::BinaryFull {
                lhs,
                rhs,
                dst,
                len,
                lhs_len,
                rhs_len,
                op,
                out_dims_bcast,
                bcast_lhs_strides,
                bcast_rhs_strides,
                elem_bytes,
            } => {
                let len = *len as usize;
                let ll = (*lhs_len as usize).max(1);
                let rl = (*rhs_len as usize).max(1);
                let eb = (*elem_bytes).max(1) as usize;
                let arena_len = arena_buf.len();
                let ll = ll.min((arena_len.saturating_sub(*lhs)) / eb);
                let rl = rl.min((arena_len.saturating_sub(*rhs)) / eb);
                let len = len.min((arena_len.saturating_sub(*dst)) / eb);
                unsafe {
                    if eb == 8 {
                        let l = sl_i64(*lhs, base, ll);
                        let r = sl_i64(*rhs, base, rl);
                        let o = sl_mut_i64(*dst, base, len);
                        if !out_dims_bcast.is_empty() {
                            let rank = out_dims_bcast.len();
                            let mut coords = vec![0u32; rank];
                            for i in 0..len {
                                let mut rem = i;
                                for ax in (0..rank).rev() {
                                    let sz = out_dims_bcast[ax] as usize;
                                    coords[ax] = (rem % sz) as u32;
                                    rem /= sz;
                                }
                                let mut li = 0usize;
                                let mut ri = 0usize;
                                for ax in 0..rank {
                                    li += coords[ax] as usize * bcast_lhs_strides[ax] as usize;
                                    ri += coords[ax] as usize * bcast_rhs_strides[ax] as usize;
                                }
                                o[i] = match op {
                                    BinaryOp::Add => l[li].wrapping_add(r[ri]),
                                    BinaryOp::Sub => l[li].wrapping_sub(r[ri]),
                                    BinaryOp::Mul => l[li].wrapping_mul(r[ri]),
                                    BinaryOp::Div => {
                                        if r[ri] == 0 {
                                            0
                                        } else {
                                            l[li] / r[ri]
                                        }
                                    }
                                    BinaryOp::Max => l[li].max(r[ri]),
                                    BinaryOp::Min => l[li].min(r[ri]),
                                    BinaryOp::Pow => l[li].pow(r[ri].max(0) as u32),
                                };
                            }
                        } else {
                            for i in 0..len {
                                let li = if ll == 1 { 0 } else { i % ll };
                                let ri = if rl == 1 { 0 } else { i % rl };
                                o[i] = match op {
                                    BinaryOp::Add => l[li].wrapping_add(r[ri]),
                                    BinaryOp::Sub => l[li].wrapping_sub(r[ri]),
                                    BinaryOp::Mul => l[li].wrapping_mul(r[ri]),
                                    BinaryOp::Div => {
                                        if r[ri] == 0 {
                                            0
                                        } else {
                                            l[li] / r[ri]
                                        }
                                    }
                                    BinaryOp::Max => l[li].max(r[ri]),
                                    BinaryOp::Min => l[li].min(r[ri]),
                                    BinaryOp::Pow => l[li].pow(r[ri].max(0) as u32),
                                };
                            }
                        }
                    } else {
                        let l = sl(*lhs, base, ll);
                        let r = sl(*rhs, base, rl);
                        let o = sl_mut(*dst, base, len);
                        if ll == len && rl == len {
                            #[cfg(target_arch = "aarch64")]
                            if matches!(op, BinaryOp::Add | BinaryOp::Mul) {
                                use std::arch::aarch64::*;
                                let chunks = len / 4;
                                for c in 0..chunks {
                                    let off = c * 4;
                                    let vl = vld1q_f32(l.as_ptr().add(off));
                                    let vr = vld1q_f32(r.as_ptr().add(off));
                                    let res = match op {
                                        BinaryOp::Add => vaddq_f32(vl, vr),
                                        BinaryOp::Mul => vmulq_f32(vl, vr),
                                        _ => unreachable!(),
                                    };
                                    vst1q_f32(o.as_mut_ptr().add(off), res);
                                }
                                for i in (chunks * 4)..len {
                                    o[i] = match op {
                                        BinaryOp::Add => l[i] + r[i],
                                        BinaryOp::Mul => l[i] * r[i],
                                        _ => unreachable!(),
                                    };
                                }
                                continue;
                            }
                        }
                        if !out_dims_bcast.is_empty() {
                            let rank = out_dims_bcast.len();
                            let mut coords = vec![0u32; rank];
                            for i in 0..len {
                                let mut rem = i;
                                for ax in (0..rank).rev() {
                                    let sz = out_dims_bcast[ax] as usize;
                                    coords[ax] = (rem % sz) as u32;
                                    rem /= sz;
                                }
                                let mut li = 0usize;
                                let mut ri = 0usize;
                                for ax in 0..rank {
                                    li += coords[ax] as usize * bcast_lhs_strides[ax] as usize;
                                    ri += coords[ax] as usize * bcast_rhs_strides[ax] as usize;
                                }
                                o[i] = match op {
                                    BinaryOp::Add => l[li] + r[ri],
                                    BinaryOp::Sub => l[li] - r[ri],
                                    BinaryOp::Mul => l[li] * r[ri],
                                    BinaryOp::Div => l[li] / r[ri],
                                    BinaryOp::Max => l[li].max(r[ri]),
                                    BinaryOp::Min => l[li].min(r[ri]),
                                    BinaryOp::Pow => l[li].powf(r[ri]),
                                };
                            }
                        } else {
                            for i in 0..len {
                                let li = if ll == 1 { 0 } else { i % ll };
                                let ri = if rl == 1 { 0 } else { i % rl };
                                o[i] = match op {
                                    BinaryOp::Add => l[li] + r[ri],
                                    BinaryOp::Sub => l[li] - r[ri],
                                    BinaryOp::Mul => l[li] * r[ri],
                                    BinaryOp::Div => l[li] / r[ri],
                                    BinaryOp::Max => l[li].max(r[ri]),
                                    BinaryOp::Min => l[li].min(r[ri]),
                                    BinaryOp::Pow => l[li].powf(r[ri]),
                                };
                            }
                        }
                    }
                }
            }

            Thunk::Gather {
                table,
                table_len,
                idx,
                dst,
                num_idx,
                trailing,
                idx_i64,
                table_bytes,
            } => {
                let (ni, tr) = (*num_idx as usize, *trailing as usize);
                let rows = *table_len as usize / tr.max(1);
                unsafe {
                    if *table_bytes == 8 {
                        let tab = sl_i64(*table, base, *table_len as usize);
                        let out = sl_mut_i64(*dst, base, ni * tr);
                        if *idx_i64 != 0 {
                            let ids = sl_i64(*idx, base, ni);
                            for i in 0..ni {
                                let row = ids[i].max(0) as usize;
                                if row < rows {
                                    out[i * tr..(i + 1) * tr]
                                        .copy_from_slice(&tab[row * tr..(row + 1) * tr]);
                                }
                            }
                        } else {
                            let ids = sl(*idx, base, ni);
                            for i in 0..ni {
                                let row = ids[i] as usize;
                                if row < rows {
                                    out[i * tr..(i + 1) * tr]
                                        .copy_from_slice(&tab[row * tr..(row + 1) * tr]);
                                }
                            }
                        }
                    } else {
                        let tab = sl(*table, base, *table_len as usize);
                        let out = sl_mut(*dst, base, ni * tr);
                        if *idx_i64 != 0 {
                            let ids = sl_i64(*idx, base, ni);
                            for i in 0..ni {
                                let row = ids[i].max(0) as usize;
                                if row < rows {
                                    out[i * tr..(i + 1) * tr]
                                        .copy_from_slice(&tab[row * tr..(row + 1) * tr]);
                                }
                            }
                        } else {
                            let ids = sl(*idx, base, ni);
                            for i in 0..ni {
                                let row = ids[i] as usize;
                                if row < rows {
                                    out[i * tr..(i + 1) * tr]
                                        .copy_from_slice(&tab[row * tr..(row + 1) * tr]);
                                }
                            }
                        }
                    }
                }
            }

            Thunk::Narrow {
                src,
                dst,
                outer,
                src_stride,
                dst_stride,
                inner,
                elem_bytes,
            } => {
                let (outer, ss, ds, inner, eb) = (
                    *outer as usize,
                    *src_stride as usize,
                    *dst_stride as usize,
                    *inner as usize,
                    *elem_bytes as usize,
                );
                let row_bytes = inner.saturating_mul(eb);
                let src_row_stride = ss.saturating_mul(eb);
                let dst_row_stride = ds.saturating_mul(eb);
                if trace_thunks {
                    eprintln!(
                        "[narrow] src={} dst={} outer={outer} ss={ss} ds={ds} inner={inner} eb={eb} row={row_bytes} arena={}",
                        *src,
                        *dst,
                        arena_buf.len()
                    );
                }
                if row_bytes > 0 && *src != *dst {
                    let arena_len = arena_buf.len();
                    for o in 0..outer {
                        let s_off = *src + o * src_row_stride;
                        let d_off = *dst + o * dst_row_stride;
                        if s_off == d_off {
                            continue;
                        }
                        if s_off.saturating_add(row_bytes) > arena_len
                            || d_off.saturating_add(row_bytes) > arena_len
                        {
                            break;
                        }
                        unsafe {
                            std::ptr::copy_nonoverlapping(
                                base.add(s_off),
                                base.add(d_off),
                                row_bytes,
                            );
                        }
                    }
                }
            }

            Thunk::Copy { src, dst, len } => {
                let mut len = *len as usize;
                if *src == *dst || len == 0 {
                    continue;
                }
                let arena_len = arena_buf.len();
                let max_from_src = (arena_len.saturating_sub(*src)) / 4;
                let max_from_dst = (arena_len.saturating_sub(*dst)) / 4;
                len = len.min(max_from_src).min(max_from_dst);
                if len == 0 {
                    continue;
                }
                let byte_len = len.saturating_mul(4);
                unsafe {
                    std::ptr::copy(base.add(*src), base.add(*dst), byte_len);
                }
            }

            Thunk::LayerNorm {
                src,
                g,
                b,
                dst,
                rows,
                h,
                eps,
            } => {
                let (rows, h) = (*rows as usize, *h as usize);
                unsafe {
                    let input = sl(*src, base, rows * h);
                    let gamma = sl(*g, base, h);
                    let beta = sl(*b, base, h);
                    let output = sl_mut(*dst, base, rows * h);
                    // Parallelize across rows (same pattern as FusedResidualLN)
                    if rows >= 4 && rows * h >= 30_000 {
                        let i_ptr = input.as_ptr() as usize;
                        let o_ptr = output.as_mut_ptr() as usize;
                        let g_ptr = gamma.as_ptr() as usize;
                        let b_ptr = beta.as_ptr() as usize;
                        let e = *eps;
                        crate::pool::par_for(rows, 4, &|off, cnt| {
                            let inp = std::slice::from_raw_parts(
                                (i_ptr as *const f32).add(off * h),
                                cnt * h,
                            );
                            let out = std::slice::from_raw_parts_mut(
                                (o_ptr as *mut f32).add(off * h),
                                cnt * h,
                            );
                            let g = std::slice::from_raw_parts(g_ptr as *const f32, h);
                            let b = std::slice::from_raw_parts(b_ptr as *const f32, h);
                            for row in 0..cnt {
                                crate::kernels::layer_norm_row(
                                    &inp[row * h..(row + 1) * h],
                                    g,
                                    b,
                                    &mut out[row * h..(row + 1) * h],
                                    h,
                                    e,
                                );
                            }
                        });
                    } else {
                        for row in 0..rows {
                            crate::kernels::layer_norm_row(
                                &input[row * h..(row + 1) * h],
                                gamma,
                                beta,
                                &mut output[row * h..(row + 1) * h],
                                h,
                                *eps,
                            );
                        }
                    }
                }
            }

            Thunk::GroupNorm {
                src,
                g,
                b,
                dst,
                n,
                c,
                h,
                w,
                num_groups,
                eps,
            } => {
                let (n, c, h, w) = (*n as usize, *c as usize, *h as usize, *w as usize);
                let plane = c * h * w;
                unsafe {
                    // Per-batch plane stride is `plane` *f32 elements*; `base`
                    // is a byte pointer, so advance by `plane * size_of::<f32>()`.
                    // (Was `base.add(ni * plane)` — a 4× under-advance that read
                    // batch row 1+ from the wrong offset; only n=1 was tested.)
                    let stride = plane * std::mem::size_of::<f32>();
                    for ni in 0..n {
                        let input = sl(*src, base.add(ni * stride), plane);
                        let gamma = sl(*g, base, c);
                        let beta = sl(*b, base, c);
                        let output = sl_mut(*dst, base.add(ni * stride), plane);
                        crate::kernels::group_norm_nchw(
                            input,
                            gamma,
                            beta,
                            output,
                            1,
                            c,
                            h,
                            w,
                            *num_groups as usize,
                            *eps,
                        );
                    }
                }
            }

            Thunk::BatchNormInference {
                src,
                g,
                b,
                mean,
                var,
                dst,
                count,
                channels,
                eps,
            } => {
                let count = *count as usize;
                let c = *channels as usize;
                let n = count * c;
                unsafe {
                    crate::kernels::batch_norm_inference(
                        sl(*src, base, n),
                        sl(*g, base, c),
                        sl(*b, base, c),
                        sl(*mean, base, c),
                        sl(*var, base, c),
                        sl_mut(*dst, base, n),
                        c,
                        *eps,
                    );
                }
            }

            Thunk::LayerNorm2d {
                src,
                g,
                b,
                dst,
                n,
                c,
                h,
                w,
                eps,
            } => {
                let (n, c, h, w) = (*n as usize, *c as usize, *h as usize, *w as usize);
                let plane = c * h * w;
                unsafe {
                    let input = sl(*src, base, n * plane);
                    let gamma = sl(*g, base, c);
                    let beta = sl(*b, base, c);
                    let output = sl_mut(*dst, base, n * plane);
                    crate::kernels::layer_norm2d_nchw(input, gamma, beta, output, n, c, h, w, *eps);
                }
            }

            Thunk::ConvTranspose2d {
                src,
                weight,
                dst,
                n,
                c_in,
                h,
                w_in,
                c_out,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
                dh,
                dw,
                groups,
            } => {
                let n = *n as usize;
                let c_in = *c_in as usize;
                let h = *h as usize;
                let w_in = *w_in as usize;
                let c_out = *c_out as usize;
                let h_out = *h_out as usize;
                let w_out = *w_out as usize;
                unsafe {
                    let inp = sl(*src, base, n * c_in * h * w_in);
                    let wt = sl(
                        *weight,
                        base,
                        c_in * (c_out / *groups as usize) * (*kh as usize) * (*kw as usize),
                    );
                    let out = sl_mut(*dst, base, n * c_out * h_out * w_out);
                    crate::kernels::conv_transpose2d_nchw(
                        inp,
                        wt,
                        out,
                        n,
                        c_in,
                        h,
                        w_in,
                        c_out,
                        h_out,
                        w_out,
                        *kh as usize,
                        *kw as usize,
                        *sh as usize,
                        *sw as usize,
                        *ph as usize,
                        *pw as usize,
                        *dh as usize,
                        *dw as usize,
                        *groups as usize,
                    );
                }
            }

            Thunk::ResizeNearest2x {
                src,
                dst,
                n,
                c,
                h,
                w,
            } => {
                let (n, c, h, w) = (*n as usize, *c as usize, *h as usize, *w as usize);
                let in_plane = c * h * w;
                let out_plane = c * h * 2 * w * 2;
                // `base` is a byte pointer; batch planes are f32 elements, so
                // advance by `plane * size_of::<f32>()`. (Was `base.add(ni *
                // plane)` — a 4× under-advance; only n=1 was ever tested.)
                let fsz = std::mem::size_of::<f32>();
                unsafe {
                    for ni in 0..n {
                        let input = sl(*src, base.add(ni * in_plane * fsz), in_plane);
                        let output = sl_mut(*dst, base.add(ni * out_plane * fsz), out_plane);
                        crate::kernels::resize_nearest_2x_nchw(input, output, c, h, w);
                    }
                }
            }

            Thunk::AxialRope2d {
                src,
                dst,
                batch,
                seq,
                hidden,
                end_x,
                end_y,
                head_dim,
                num_heads,
                theta,
                repeat_factor,
            } => {
                let b = *batch as usize;
                let s = *seq as usize;
                let hdim = *head_dim as usize;
                let nh = *num_heads as usize;
                let plane = s * (*hidden as usize);
                // `base` is a byte pointer; advance per-batch by element-stride
                // bytes. (Was `base.add(bi * plane)` — 4× under-advance for
                // batch>1; matches the corrected `execute_axial_rope2d_f32`.)
                let plane_bytes = plane * std::mem::size_of::<f32>();
                unsafe {
                    for bi in 0..b {
                        let input = sl(*src, base.add(bi * plane_bytes), plane);
                        let output = sl_mut(*dst, base.add(bi * plane_bytes), plane);
                        let rotated = rlx_ir::ops::axial_rope2d::apply_axial_rope2d(
                            input,
                            nh,
                            s,
                            hdim,
                            *end_x as usize,
                            *end_y as usize,
                            *theta,
                            *repeat_factor as usize,
                        );
                        output.copy_from_slice(&rotated);
                    }
                }
            }

            Thunk::RmsNorm {
                src,
                g,
                b,
                dst,
                rows,
                h,
                eps,
            } => {
                let (rows, h) = (*rows as usize, *h as usize);
                unsafe {
                    let input = sl(*src, base, rows * h);
                    let gamma = sl(*g, base, h);
                    let beta = sl(*b, base, h);
                    let output = sl_mut(*dst, base, rows * h);
                    let inv_h = 1.0 / h as f32;
                    for row in 0..rows {
                        let in_row = &input[row * h..(row + 1) * h];
                        let out_row = &mut output[row * h..(row + 1) * h];
                        // RMS = sqrt(mean(x^2) + eps); scale = 1/RMS.
                        let mut sumsq = 0f32;
                        for &v in in_row {
                            sumsq += v * v;
                        }
                        let inv_rms = (sumsq * inv_h + *eps).sqrt().recip();
                        for i in 0..h {
                            out_row[i] = in_row[i] * inv_rms * gamma[i] + beta[i];
                        }
                    }
                }
            }

            Thunk::Softmax { data, rows, cols } => {
                let (rows, cols) = (*rows as usize, *cols as usize);
                unsafe {
                    crate::kernels::neon_softmax(sl_mut(*data, base, rows * cols), rows, cols);
                }
            }

            Thunk::Cumsum {
                src,
                dst,
                rows,
                cols,
                exclusive,
            } => {
                let (rows, cols) = (*rows as usize, *cols as usize);
                unsafe {
                    let s = sl(*src, base, rows * cols);
                    let d = sl_mut(*dst, base, rows * cols);
                    if *exclusive {
                        for r in 0..rows {
                            let mut acc = 0.0f32;
                            for c in 0..cols {
                                d[r * cols + c] = acc;
                                acc += s[r * cols + c];
                            }
                        }
                    } else {
                        for r in 0..rows {
                            let mut acc = 0.0f32;
                            for c in 0..cols {
                                acc += s[r * cols + c];
                                d[r * cols + c] = acc;
                            }
                        }
                    }
                }
            }

            Thunk::Sample {
                logits,
                dst,
                batch,
                vocab,
                top_k,
                top_p,
                temperature,
                seed,
            } => unsafe {
                execute_sample_f32(
                    *logits,
                    *dst,
                    *batch as usize,
                    *vocab as usize,
                    *top_k as usize,
                    *top_p,
                    *temperature,
                    *seed,
                    base,
                );
            },

            Thunk::RngNormal {
                dst,
                len,
                mean,
                scale,
                key,
                op_seed,
            } => {
                let n = *len as usize;
                unsafe {
                    let out = sl_mut(*dst, base, n);
                    let opts = *schedule.rng.read().unwrap();
                    rlx_ir::fill_normal_like(out, *mean, *scale, opts, *key, *op_seed);
                }
            }

            Thunk::RngUniform {
                dst,
                len,
                low,
                high,
                key,
                op_seed,
            } => {
                let n = *len as usize;
                unsafe {
                    let out = sl_mut(*dst, base, n);
                    let opts = *schedule.rng.read().unwrap();
                    rlx_ir::fill_uniform_like(out, *low, *high, opts, *key, *op_seed);
                }
            }

            Thunk::GatedDeltaNet {
                q,
                k,
                v,
                g,
                beta,
                state,
                dst,
                batch,
                seq,
                heads,
                state_size,
            } => unsafe {
                execute_gated_delta_net_f32(
                    *q,
                    *k,
                    *v,
                    *g,
                    *beta,
                    *state,
                    *dst,
                    *batch as usize,
                    *seq as usize,
                    *heads as usize,
                    *state_size as usize,
                    base,
                );
            },

            Thunk::Lstm {
                x,
                w_ih,
                w_hh,
                bias,
                h0,
                c0,
                dst,
                batch,
                seq,
                input_size,
                hidden,
                num_layers,
                bidirectional,
                carry,
            } => unsafe {
                execute_lstm_f32(
                    *x,
                    *w_ih,
                    *w_hh,
                    *bias,
                    *h0,
                    *c0,
                    *dst,
                    *batch as usize,
                    *seq as usize,
                    *input_size as usize,
                    *hidden as usize,
                    *num_layers as usize,
                    *bidirectional,
                    *carry,
                    base,
                );
            },

            Thunk::Gru {
                x,
                w_ih,
                w_hh,
                b_ih,
                b_hh,
                h0,
                dst,
                batch,
                seq,
                input_size,
                hidden,
                num_layers,
                bidirectional,
                carry,
            } => unsafe {
                execute_gru_f32(
                    *x,
                    *w_ih,
                    *w_hh,
                    *b_ih,
                    *b_hh,
                    *h0,
                    *dst,
                    *batch as usize,
                    *seq as usize,
                    *input_size as usize,
                    *hidden as usize,
                    *num_layers as usize,
                    *bidirectional,
                    *carry,
                    base,
                );
            },

            Thunk::Rnn {
                x,
                w_ih,
                w_hh,
                bias,
                h0,
                dst,
                batch,
                seq,
                input_size,
                hidden,
                num_layers,
                bidirectional,
                carry,
                relu,
            } => unsafe {
                execute_rnn_f32(
                    *x,
                    *w_ih,
                    *w_hh,
                    *bias,
                    *h0,
                    *dst,
                    *batch as usize,
                    *seq as usize,
                    *input_size as usize,
                    *hidden as usize,
                    *num_layers as usize,
                    *bidirectional,
                    *carry,
                    *relu,
                    base,
                );
            },

            Thunk::Mamba2 {
                x,
                dt,
                a,
                b,
                c,
                dst,
                batch,
                seq,
                heads,
                head_dim,
                state_size,
            } => unsafe {
                execute_mamba2_f32(
                    *x,
                    *dt,
                    *a,
                    *b,
                    *c,
                    *dst,
                    *batch as usize,
                    *seq as usize,
                    *heads as usize,
                    *head_dim as usize,
                    *state_size as usize,
                    base,
                );
            },

            Thunk::SelectiveScan {
                x,
                delta,
                a,
                b: bp,
                c: cp,
                dst,
                batch,
                seq,
                hidden,
                state_size,
            } => unsafe {
                execute_selective_scan_f32(
                    *x,
                    *delta,
                    *a,
                    *bp,
                    *cp,
                    *dst,
                    *batch as usize,
                    *seq as usize,
                    *hidden as usize,
                    *state_size as usize,
                    base,
                );
            },

            Thunk::DequantMatMul {
                x,
                w_q,
                scale,
                zp,
                dst,
                m,
                k,
                n,
                block_size,
                is_asymmetric,
            } => {
                let (m, k, n, bs) = (*m as usize, *k as usize, *n as usize, *block_size as usize);
                let n_blocks = k.div_ceil(bs);
                unsafe {
                    let xs = sl(*x, base, m * k);
                    let w_bytes = std::slice::from_raw_parts(base.add(*w_q) as *const i8, k * n);
                    let scales = sl(*scale, base, n_blocks * n);
                    let zps = if *is_asymmetric {
                        sl(*zp, base, n_blocks * n)
                    } else {
                        &[][..]
                    };
                    let out = sl_mut(*dst, base, m * n);
                    dequant_matmul_int8(xs, w_bytes, scales, zps, out, m, k, n, bs, *is_asymmetric);
                }
            }

            Thunk::DequantMatMulGguf {
                x,
                w_q,
                dst,
                m,
                k,
                n,
                scheme,
            } => {
                let (m, k, n) = (*m as usize, *k as usize, *n as usize);
                let block_bytes = scheme.gguf_block_bytes() as usize;
                let block_elems = scheme.gguf_block_size() as usize;
                debug_assert!(
                    block_bytes > 0 && block_elems > 0,
                    "non-GGUF scheme in GGUF arm"
                );
                debug_assert!(
                    (k * n).is_multiple_of(block_elems),
                    "k*n={} not aligned to GGUF block size {}",
                    k * n,
                    block_elems
                );
                let total_bytes = (k * n) / block_elems * block_bytes;
                unsafe {
                    let xs = sl(*x, base, m * k);
                    let w_bytes_ptr = base.add(*w_q) as *const u8;
                    let w_bytes = std::slice::from_raw_parts(w_bytes_ptr, total_bytes);
                    let out = sl_mut(*dst, base, m * n);
                    crate::gguf_matmul::gguf_matmul_bt(xs, w_bytes, out, m, k, n, *scheme);
                }
            }

            Thunk::DequantMatMulInt4 {
                x,
                w_q,
                scale,
                zp,
                dst,
                m,
                k,
                n,
                block_size,
                is_asymmetric,
            } => {
                let (m, k, n, bs) = (*m as usize, *k as usize, *n as usize, *block_size as usize);
                let n_blocks = k.div_ceil(bs);
                unsafe {
                    let xs = sl(*x, base, m * k);
                    let w_bytes = std::slice::from_raw_parts(
                        base.add(*w_q) as *const u8,
                        (k * n).div_ceil(2),
                    );
                    let scales = sl(*scale, base, n_blocks * n);
                    let zps = if *is_asymmetric {
                        sl(*zp, base, n_blocks * n)
                    } else {
                        &[][..]
                    };
                    let out = sl_mut(*dst, base, m * n);
                    dequant_matmul_int4(xs, w_bytes, scales, zps, out, m, k, n, bs, *is_asymmetric);
                }
            }

            Thunk::DequantMatMulFp8 {
                x,
                w_q,
                scale,
                dst,
                m,
                k,
                n,
                e5m2,
            } => {
                let (m, k, n) = (*m as usize, *k as usize, *n as usize);
                unsafe {
                    let xs = sl(*x, base, m * k);
                    let w_bytes = std::slice::from_raw_parts(base.add(*w_q) as *const u8, k * n);
                    let scales = sl(*scale, base, n);
                    let out = sl_mut(*dst, base, m * n);
                    dequant_matmul_fp8(xs, w_bytes, scales, out, m, k, n, *e5m2);
                }
            }

            Thunk::DequantMatMulNvfp4 {
                x,
                w_q,
                scale,
                global_scale,
                dst,
                m,
                k,
                n,
            } => {
                let (m, k, n) = (*m as usize, *k as usize, *n as usize);
                let n_scale = k.div_ceil(rlx_ir::NVFP4_GROUP_SIZE) * n;
                unsafe {
                    let xs = sl(*x, base, m * k);
                    let w_bytes = std::slice::from_raw_parts(
                        base.add(*w_q) as *const u8,
                        (k * n).div_ceil(2),
                    );
                    let scale_bytes =
                        std::slice::from_raw_parts(base.add(*scale) as *const u8, n_scale);
                    let gs = sl(*global_scale, base, 1)[0];
                    let out = sl_mut(*dst, base, m * n);
                    dequant_matmul_nvfp4(xs, w_bytes, scale_bytes, gs, out, m, k, n);
                }
            }

            Thunk::ScaledMatMul {
                lhs,
                rhs,
                lhs_scale,
                rhs_scale,
                bias,
                dst,
                m,
                k,
                n,
                lhs_fmt,
                rhs_fmt,
                layout,
                has_bias,
            } => {
                let (m, k, n) = (*m as usize, *k as usize, *n as usize);
                let layout = *layout;
                let nblk = lowp_nblk(k, layout);
                let per_tensor = matches!(layout, rlx_ir::ScaleLayout::PerTensor);
                let n_lscale = if per_tensor { 1 } else { m * nblk };
                let n_rscale = if per_tensor { 1 } else { n * nblk };
                unsafe {
                    let lhs_b = std::slice::from_raw_parts(base.add(*lhs) as *const u8, m * k);
                    let rhs_b = std::slice::from_raw_parts(base.add(*rhs) as *const u8, n * k);
                    let ls = lowp_read_scales(layout, base, *lhs_scale, n_lscale);
                    let rs = lowp_read_scales(layout, base, *rhs_scale, n_rscale);
                    let bias_s = if *has_bias {
                        Some(sl(*bias, base, n))
                    } else {
                        None
                    };
                    let out = sl_mut(*dst, base, m * n);
                    lowp_scaled_matmul(
                        lhs_b, rhs_b, &ls, &rs, bias_s, out, m, n, k, layout, *lhs_fmt, *rhs_fmt,
                    );
                }
            }

            Thunk::ScaledQuantize {
                x,
                scale,
                dst,
                rows,
                cols,
                fmt,
                layout,
            } => {
                let (rows, cols) = (*rows as usize, *cols as usize);
                let layout = *layout;
                let nblk = lowp_nblk(cols, layout);
                let n_scale = if matches!(layout, rlx_ir::ScaleLayout::PerTensor) {
                    1
                } else {
                    rows * nblk
                };
                unsafe {
                    let xs = sl(*x, base, rows * cols);
                    let scales = lowp_read_scales(layout, base, *scale, n_scale);
                    let out = std::slice::from_raw_parts_mut(base.add(*dst), rows * cols);
                    lowp_quantize(xs, &scales, *fmt, layout, rows, cols, out);
                }
            }

            Thunk::ScaledQuantScale {
                x,
                dst,
                rows,
                cols,
                fmt,
                layout,
            } => {
                let (rows, cols) = (*rows as usize, *cols as usize);
                let layout = *layout;
                let nblk = lowp_nblk(cols, layout);
                unsafe {
                    let xs = sl(*x, base, rows * cols);
                    let scales = lowp_compute_scales(xs, *fmt, layout, rows, cols);
                    match layout {
                        rlx_ir::ScaleLayout::PerTensor => {
                            sl_mut(*dst, base, 1)[0] = scales[0];
                        }
                        rlx_ir::ScaleLayout::BlockMxE8M0 { .. } => {
                            let out = std::slice::from_raw_parts_mut(base.add(*dst), rows * nblk);
                            for (o, &s) in out.iter_mut().zip(&scales) {
                                *o = rlx_ir::lowp_codec::f32_to_e8m0(s);
                            }
                        }
                        rlx_ir::ScaleLayout::Nvfp4 { .. } => {
                            let out = std::slice::from_raw_parts_mut(base.add(*dst), rows * nblk);
                            for (o, &s) in out.iter_mut().zip(&scales) {
                                *o = rlx_ir::lowp_codec::encode(rlx_ir::ScaledFormat::F8E4M3, s);
                            }
                        }
                    }
                }
            }

            Thunk::ScaledDequantize {
                codes,
                scale,
                dst,
                rows,
                cols,
                fmt,
                layout,
            } => unsafe {
                execute_scaled_dequantize_f32(
                    *codes,
                    *scale,
                    *dst,
                    *rows as usize,
                    *cols as usize,
                    *fmt,
                    *layout,
                    base,
                );
            },

            Thunk::LoraMatMul {
                x,
                w,
                a,
                b,
                dst,
                m,
                k,
                n,
                r,
                scale,
            } => {
                let (m, k, n, r) = (*m as usize, *k as usize, *n as usize, *r as usize);
                unsafe {
                    let xs = sl(*x, base, m * k);
                    let ws = sl(*w, base, k * n);
                    let a_s = sl(*a, base, k * r);
                    let bs = sl(*b, base, r * n);
                    let out = sl_mut(*dst, base, m * n);
                    crate::blas::sgemm(xs, ws, out, m, k, n);
                    let mut tmp = vec![0f32; m * r];
                    crate::blas::sgemm(xs, a_s, &mut tmp, m, k, r);
                    if *scale != 1.0 {
                        for v in tmp.iter_mut() {
                            *v *= *scale;
                        }
                    }
                    crate::blas::sgemm_accumulate(&tmp, bs, out, m, r, n);
                }
            }

            Thunk::Attention {
                q,
                k,
                v,
                mask,
                out,
                batch,
                seq,
                kv_seq,
                heads,
                head_dim,
                mask_kind,
                scale,
                q_row_stride,
                k_row_stride,
                v_row_stride,
                bhsd,
            } => {
                let (b, q_s, k_s, nh, dh) = (
                    *batch as usize,
                    *seq as usize,
                    *kv_seq as usize,
                    *heads as usize,
                    *head_dim as usize,
                );
                let hs = nh * dh;
                // For [B, H, S, D] layout each (b, h) tile is dense
                // contiguous; the qrs/krs/vrs strides are not used.
                let (qrs, krs, vrs) = if *bhsd {
                    (dh, dh, dh)
                } else {
                    (
                        *q_row_stride as usize,
                        *k_row_stride as usize,
                        *v_row_stride as usize,
                    )
                };
                let bhsd = *bhsd;
                let _ = (q_row_stride, k_row_stride, v_row_stride);
                let scale = *scale;
                let ss = q_s * k_s;
                let cfg = crate::config::RuntimeConfig::global();
                unsafe {
                    // Slice lengths cover the strided span. When Q/K/V
                    // alias the parent QKV (post-#46-fusion), the same
                    // bytes back all three slices — compiler bounds
                    // checks see the right size. For [B, H, S, D] the
                    // buffer is densely B*H*S*D elements; the row
                    // strides aren't used.
                    let q_len = if bhsd {
                        b * nh * q_s * dh
                    } else {
                        b * q_s * qrs
                    };
                    let k_len = if bhsd {
                        b * nh * k_s * dh
                    } else {
                        b * k_s * krs
                    };
                    let v_len = if bhsd {
                        b * nh * k_s * dh
                    } else {
                        b * k_s * vrs
                    };
                    let q_data = sl(*q, base, q_len);
                    let k_data = sl(*k, base, k_len);
                    let v_data = sl(*v, base, v_len);
                    let mask_data: &[f32] = match mask_kind {
                        rlx_ir::op::MaskKind::Custom => sl(*mask, base, b * k_s),
                        rlx_ir::op::MaskKind::Bias => sl(*mask, base, b * nh * q_s * k_s),
                        _ => &[],
                    };
                    let out_len = if bhsd {
                        b * nh * q_s * dh
                    } else {
                        b * q_s * hs
                    };
                    let out_data = sl_mut(*out, base, out_len);

                    // ── [B, H, S, D] fallback ──────────────────────
                    // The NEON / strided-BLAS specializations below
                    // are written for the [B, S, H, D] layout. When
                    // the input is head-major ([B, H, S, D] —
                    // matching rlx-cuda / rlx-rocm / rlx-tpu), bypass
                    // them and run a simple (correct but slower)
                    // scalar implementation. Production-CPU inference
                    // graphs use [B, S, H, D] so they still hit the
                    // hot path; cross-backend parity tests use
                    // [B, H, S, D] and land here.
                    if bhsd {
                        let scores = &mut sdpa_scores[..ss];
                        for bi in 0..b {
                            for hi in 0..nh {
                                let q_head_base = bi * nh * q_s * dh + hi * q_s * dh;
                                let k_head_base = bi * nh * k_s * dh + hi * k_s * dh;
                                // Q@K^T
                                for qi in 0..q_s {
                                    let q_base = q_head_base + qi * dh;
                                    for ki in 0..k_s {
                                        let k_base = k_head_base + ki * dh;
                                        let mut dot = 0f32;
                                        for d in 0..dh {
                                            dot += q_data[q_base + d] * k_data[k_base + d];
                                        }
                                        scores[qi * k_s + ki] = dot * scale;
                                        if matches!(mask_kind, rlx_ir::op::MaskKind::Custom)
                                            && !mask_data.is_empty()
                                            && mask_data[bi * k_s + ki] < mask_thr
                                        {
                                            scores[qi * k_s + ki] = mask_neg;
                                        }
                                    }
                                }
                                if matches!(mask_kind, rlx_ir::op::MaskKind::Bias) {
                                    let off = (bi * nh + hi) * q_s * k_s;
                                    for i in 0..q_s * k_s {
                                        scores[i] += mask_data[off + i];
                                    }
                                }
                                apply_synthetic_mask(scores, q_s, k_s, *mask_kind);
                                crate::kernels::neon_softmax(scores, q_s, k_s);
                                // score @ V
                                for qi in 0..q_s {
                                    let o_base = q_head_base + qi * dh;
                                    for d in 0..dh {
                                        out_data[o_base + d] = 0.0;
                                    }
                                    for ki in 0..k_s {
                                        let sc = scores[qi * k_s + ki];
                                        if sc > score_thr {
                                            let v_base = k_head_base + ki * dh;
                                            for d in 0..dh {
                                                out_data[o_base + d] += sc * v_data[v_base + d];
                                            }
                                        }
                                    }
                                }
                            }
                        }
                        continue;
                    }

                    // ── Auto-select kernel: NEON dots vs strided BLAS ───
                    // For tiny inputs (batch=1, short seq), per-head BLAS call
                    // overhead (~0.5µs × 2 calls × num_heads × num_layers)
                    // exceeds the NEON compute cost. Use direct strided NEON
                    // with zero dispatch overhead.
                    // For batch≥2: always BLAS + par_for (parallelism wins).
                    if b == 1 && q_s.max(k_s) <= cfg.sdpa_seq_threshold {
                        // ── Sequential NEON path (zero overhead) ──
                        let scores = &mut sdpa_scores[..ss];
                        #[cfg(target_arch = "aarch64")]
                        let neon_chunks = dh / 4;

                        for bi in 0..b {
                            for hi in 0..nh {
                                // Q@K^T via strided NEON dot products
                                for qi in 0..q_s {
                                    let q_off = bi * q_s * qrs + qi * qrs + hi * dh;
                                    for ki in 0..k_s {
                                        let k_off = bi * k_s * krs + ki * krs + hi * dh;
                                        #[cfg(target_arch = "aarch64")]
                                        let mut dot;
                                        #[cfg(not(target_arch = "aarch64"))]
                                        let mut dot = 0f32;
                                        #[cfg(target_arch = "aarch64")]
                                        {
                                            use std::arch::aarch64::*;
                                            let mut acc = vdupq_n_f32(0.0);
                                            for c in 0..neon_chunks {
                                                let vq =
                                                    vld1q_f32(q_data.as_ptr().add(q_off + c * 4));
                                                let vk =
                                                    vld1q_f32(k_data.as_ptr().add(k_off + c * 4));
                                                acc = vfmaq_f32(acc, vq, vk);
                                            }
                                            dot = vaddvq_f32(acc);
                                            for d in (neon_chunks * 4)..dh {
                                                dot += q_data[q_off + d] * k_data[k_off + d];
                                            }
                                        }
                                        #[cfg(not(target_arch = "aarch64"))]
                                        for d in 0..dh {
                                            dot += q_data[q_off + d] * k_data[k_off + d];
                                        }
                                        scores[qi * k_s + ki] = dot * scale;
                                        // Inner-loop Custom mask check —
                                        // Causal / SlidingWindow / None
                                        // apply outside the loop below.
                                        // Skip for Bias — that mask is a
                                        // per-head additive tensor, not a
                                        // 0/1 key-padding mask.
                                        if matches!(mask_kind, rlx_ir::op::MaskKind::Custom)
                                            && !mask_data.is_empty()
                                            && mask_data[bi * k_s + ki] < mask_thr
                                        {
                                            scores[qi * k_s + ki] = mask_neg;
                                        }
                                    }
                                }

                                if matches!(mask_kind, rlx_ir::op::MaskKind::Bias) {
                                    let off = (bi * nh + hi) * q_s * k_s;
                                    for i in 0..q_s * k_s {
                                        scores[i] += mask_data[off + i];
                                    }
                                }
                                apply_synthetic_mask(scores, q_s, k_s, *mask_kind);
                                crate::kernels::neon_softmax(scores, q_s, k_s);

                                // Score@V via strided NEON accumulation (zero-copy)
                                for qi in 0..q_s {
                                    let o_off = bi * q_s * hs + qi * hs + hi * dh;
                                    // Zero output for this head position
                                    for d in 0..dh {
                                        out_data[o_off + d] = 0.0;
                                    }
                                    for ki in 0..k_s {
                                        let sc = scores[qi * k_s + ki];
                                        if sc > score_thr {
                                            let v_off = bi * k_s * vrs + ki * vrs + hi * dh;
                                            #[cfg(target_arch = "aarch64")]
                                            {
                                                use std::arch::aarch64::*;
                                                let vsc = vdupq_n_f32(sc);
                                                for c in 0..neon_chunks {
                                                    let off = c * 4;
                                                    let vo = vld1q_f32(
                                                        out_data.as_ptr().add(o_off + off),
                                                    );
                                                    let vv =
                                                        vld1q_f32(v_data.as_ptr().add(v_off + off));
                                                    vst1q_f32(
                                                        out_data.as_mut_ptr().add(o_off + off),
                                                        vfmaq_f32(vo, vsc, vv),
                                                    );
                                                }
                                            }
                                            #[cfg(not(target_arch = "aarch64"))]
                                            for d in 0..dh {
                                                out_data[o_off + d] += sc * v_data[v_off + d];
                                            }
                                        }
                                    }
                                }
                            }
                        }
                    } else {
                        // ── Parallel strided BLAS path (high throughput) ──
                        let total_work = b * nh;
                        let q_addr = q_data.as_ptr() as usize;
                        let k_addr = k_data.as_ptr() as usize;
                        let v_addr = v_data.as_ptr() as usize;
                        let m_addr = mask_data.as_ptr() as usize;
                        let o_addr = out_data.as_mut_ptr() as usize;
                        let sc_addr = sdpa_scores.as_mut_ptr() as usize;

                        crate::pool::par_for(total_work, 1, &|off, cnt| {
                            for idx in off..off + cnt {
                                let bi = idx / nh;
                                let hi = idx % nh;

                                let q_start = (q_addr as *const f32).add(bi * q_s * qrs + hi * dh);
                                let k_start = (k_addr as *const f32).add(bi * k_s * krs + hi * dh);
                                let v_start = (v_addr as *const f32).add(bi * k_s * vrs + hi * dh);
                                let o_start = (o_addr as *mut f32).add(bi * q_s * hs + hi * dh);
                                let sc = std::slice::from_raw_parts_mut(
                                    (sc_addr as *mut f32).add(idx * ss),
                                    ss,
                                );

                                // LDA = qrs, LDB = krs (parent row strides
                                // when fused; hs otherwise).
                                crate::blas::sgemm_general(
                                    q_start,
                                    k_start,
                                    sc.as_mut_ptr(),
                                    q_s,
                                    k_s,
                                    dh,
                                    scale,
                                    0.0,
                                    qrs,
                                    krs,
                                    k_s,
                                    false,
                                    true,
                                );

                                match mask_kind {
                                    rlx_ir::op::MaskKind::Custom => {
                                        let mask_bi = std::slice::from_raw_parts(
                                            (m_addr as *const f32).add(bi * k_s),
                                            k_s,
                                        );
                                        for ki in 0..k_s {
                                            if mask_bi[ki] < mask_thr {
                                                for qi in 0..q_s {
                                                    sc[qi * k_s + ki] = mask_neg;
                                                }
                                            }
                                        }
                                    }
                                    rlx_ir::op::MaskKind::Bias => {
                                        // Per-head additive bias slice.
                                        let bias = std::slice::from_raw_parts(
                                            (m_addr as *const f32).add((bi * nh + hi) * q_s * k_s),
                                            q_s * k_s,
                                        );
                                        for i in 0..q_s * k_s {
                                            sc[i] += bias[i];
                                        }
                                    }
                                    _ => apply_synthetic_mask(sc, q_s, k_s, *mask_kind),
                                }

                                crate::kernels::neon_softmax(sc, q_s, k_s);

                                // LDB = vrs (parent row stride when
                                // fused; hs otherwise). LDC stays hs —
                                // output is its own contiguous buffer.
                                crate::blas::sgemm_general(
                                    sc.as_ptr(),
                                    v_start,
                                    o_start,
                                    q_s,
                                    dh,
                                    k_s,
                                    1.0,
                                    0.0,
                                    k_s,
                                    vrs,
                                    hs,
                                    false,
                                    false,
                                );
                            }
                        });
                    }
                }
            }

            Thunk::AttentionBackward {
                q,
                k,
                v,
                dy,
                mask,
                out,
                batch,
                seq,
                kv_seq,
                heads,
                head_dim,
                mask_kind,
                wrt,
                bhsd,
            } => {
                let (b, q_s, k_s, nh, dh) = (
                    *batch as usize,
                    *seq as usize,
                    *kv_seq as usize,
                    *heads as usize,
                    *head_dim as usize,
                );
                unsafe {
                    let q_len = if *bhsd {
                        b * nh * q_s * dh
                    } else {
                        b * q_s * nh * dh
                    };
                    let k_len = if *bhsd {
                        b * nh * k_s * dh
                    } else {
                        b * k_s * nh * dh
                    };
                    let out_len = match wrt {
                        rlx_ir::op::AttentionBwdWrt::Key | rlx_ir::op::AttentionBwdWrt::Value => {
                            k_len
                        }
                        rlx_ir::op::AttentionBwdWrt::Query => q_len,
                    };
                    let q_data = sl(*q, base, q_len);
                    let k_data = sl(*k, base, k_len);
                    let v_data = sl(*v, base, k_len);
                    let dy_data = sl(*dy, base, q_len);
                    let out_data = sl_mut(*out, base, out_len);
                    let mask_data: &[f32] = if *mask != 0 {
                        let ml = match mask_kind {
                            rlx_ir::op::MaskKind::Custom => b * k_s,
                            rlx_ir::op::MaskKind::Bias => b * nh * q_s * k_s,
                            _ => 0,
                        };
                        sl(*mask, base, ml)
                    } else {
                        &[]
                    };
                    crate::attention_bwd::attention_backward(
                        *wrt, q_data, k_data, v_data, dy_data, out_data, b, nh, q_s, k_s, dh,
                        *mask_kind, mask_data, *bhsd,
                    );
                }
            }

            Thunk::ActivationInPlace { data, len, act } => {
                let len = *len as usize;
                unsafe {
                    let d = sl_mut(*data, base, len);
                    match act {
                        Activation::Gelu => crate::kernels::par_gelu_inplace(d),
                        Activation::GeluApprox => crate::kernels::par_gelu_approx_inplace(d),
                        Activation::Silu => crate::kernels::par_silu_inplace(d),
                        Activation::Relu => {
                            for v in d.iter_mut() {
                                *v = v.max(0.0);
                            }
                        }
                        Activation::Sigmoid => {
                            for v in d.iter_mut() {
                                *v = 1.0 / (1.0 + (-*v).exp());
                            }
                        }
                        Activation::Tanh => {
                            for v in d.iter_mut() {
                                *v = v.tanh();
                            }
                        }
                        Activation::Exp => {
                            for v in d.iter_mut() {
                                *v = v.exp();
                            }
                        }
                        Activation::Log => {
                            for v in d.iter_mut() {
                                *v = v.ln();
                            }
                        }
                        Activation::Sqrt => {
                            for v in d.iter_mut() {
                                *v = v.sqrt();
                            }
                        }
                        Activation::Rsqrt => {
                            for v in d.iter_mut() {
                                *v = 1.0 / v.sqrt();
                            }
                        }
                        Activation::Neg => {
                            for v in d.iter_mut() {
                                *v = -*v;
                            }
                        }
                        Activation::Abs => {
                            for v in d.iter_mut() {
                                *v = v.abs();
                            }
                        }
                        Activation::Round => {
                            for v in d.iter_mut() {
                                *v = v.round();
                            }
                        }
                        Activation::Sin => {
                            for v in d.iter_mut() {
                                *v = v.sin();
                            }
                        }
                        Activation::Cos => {
                            for v in d.iter_mut() {
                                *v = v.cos();
                            }
                        }
                        Activation::Tan => {
                            for v in d.iter_mut() {
                                *v = v.tan();
                            }
                        }
                        Activation::Atan => {
                            for v in d.iter_mut() {
                                *v = v.atan();
                            }
                        }
                    }
                }
            }

            Thunk::FusedAttnBlock {
                hidden,
                qkv_w,
                out_w,
                mask,
                mask_kind,
                out,
                qkv_b,
                out_b,
                cos,
                sin,
                cos_len,
                batch,
                seq,
                hs,
                nh,
                dh,
                has_bias,
                has_rope,
                interleaved,
            } => {
                let (b, s) = (*batch as usize, *seq as usize);
                let (h, n_h, d_h) = (*hs as usize, *nh as usize, *dh as usize);
                let interleaved = *interleaved;
                let m = b * s;
                let scale = (d_h as f32).powf(-0.5);
                let half = d_h / 2;
                // Only `Custom` consumes the per-key padding buffer; `Causal` /
                // `SlidingWindow` are synthesized from (qi, ki) below, and have
                // no mask buffer (so reading one would touch unrelated arena
                // bytes). q_seq == kv_seq here (guaranteed at fusion time), so
                // the absolute query position is just `qi`.
                let use_custom_mask = matches!(mask_kind, rlx_ir::op::MaskKind::Custom);
                unsafe {
                    let inp = sl(*hidden, base, m * h);
                    let wq = sl(*qkv_w, base, h * 3 * h);
                    let wo = sl(*out_w, base, h * h);
                    let mk = if use_custom_mask {
                        sl(*mask, base, b * s)
                    } else {
                        &[]
                    };
                    let dst = sl_mut(*out, base, m * h);

                    // Stack-allocated intermediates — all fit in L1 cache for small batch
                    let mut qkv = vec![0f32; m * 3 * h];
                    let mut attn_out = vec![0f32; m * h];
                    let mut scores_buf = vec![0f32; s * s]; // one head at a time

                    // 1. QKV projection: [m, h] @ [h, 3h] → [m, 3h]
                    crate::blas::sgemm(inp, wq, &mut qkv, m, h, 3 * h);
                    if *has_bias {
                        let bias = sl(*qkv_b, base, 3 * h);
                        crate::blas::bias_add(&mut qkv, bias, m, 3 * h);
                    }

                    // 2. Multi-head SDPA (Q/K/V are views into qkv at offsets 0, h, 2h)
                    //    Process heads sequentially with inline RoPE — zero copy.
                    #[cfg(target_arch = "aarch64")]
                    let neon_chunks = d_h / 4;
                    #[cfg(target_arch = "aarch64")]
                    let _rope_chunks = half / 4;

                    for bi in 0..b {
                        for hi in 0..n_h {
                            // For each (query_pos, key_pos): compute Q@K^T with inline RoPE
                            for qi in 0..s {
                                let q_base = bi * s * 3 * h + qi * 3 * h + hi * d_h;
                                for ki in 0..s {
                                    let k_base = bi * s * 3 * h + ki * 3 * h + h + hi * d_h;
                                    let mut dot = 0f32;

                                    if *has_rope {
                                        // Apply RoPE inline during dot product
                                        let q_cos = qi * half;
                                        let k_cos = ki * half;
                                        let cos_tab = sl(*cos, base, *cos_len as usize);
                                        let sin_tab = sl(*sin, base, *cos_len as usize);
                                        // Rotate per pair, then dot. The q·k sum
                                        // is layout-independent, so only the pair
                                        // element offsets differ by style:
                                        //   NeoX:  (i, i+half)   GPT-J: (2i, 2i+1)
                                        // angle index is the pair index `i` for both.
                                        for i in 0..half {
                                            let (qo1, qo2, ko1, ko2) = if interleaved {
                                                (2 * i, 2 * i + 1, 2 * i, 2 * i + 1)
                                            } else {
                                                (i, half + i, i, half + i)
                                            };
                                            let q1 = qkv[q_base + qo1];
                                            let q2 = qkv[q_base + qo2];
                                            let k1 = qkv[k_base + ko1];
                                            let k2 = qkv[k_base + ko2];
                                            let c_q = cos_tab[q_cos + i];
                                            let s_q = sin_tab[q_cos + i];
                                            let c_k = cos_tab[k_cos + i];
                                            let s_k = sin_tab[k_cos + i];
                                            let qr1 = q1 * c_q - q2 * s_q;
                                            let kr1 = k1 * c_k - k2 * s_k;
                                            let qr2 = q2 * c_q + q1 * s_q;
                                            let kr2 = k2 * c_k + k1 * s_k;
                                            dot += qr1 * kr1 + qr2 * kr2;
                                        }
                                    } else {
                                        // Standard dot product
                                        #[cfg(target_arch = "aarch64")]
                                        {
                                            use std::arch::aarch64::*;
                                            let mut acc = vdupq_n_f32(0.0);
                                            for c in 0..neon_chunks {
                                                let vq =
                                                    vld1q_f32(qkv.as_ptr().add(q_base + c * 4));
                                                let vk =
                                                    vld1q_f32(qkv.as_ptr().add(k_base + c * 4));
                                                acc = vfmaq_f32(acc, vq, vk);
                                            }
                                            dot = vaddvq_f32(acc);
                                            for d in (neon_chunks * 4)..d_h {
                                                dot += qkv[q_base + d] * qkv[k_base + d];
                                            }
                                        }
                                        #[cfg(not(target_arch = "aarch64"))]
                                        for d in 0..d_h {
                                            dot += qkv[q_base + d] * qkv[k_base + d];
                                        }
                                    }

                                    scores_buf[qi * s + ki] = dot * scale;
                                    // Synthesized position masks (q_offset == 0):
                                    //   Causal         → mask future keys ki > qi
                                    //   SlidingWindow  → also mask ki + w < qi
                                    let pos_masked = match mask_kind {
                                        rlx_ir::op::MaskKind::Causal => ki > qi,
                                        rlx_ir::op::MaskKind::SlidingWindow(w) => {
                                            ki > qi || ki + *w < qi
                                        }
                                        _ => false,
                                    };
                                    if pos_masked || (use_custom_mask && mk[bi * s + ki] < mask_thr)
                                    {
                                        scores_buf[qi * s + ki] = mask_neg;
                                    }
                                }
                            }

                            // Softmax
                            crate::kernels::neon_softmax(&mut scores_buf[..s * s], s, s);

                            // Score @ V accumulation (V at offset 2h in QKV)
                            for qi in 0..s {
                                let o_base = bi * s * h + qi * h + hi * d_h;
                                for d in 0..d_h {
                                    attn_out[o_base + d] = 0.0;
                                }
                                for ki in 0..s {
                                    let sc = scores_buf[qi * s + ki];
                                    if sc > score_thr {
                                        let v_base = bi * s * 3 * h + ki * 3 * h + 2 * h + hi * d_h;
                                        #[cfg(target_arch = "aarch64")]
                                        {
                                            use std::arch::aarch64::*;
                                            let vsc = vdupq_n_f32(sc);
                                            for c in 0..neon_chunks {
                                                let off = c * 4;
                                                let vo =
                                                    vld1q_f32(attn_out.as_ptr().add(o_base + off));
                                                let vv = vld1q_f32(qkv.as_ptr().add(v_base + off));
                                                vst1q_f32(
                                                    attn_out.as_mut_ptr().add(o_base + off),
                                                    vfmaq_f32(vo, vsc, vv),
                                                );
                                            }
                                        }
                                        #[cfg(not(target_arch = "aarch64"))]
                                        for d in 0..d_h {
                                            attn_out[o_base + d] += sc * qkv[v_base + d];
                                        }
                                    }
                                }
                            }
                        }
                    }

                    // 3. Output projection: [m, h] @ [h, h] → dst
                    crate::blas::sgemm(&attn_out, wo, dst, m, h, h);
                    if *has_bias {
                        let bias = sl(*out_b, base, h);
                        crate::blas::bias_add(dst, bias, m, h);
                    }
                }
            }

            Thunk::Rope {
                src,
                cos,
                sin,
                dst,
                batch,
                seq,
                hidden,
                head_dim,
                n_rot,
                cos_len,
                src_row_stride,
                interleaved,
            } => {
                let interleaved = *interleaved;
                let (b, s, hs, dh, nr) = (
                    *batch as usize,
                    *seq as usize,
                    *hidden as usize,
                    *head_dim as usize,
                    *n_rot as usize,
                );
                let tab_half = dh / 2;
                let rot_half = nr / 2;
                let nh = hs / dh;
                let cl = *cos_len as usize;
                let src_rs = *src_row_stride as usize;
                // Number of rows in the RoPE table. A per-(batch·seq) table
                // (`cos_rows == b*s`, distinct from the shared per-seq table) is
                // indexed by the *global* token so ragged batched decode can
                // give each sequence its own absolute position.
                let cos_rows = cl / tab_half.max(1);
                let per_token = cos_rows == b * s && cos_rows != s;
                unsafe {
                    let x = sl(*src, base, b * s * src_rs);
                    let cos_tab = sl(*cos, base, cl);
                    let sin_tab = sl(*sin, base, cl);
                    let out = sl_mut(*dst, base, b * s * hs);

                    let total = b * s;
                    let x_ptr = x.as_ptr() as usize;
                    let o_ptr = out.as_mut_ptr() as usize;
                    let c_ptr = cos_tab.as_ptr() as usize;
                    let s_ptr = sin_tab.as_ptr() as usize;

                    crate::pool::par_for(total, 4, &|off, cnt| {
                        for idx in off..off + cnt {
                            let bi = idx / s;
                            let si = idx % s;
                            let tab_off = if per_token { idx } else { si } * tab_half;

                            for hi in 0..nh {
                                let src_base = bi * s * src_rs + si * src_rs + hi * dh;
                                let dst_base = bi * s * hs + si * hs + hi * dh;
                                let xp = (x_ptr as *const f32).add(src_base);
                                let op = (o_ptr as *mut f32).add(dst_base);
                                let cp = (c_ptr as *const f32).add(tab_off);
                                let sp = (s_ptr as *const f32).add(tab_off);

                                if interleaved {
                                    // GPT-J / llama.cpp-NORM: rotate adjacent
                                    // pairs (2i, 2i+1) by angle i.
                                    for i in 0..rot_half {
                                        let x1 = *xp.add(2 * i);
                                        let x2 = *xp.add(2 * i + 1);
                                        let cv = *cp.add(i);
                                        let sv = *sp.add(i);
                                        *op.add(2 * i) = x1 * cv - x2 * sv;
                                        *op.add(2 * i + 1) = x2 * cv + x1 * sv;
                                    }
                                } else {
                                    // HF / NeoX rotate-half: pair (i, i+rot_half).
                                    for i in 0..rot_half {
                                        let x1 = *xp.add(i);
                                        let x2 = *xp.add(rot_half + i);
                                        let cv = *cp.add(i);
                                        let sv = *sp.add(i);
                                        *op.add(i) = x1 * cv - x2 * sv;
                                        *op.add(rot_half + i) = x2 * cv + x1 * sv;
                                    }
                                }
                                for j in nr..dh {
                                    *op.add(j) = *xp.add(j);
                                }
                            }
                        }
                    });
                }
            }
            Thunk::FusedBertLayer {
                hidden,
                qkv_w,
                qkv_b,
                out_w,
                out_b,
                mask,
                ln1_g,
                ln1_b,
                eps1,
                fc1_w,
                fc1_b,
                fc2_w,
                fc2_b,
                ln2_g,
                ln2_b,
                eps2,
                out,
                batch,
                seq,
                hs,
                nh,
                dh,
                int_dim,
            } => {
                let (b, s, h, n_h, d_h) = (
                    *batch as usize,
                    *seq as usize,
                    *hs as usize,
                    *nh as usize,
                    *dh as usize,
                );
                let m = b * s;
                let id = *int_dim as usize;
                let scale = (d_h as f32).powf(-0.5);
                let _half = d_h / 2;
                #[cfg(target_arch = "aarch64")]
                let neon_chunks = d_h / 4;
                unsafe {
                    let inp = sl(*hidden, base, m * h);
                    let dst = sl_mut(*out, base, m * h);
                    let mk = sl(*mask, base, b * s);

                    // Pre-allocated buffers (zero malloc per layer — allocated once before thunk loop)
                    let qkv = std::slice::from_raw_parts_mut(fl_qkv.as_mut_ptr(), m * 3 * h);
                    let attn = std::slice::from_raw_parts_mut(fl_attn.as_mut_ptr(), m * h);
                    let res = std::slice::from_raw_parts_mut(fl_res.as_mut_ptr(), m * h);
                    let normed = std::slice::from_raw_parts_mut(fl_normed.as_mut_ptr(), m * h);
                    let ffn = std::slice::from_raw_parts_mut(fl_ffn.as_mut_ptr(), m * id);
                    let sc = std::slice::from_raw_parts_mut(fl_sc.as_mut_ptr(), s * s);

                    // QKV (parallelized across cores — multiple AMX coprocessors)
                    crate::blas::par_sgemm_bias(
                        inp,
                        sl(*qkv_w, base, h * 3 * h),
                        sl(*qkv_b, base, 3 * h),
                        qkv,
                        m,
                        h,
                        3 * h,
                    );

                    // SDPA per head (sequential NEON, inline — zero overhead)
                    for bi in 0..b {
                        for hi in 0..n_h {
                            for qi in 0..s {
                                for ki in 0..s {
                                    let q_base = bi * s * 3 * h + qi * 3 * h + hi * d_h;
                                    let k_base = bi * s * 3 * h + ki * 3 * h + h + hi * d_h;
                                    #[cfg(target_arch = "aarch64")]
                                    let dot;
                                    #[cfg(not(target_arch = "aarch64"))]
                                    let mut dot = 0f32;
                                    #[cfg(target_arch = "aarch64")]
                                    {
                                        use std::arch::aarch64::*;
                                        let mut acc = vdupq_n_f32(0.0);
                                        for c in 0..neon_chunks {
                                            acc = vfmaq_f32(
                                                acc,
                                                vld1q_f32(qkv.as_ptr().add(q_base + c * 4)),
                                                vld1q_f32(qkv.as_ptr().add(k_base + c * 4)),
                                            );
                                        }
                                        dot = vaddvq_f32(acc);
                                    }
                                    #[cfg(not(target_arch = "aarch64"))]
                                    for d in 0..d_h {
                                        dot += qkv[q_base + d] * qkv[k_base + d];
                                    }
                                    sc[qi * s + ki] = dot * scale;
                                    if mk[bi * s + ki] < mask_thr {
                                        sc[qi * s + ki] = mask_neg;
                                    }
                                }
                            }
                            crate::kernels::neon_softmax(&mut sc[..s * s], s, s);
                            for qi in 0..s {
                                let o = bi * s * h + qi * h + hi * d_h;
                                for d in 0..d_h {
                                    attn[o + d] = 0.0;
                                }
                                for ki in 0..s {
                                    let w = sc[qi * s + ki];
                                    if w > score_thr {
                                        let v = bi * s * 3 * h + ki * 3 * h + 2 * h + hi * d_h;
                                        #[cfg(target_arch = "aarch64")]
                                        {
                                            use std::arch::aarch64::*;
                                            let vw = vdupq_n_f32(w);
                                            for c in 0..neon_chunks {
                                                let off = c * 4;
                                                vst1q_f32(
                                                    attn.as_mut_ptr().add(o + off),
                                                    vfmaq_f32(
                                                        vld1q_f32(attn.as_ptr().add(o + off)),
                                                        vw,
                                                        vld1q_f32(qkv.as_ptr().add(v + off)),
                                                    ),
                                                );
                                            }
                                        }
                                        #[cfg(not(target_arch = "aarch64"))]
                                        for d in 0..d_h {
                                            attn[o + d] += w * qkv[v + d];
                                        }
                                    }
                                }
                            }
                        }
                    }

                    // Out proj (sgemm + bias fused) + residual add with NEON
                    crate::blas::sgemm_bias(
                        attn,
                        sl(*out_w, base, h * h),
                        sl(*out_b, base, h),
                        res,
                        m,
                        h,
                        h,
                    );
                    #[cfg(target_arch = "aarch64")]
                    {
                        use std::arch::aarch64::*;
                        let chunks_h = (m * h) / 4;
                        for c in 0..chunks_h {
                            let off = c * 4;
                            vst1q_f32(
                                res.as_mut_ptr().add(off),
                                vaddq_f32(
                                    vld1q_f32(res.as_ptr().add(off)),
                                    vld1q_f32(inp.as_ptr().add(off)),
                                ),
                            );
                        }
                        for i in (chunks_h * 4)..(m * h) {
                            res[i] += inp[i];
                        }
                    }
                    #[cfg(not(target_arch = "aarch64"))]
                    for i in 0..m * h {
                        res[i] += inp[i];
                    }

                    // LN1 (fused residual already done above — just normalize)
                    let g1 = sl(*ln1_g, base, h);
                    let b1 = sl(*ln1_b, base, h);
                    for r in 0..m {
                        crate::kernels::layer_norm_row(
                            &res[r * h..(r + 1) * h],
                            g1,
                            b1,
                            &mut normed[r * h..(r + 1) * h],
                            h,
                            *eps1,
                        );
                    }

                    // FFN: fc1 (parallel across cores) + GELU
                    crate::blas::par_sgemm_bias(
                        normed,
                        sl(*fc1_w, base, h * id),
                        sl(*fc1_b, base, id),
                        ffn,
                        m,
                        h,
                        id,
                    );
                    crate::kernels::par_gelu_inplace(ffn);

                    // fc2 + bias (parallel across cores) + residual with NEON
                    crate::blas::par_sgemm_bias(
                        ffn,
                        sl(*fc2_w, base, id * h),
                        sl(*fc2_b, base, h),
                        res,
                        m,
                        id,
                        h,
                    );
                    #[cfg(target_arch = "aarch64")]
                    {
                        use std::arch::aarch64::*;
                        let chunks_h = (m * h) / 4;
                        for c in 0..chunks_h {
                            let off = c * 4;
                            vst1q_f32(
                                res.as_mut_ptr().add(off),
                                vaddq_f32(
                                    vld1q_f32(res.as_ptr().add(off)),
                                    vld1q_f32(normed.as_ptr().add(off)),
                                ),
                            );
                        }
                        for i in (chunks_h * 4)..(m * h) {
                            res[i] += normed[i];
                        }
                    }
                    #[cfg(not(target_arch = "aarch64"))]
                    for i in 0..m * h {
                        res[i] += normed[i];
                    }

                    // LN2 → output
                    let g2 = sl(*ln2_g, base, h);
                    let b2 = sl(*ln2_b, base, h);
                    for r in 0..m {
                        crate::kernels::layer_norm_row(
                            &res[r * h..(r + 1) * h],
                            g2,
                            b2,
                            &mut dst[r * h..(r + 1) * h],
                            h,
                            *eps2,
                        );
                    }
                }
            }

            Thunk::FusedNomicLayer {
                hidden,
                qkv_w,
                out_w,
                mask,
                cos,
                sin,
                cos_len,
                ln1_g,
                ln1_b,
                eps1,
                fc11_w,
                fc12_w: _,
                fc2_w,
                ln2_g,
                ln2_b,
                eps2,
                out,
                batch,
                seq,
                hs,
                nh,
                dh,
                int_dim,
                interleaved,
            } => {
                let interleaved = *interleaved;
                let (b, s, h, n_h, d_h) = (
                    *batch as usize,
                    *seq as usize,
                    *hs as usize,
                    *nh as usize,
                    *dh as usize,
                );
                let m = b * s;
                let id = *int_dim as usize;
                let scale = (d_h as f32).powf(-0.5);
                let half_dh = d_h / 2;
                #[cfg(target_arch = "aarch64")]
                let neon_chunks = d_h / 4;
                unsafe {
                    let inp = sl(*hidden, base, m * h);
                    let dst = sl_mut(*out, base, m * h);
                    let mk = sl(*mask, base, b * s);
                    let cos_tab = sl(*cos, base, *cos_len as usize);
                    let sin_tab = sl(*sin, base, *cos_len as usize);
                    // fc11_w is the fused [h, 2*int_dim] weight (fc11 || fc12 concatenated)
                    let fused_fc_w = sl(*fc11_w, base, h * 2 * id);

                    let mut qkv = vec![0f32; m * 3 * h];
                    let mut attn = vec![0f32; m * h];
                    let mut res = vec![0f32; m * h];
                    let mut normed = vec![0f32; m * h];
                    let mut ffn_concat = vec![0f32; m * 2 * id]; // fc11||fc12 output
                    let mut sc = vec![0f32; s * s];

                    // QKV (no bias)
                    crate::blas::sgemm(inp, sl(*qkv_w, base, h * 3 * h), &mut qkv, m, h, 3 * h);

                    // SDPA with inline RoPE
                    for bi in 0..b {
                        for hi in 0..n_h {
                            for qi in 0..s {
                                for ki in 0..s {
                                    let q_base = bi * s * 3 * h + qi * 3 * h + hi * d_h;
                                    let k_base = bi * s * 3 * h + ki * 3 * h + h + hi * d_h;
                                    let mut dot = 0f32;
                                    for i in 0..half_dh {
                                        // NeoX pairs (i, i+half); GPT-J pairs (2i, 2i+1).
                                        let (o1, o2) = if interleaved {
                                            (2 * i, 2 * i + 1)
                                        } else {
                                            (i, half_dh + i)
                                        };
                                        let q1 = qkv[q_base + o1];
                                        let q2 = qkv[q_base + o2];
                                        let k1 = qkv[k_base + o1];
                                        let k2 = qkv[k_base + o2];
                                        let cq = cos_tab[qi * half_dh + i];
                                        let sq = sin_tab[qi * half_dh + i];
                                        let ck = cos_tab[ki * half_dh + i];
                                        let sk = sin_tab[ki * half_dh + i];
                                        dot += (q1 * cq - q2 * sq) * (k1 * ck - k2 * sk)
                                            + (q2 * cq + q1 * sq) * (k2 * ck + k1 * sk);
                                    }
                                    sc[qi * s + ki] = dot * scale;
                                    if mk[bi * s + ki] < mask_thr {
                                        sc[qi * s + ki] = mask_neg;
                                    }
                                }
                            }
                            crate::kernels::neon_softmax(&mut sc[..s * s], s, s);
                            for qi in 0..s {
                                let o = bi * s * h + qi * h + hi * d_h;
                                for d in 0..d_h {
                                    attn[o + d] = 0.0;
                                }
                                for ki in 0..s {
                                    let w = sc[qi * s + ki];
                                    if w > score_thr {
                                        let v = bi * s * 3 * h + ki * 3 * h + 2 * h + hi * d_h;
                                        #[cfg(target_arch = "aarch64")]
                                        {
                                            use std::arch::aarch64::*;
                                            let vw = vdupq_n_f32(w);
                                            for c in 0..neon_chunks {
                                                let off = c * 4;
                                                vst1q_f32(
                                                    attn.as_mut_ptr().add(o + off),
                                                    vfmaq_f32(
                                                        vld1q_f32(attn.as_ptr().add(o + off)),
                                                        vw,
                                                        vld1q_f32(qkv.as_ptr().add(v + off)),
                                                    ),
                                                );
                                            }
                                        }
                                        #[cfg(not(target_arch = "aarch64"))]
                                        for d in 0..d_h {
                                            attn[o + d] += w * qkv[v + d];
                                        }
                                    }
                                }
                            }
                        }
                    }

                    // Out proj (no bias) + residual
                    crate::blas::sgemm(&attn, sl(*out_w, base, h * h), &mut res, m, h, h);
                    for i in 0..m * h {
                        res[i] += inp[i];
                    }

                    // LN1
                    let g1 = sl(*ln1_g, base, h);
                    let b1 = sl(*ln1_b, base, h);
                    for r in 0..m {
                        crate::kernels::layer_norm_row(
                            &res[r * h..(r + 1) * h],
                            g1,
                            b1,
                            &mut normed[r * h..(r + 1) * h],
                            h,
                            *eps1,
                        );
                    }

                    // SwiGLU: fused fc11+fc12 sgemm, then split, silu, mul
                    crate::blas::sgemm(&normed, fused_fc_w, &mut ffn_concat, m, h, 2 * id);
                    // Split: first id cols = fc11 (up), second id cols = fc12 (gate)
                    // SiLU on gate, then multiply up * gate → store in up region
                    for row in 0..m {
                        let bo = row * 2 * id;
                        // SiLU in-place on gate portion
                        for j in 0..id {
                            let x = ffn_concat[bo + id + j];
                            ffn_concat[bo + id + j] = x / (1.0 + (-x).exp());
                        }
                        // Multiply: up[j] *= gate[j]
                        for j in 0..id {
                            ffn_concat[bo + j] *= ffn_concat[bo + id + j];
                        }
                    }

                    // fc2 (no bias) + residual. The up*silu(gate) product lives in
                    // the FIRST `id` cols of each 2*id-wide ffn_concat row; gather
                    // it contiguous and run the SAME `sgemm` dispatch the unfused
                    // path uses (a strided `sgemm_general` here would force the
                    // BLAS/scalar path and diverge from the unfused NEON sgemm).
                    let mut swiglu_contig = vec![0f32; m * id];
                    for row in 0..m {
                        let bo = row * 2 * id;
                        swiglu_contig[row * id..(row + 1) * id]
                            .copy_from_slice(&ffn_concat[bo..bo + id]);
                    }
                    crate::blas::sgemm(
                        &swiglu_contig,
                        sl(*fc2_w, base, id * h),
                        &mut res,
                        m,
                        id,
                        h,
                    );
                    for i in 0..m * h {
                        res[i] += normed[i];
                    }

                    // LN2 → output
                    let g2 = sl(*ln2_g, base, h);
                    let b2 = sl(*ln2_b, base, h);
                    for r in 0..m {
                        crate::kernels::layer_norm_row(
                            &res[r * h..(r + 1) * h],
                            g2,
                            b2,
                            &mut dst[r * h..(r + 1) * h],
                            h,
                            *eps2,
                        );
                    }
                }
            }

            Thunk::FusedSwiGLU {
                src,
                dst,
                n_half,
                total,
                gate_first,
            } => {
                let n = *n_half as usize;
                let t = *total as usize;
                let outer = t / n;
                let in_total = outer * 2 * n;
                let gate_first = *gate_first;
                unsafe {
                    let inp = sl(*src, base, in_total);
                    let out = sl_mut(*dst, base, t);
                    for o in 0..outer {
                        let in_row = &inp[o * 2 * n..(o + 1) * 2 * n];
                        let out_row = &mut out[o * n..(o + 1) * n];
                        for i in 0..n {
                            let (up, gate) = if gate_first {
                                (in_row[n + i], in_row[i])
                            } else {
                                (in_row[i], in_row[n + i])
                            };
                            out_row[i] = up * (gate / (1.0 + (-gate).exp()));
                        }
                    }
                }
            }

            Thunk::Concat {
                dst,
                outer,
                inner,
                total_axis,
                inputs,
            } => {
                let outer = *outer as usize;
                let inner = *inner as usize;
                let total_axis = *total_axis as usize;
                let row_stride = total_axis * inner;
                let out_total = outer * row_stride;
                unsafe {
                    let out = sl_mut(*dst, base, out_total);
                    let mut cum: usize = 0;
                    for (src_off, in_axis, in_numel) in inputs {
                        let in_axis = *in_axis as usize;
                        let copy_per_row = in_axis * inner;
                        let dst_col_off = cum * inner;
                        let inp = sl(*src_off, base, (*in_numel as usize).max(1));
                        concat_copy_rows_f32(
                            out,
                            inp,
                            outer,
                            copy_per_row,
                            row_stride,
                            dst_col_off,
                            *in_numel as usize,
                        );
                        cum += in_axis;
                    }
                }
            }

            Thunk::ConcatF64 {
                dst,
                outer,
                inner,
                total_axis,
                inputs,
            } => {
                let outer = *outer as usize;
                let inner = *inner as usize;
                let total_axis = *total_axis as usize;
                let row_stride = total_axis * inner;
                let out_total = outer * row_stride;
                unsafe {
                    let out = sl_mut_f64(*dst, base, out_total);
                    let mut cum: usize = 0;
                    for (src_off, in_axis, in_numel) in inputs {
                        let in_axis = *in_axis as usize;
                        let copy_per_row = in_axis * inner;
                        let dst_col_off = cum * inner;
                        let inp = sl_f64(*src_off, base, (*in_numel as usize).max(1));
                        concat_copy_rows_f64(
                            out,
                            inp,
                            outer,
                            copy_per_row,
                            row_stride,
                            dst_col_off,
                            *in_numel as usize,
                        );
                        cum += in_axis;
                    }
                }
            }

            Thunk::Compare {
                lhs,
                rhs,
                dst,
                len,
                op,
                inputs_i64,
                inputs_elem_bytes,
                dst_elem_bytes,
            } => {
                let len = *len as usize;
                let arena_len = arena_buf.len();
                let elem = (*inputs_elem_bytes).max(1) as usize;
                let dst_eb = (*dst_elem_bytes).max(1) as usize;
                let max_l = (arena_len.saturating_sub(*lhs)) / elem;
                let max_r = (arena_len.saturating_sub(*rhs)) / elem;
                let max_d = (arena_len.saturating_sub(*dst)) / dst_eb;
                let len = len.min(max_l).min(max_r).min(max_d);
                if trace_thunks && len > 0 {
                    eprintln!("[compare] len={len} lhs={} rhs={} dst={}", *lhs, *rhs, *dst);
                }
                if elem == 1 {
                    let l = arena_buf[*lhs..*lhs + len].to_vec();
                    let r = arena_buf[*rhs..*rhs + len].to_vec();
                    for i in 0..len {
                        let v = match op {
                            CmpOp::Eq => l[i] == r[i],
                            CmpOp::Ne => l[i] != r[i],
                            CmpOp::Lt => l[i] < r[i],
                            CmpOp::Le => l[i] <= r[i],
                            CmpOp::Gt => l[i] > r[i],
                            CmpOp::Ge => l[i] >= r[i],
                        };
                        if *dst_elem_bytes == 1 {
                            arena_buf[*dst + i] = u8::from(v);
                        } else {
                            unsafe {
                                let o = sl_mut(*dst, base, len);
                                o[i] = if v { 1.0 } else { 0.0 };
                            }
                        }
                    }
                } else if *inputs_i64 != 0 {
                    unsafe {
                        let l = sl_i64(*lhs, base, len);
                        let r = sl_i64(*rhs, base, len);
                        for i in 0..len {
                            let v = match op {
                                CmpOp::Eq => l[i] == r[i],
                                CmpOp::Ne => l[i] != r[i],
                                CmpOp::Lt => l[i] < r[i],
                                CmpOp::Le => l[i] <= r[i],
                                CmpOp::Gt => l[i] > r[i],
                                CmpOp::Ge => l[i] >= r[i],
                            };
                            if *dst_elem_bytes == 1 {
                                arena_buf[*dst + i] = u8::from(v);
                            } else {
                                let o = sl_mut(*dst, base, len);
                                o[i] = if v { 1.0 } else { 0.0 };
                            }
                        }
                    }
                } else {
                    unsafe {
                        let l = sl(*lhs, base, len);
                        let r = sl(*rhs, base, len);
                        for i in 0..len {
                            let v = match op {
                                CmpOp::Eq => l[i] == r[i],
                                CmpOp::Ne => l[i] != r[i],
                                CmpOp::Lt => l[i] < r[i],
                                CmpOp::Le => l[i] <= r[i],
                                CmpOp::Gt => l[i] > r[i],
                                CmpOp::Ge => l[i] >= r[i],
                            };
                            if *dst_elem_bytes == 1 {
                                arena_buf[*dst + i] = u8::from(v);
                            } else {
                                let o = sl_mut(*dst, base, len);
                                o[i] = if v { 1.0 } else { 0.0 };
                            }
                        }
                    }
                }
            }

            Thunk::Where {
                cond,
                on_true,
                on_false,
                dst,
                len,
                elem_bytes,
                cond_elem_bytes,
            } => {
                let len = *len as usize;
                let eb = *elem_bytes as usize;
                let cond_eb = (*cond_elem_bytes).max(1) as usize;
                let arena_len = arena_buf.len();
                let len = len
                    .min((arena_len.saturating_sub(*cond)) / cond_eb)
                    .min((arena_len.saturating_sub(*on_true)) / eb)
                    .min((arena_len.saturating_sub(*on_false)) / eb)
                    .min((arena_len.saturating_sub(*dst)) / eb);
                unsafe {
                    if *elem_bytes == 8 {
                        let t = sl_i64(*on_true, base, len);
                        let e = sl_i64(*on_false, base, len);
                        let o = sl_mut_i64(*dst, base, len);
                        if *cond_elem_bytes == 1 {
                            let c = &arena_buf[*cond..*cond + len];
                            for i in 0..len {
                                o[i] = if c[i] != 0 { t[i] } else { e[i] };
                            }
                        } else {
                            let c = sl_i64(*cond, base, len);
                            for i in 0..len {
                                o[i] = if c[i] != 0 { t[i] } else { e[i] };
                            }
                        }
                    } else if *cond_elem_bytes == 1 {
                        let c = &arena_buf[*cond..*cond + len];
                        let t = sl(*on_true, base, len);
                        let e = sl(*on_false, base, len);
                        let o = sl_mut(*dst, base, len);
                        for i in 0..len {
                            o[i] = if c[i] != 0 { t[i] } else { e[i] };
                        }
                    } else {
                        let c = sl(*cond, base, len);
                        let t = sl(*on_true, base, len);
                        let e = sl(*on_false, base, len);
                        let o = sl_mut(*dst, base, len);
                        for i in 0..len {
                            o[i] = if c[i] != 0.0 { t[i] } else { e[i] };
                        }
                    }
                }
            }

            Thunk::Fma {
                a,
                b,
                c,
                dst,
                len,
                elem_bytes,
            } => {
                let len = *len as usize;
                let eb = (*elem_bytes).max(1) as usize;
                let arena_len = arena_buf.len();
                let len = len
                    .min(arena_len.saturating_sub(*a) / eb)
                    .min(arena_len.saturating_sub(*b) / eb)
                    .min(arena_len.saturating_sub(*c) / eb)
                    .min(arena_len.saturating_sub(*dst) / eb);
                unsafe {
                    if *elem_bytes == 8 {
                        let av = sl_f64(*a, base, len);
                        let bv = sl_f64(*b, base, len);
                        let cv = sl_f64(*c, base, len);
                        let o = sl_mut_f64(*dst, base, len);
                        for i in 0..len {
                            o[i] = av[i].mul_add(bv[i], cv[i]);
                        }
                    } else {
                        let av = sl(*a, base, len);
                        let bv = sl(*b, base, len);
                        let cv = sl(*c, base, len);
                        let o = sl_mut(*dst, base, len);
                        for i in 0..len {
                            o[i] = av[i].mul_add(bv[i], cv[i]);
                        }
                    }
                }
            }

            Thunk::ScatterAdd {
                updates,
                indices,
                dst,
                num_updates,
                out_dim,
                trailing,
            } => {
                let num_updates = *num_updates as usize;
                let out_dim = *out_dim as usize;
                let trailing = *trailing as usize;
                unsafe {
                    let upd = sl(*updates, base, num_updates * trailing);
                    let ids = sl(*indices, base, num_updates);
                    let out = sl_mut(*dst, base, out_dim * trailing);
                    // Zero the output first — semantics are accumulate-into-zeros.
                    for v in out.iter_mut() {
                        *v = 0.0;
                    }
                    for i in 0..num_updates {
                        let row = ids[i] as usize;
                        debug_assert!(row < out_dim, "ScatterAdd index out of range");
                        let src_off = i * trailing;
                        let dst_off = row * trailing;
                        for j in 0..trailing {
                            out[dst_off + j] += upd[src_off + j];
                        }
                    }
                }
            }

            Thunk::GroupedMatMul {
                input,
                weight,
                expert_idx,
                dst,
                m,
                k_dim,
                n,
                num_experts,
            } => {
                let m = *m as usize;
                let k_dim = *k_dim as usize;
                let n = *n as usize;
                let num_experts = *num_experts as usize;
                unsafe {
                    let inp = sl(*input, base, m * k_dim);
                    let wt = sl(*weight, base, num_experts * k_dim * n);
                    let ids = sl(*expert_idx, base, m);
                    let out = sl_mut(*dst, base, m * n);

                    // Counting-sort tokens by their assigned expert.
                    // counts[e] = how many tokens routed to expert e.
                    let mut counts = vec![0usize; num_experts];
                    for i in 0..m {
                        let e = ids[i] as usize;
                        debug_assert!(
                            e < num_experts,
                            "expert_idx out of range: {e} >= {num_experts}"
                        );
                        counts[e] += 1;
                    }
                    // Cumulative offsets into the packed buffer.
                    let mut offsets = vec![0usize; num_experts + 1];
                    for e in 0..num_experts {
                        offsets[e + 1] = offsets[e] + counts[e];
                    }
                    // Pack: each expert's rows land contiguously in `packed_in`.
                    // `original_pos[packed_idx] = original_token_idx` for the
                    // unpermute step at the end.
                    let mut packed_in = vec![0f32; m * k_dim];
                    let mut original_pos = vec![0usize; m];
                    let mut write_idx = vec![0usize; num_experts];
                    for i in 0..m {
                        let e = ids[i] as usize;
                        let dst_row = offsets[e] + write_idx[e];
                        packed_in[dst_row * k_dim..(dst_row + 1) * k_dim]
                            .copy_from_slice(&inp[i * k_dim..(i + 1) * k_dim]);
                        original_pos[dst_row] = i;
                        write_idx[e] += 1;
                    }

                    // One BLAS sgemm per expert. Skip experts with no
                    // tokens — common at the tail when M is much smaller
                    // than num_experts × k.
                    let mut packed_out = vec![0f32; m * n];
                    let expert_stride = k_dim * n;
                    let gmm_ord = crate::moe_residency::next_gmm_ord();
                    let moe_layer = gmm_ord / 3;
                    for e in 0..num_experts {
                        let count = counts[e];
                        if count == 0 {
                            continue;
                        }
                        crate::moe_residency::record_expert_tokens(moe_layer, e, count);
                        let in_start = offsets[e];
                        let in_slice = &packed_in[in_start * k_dim..(in_start + count) * k_dim];
                        let w_slab: &[f32] =
                            if !crate::moe_residency::expert_on_device_for_layer(moe_layer, e) {
                                if let Some(ptr) =
                                    crate::moe_residency::host_expert_weight_ptr(gmm_ord, e)
                                {
                                    std::slice::from_raw_parts(ptr, expert_stride)
                                } else {
                                    &wt[e * expert_stride..(e + 1) * expert_stride]
                                }
                            } else {
                                &wt[e * expert_stride..(e + 1) * expert_stride]
                            };
                        let out_slice = &mut packed_out[in_start * n..(in_start + count) * n];
                        crate::blas::sgemm(in_slice, w_slab, out_slice, count, k_dim, n);
                    }

                    // Unpermute back to original token order.
                    for packed_idx in 0..m {
                        let i = original_pos[packed_idx];
                        out[i * n..(i + 1) * n]
                            .copy_from_slice(&packed_out[packed_idx * n..(packed_idx + 1) * n]);
                    }
                }
            }

            Thunk::DequantGroupedMatMulGguf {
                input,
                w_q,
                expert_idx,
                dst,
                m,
                k_dim,
                n,
                num_experts,
                scheme,
            } => {
                let m = *m as usize;
                let k_dim = *k_dim as usize;
                let n = *n as usize;
                let num_experts = *num_experts as usize;
                let block_elems = scheme.gguf_block_size() as usize;
                let block_bytes = scheme.gguf_block_bytes() as usize;
                let slab_bytes = (k_dim * n) / block_elems * block_bytes;
                unsafe {
                    let inp = sl(*input, base, m * k_dim);
                    let wt = std::slice::from_raw_parts(
                        base.add(*w_q) as *const u8,
                        num_experts * slab_bytes,
                    );
                    let ids = sl(*expert_idx, base, m);
                    let out = sl_mut(*dst, base, m * n);
                    crate::gguf_matmul::gguf_grouped_matmul_bt(
                        inp,
                        wt,
                        ids,
                        out,
                        m,
                        k_dim,
                        n,
                        num_experts,
                        *scheme,
                    );
                }
            }

            Thunk::DequantMoEWeightsGguf {
                w_q,
                dst,
                k_dim,
                n,
                num_experts,
                scheme,
            } => {
                let k_dim = *k_dim as usize;
                let n = *n as usize;
                let num_experts = *num_experts as usize;
                let block_elems = scheme.gguf_block_size() as usize;
                let block_bytes = scheme.gguf_block_bytes() as usize;
                let slab_bytes = (k_dim * n) / block_elems * block_bytes;
                unsafe {
                    let wt = std::slice::from_raw_parts(
                        base.add(*w_q) as *const u8,
                        num_experts * slab_bytes,
                    );
                    let out = sl_mut(*dst, base, num_experts * k_dim * n);
                    crate::gguf_matmul::dequant_moe_weights_to_grouped_f32(
                        wt,
                        out,
                        num_experts,
                        k_dim,
                        n,
                        *scheme,
                    );
                }
            }

            Thunk::TopK {
                src,
                dst,
                outer,
                axis_dim,
                k,
                indices_i64,
            } => {
                let outer = *outer as usize;
                let axis_dim = *axis_dim as usize;
                let k = *k as usize;
                unsafe {
                    let inp = sl(*src, base, outer * axis_dim);
                    // Repeated argmax with masking. O(k * axis_dim) per row;
                    // good enough for small k (MoE typical k=2–8). For larger
                    // k a partial heap would win.
                    let mut row_buf: Vec<f32> = vec![0.0; axis_dim];
                    if *indices_i64 != 0 {
                        let out = sl_mut_i64(*dst, base, outer * k);
                        for o in 0..outer {
                            row_buf.copy_from_slice(&inp[o * axis_dim..(o + 1) * axis_dim]);
                            for ki in 0..k {
                                let mut best_i = 0usize;
                                let mut best_v = row_buf[0];
                                for i in 1..axis_dim {
                                    let v = row_buf[i];
                                    if v > best_v {
                                        best_v = v;
                                        best_i = i;
                                    }
                                }
                                out[o * k + ki] = best_i as i64;
                                row_buf[best_i] = f32::NEG_INFINITY;
                            }
                        }
                    } else {
                        let out = sl_mut(*dst, base, outer * k);
                        for o in 0..outer {
                            row_buf.copy_from_slice(&inp[o * axis_dim..(o + 1) * axis_dim]);
                            for ki in 0..k {
                                let mut best_i = 0usize;
                                let mut best_v = row_buf[0];
                                for i in 1..axis_dim {
                                    let v = row_buf[i];
                                    if v > best_v {
                                        best_v = v;
                                        best_i = i;
                                    }
                                }
                                out[o * k + ki] = best_i as f32;
                                row_buf[best_i] = f32::NEG_INFINITY;
                            }
                        }
                        if let Some(cap) = schedule.moe_topk_capture.as_ref() {
                            cap.push_topk_f32(&out[..outer * k], axis_dim);
                        }
                    }
                }
            }

            Thunk::Reduce {
                src,
                dst,
                outer,
                reduced,
                inner,
                op,
            } => {
                let outer = *outer as usize;
                let reduced = *reduced as usize;
                let inner = *inner as usize;
                let in_total = outer * reduced * inner;
                let out_total = outer * inner;
                unsafe {
                    let inp = sl(*src, base, in_total);
                    let out = sl_mut(*dst, base, out_total);
                    // Each output element reduces a disjoint strided strip, so
                    // the output range parallelizes (the bias-gradient reductions
                    // read the big [N,C,H,W] tensors down to [C]; that's C-way).
                    let reduce_one = |oi: usize| -> f32 {
                        let o = oi / inner;
                        let i = oi % inner;
                        let mut acc = match op {
                            ReduceOp::Max => f32::NEG_INFINITY,
                            ReduceOp::Min => f32::INFINITY,
                            ReduceOp::Prod => 1.0f32,
                            _ => 0.0f32, // Sum / Mean
                        };
                        for r in 0..reduced {
                            let v = inp[o * reduced * inner + r * inner + i];
                            acc = match op {
                                ReduceOp::Sum | ReduceOp::Mean => acc + v,
                                ReduceOp::Max => acc.max(v),
                                ReduceOp::Min => acc.min(v),
                                ReduceOp::Prod => acc * v,
                            };
                        }
                        if matches!(op, ReduceOp::Mean) {
                            acc /= reduced as f32;
                        }
                        acc
                    };
                    if fast_conv_enabled()
                        && crate::pool::should_parallelize(in_total)
                        && out_total > 1
                    {
                        let out_addr = out.as_mut_ptr() as usize;
                        crate::pool::par_for(
                            out_total,
                            crate::pool::outer_chunk(out_total),
                            &|off, cnt| {
                                for oi in off..off + cnt {
                                    *((out_addr as *mut f32).add(oi)) = reduce_one(oi);
                                }
                            },
                        );
                    } else {
                        for oi in 0..out_total {
                            out[oi] = reduce_one(oi);
                        }
                    }
                }
            }

            Thunk::ArgReduce {
                src,
                dst,
                outer,
                reduced,
                inner,
                is_max,
            } => {
                let outer = *outer as usize;
                let reduced = *reduced as usize;
                let inner = *inner as usize;
                let in_total = outer * reduced * inner;
                let out_total = outer * inner;
                unsafe {
                    let inp = sl(*src, base, in_total);
                    let out = sl_mut(*dst, base, out_total);
                    for o in 0..outer {
                        for i in 0..inner {
                            let mut best = inp[o * reduced * inner + i];
                            let mut best_idx = 0usize;
                            for r in 1..reduced {
                                let v = inp[o * reduced * inner + r * inner + i];
                                let better = if *is_max { v > best } else { v < best };
                                if better {
                                    best = v;
                                    best_idx = r;
                                }
                            }
                            out[o * inner + i] = best_idx as f32;
                        }
                    }
                }
            }

            Thunk::Conv2D1x1 {
                src,
                weight,
                dst,
                n,
                c_in,
                c_out,
                hw,
            } => {
                let n = *n as usize;
                let c_in = *c_in as usize;
                let c_out = *c_out as usize;
                let hw = *hw as usize;
                unsafe {
                    let inp = sl(*src, base, n * c_in * hw);
                    let wt = sl(*weight, base, c_out * c_in);
                    let out = sl_mut(*dst, base, n * c_out * hw);
                    // Per-batch sgemm: weight [c_out, c_in] @ input
                    // [c_in, hw] = output [c_out, hw]. The weight is
                    // shared across batches, so we get to dispatch
                    // BLAS once per N (typically 1).
                    for ni in 0..n {
                        let in_off = ni * c_in * hw;
                        let out_off = ni * c_out * hw;
                        crate::blas::sgemm(
                            wt,
                            &inp[in_off..in_off + c_in * hw],
                            &mut out[out_off..out_off + c_out * hw],
                            c_out,
                            c_in,
                            hw,
                        );
                    }
                }
            }

            Thunk::Conv2D {
                src,
                weight,
                dst,
                n,
                c_in,
                h,
                w,
                c_out,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
                dh,
                dw,
                groups,
            } => {
                let n = *n as usize;
                let c_in = *c_in as usize;
                let h = *h as usize;
                let w = *w as usize;
                let c_out = *c_out as usize;
                let h_out = *h_out as usize;
                let w_out = *w_out as usize;
                let kh = *kh as usize;
                let kw = *kw as usize;
                let sh = *sh as usize;
                let sw = *sw as usize;
                let ph = *ph as usize;
                let pw = *pw as usize;
                let dh = *dh as usize;
                let dw = *dw as usize;
                let groups = *groups as usize;
                let c_in_per_g = c_in / groups;
                unsafe {
                    let inp = sl(*src, base, n * c_in * h * w);
                    let wt = sl(*weight, base, c_out * c_in_per_g * kh * kw);
                    let out = sl_mut(*dst, base, n * c_out * h_out * w_out);
                    // Forward conv has two interchangeable kernels:
                    //   * a reference scalar nested loop (default), and
                    //   * im2col + BLAS sgemm, enabled with RLX_FAST_CONV=1.
                    // The fast path mirrors Conv2D1x1 / Conv2dBackwardWeight:
                    // gather patches per (batch, group) then dispatch one
                    // sgemm. Results match up to float reassociation.
                    // Eligibility: stride-1, no padding, dilation-1 — the direct
                    // kernel's no-bounds SAXPY form (and Winograd's tiling).
                    let s1_nopad = sh == 1 && sw == 1 && ph == 0 && pw == 0 && dh == 1 && dw == 1;
                    let winograd_ok = s1_nopad && kh == 3 && kw == 3 && groups == 1;
                    // im2col+BLAS is the measured CPU optimum for these shapes;
                    // Winograd (RLX_WINOGRAD) and the direct kernel
                    // (RLX_DIRECT_CONV) are opt-in alternatives that win only at
                    // higher channel counts — both measured slower on TinyConv.
                    if fast_conv_enabled() && winograd_enabled() && winograd_ok {
                        conv2d_forward_winograd(inp, wt, out, n, c_in, h, w, c_out, h_out, w_out);
                    } else if fast_conv_enabled() && direct_conv_enabled() && s1_nopad {
                        conv2d_forward_direct(
                            inp, wt, out, n, c_in, h, w, c_out, h_out, w_out, kh, kw, groups,
                        );
                    } else if fast_conv_enabled() {
                        conv2d_forward_im2col(
                            inp, wt, out, n, c_in, h, w, c_out, h_out, w_out, kh, kw, sh, sw, ph,
                            pw, dh, dw, groups,
                        );
                    } else {
                        conv2d_forward_naive(
                            inp, wt, out, n, c_in, h, w, c_out, h_out, w_out, kh, kw, sh, sw, ph,
                            pw, dh, dw, groups,
                        );
                    }
                }
            }

            Thunk::Pool2D {
                src,
                dst,
                n,
                c,
                h,
                w,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
                kind,
            } => {
                let n = *n as usize;
                let c = *c as usize;
                let h = *h as usize;
                let w = *w as usize;
                let h_out = *h_out as usize;
                let w_out = *w_out as usize;
                let kh = *kh as usize;
                let kw = *kw as usize;
                let sh = *sh as usize;
                let sw = *sw as usize;
                let ph = *ph as usize;
                let pw = *pw as usize;
                let kernel_area = (kh * kw) as f32;
                unsafe {
                    let inp = sl(*src, base, n * c * h * w);
                    let out = sl_mut(*dst, base, n * c * h_out * w_out);
                    // Each (n, c) plane is independent and writes a disjoint
                    // output region, so pooling fans out over the channel-batch
                    // when RLX_FAST_CONV is set.
                    let out_addr = out.as_mut_ptr() as usize;
                    let is_max = matches!(kind, ReduceOp::Max);
                    let is_mean = matches!(kind, ReduceOp::Mean);
                    // No-padding windows (the conv-net case) are always fully
                    // in-bounds, so the hot path drops the per-element bounds
                    // branches and hoists the reduce-op choice out of the loop.
                    let nopad = ph == 0 && pw == 0;
                    let pool_plane = |nc: usize| {
                        let ni = nc / c;
                        let ci = nc % c;
                        let in_chan = ni * c * h * w + ci * h * w;
                        let out_chan = ni * c * h_out * w_out + ci * h_out * w_out;
                        let op = out_addr as *mut f32;
                        for ho in 0..h_out {
                            for wo in 0..w_out {
                                let acc = if nopad {
                                    let row0 = in_chan + (ho * sh) * w + wo * sw;
                                    let mut a = if is_max { f32::NEG_INFINITY } else { 0.0 };
                                    for ki in 0..kh {
                                        let row = row0 + ki * w;
                                        if is_max {
                                            for kj in 0..kw {
                                                a = a.max(inp[row + kj]);
                                            }
                                        } else {
                                            for kj in 0..kw {
                                                a += inp[row + kj];
                                            }
                                        }
                                    }
                                    a
                                } else {
                                    let mut a = if is_max { f32::NEG_INFINITY } else { 0.0 };
                                    for ki in 0..kh {
                                        for kj in 0..kw {
                                            let hi = ho * sh + ki;
                                            let wi = wo * sw + kj;
                                            if hi < ph || wi < pw {
                                                continue;
                                            }
                                            let hi = hi - ph;
                                            let wi = wi - pw;
                                            if hi >= h || wi >= w {
                                                continue;
                                            }
                                            let v = inp[in_chan + hi * w + wi];
                                            if is_max {
                                                a = a.max(v);
                                            } else {
                                                a += v;
                                            }
                                        }
                                    }
                                    a
                                };
                                let acc = if is_mean { acc / kernel_area } else { acc };
                                *op.add(out_chan + ho * w_out + wo) = acc;
                            }
                        }
                    };
                    if fast_conv_enabled() && crate::pool::should_parallelize(n * c * h_out * w_out)
                    {
                        crate::pool::par_for(
                            n * c,
                            crate::pool::outer_chunk(n * c),
                            &|off, cnt| {
                                for nc in off..off + cnt {
                                    pool_plane(nc);
                                }
                            },
                        );
                    } else {
                        for nc in 0..n * c {
                            pool_plane(nc);
                        }
                    }
                }
            }

            Thunk::ReluBackward { x, dy, dx, len } => {
                let len = *len as usize;
                unsafe {
                    let xs = sl(*x, base, len);
                    let dys = sl(*dy, base, len);
                    let out = sl_mut(*dx, base, len);
                    if fast_conv_enabled() && crate::pool::should_parallelize(len) {
                        let oa = out.as_mut_ptr() as usize;
                        crate::pool::par_for(len, crate::pool::chunk_floor(len), &|off, cnt| {
                            for i in off..off + cnt {
                                *((oa as *mut f32).add(i)) = if xs[i] > 0.0 { dys[i] } else { 0.0 };
                            }
                        });
                    } else {
                        for i in 0..len {
                            out[i] = if xs[i] > 0.0 { dys[i] } else { 0.0 };
                        }
                    }
                }
            }

            Thunk::ReluBackwardF64 { x, dy, dx, len } => {
                let len = *len as usize;
                unsafe {
                    let xs = sl_f64(*x, base, len);
                    let dys = sl_f64(*dy, base, len);
                    let out = sl_mut_f64(*dx, base, len);
                    for i in 0..len {
                        out[i] = if xs[i] > 0.0 { dys[i] } else { 0.0 };
                    }
                }
            }

            Thunk::QMatMul {
                x,
                w,
                bias,
                out,
                m,
                k,
                n,
                x_zp,
                w_zp,
                out_zp,
                mult,
            } => {
                let m = *m as usize;
                let k = *k as usize;
                let n = *n as usize;
                unsafe {
                    let x_ptr = base.add(*x) as *const i8;
                    let w_ptr = base.add(*w) as *const i8;
                    let bias_ptr = base.add(*bias) as *const i32;
                    let out_ptr = base.add(*out) as *mut i8;
                    for mi in 0..m {
                        for ni in 0..n {
                            let mut acc: i32 = *bias_ptr.add(ni);
                            for ki in 0..k {
                                let xv = *x_ptr.add(mi * k + ki) as i32 - *x_zp;
                                let wv = *w_ptr.add(ki * n + ni) as i32 - *w_zp;
                                acc += xv * wv;
                            }
                            // Requantize: round(acc · mult) + out_zp,
                            // clamped to i8.
                            let r = (acc as f32 * *mult).round() as i32 + *out_zp;
                            let r = r.clamp(-128, 127) as i8;
                            *out_ptr.add(mi * n + ni) = r;
                        }
                    }
                }
            }

            Thunk::QConv2d {
                x,
                w,
                bias,
                out,
                n,
                c_in,
                h,
                w_in,
                c_out,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
                dh,
                dw,
                groups,
                x_zp,
                w_zp,
                out_zp,
                mult,
            } => {
                let n = *n as usize;
                let c_in = *c_in as usize;
                let h = *h as usize;
                let w_in = *w_in as usize;
                let c_out = *c_out as usize;
                let h_out = *h_out as usize;
                let w_out = *w_out as usize;
                let kh = *kh as usize;
                let kw = *kw as usize;
                let sh = *sh as usize;
                let sw = *sw as usize;
                let ph = *ph as usize;
                let pw = *pw as usize;
                let dh = *dh as usize;
                let dw = *dw as usize;
                let groups = *groups as usize;
                let c_in_per_g = c_in / groups;
                let c_out_per_g = c_out / groups;
                unsafe {
                    let x_ptr = base.add(*x) as *const i8;
                    let w_ptr = base.add(*w) as *const i8;
                    let bias_ptr = base.add(*bias) as *const i32;
                    let out_ptr = base.add(*out) as *mut i8;
                    for ni in 0..n {
                        for co in 0..c_out {
                            let g = co / c_out_per_g;
                            let ci_start = g * c_in_per_g;
                            for ho in 0..h_out {
                                for wo in 0..w_out {
                                    let mut acc: i32 = *bias_ptr.add(co);
                                    for ci_off in 0..c_in_per_g {
                                        let ci = ci_start + ci_off;
                                        let in_chan = ((ni * c_in) + ci) * h * w_in;
                                        let wt_chan = ((co * c_in_per_g) + ci_off) * kh * kw;
                                        for ki in 0..kh {
                                            for kj in 0..kw {
                                                let hi = ho * sh + ki * dh;
                                                let wi = wo * sw + kj * dw;
                                                if hi < ph || wi < pw {
                                                    continue;
                                                }
                                                let hi = hi - ph;
                                                let wi = wi - pw;
                                                if hi >= h || wi >= w_in {
                                                    continue;
                                                }
                                                let xv = *x_ptr.add(in_chan + hi * w_in + wi)
                                                    as i32
                                                    - *x_zp;
                                                let wv = *w_ptr.add(wt_chan + ki * kw + kj) as i32
                                                    - *w_zp;
                                                acc += xv * wv;
                                            }
                                        }
                                    }
                                    let r = (acc as f32 * *mult).round() as i32 + *out_zp;
                                    let r = r.clamp(-128, 127) as i8;
                                    let dst = ((ni * c_out) + co) * h_out * w_out + ho * w_out + wo;
                                    *out_ptr.add(dst) = r;
                                }
                            }
                        }
                    }
                }
            }

            Thunk::Quantize {
                x,
                q,
                len,
                chan_axis: _,
                chan_dim,
                inner,
                scales,
                zero_points,
            } => {
                let len = *len as usize;
                let chan_dim = *chan_dim as usize;
                let inner = *inner as usize;
                unsafe {
                    let xs = sl(*x, base, len);
                    let q_ptr = base.add(*q) as *mut i8;
                    for i in 0..len {
                        let c = if chan_dim == 1 {
                            0
                        } else {
                            (i / inner) % chan_dim
                        };
                        let inv_scale = 1.0 / scales[c];
                        let zp = zero_points[c];
                        let v = (xs[i] * inv_scale).round() as i32 + zp;
                        *q_ptr.add(i) = v.clamp(-128, 127) as i8;
                    }
                }
            }

            Thunk::Dequantize {
                q,
                x,
                len,
                chan_axis: _,
                chan_dim,
                inner,
                scales,
                zero_points,
            } => {
                let len = *len as usize;
                let chan_dim = *chan_dim as usize;
                let inner = *inner as usize;
                unsafe {
                    let q_ptr = base.add(*q) as *const i8;
                    let out = sl_mut(*x, base, len);
                    for i in 0..len {
                        let c = if chan_dim == 1 {
                            0
                        } else {
                            (i / inner) % chan_dim
                        };
                        let scale = scales[c];
                        let zp = zero_points[c];
                        let qv = *q_ptr.add(i) as i32;
                        out[i] = (qv - zp) as f32 * scale;
                    }
                }
            }

            Thunk::FakeQuantize {
                x,
                out,
                len,
                chan_axis: _,
                chan_dim,
                inner,
                bits,
                ste: _,
                scale_mode,
                state_off,
            } => {
                use rlx_ir::op::ScaleMode;
                let len = *len as usize;
                let chan_dim = *chan_dim as usize;
                let inner = *inner as usize;
                let q_max: f32 = match *bits {
                    8 => 127.0,
                    4 => 7.0,
                    2 => 1.0,
                    n => panic!("FakeQuantize: unsupported bits {n}"),
                };
                unsafe {
                    let xs = sl(*x, base, len);
                    let outs = sl_mut(*out, base, len);

                    let mut scale = vec![0f32; chan_dim];
                    match scale_mode {
                        ScaleMode::PerBatch => {
                            let mut max_abs = vec![0f32; chan_dim];
                            for i in 0..len {
                                let c = if chan_dim == 1 {
                                    0
                                } else {
                                    (i / inner) % chan_dim
                                };
                                let a = xs[i].abs();
                                if a > max_abs[c] {
                                    max_abs[c] = a;
                                }
                            }
                            for c in 0..chan_dim {
                                scale[c] = (max_abs[c] / q_max).max(1e-12);
                            }
                        }
                        ScaleMode::EMA { decay } => {
                            // Per-channel current max-abs, then blend
                            // into the running state in place.
                            let mut max_abs = vec![0f32; chan_dim];
                            for i in 0..len {
                                let c = if chan_dim == 1 {
                                    0
                                } else {
                                    (i / inner) % chan_dim
                                };
                                let a = xs[i].abs();
                                if a > max_abs[c] {
                                    max_abs[c] = a;
                                }
                            }
                            let state =
                                sl_mut(state_off.expect("EMA needs state_off"), base, chan_dim);
                            for c in 0..chan_dim {
                                let cur = (max_abs[c] / q_max).max(1e-12);
                                // Cold-start: state==0 → seed directly.
                                let blended = if state[c] <= 0.0 {
                                    cur
                                } else {
                                    *decay * state[c] + (1.0 - *decay) * cur
                                };
                                state[c] = blended;
                                scale[c] = blended;
                            }
                        }
                        ScaleMode::Fixed => {
                            let state =
                                sl(state_off.expect("Fixed needs state_off"), base, chan_dim);
                            for c in 0..chan_dim {
                                scale[c] = state[c].max(1e-12);
                            }
                        }
                    }

                    for i in 0..len {
                        let c = if chan_dim == 1 {
                            0
                        } else {
                            (i / inner) % chan_dim
                        };
                        let s = scale[c];
                        let qv = (xs[i] / s).round().clamp(-q_max, q_max);
                        outs[i] = qv * s;
                    }
                }
            }

            Thunk::ActivationBackward {
                x,
                dy,
                dx,
                len,
                kind,
            } => {
                let len = *len as usize;
                unsafe {
                    let xs = sl(*x, base, len);
                    let dys = sl(*dy, base, len);
                    let out = sl_mut(*dx, base, len);
                    activation_backward_kernel(*kind, xs, dys, out);
                }
            }

            Thunk::ActivationBackwardF64 {
                x,
                dy,
                dx,
                len,
                kind,
            } => {
                let len = *len as usize;
                unsafe {
                    let xs = sl_f64(*x, base, len);
                    let dys = sl_f64(*dy, base, len);
                    let out = sl_mut_f64(*dx, base, len);
                    activation_backward_kernel_f64(*kind, xs, dys, out);
                }
            }

            Thunk::FakeQuantizeLSQ {
                x,
                scale_off,
                out,
                len,
                chan_axis: _,
                chan_dim,
                inner,
                bits,
            } => {
                let len = *len as usize;
                let chan_dim = *chan_dim as usize;
                let inner = *inner as usize;
                let q_max: f32 = match *bits {
                    8 => 127.0,
                    4 => 7.0,
                    2 => 1.0,
                    n => panic!("FakeQuantizeLSQ: bad bits {n}"),
                };
                unsafe {
                    let xs = sl(*x, base, len);
                    let scale = sl(*scale_off, base, chan_dim);
                    let outs = sl_mut(*out, base, len);
                    for i in 0..len {
                        let c = if chan_dim == 1 {
                            0
                        } else {
                            (i / inner) % chan_dim
                        };
                        let s = scale[c].max(1e-12);
                        let qv = (xs[i] / s).round().clamp(-q_max, q_max);
                        outs[i] = qv * s;
                    }
                }
            }

            Thunk::FakeQuantizeLSQBackwardX {
                x,
                scale_off,
                dy,
                dx,
                len,
                chan_axis: _,
                chan_dim,
                inner,
                bits,
            } => {
                let len = *len as usize;
                let chan_dim = *chan_dim as usize;
                let inner = *inner as usize;
                let q_max: f32 = match *bits {
                    8 => 127.0,
                    4 => 7.0,
                    2 => 1.0,
                    n => panic!("FakeQuantizeLSQBackwardX: bad bits {n}"),
                };
                unsafe {
                    let xs = sl(*x, base, len);
                    let scale = sl(*scale_off, base, chan_dim);
                    let dys = sl(*dy, base, len);
                    let outs = sl_mut(*dx, base, len);
                    // STE-clipped: dx = dy when |x/s| ≤ q_max, else 0.
                    for i in 0..len {
                        let c = if chan_dim == 1 {
                            0
                        } else {
                            (i / inner) % chan_dim
                        };
                        let z = xs[i] / scale[c].max(1e-12);
                        outs[i] = if z.abs() <= q_max { dys[i] } else { 0.0 };
                    }
                }
            }

            Thunk::FakeQuantizeLSQBackwardScale {
                x,
                scale_off,
                dy,
                dscale,
                len,
                chan_axis: _,
                chan_dim,
                inner,
                bits,
            } => {
                let len = *len as usize;
                let chan_dim = *chan_dim as usize;
                let inner = *inner as usize;
                let q_max: f32 = match *bits {
                    8 => 127.0,
                    4 => 7.0,
                    2 => 1.0,
                    n => panic!("FakeQuantizeLSQBackwardScale: bad bits {n}"),
                };
                unsafe {
                    let xs = sl(*x, base, len);
                    let scale = sl(*scale_off, base, chan_dim);
                    let dys = sl(*dy, base, len);
                    let outs = sl_mut(*dscale, base, chan_dim);
                    for v in outs.iter_mut() {
                        *v = 0.0;
                    }
                    // ψ(z) = -z + round(z) inside range, sign(z)·q_max outside.
                    // dscale[c] = sum_i ψ(x_i/s[c]) * upstream[i].
                    for i in 0..len {
                        let c = if chan_dim == 1 {
                            0
                        } else {
                            (i / inner) % chan_dim
                        };
                        let s = scale[c].max(1e-12);
                        let z = xs[i] / s;
                        let psi = if z.abs() <= q_max {
                            -z + z.round()
                        } else if z > 0.0 {
                            q_max
                        } else {
                            -q_max
                        };
                        outs[c] += psi * dys[i];
                    }
                }
            }

            Thunk::FakeQuantizeBackward {
                x,
                dy,
                dx,
                len,
                chan_axis: _,
                chan_dim,
                inner,
                bits,
                ste,
            } => {
                use rlx_ir::op::SteKind;
                let len = *len as usize;
                let chan_dim = *chan_dim as usize;
                let inner = *inner as usize;
                let q_max: f32 = match *bits {
                    8 => 127.0,
                    4 => 7.0,
                    2 => 1.0,
                    n => panic!("FakeQuantizeBackward: bad bits {n}"),
                };
                unsafe {
                    let xs = sl(*x, base, len);
                    let dys = sl(*dy, base, len);
                    let outs = sl_mut(*dx, base, len);

                    // Per-channel max-abs → scale, same as forward.
                    let mut max_abs = vec![0f32; chan_dim];
                    for i in 0..len {
                        let c = if chan_dim == 1 {
                            0
                        } else {
                            (i / inner) % chan_dim
                        };
                        let a = xs[i].abs();
                        if a > max_abs[c] {
                            max_abs[c] = a;
                        }
                    }
                    let mut scale = vec![0f32; chan_dim];
                    for c in 0..chan_dim {
                        scale[c] = (max_abs[c] / q_max).max(1e-12);
                    }

                    match *ste {
                        SteKind::Identity => {
                            // dx = dy unchanged.
                            outs.copy_from_slice(dys);
                        }
                        SteKind::ClippedIdentity => {
                            // dx = dy * (|x| <= q_max·s); zero if the
                            // forward saturated.
                            for i in 0..len {
                                let c = if chan_dim == 1 {
                                    0
                                } else {
                                    (i / inner) % chan_dim
                                };
                                let bound = q_max * scale[c];
                                outs[i] = if xs[i].abs() <= bound { dys[i] } else { 0.0 };
                            }
                        }
                        SteKind::Tanh => {
                            // dx = dy * (1 - tanh²(x/s)).
                            for i in 0..len {
                                let c = if chan_dim == 1 {
                                    0
                                } else {
                                    (i / inner) % chan_dim
                                };
                                let t = (xs[i] / scale[c]).tanh();
                                outs[i] = dys[i] * (1.0 - t * t);
                            }
                        }
                        SteKind::HardTanh => {
                            // dx = dy * max(0, 1 - |x/(q_max·s)|).
                            for i in 0..len {
                                let c = if chan_dim == 1 {
                                    0
                                } else {
                                    (i / inner) % chan_dim
                                };
                                let bound = q_max * scale[c];
                                let attenuation = (1.0 - (xs[i] / bound).abs()).max(0.0);
                                outs[i] = dys[i] * attenuation;
                            }
                        }
                    }
                }
            }

            Thunk::LayerNormBackwardInput {
                x,
                gamma,
                dy,
                dx,
                rows,
                h,
                eps,
            } => {
                let rows = *rows as usize;
                let h = *h as usize;
                let eps = *eps;
                unsafe {
                    let xs = sl(*x, base, rows * h);
                    let g = sl(*gamma, base, h);
                    let dys = sl(*dy, base, rows * h);
                    let out = sl_mut(*dx, base, rows * h);
                    let n_inv = 1.0 / h as f32;
                    for r in 0..rows {
                        let xr = &xs[r * h..(r + 1) * h];
                        let dyr = &dys[r * h..(r + 1) * h];
                        // Per-row mean and inv_std (recompute — no saved
                        // tensor from the forward pass).
                        let mut sum = 0f32;
                        for &v in xr {
                            sum += v;
                        }
                        let mean = sum * n_inv;
                        let mut var = 0f32;
                        for &v in xr {
                            let d = v - mean;
                            var += d * d;
                        }
                        let inv_std = 1.0 / (var * n_inv + eps).sqrt();

                        // sums needed for the closed-form:
                        //   mean(dy·γ) and mean(dy·γ·x̂)
                        let mut s_sy = 0f32;
                        let mut s_sxh = 0f32;
                        for d in 0..h {
                            let xh = (xr[d] - mean) * inv_std;
                            let sy = dyr[d] * g[d];
                            s_sy += sy;
                            s_sxh += sy * xh;
                        }
                        let m_sy = s_sy * n_inv;
                        let m_sxh = s_sxh * n_inv;

                        for d in 0..h {
                            let xh = (xr[d] - mean) * inv_std;
                            let sy = dyr[d] * g[d];
                            out[r * h + d] = inv_std * (sy - m_sy - xh * m_sxh);
                        }
                    }
                }
            }

            Thunk::BatchNormInferenceBackwardInput {
                x,
                gamma,
                mean,
                var,
                dy,
                dx,
                count,
                channels,
                eps,
            } => {
                let count = *count as usize;
                let c = *channels as usize;
                let n = count * c;
                let eps = *eps;
                unsafe {
                    crate::kernels::batch_norm_inference_backward_input(
                        sl(*x, base, n),
                        sl(*gamma, base, c),
                        sl(*mean, base, c),
                        sl(*var, base, c),
                        sl(*dy, base, n),
                        sl_mut(*dx, base, n),
                        c,
                        eps,
                    );
                }
            }

            Thunk::BatchNormInferenceBackwardGamma {
                x,
                mean,
                var,
                dy,
                dgamma,
                count,
                channels,
                eps,
            } => {
                let count = *count as usize;
                let c = *channels as usize;
                let n = count * c;
                let eps = *eps;
                unsafe {
                    crate::kernels::batch_norm_inference_backward_gamma(
                        sl(*x, base, n),
                        sl(*mean, base, c),
                        sl(*var, base, c),
                        sl(*dy, base, n),
                        sl_mut(*dgamma, base, c),
                        c,
                        eps,
                    );
                }
            }

            Thunk::BatchNormInferenceBackwardBeta {
                dy,
                dbeta,
                count,
                channels,
            } => {
                let count = *count as usize;
                let c = *channels as usize;
                let n = count * c;
                unsafe {
                    crate::kernels::batch_norm_inference_backward_beta(
                        sl(*dy, base, n),
                        sl_mut(*dbeta, base, c),
                        c,
                    );
                }
            }

            Thunk::LayerNormBackwardGamma {
                x,
                dy,
                dgamma,
                rows,
                h,
                eps,
            } => {
                let rows = *rows as usize;
                let h = *h as usize;
                let eps = *eps;
                unsafe {
                    let xs = sl(*x, base, rows * h);
                    let dys = sl(*dy, base, rows * h);
                    let out = sl_mut(*dgamma, base, h);
                    for v in out.iter_mut() {
                        *v = 0.0;
                    }
                    let n_inv = 1.0 / h as f32;
                    for r in 0..rows {
                        let xr = &xs[r * h..(r + 1) * h];
                        let dyr = &dys[r * h..(r + 1) * h];
                        let mut sum = 0f32;
                        for &v in xr {
                            sum += v;
                        }
                        let mean = sum * n_inv;
                        let mut var = 0f32;
                        for &v in xr {
                            let d = v - mean;
                            var += d * d;
                        }
                        let inv_std = 1.0 / (var * n_inv + eps).sqrt();
                        for d in 0..h {
                            let xh = (xr[d] - mean) * inv_std;
                            out[d] += dyr[d] * xh;
                        }
                    }
                }
            }

            Thunk::RmsNormBackwardInput {
                x,
                gamma,
                beta,
                dy,
                dx,
                rows,
                h,
                eps,
            } => {
                let (rows, h) = (*rows as usize, *h as usize);
                unsafe {
                    let xs = sl(*x, base, rows * h);
                    let g = sl(*gamma, base, h);
                    let b = sl(*beta, base, h);
                    let dys = sl(*dy, base, rows * h);
                    let out = sl_mut(*dx, base, rows * h);
                    let mut dg = vec![0f32; h];
                    let mut db = vec![0f32; h];
                    for r in 0..rows {
                        crate::training_bwd::rms_norm_backward_row(
                            &xs[r * h..(r + 1) * h],
                            g,
                            b,
                            &dys[r * h..(r + 1) * h],
                            &mut out[r * h..(r + 1) * h],
                            &mut dg,
                            &mut db,
                            *eps,
                        );
                    }
                }
            }

            Thunk::RmsNormBackwardGamma {
                x,
                gamma,
                beta,
                dy,
                dgamma,
                rows,
                h,
                eps,
            } => {
                let (rows, h) = (*rows as usize, *h as usize);
                unsafe {
                    let xs = sl(*x, base, rows * h);
                    let g = sl(*gamma, base, h);
                    let b = sl(*beta, base, h);
                    let dys = sl(*dy, base, rows * h);
                    let out = sl_mut(*dgamma, base, h);
                    for v in out.iter_mut() {
                        *v = 0.0;
                    }
                    let mut dx = vec![0f32; h];
                    let mut db = vec![0f32; h];
                    for r in 0..rows {
                        crate::training_bwd::rms_norm_backward_row(
                            &xs[r * h..(r + 1) * h],
                            g,
                            b,
                            &dys[r * h..(r + 1) * h],
                            &mut dx,
                            &mut *out,
                            &mut db,
                            *eps,
                        );
                    }
                }
            }

            Thunk::RmsNormBackwardBeta {
                x,
                gamma,
                beta,
                dy,
                dbeta,
                rows,
                h,
                eps,
            } => {
                let (rows, h) = (*rows as usize, *h as usize);
                unsafe {
                    let xs = sl(*x, base, rows * h);
                    let g = sl(*gamma, base, h);
                    let b = sl(*beta, base, h);
                    let dys = sl(*dy, base, rows * h);
                    let out = sl_mut(*dbeta, base, h);
                    for v in out.iter_mut() {
                        *v = 0.0;
                    }
                    let mut dx = vec![0f32; h];
                    let mut dg = vec![0f32; h];
                    for r in 0..rows {
                        crate::training_bwd::rms_norm_backward_row(
                            &xs[r * h..(r + 1) * h],
                            g,
                            b,
                            &dys[r * h..(r + 1) * h],
                            &mut dx,
                            &mut dg,
                            &mut *out,
                            *eps,
                        );
                    }
                }
            }

            Thunk::RopeBackward {
                dy,
                cos,
                sin,
                dx,
                batch,
                seq,
                hidden,
                head_dim,
                n_rot,
                cos_len,
            } => {
                let (b, s, hs, dh, nr, cl) = (
                    *batch as usize,
                    *seq as usize,
                    *hidden as usize,
                    *head_dim as usize,
                    *n_rot as usize,
                    *cos_len as usize,
                );
                let nh = hs / dh;
                let tab_half = dh / 2;
                unsafe {
                    let dys = sl(*dy, base, b * s * hs);
                    let cos_tab = sl(*cos, base, cl);
                    let sin_tab = sl(*sin, base, cl);
                    let out = sl_mut(*dx, base, b * s * hs);
                    for bi in 0..b {
                        for si in 0..s {
                            let tab_off = si.saturating_mul(tab_half) % cl.max(1);
                            let cp = &cos_tab[tab_off..tab_off + tab_half.min(cl)];
                            let sp = &sin_tab[tab_off..tab_off + tab_half.min(cl)];
                            for hi in 0..nh {
                                let base_idx = bi * s * hs + si * hs + hi * dh;
                                crate::training_bwd::rope_backward_row(
                                    &dys[base_idx..base_idx + dh],
                                    cp,
                                    sp,
                                    &mut out[base_idx..base_idx + dh],
                                    dh,
                                    nr,
                                );
                            }
                        }
                    }
                }
            }

            Thunk::CumsumBackward {
                dy,
                dx,
                rows,
                cols,
                exclusive,
            } => {
                let (rows, cols) = (*rows as usize, *cols as usize);
                unsafe {
                    let dys = sl(*dy, base, rows * cols);
                    let out = sl_mut(*dx, base, rows * cols);
                    for r in 0..rows {
                        crate::training_bwd::cumsum_backward_row(
                            &dys[r * cols..(r + 1) * cols],
                            &mut out[r * cols..(r + 1) * cols],
                            *exclusive,
                        );
                    }
                }
            }

            Thunk::GroupNormBackwardInput {
                x,
                gamma,
                beta: _beta,
                dy,
                dx,
                n,
                c,
                h,
                w,
                num_groups,
                eps,
            } => {
                let (n, c, h, w) = (*n as usize, *c as usize, *h as usize, *w as usize);
                let plane = c * h * w;
                unsafe {
                    let xs = sl(*x, base, n * plane);
                    let g = sl(*gamma, base, c);
                    let dys = sl(*dy, base, n * plane);
                    let out = sl_mut(*dx, base, n * plane);
                    crate::training_bwd::group_norm_backward_input_nchw(
                        xs,
                        g,
                        dys,
                        out,
                        n,
                        c,
                        h,
                        w,
                        *num_groups as usize,
                        *eps,
                    );
                }
            }

            Thunk::GroupNormBackwardGamma {
                x,
                dy,
                dgamma,
                n,
                c,
                h,
                w,
                num_groups,
                eps,
            } => {
                let (n, c, h, w) = (*n as usize, *c as usize, *h as usize, *w as usize);
                let plane = c * h * w;
                unsafe {
                    let xs = sl(*x, base, n * plane);
                    let dys = sl(*dy, base, n * plane);
                    let out = sl_mut(*dgamma, base, c);
                    crate::training_bwd::group_norm_backward_gamma_nchw(
                        xs,
                        dys,
                        out,
                        n,
                        c,
                        h,
                        w,
                        *num_groups as usize,
                        *eps,
                    );
                }
            }

            Thunk::GroupNormBackwardBeta {
                dy,
                dbeta,
                n,
                c,
                h,
                w,
            } => {
                let (n, c, h, w) = (*n as usize, *c as usize, *h as usize, *w as usize);
                let plane = c * h * w;
                unsafe {
                    let dys = sl(*dy, base, n * plane);
                    let out = sl_mut(*dbeta, base, c);
                    crate::training_bwd::group_norm_backward_beta_nchw(dys, out, n, c, h, w);
                }
            }

            Thunk::GatherBackward {
                dy,
                indices,
                dst,
                outer,
                axis_dim,
                num_idx,
                trailing,
            } => {
                let (outer, axis_dim, num_idx, trailing) = (
                    *outer as usize,
                    *axis_dim as usize,
                    *num_idx as usize,
                    *trailing as usize,
                );
                unsafe {
                    let dys = sl(*dy, base, outer * num_idx * trailing);
                    let ids = sl(*indices, base, num_idx);
                    let out = sl_mut(*dst, base, outer * axis_dim * trailing);
                    for v in out.iter_mut() {
                        *v = 0.0;
                    }
                    crate::training_bwd::gather_axis_backward(
                        dys, ids, out, outer, axis_dim, num_idx, trailing,
                    );
                }
            }

            Thunk::MaxPool2dBackward {
                x,
                dy,
                dx,
                n,
                c,
                h,
                w,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
            } => unsafe {
                execute_maxpool2d_backward_f32(
                    *x, *dy, *dx, *n, *c, *h, *w, *h_out, *w_out, *kh, *kw, *sh, *sw, *ph, *pw,
                    base,
                );
            },

            Thunk::Conv2dBackwardInput {
                dy,
                w,
                dx,
                n,
                c_in,
                h,
                w_in,
                c_out,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
                dh,
                dw,
                groups,
            } => {
                // Per-group GEMM + col2im. Two orders of magnitude faster
                // than the naive 6-deep nested loop on training shapes.
                //
                //   dcol_n_g = w_g^T  @  dy_n_g            (sgemm)
                //   dx_n_g  += col2im(dcol_n_g)            (scatter-add)
                //
                // Layouts (all row-major):
                //   w_g       [c_out_per_g, c_in_per_g · kh · kw]
                //   dy_n_g    [c_out_per_g, h_out · w_out]
                //   dcol_n_g  [c_in_per_g · kh · kw, h_out · w_out]
                //   dx_n_g    [c_in_per_g, h · w_in]
                let n = *n as usize;
                let c_in = *c_in as usize;
                let h = *h as usize;
                let w_in = *w_in as usize;
                let c_out = *c_out as usize;
                let h_out = *h_out as usize;
                let w_out = *w_out as usize;
                let kh = *kh as usize;
                let kw = *kw as usize;
                let sh = *sh as usize;
                let sw = *sw as usize;
                let ph = *ph as usize;
                let pw = *pw as usize;
                let dh = *dh as usize;
                let dw = *dw as usize;
                let groups = *groups as usize;
                let c_in_per_g = c_in / groups;
                let c_out_per_g = c_out / groups;

                let m_dim = c_in_per_g * kh * kw;
                let n_dim = h_out * w_out;
                let k_dim = c_out_per_g;

                let dy_stride_n = c_out * h_out * w_out;
                let dy_stride_g = c_out_per_g * h_out * w_out;
                let w_stride_g = c_out_per_g * c_in_per_g * kh * kw;
                let dx_stride_n = c_in * h * w_in;
                let dx_stride_g = c_in_per_g * h * w_in;

                unsafe {
                    let dys = sl(*dy, base, n * c_out * h_out * w_out);
                    let ws = sl(*w, base, c_out * c_in_per_g * kh * kw);
                    let dxs = sl_mut(*dx, base, n * c_in * h * w_in);
                    for v in dxs.iter_mut() {
                        *v = 0.0;
                    }

                    // Each (ni, g) writes a disjoint dx_n_g region (col2im
                    // scatter-adds into the freshly-zeroed slice), so the batch
                    // loop fans out over the pool when RLX_FAST_CONV is set —
                    // each worker owns a `dcol` scratch and a raw pointer it
                    // offsets into its own dx window.
                    if fast_conv_enabled() {
                        let dx_addr = dxs.as_mut_ptr() as usize;
                        crate::pool::par_for(n, 1, &|off, cnt| {
                            let mut dcol = vec![0f32; m_dim * n_dim];
                            for ni in off..off + cnt {
                                for g in 0..groups {
                                    let w_g_off = g * w_stride_g;
                                    let dy_n_g_off = ni * dy_stride_n + g * dy_stride_g;
                                    let dx_n_g_off = ni * dx_stride_n + g * dx_stride_g;
                                    crate::blas::sgemm_general(
                                        ws.as_ptr().add(w_g_off),
                                        dys.as_ptr().add(dy_n_g_off),
                                        dcol.as_mut_ptr(),
                                        m_dim,
                                        n_dim,
                                        k_dim,
                                        1.0,
                                        0.0,
                                        m_dim,
                                        n_dim,
                                        n_dim,
                                        true,
                                        false,
                                    );
                                    let dx_g = std::slice::from_raw_parts_mut(
                                        (dx_addr as *mut f32).add(dx_n_g_off),
                                        dx_stride_g,
                                    );
                                    col2im(
                                        &dcol, dx_g, c_in_per_g, h, w_in, h_out, w_out, kh, kw, sh,
                                        sw, ph, pw, dh, dw,
                                    );
                                }
                            }
                        });
                    } else {
                        // Reused scratch buffer for the [m_dim, n_dim] dcol.
                        let mut dcol = vec![0f32; m_dim * n_dim];
                        for ni in 0..n {
                            for g in 0..groups {
                                let w_g_off = g * w_stride_g;
                                let dy_n_g_off = ni * dy_stride_n + g * dy_stride_g;
                                let dx_n_g_off = ni * dx_stride_n + g * dx_stride_g;

                                // dcol = w_g^T @ dy_n_g
                                // w_g  is stored as [k_dim rows, m_dim cols] row-major
                                // (i.e. K×M storage with lda = M = m_dim — exactly what
                                // sgemm_general wants for trans_a=true).
                                crate::blas::sgemm_general(
                                    ws.as_ptr().add(w_g_off),
                                    dys.as_ptr().add(dy_n_g_off),
                                    dcol.as_mut_ptr(),
                                    m_dim,
                                    n_dim,
                                    k_dim,
                                    1.0,
                                    0.0,
                                    /*lda=*/ m_dim,
                                    /*ldb=*/ n_dim,
                                    /*ldc=*/ n_dim,
                                    /*trans_a=*/ true,
                                    /*trans_b=*/ false,
                                );

                                // dx_n_g += col2im(dcol)
                                col2im(
                                    &dcol,
                                    &mut dxs[dx_n_g_off..dx_n_g_off + dx_stride_g],
                                    c_in_per_g,
                                    h,
                                    w_in,
                                    h_out,
                                    w_out,
                                    kh,
                                    kw,
                                    sh,
                                    sw,
                                    ph,
                                    pw,
                                    dh,
                                    dw,
                                );
                            }
                        }
                    }
                }
            }

            Thunk::Conv2dBackwardWeight {
                x,
                dy,
                dw,
                n,
                c_in,
                h,
                w,
                c_out,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
                dh,
                dw_dil,
                groups,
            } => {
                let n = *n as usize;
                let c_in = *c_in as usize;
                let h = *h as usize;
                let w = *w as usize;
                // Per-group im2col + GEMM, summed across batch.
                //
                //   col_n_g  = im2col(x_n_g)               (gather)
                //   dw_g    += dy_n_g  @  col_n_g^T        (sgemm, β=1)
                //
                // Layouts:
                //   x_n_g     [c_in_per_g, h · w]
                //   col_n_g   [c_in_per_g · kh · kw, h_out · w_out]
                //   dy_n_g    [c_out_per_g, h_out · w_out]
                //   dw_g      [c_out_per_g, c_in_per_g · kh · kw]
                let c_out = *c_out as usize;
                let h_out = *h_out as usize;
                let w_out = *w_out as usize;
                let kh = *kh as usize;
                let kw = *kw as usize;
                let sh = *sh as usize;
                let sw = *sw as usize;
                let ph = *ph as usize;
                let pw = *pw as usize;
                let dh = *dh as usize;
                let dw_dil = *dw_dil as usize;
                let groups = *groups as usize;
                let c_in_per_g = c_in / groups;
                let c_out_per_g = c_out / groups;

                let m_dim = c_out_per_g;
                let n_dim = c_in_per_g * kh * kw;
                let k_dim = h_out * w_out;

                let x_stride_n = c_in * h * w;
                let x_stride_g = c_in_per_g * h * w;
                let dy_stride_n = c_out * h_out * w_out;
                let dy_stride_g = c_out_per_g * h_out * w_out;
                let dw_stride_g = c_out_per_g * c_in_per_g * kh * kw;

                unsafe {
                    let xs = sl(*x, base, n * c_in * h * w);
                    let dys = sl(*dy, base, n * c_out * h_out * w_out);
                    let dws = sl_mut(*dw, base, c_out * c_in_per_g * kh * kw);
                    for v in dws.iter_mut() {
                        *v = 0.0;
                    }

                    // dw is a cross-batch reduction (β=1 accumulate), so the
                    // parallel path gives each worker a private `local` dw,
                    // accumulates into it over its slice of the batch, then adds
                    // it into the shared `dws` once under a lock — O(threads)
                    // contention, not O(batch). RLX_FAST_CONV gates it.
                    if fast_conv_enabled() {
                        let dw_len = dws.len();
                        let dws_addr = dws.as_mut_ptr() as usize;
                        let lock = std::sync::Mutex::new(());
                        crate::pool::par_for(n, 1, &|off, cnt| {
                            let mut col = vec![0f32; n_dim * k_dim];
                            let mut local = vec![0f32; dw_len];
                            for ni in off..off + cnt {
                                for g in 0..groups {
                                    let x_n_g_off = ni * x_stride_n + g * x_stride_g;
                                    let dy_n_g_off = ni * dy_stride_n + g * dy_stride_g;
                                    let dw_g_off = g * dw_stride_g;
                                    // Rows-layout im2col: col is [P, K] row-major, so
                                    // the GEMM dy[c_out,P] @ col[P,K] reads col
                                    // contiguously (trans_b=false) instead of the
                                    // strided transposed read the [K,P] layout forced.
                                    crate::im2col::im2col_rows_layout(
                                        &xs[x_n_g_off..x_n_g_off + x_stride_g],
                                        &mut col,
                                        1,
                                        c_in_per_g,
                                        h,
                                        w,
                                        h_out,
                                        w_out,
                                        kh,
                                        kw,
                                        sh,
                                        sw,
                                        ph,
                                        pw,
                                        dh,
                                        dw_dil,
                                    );
                                    crate::blas::sgemm_general(
                                        dys.as_ptr().add(dy_n_g_off),
                                        col.as_ptr(),
                                        local.as_mut_ptr().add(dw_g_off),
                                        m_dim,
                                        n_dim,
                                        k_dim,
                                        1.0,
                                        1.0,
                                        k_dim,
                                        n_dim,
                                        n_dim,
                                        false,
                                        false,
                                    );
                                }
                            }
                            let _guard = lock.lock().unwrap();
                            let dws = std::slice::from_raw_parts_mut(dws_addr as *mut f32, dw_len);
                            for (d, l) in dws.iter_mut().zip(local.iter()) {
                                *d += *l;
                            }
                        });
                    } else {
                        let mut col = vec![0f32; n_dim * k_dim];
                        for ni in 0..n {
                            for g in 0..groups {
                                let x_n_g_off = ni * x_stride_n + g * x_stride_g;
                                im2col(
                                    &xs[x_n_g_off..x_n_g_off + x_stride_g],
                                    &mut col,
                                    c_in_per_g,
                                    h,
                                    w,
                                    h_out,
                                    w_out,
                                    kh,
                                    kw,
                                    sh,
                                    sw,
                                    ph,
                                    pw,
                                    dh,
                                    dw_dil,
                                );

                                let dy_n_g_off = ni * dy_stride_n + g * dy_stride_g;
                                let dw_g_off = g * dw_stride_g;

                                // dw_g += dy_n_g @ col^T
                                //
                                // Output shape m × n_out = c_out_per_g × (c_in_per_g·kh·kw).
                                // dy_n_g is stored M×K row-major (lda = K = k_dim).
                                // col is stored as N×K row-major; with trans_b=true,
                                // sgemm_general uses ldb = K = k_dim and treats it as
                                // transposed. β=1 accumulates across the batch loop.
                                crate::blas::sgemm_general(
                                    dys.as_ptr().add(dy_n_g_off),
                                    col.as_ptr(),
                                    dws.as_mut_ptr().add(dw_g_off),
                                    m_dim,
                                    n_dim,
                                    k_dim,
                                    1.0,
                                    1.0,
                                    /*lda=*/ k_dim,
                                    /*ldb=*/ k_dim,
                                    /*ldc=*/ n_dim,
                                    /*trans_a=*/ false,
                                    /*trans_b=*/ true,
                                );
                            }
                        }
                    }
                }
            }

            Thunk::Im2Col {
                x,
                col,
                n,
                c_in,
                h,
                w,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
                dh,
                dw_dil,
            } => {
                let c_in = *c_in as usize;
                let h = *h as usize;
                let w = *w as usize;
                let h_out = *h_out as usize;
                let w_out = *w_out as usize;
                let kh = *kh as usize;
                let kw = *kw as usize;
                let sh = *sh as usize;
                let sw = *sw as usize;
                let ph = *ph as usize;
                let pw = *pw as usize;
                let dh = *dh as usize;
                let dw_dil = *dw_dil as usize;
                let per_batch = c_in * h * w;
                unsafe {
                    let n_eff = if *n == 0 { 0usize } else { *n as usize };
                    let x_floats = if n_eff == 0 {
                        per_batch.max(1)
                    } else {
                        n_eff * per_batch
                    };
                    let xs = sl(*x, base, x_floats);
                    let n = if *n == 0 {
                        xs.len() / per_batch.max(1)
                    } else {
                        n_eff
                    };
                    let m = n * h_out * w_out;
                    let k = c_in * kh * kw;
                    let cols = sl_mut(*col, base, m * k);
                    crate::im2col::im2col_rows_layout(
                        xs, cols, n, c_in, h, w, h_out, w_out, kh, kw, sh, sw, ph, pw, dh, dw_dil,
                    );
                }
            }

            Thunk::SoftmaxCrossEntropyDense {
                logits,
                targets,
                dst,
                n,
                c,
            } => {
                let n = *n as usize;
                let c = *c as usize;
                unsafe {
                    let lg = sl(*logits, base, n * c);
                    let tg = sl(*targets, base, n * c);
                    let out = sl_mut(*dst, base, n);
                    for ni in 0..n {
                        let row = &lg[ni * c..(ni + 1) * c];
                        let trow = &tg[ni * c..(ni + 1) * c];
                        // log-sum-exp: max-subtract for stability.
                        let mut m = f32::NEG_INFINITY;
                        for &v in row {
                            if v > m {
                                m = v;
                            }
                        }
                        let mut sum = 0f32;
                        for &v in row {
                            sum += (v - m).exp();
                        }
                        let lse = m + sum.ln();
                        // loss = lse - Σ_c targets[c]·logits[c].
                        let mut dot = 0f32;
                        for k in 0..c {
                            dot += trow[k] * row[k];
                        }
                        out[ni] = lse - dot;
                    }
                }
            }

            Thunk::SoftmaxCrossEntropy {
                logits,
                labels,
                dst,
                n,
                c,
            } => {
                let n = *n as usize;
                let c = *c as usize;
                unsafe {
                    let lg = sl(*logits, base, n * c);
                    let lb = sl(*labels, base, n);
                    let out = sl_mut(*dst, base, n);
                    for ni in 0..n {
                        let row = &lg[ni * c..(ni + 1) * c];
                        // log-sum-exp: max-subtract for stability.
                        let mut m = f32::NEG_INFINITY;
                        for &v in row {
                            if v > m {
                                m = v;
                            }
                        }
                        let mut sum = 0f32;
                        for &v in row {
                            sum += (v - m).exp();
                        }
                        let lse = m + sum.ln();
                        let label_idx = lb[ni] as usize;
                        // loss = -(logits[label] - lse) = lse - logits[label].
                        out[ni] = lse - row[label_idx];
                    }
                }
            }

            Thunk::SoftmaxCrossEntropyBackward {
                logits,
                labels,
                d_loss,
                dlogits,
                n,
                c,
            } => {
                let n = *n as usize;
                let c = *c as usize;
                unsafe {
                    let lg = sl(*logits, base, n * c);
                    let lb = sl(*labels, base, n);
                    let dl = sl(*d_loss, base, n);
                    let out = sl_mut(*dlogits, base, n * c);
                    for ni in 0..n {
                        let row = &lg[ni * c..(ni + 1) * c];
                        let label_idx = lb[ni] as usize;
                        let scale = dl[ni];
                        let mut m = f32::NEG_INFINITY;
                        for &v in row {
                            if v > m {
                                m = v;
                            }
                        }
                        let mut sum = 0f32;
                        for &v in row {
                            sum += (v - m).exp();
                        }
                        let inv_sum = 1.0 / sum;
                        let dst_row = &mut out[ni * c..(ni + 1) * c];
                        for k in 0..c {
                            let p = (row[k] - m).exp() * inv_sum;
                            let one_hot = if k == label_idx { 1.0 } else { 0.0 };
                            dst_row[k] = (p - one_hot) * scale;
                        }
                    }
                }
            }

            Thunk::GatherAxis {
                table,
                idx,
                dst,
                outer,
                axis_dim,
                num_idx,
                trailing,
                idx_i64,
                table_bytes,
            } => {
                let outer = *outer as usize;
                let axis_dim = *axis_dim as usize;
                let num_idx = *num_idx as usize;
                let trailing = *trailing as usize;
                unsafe {
                    if *table_bytes == 8 {
                        let tab = sl_i64(*table, base, outer * axis_dim * trailing);
                        let out = sl_mut_i64(*dst, base, outer * num_idx * trailing);
                        for o in 0..outer {
                            let tab_outer = o * axis_dim * trailing;
                            let out_outer = o * num_idx * trailing;
                            if *idx_i64 != 0 {
                                let ids = sl_i64(*idx, base, num_idx);
                                for k in 0..num_idx {
                                    let row = ids[k].max(0) as usize;
                                    if row < axis_dim {
                                        let tab_row = tab_outer + row * trailing;
                                        let out_row = out_outer + k * trailing;
                                        out[out_row..out_row + trailing]
                                            .copy_from_slice(&tab[tab_row..tab_row + trailing]);
                                    }
                                }
                            } else {
                                let ids = sl(*idx, base, num_idx);
                                for k in 0..num_idx {
                                    let row = ids[k] as usize;
                                    if row < axis_dim {
                                        let tab_row = tab_outer + row * trailing;
                                        let out_row = out_outer + k * trailing;
                                        out[out_row..out_row + trailing]
                                            .copy_from_slice(&tab[tab_row..tab_row + trailing]);
                                    }
                                }
                            }
                        }
                    } else {
                        let tab = sl(*table, base, outer * axis_dim * trailing);
                        let out = sl_mut(*dst, base, outer * num_idx * trailing);
                        for o in 0..outer {
                            let tab_outer = o * axis_dim * trailing;
                            let out_outer = o * num_idx * trailing;
                            if *idx_i64 != 0 {
                                let ids = sl_i64(*idx, base, num_idx);
                                for k in 0..num_idx {
                                    let row = ids[k].max(0) as usize;
                                    if row < axis_dim {
                                        let tab_row = tab_outer + row * trailing;
                                        let out_row = out_outer + k * trailing;
                                        out[out_row..out_row + trailing]
                                            .copy_from_slice(&tab[tab_row..tab_row + trailing]);
                                    }
                                }
                            } else {
                                let ids = sl(*idx, base, num_idx);
                                for k in 0..num_idx {
                                    let row = ids[k] as usize;
                                    if row < axis_dim {
                                        let tab_row = tab_outer + row * trailing;
                                        let out_row = out_outer + k * trailing;
                                        out[out_row..out_row + trailing]
                                            .copy_from_slice(&tab[tab_row..tab_row + trailing]);
                                    }
                                }
                            }
                        }
                    }
                }
            }

            Thunk::Transpose {
                src,
                dst,
                in_total,
                out_dims,
                in_strides,
                elem_bytes,
            } => {
                // N-D index walk: for each output flat index, decompose into
                // multi-dim coords using out_dims, then dot with in_strides
                // to find the source flat index. Stride 0 = broadcast (read
                // the same input element repeatedly along that dim).
                let rank = out_dims.len();
                let total: usize = out_dims.iter().map(|&d| d as usize).product();
                let in_total = *in_total as usize;
                unsafe {
                    if *elem_bytes == 1 {
                        // 1-byte dtypes (Bool / I8 / U8). Without this branch the
                        // `else` path below reads/writes 4 bytes per element via the
                        // f32 slice, corrupting e.g. a broadcast of the VITS attention
                        // mask (Bool, expanded over heads) — masking wrong positions.
                        let inp = arena_buf[*src..*src + in_total].to_vec();
                        let out = &mut arena_buf[*dst..*dst + total];
                        let mut idx = vec![0usize; rank];
                        for o in 0..total {
                            let mut src_idx = 0usize;
                            for d in 0..rank {
                                src_idx += idx[d] * in_strides[d] as usize;
                            }
                            out[o] = inp[broadcast_src_index(src_idx, in_total)];
                            for d in (0..rank).rev() {
                                idx[d] += 1;
                                if idx[d] < out_dims[d] as usize {
                                    break;
                                }
                                idx[d] = 0;
                            }
                        }
                    } else if *elem_bytes == 8 {
                        let inp = sl_i64(*src, base, in_total);
                        let out = sl_mut_i64(*dst, base, total);
                        let mut idx = vec![0usize; rank];
                        for o in 0..total {
                            let mut src_idx = 0usize;
                            for d in 0..rank {
                                src_idx += idx[d] * in_strides[d] as usize;
                            }
                            out[o] = inp[broadcast_src_index(src_idx, in_total)];
                            for d in (0..rank).rev() {
                                idx[d] += 1;
                                if idx[d] < out_dims[d] as usize {
                                    break;
                                }
                                idx[d] = 0;
                            }
                        }
                    } else {
                        let inp = sl(*src, base, in_total);
                        let out = sl_mut(*dst, base, total);
                        if rank == 4
                            && in_strides[0] == 0
                            && in_strides[2] == 0
                            && in_strides[3] == 0
                            && in_strides[1] != 0
                        {
                            // Per-channel broadcast out[n,c,h,w] = in[c*sc] — how
                            // a conv bias `[C]` reaches `[N,C,H,W]` (and its
                            // recompute in the backward graph). Fill each (n,c)
                            // plane with its scalar instead of an N-D index walk
                            // over every element; parallel over the channel-batch.
                            let d1 = out_dims[1] as usize;
                            let sc = in_strides[1] as usize;
                            let plane = (out_dims[2] as usize) * (out_dims[3] as usize);
                            let nc_total = (out_dims[0] as usize) * d1;
                            let out_addr = out.as_mut_ptr() as usize;
                            let fill = |nc0: usize, nc1: usize| {
                                let op = out_addr as *mut f32;
                                for nc in nc0..nc1 {
                                    let v = inp[(nc % d1) * sc];
                                    let base_off = nc * plane;
                                    for k in 0..plane {
                                        *op.add(base_off + k) = v;
                                    }
                                }
                            };
                            if fast_conv_enabled() && crate::pool::should_parallelize(total) {
                                crate::pool::par_for(
                                    nc_total,
                                    crate::pool::outer_chunk(nc_total),
                                    &|off, cnt| fill(off, off + cnt),
                                );
                            } else {
                                fill(0, nc_total);
                            }
                        } else if rank == 2 && in_strides[0] != 0 && in_strides[1] != 0 {
                            // Fast 2D transpose (the common matmul-backward case:
                            // xᵀ, wᵀ). out[i,j] = in[i*s0 + j*s1]; tiled over
                            // columns for write-locality, parallel over rows. Far
                            // cheaper than the general per-element index walk.
                            let d0 = out_dims[0] as usize;
                            let d1 = out_dims[1] as usize;
                            let s0 = in_strides[0] as usize;
                            let s1 = in_strides[1] as usize;
                            let out_addr = out.as_mut_ptr() as usize;
                            let tile = |i0: usize, i1: usize| {
                                let op = out_addr as *mut f32;
                                const T: usize = 32;
                                let mut j0 = 0;
                                while j0 < d1 {
                                    let j1 = (j0 + T).min(d1);
                                    for i in i0..i1 {
                                        let inb = i * s0;
                                        let outb = i * d1;
                                        for j in j0..j1 {
                                            *op.add(outb + j) = inp[inb + j * s1];
                                        }
                                    }
                                    j0 = j1;
                                }
                            };
                            if fast_conv_enabled() && crate::pool::should_parallelize(total) {
                                crate::pool::par_for(
                                    d0,
                                    crate::pool::outer_chunk(d0),
                                    &|off, cnt| tile(off, off + cnt),
                                );
                            } else {
                                tile(0, d0);
                            }
                        } else if fast_conv_enabled() && crate::pool::should_parallelize(total) {
                            // Parallel: each chunk seeds its starting multi-index
                            // from `off`, then walks incrementally. Output writes
                            // are disjoint per `o`.
                            let out_addr = out.as_mut_ptr() as usize;
                            crate::pool::par_for(
                                total,
                                crate::pool::chunk_floor(total),
                                &|off, cnt| {
                                    let mut idx = vec![0usize; rank];
                                    let mut rem = off;
                                    for d in (0..rank).rev() {
                                        let dim = out_dims[d] as usize;
                                        idx[d] = rem % dim;
                                        rem /= dim;
                                    }
                                    for o in off..off + cnt {
                                        let mut src_idx = 0usize;
                                        for d in 0..rank {
                                            src_idx += idx[d] * in_strides[d] as usize;
                                        }
                                        let v = inp[broadcast_src_index(src_idx, in_total)];
                                        *((out_addr as *mut f32).add(o)) = v;
                                        for d in (0..rank).rev() {
                                            idx[d] += 1;
                                            if idx[d] < out_dims[d] as usize {
                                                break;
                                            }
                                            idx[d] = 0;
                                        }
                                    }
                                },
                            );
                        } else {
                            let mut idx = vec![0usize; rank];
                            for o in 0..total {
                                let mut src_idx = 0usize;
                                for d in 0..rank {
                                    src_idx += idx[d] * in_strides[d] as usize;
                                }
                                out[o] = inp[broadcast_src_index(src_idx, in_total)];
                                for d in (0..rank).rev() {
                                    idx[d] += 1;
                                    if idx[d] < out_dims[d] as usize {
                                        break;
                                    }
                                    idx[d] = 0;
                                }
                            }
                        }
                    }
                }
            }

            // (Thunk::DenseSolveF64 / Thunk::ScanBackward had panic
            // stubs here as placeholders during the wire-up; both
            // are now reached by the real implementations earlier in
            // this same match — the stubs were dead code shadowed by
            // the specific-pattern arms above. Removed.)
            Thunk::CustomOp {
                kernel,
                inputs,
                output,
                attrs,
            } => {
                let (out_off, out_len, out_shape) = output;
                unsafe {
                    dispatch_custom_op(
                        &**kernel, inputs, *out_off, *out_len, out_shape, attrs, base,
                    );
                }
            }

            Thunk::Reverse {
                src,
                dst,
                dims,
                rev_mask,
                elem_bytes,
            } => {
                let eb = *elem_bytes as usize;
                let rank = dims.len();
                let total: usize = dims.iter().map(|&d| d as usize).product::<usize>().max(1);
                let mut strides = vec![1usize; rank];
                for i in (0..rank.saturating_sub(1)).rev() {
                    strides[i] = strides[i + 1] * dims[i + 1] as usize;
                }
                unsafe {
                    let src_base = base.add(*src);
                    let dst_base = base.add(*dst);
                    for o in 0..total {
                        let mut rem = o;
                        let mut in_flat = 0usize;
                        for ax in 0..rank {
                            let idx = rem / strides[ax];
                            rem %= strides[ax];
                            let in_idx = if rev_mask[ax] {
                                dims[ax] as usize - 1 - idx
                            } else {
                                idx
                            };
                            in_flat += in_idx * strides[ax];
                        }
                        std::ptr::copy_nonoverlapping(
                            src_base.add(in_flat * eb),
                            dst_base.add(o * eb),
                            eb,
                        );
                    }
                }
            }
        }
        if trace_done {
            eprintln!("[thunk {i} done]");
        }
    }
}

/// Griewank treeverse: process backward iterations `[t_lo..=t_hi]` (with
/// the carry entering iteration `t_lo` supplied as `anchor_carry`) by
/// recursive binary subdivision. Total work `O((t_hi-t_lo+1) · log)`,
/// auxiliary memory `O(log · carry_bytes)` for the recursion stack.
///
/// Compared to the iterative segment-cached scheme, this trades extra
/// recompute for less working memory — each level of recursion holds
/// one `cb`-sized intermediate carry on the stack but never the whole
/// segment at once. With K saved outer checkpoints, the outer driver
/// invokes this helper once per segment.
///
/// `process_iter(t, carry_at_t)` is the per-iteration leaf action: it
/// runs `body_vjp` at iteration `t` with the supplied carry, threads
/// `dcarry` backward, and (for ScanBackwardXs) writes `dxs[t]`.
#[allow(clippy::too_many_arguments)]
unsafe fn griewank_process_segment(
    t_lo: usize,
    t_hi: usize,
    anchor_carry: &[u8],
    cb: usize,
    fwd_sched: &ThunkSchedule,
    fwd_init: &[u8],
    fwd_carry_in_off: usize,
    fwd_output_off: usize,
    fwd_x_offs: &[usize],
    base: *mut u8,
    outer_xs_offs: &[(usize, u32)],
    fwd_buf: &mut Vec<u8>,
    leaf_threshold: usize,
    process_iter: &mut dyn FnMut(usize, &[u8]),
) {
    unsafe {
        let size = t_hi - t_lo + 1;
        if size == 1 {
            process_iter(t_lo, anchor_carry);
            return;
        }
        if size <= leaf_threshold {
            // Walk forward, cache each carry, run backward in reverse.
            let mut cache: Vec<u8> = Vec::with_capacity(size * cb);
            cache.extend_from_slice(anchor_carry);
            fwd_buf.copy_from_slice(fwd_init);
            std::ptr::copy_nonoverlapping(
                anchor_carry.as_ptr(),
                fwd_buf.as_mut_ptr().add(fwd_carry_in_off),
                cb,
            );
            for i in 1..size {
                let cur_iter = t_lo + i - 1;
                for (idx, fb_x_off) in fwd_x_offs.iter().enumerate() {
                    let (outer_xs_off, x_psb) = outer_xs_offs[idx];
                    let xb = x_psb as usize;
                    std::ptr::copy_nonoverlapping(
                        base.add(outer_xs_off + cur_iter * xb),
                        fwd_buf.as_mut_ptr().add(*fb_x_off),
                        xb,
                    );
                }
                execute_thunks(fwd_sched, fwd_buf);
                if fwd_output_off != fwd_carry_in_off {
                    fwd_buf.copy_within(fwd_output_off..fwd_output_off + cb, fwd_carry_in_off);
                }
                cache.extend_from_slice(&fwd_buf[fwd_carry_in_off..fwd_carry_in_off + cb]);
            }
            // Process backward.
            for t in (t_lo..=t_hi).rev() {
                let idx = t - t_lo;
                let carry = &cache[idx * cb..(idx + 1) * cb];
                process_iter(t, carry);
            }
            return;
        }

        // Split: walk forward from anchor to compute carry entering `mid`.
        // (We need `mid - t_lo` body executions: one per iteration in
        // [t_lo, mid).)
        let mid = t_lo + size / 2;
        fwd_buf.copy_from_slice(fwd_init);
        std::ptr::copy_nonoverlapping(
            anchor_carry.as_ptr(),
            fwd_buf.as_mut_ptr().add(fwd_carry_in_off),
            cb,
        );
        for cur_iter in t_lo..mid {
            for (idx, fb_x_off) in fwd_x_offs.iter().enumerate() {
                let (outer_xs_off, x_psb) = outer_xs_offs[idx];
                let xb = x_psb as usize;
                std::ptr::copy_nonoverlapping(
                    base.add(outer_xs_off + cur_iter * xb),
                    fwd_buf.as_mut_ptr().add(*fb_x_off),
                    xb,
                );
            }
            execute_thunks(fwd_sched, fwd_buf);
            if fwd_output_off != fwd_carry_in_off {
                fwd_buf.copy_within(fwd_output_off..fwd_output_off + cb, fwd_carry_in_off);
            }
        }
        let mid_carry: Vec<u8> = fwd_buf[fwd_carry_in_off..fwd_carry_in_off + cb].to_vec();

        // Right half first (higher t values processed first to match the
        // canonical reverse-mode iteration order: dcarry threads from
        // t=length-1 down to t=0).
        griewank_process_segment(
            mid,
            t_hi,
            &mid_carry,
            cb,
            fwd_sched,
            fwd_init,
            fwd_carry_in_off,
            fwd_output_off,
            fwd_x_offs,
            base,
            outer_xs_offs,
            fwd_buf,
            leaf_threshold,
            process_iter,
        );
        // Then left half with original anchor.
        griewank_process_segment(
            t_lo,
            mid - 1,
            anchor_carry,
            cb,
            fwd_sched,
            fwd_init,
            fwd_carry_in_off,
            fwd_output_off,
            fwd_x_offs,
            base,
            outer_xs_offs,
            fwd_buf,
            leaf_threshold,
            process_iter,
        );
    }
}

/// Execute a batched 1D FFT in the f64 2N-real-block layout.
/// Each "row" is `2N` f64 elements: first `N` real, then `N` imag.
/// The `outer` rows are independent and processed sequentially.
///
/// Both forward and inverse use the same Cooley-Tukey radix-2 DIT
/// kernel — only the twiddle-factor sign differs. Power-of-2 only
/// (the IR builder rejects non-power-of-2 sizes at graph-build time).
/// Batched 1D FFT on the f64 2N-real-block layout. Public so other
/// backend crates can invoke this as a host fallback against a
/// unified-memory arena (e.g. rlx-metal: sync the command buffer,
/// pass the Metal `Buffer::contents()` pointer as `base`, restart the
/// command buffer). Self-contained — no rlx-cpu state required.
///
/// Safety: `base + src` and `base + dst` must be valid for the
/// `outer * 2 * n_complex * sizeof::<f64>()` byte range and stay
/// alive for the duration of the call.
pub unsafe fn execute_fft1d_f64(
    src: usize,
    dst: usize,
    outer: usize,
    n_complex: usize,
    inverse: bool,
    norm_tag: u32,
    base: *mut u8,
) {
    let row_elems = 2 * n_complex;
    let mut re = vec![0f64; n_complex];
    let mut im = vec![0f64; n_complex];
    let norm = rlx_ir::fft::FftNorm::from_tag(norm_tag);
    let scale = norm.output_scale(n_complex, inverse);
    // Scratch reused across rows for the Bluestein path. Empty when
    // we're on the radix-2 fast path.
    let mut scratch = if n_complex.is_power_of_two() || n_complex <= 16 {
        BluesteinScratchF64::empty()
    } else {
        BluesteinScratchF64::build(n_complex, inverse)
    };
    for o in 0..outer {
        let row_offset = src + o * row_elems * std::mem::size_of::<f64>();
        let s = unsafe { sl_f64(row_offset, base, row_elems) };
        re.copy_from_slice(&s[..n_complex]);
        im.copy_from_slice(&s[n_complex..]);
        if n_complex.is_power_of_two() {
            fft_radix2_inplace_f64(&mut re, &mut im, inverse);
        } else if n_complex <= 16 {
            fft_naive_inplace_f64(&mut re, &mut im, inverse);
        } else {
            fft_bluestein_inplace_f64(&mut re, &mut im, inverse, &mut scratch);
        }
        if scale != 1.0 {
            re.iter_mut().for_each(|v| *v *= scale);
            im.iter_mut().for_each(|v| *v *= scale);
        }
        let dst_offset = dst + o * row_elems * std::mem::size_of::<f64>();
        let d = unsafe { sl_mut_f64(dst_offset, base, row_elems) };
        d[..n_complex].copy_from_slice(&re);
        d[n_complex..].copy_from_slice(&im);
    }
}

/// f32 counterpart of `execute_fft1d_f64`. Same 2N-real-block layout
/// (first N real, second N imag per row), same unnormalized
/// convention; only the element width differs. Twiddle factors are
/// computed in f64 and cast to f32 to keep large-N error closer to
/// the f64 path (the savings from f32 are in memory bandwidth, not in
/// twiddle precision).
/// Complex (C64) dense GEMM `C[m,n] = A[m,k] · B[k,n]`. Operands are
/// interleaved `[re, im]` f32; `a_off`/`b_off`/`c_off` are byte offsets
/// into `base`. Parallel over output rows (disjoint writes).
unsafe fn cgemm_c64(
    a_off: usize,
    b_off: usize,
    c_off: usize,
    m: usize,
    k: usize,
    n: usize,
    base: *mut u8,
) {
    let bptr = base as usize;
    unsafe {
        let a = std::slice::from_raw_parts((bptr + a_off) as *const f32, 2 * m * k);
        let b = std::slice::from_raw_parts((bptr + b_off) as *const f32, 2 * k * n);
        let c_base = bptr + c_off;
        crate::pool::par_range(m, |i| {
            let crow = std::slice::from_raw_parts_mut((c_base + i * n * 8) as *mut f32, 2 * n);
            for j in 0..n {
                let mut re = 0f32;
                let mut im = 0f32;
                for l in 0..k {
                    let ar = a[2 * (i * k + l)];
                    let ai = a[2 * (i * k + l) + 1];
                    let br = b[2 * (l * n + j)];
                    let bi = b[2 * (l * n + j) + 1];
                    re += ar * br - ai * bi;
                    im += ar * bi + ai * br;
                }
                crow[2 * j] = re;
                crow[2 * j + 1] = im;
            }
        });
    }
}

/// Reference / host-fallback entry for `Op::Lstm` (multi-layer,
/// optionally bidirectional, optional decode carry). Gate order i, f, g,
/// o. Shared by the CPU backend and the CUDA / ROCm / wgpu / Metal host
/// fallbacks. Tensors are `f32` in the arena; weights are packed (see
/// `Op::Lstm`). `h0`/`c0` are byte offsets when `carry`, else ignored;
/// the final `hn`/`cn` are written back into them in place. `dst` is
/// `[batch, seq, D*hidden]`. Batch items run in parallel per direction.
#[allow(clippy::too_many_arguments)]
pub unsafe fn execute_lstm_f32(
    x: usize,
    w_ih: usize,
    w_hh: usize,
    bias: usize,
    h0: usize,
    c0: usize,
    dst: usize,
    batch: usize,
    seq: usize,
    input_size: usize,
    hidden: usize,
    num_layers: usize,
    bidirectional: bool,
    carry: bool,
    base: *mut u8,
) {
    #[inline]
    fn sigmoid(z: f32) -> f32 {
        1.0 / (1.0 + (-z).exp())
    }

    let bptr = base as usize;
    let four_h = 4 * hidden;
    let dirs = if bidirectional { 2 } else { 1 };

    unsafe {
        let f32s = |off: usize, n: usize| -> &[f32] {
            std::slice::from_raw_parts((bptr + off) as *const f32, n)
        };

        // Layer 0 reads x; later layers read the previous layer's output.
        let mut layer_in: Vec<f32> = f32s(x, batch * seq * input_size).to_vec();
        let mut in_l = input_size;
        // Running element cursor into the packed `w_ih` buffer (block width
        // `4h * in_l` varies per layer; `w_hh`/`bias` blocks are uniform).
        let mut wih_cursor = 0usize;

        for l in 0..num_layers {
            let out_width = dirs * hidden;
            let mut layer_out = vec![0f32; batch * seq * out_width];
            let lo_ptr = layer_out.as_mut_ptr() as usize;
            let li_ref: &[f32] = &layer_in;
            let wih_block = four_h * in_l;

            for dir in 0..dirs {
                let ld = l * dirs + dir;
                let wih = f32s((w_ih / 4 + wih_cursor + dir * wih_block) * 4, wih_block);
                let whh = f32s(w_hh + ld * four_h * hidden * 4, four_h * hidden);
                let bs = f32s(bias + ld * four_h * 4, four_h);
                let h0p = bptr + h0 + ld * batch * hidden * 4;
                let c0p = bptr + c0 + ld * batch * hidden * 4;

                crate::pool::par_range(batch, |b| {
                    let lo = lo_ptr as *mut f32;
                    let mut h = vec![0f32; hidden];
                    let mut c = vec![0f32; hidden];
                    if carry {
                        let hin = std::slice::from_raw_parts(
                            (h0p + b * hidden * 4) as *const f32,
                            hidden,
                        );
                        let cin = std::slice::from_raw_parts(
                            (c0p + b * hidden * 4) as *const f32,
                            hidden,
                        );
                        h.copy_from_slice(hin);
                        c.copy_from_slice(cin);
                    }
                    let mut z = vec![0f32; four_h];
                    for step in 0..seq {
                        let t = if dir == 0 { step } else { seq - 1 - step };
                        let x_t = &li_ref[(b * seq + t) * in_l..(b * seq + t + 1) * in_l];
                        for r in 0..four_h {
                            let wr = &wih[r * in_l..(r + 1) * in_l];
                            let mut acc = bs[r];
                            for j in 0..in_l {
                                acc += wr[j] * x_t[j];
                            }
                            let hr = &whh[r * hidden..(r + 1) * hidden];
                            for (j, &hj) in h.iter().enumerate() {
                                acc += hr[j] * hj;
                            }
                            z[r] = acc;
                        }
                        for k in 0..hidden {
                            let i_g = sigmoid(z[k]);
                            let f_g = sigmoid(z[hidden + k]);
                            let g_g = z[2 * hidden + k].tanh();
                            let o_g = sigmoid(z[3 * hidden + k]);
                            let c_new = f_g * c[k] + i_g * g_g;
                            c[k] = c_new;
                            let h_new = o_g * c_new.tanh();
                            h[k] = h_new;
                            // [batch, seq, D*hidden]; this direction owns the
                            // `dir*hidden .. dir*hidden+hidden` feature slice.
                            *lo.add((b * seq + t) * out_width + dir * hidden + k) = h_new;
                        }
                    }
                    if carry {
                        let hout = std::slice::from_raw_parts_mut(
                            (h0p + b * hidden * 4) as *mut f32,
                            hidden,
                        );
                        let cout = std::slice::from_raw_parts_mut(
                            (c0p + b * hidden * 4) as *mut f32,
                            hidden,
                        );
                        hout.copy_from_slice(&h);
                        cout.copy_from_slice(&c);
                    }
                });
            }

            wih_cursor += dirs * wih_block;
            layer_in = layer_out;
            in_l = out_width;
        }

        // Final layer output → dst [batch, seq, D*hidden].
        let dst_slice = std::slice::from_raw_parts_mut((bptr + dst) as *mut f32, layer_in.len());
        dst_slice.copy_from_slice(&layer_in);
    }
}

/// Reference / host-fallback for `Op::Gru` (PyTorch GRU; gate order r, z, n;
/// multi-layer / bidirectional / carry). Separate `b_ih`/`b_hh` because the
/// reset gate is applied to the hidden term *after* its bias:
/// `n = tanh(x·W_inᵀ+b_in + r ⊙ (h·W_hnᵀ+b_hn))`,
/// `h' = (1-z)⊙n + z⊙h`. Packing mirrors `Op::Lstm` with `3*hidden` gate rows.
#[allow(clippy::too_many_arguments)]
pub unsafe fn execute_gru_f32(
    x: usize,
    w_ih: usize,
    w_hh: usize,
    b_ih: usize,
    b_hh: usize,
    h0: usize,
    dst: usize,
    batch: usize,
    seq: usize,
    input_size: usize,
    hidden: usize,
    num_layers: usize,
    bidirectional: bool,
    carry: bool,
    base: *mut u8,
) {
    #[inline]
    fn sigmoid(z: f32) -> f32 {
        1.0 / (1.0 + (-z).exp())
    }

    let bptr = base as usize;
    let three_h = 3 * hidden;
    let dirs = if bidirectional { 2 } else { 1 };

    unsafe {
        let f32s = |off: usize, n: usize| -> &[f32] {
            std::slice::from_raw_parts((bptr + off) as *const f32, n)
        };

        let mut layer_in: Vec<f32> = f32s(x, batch * seq * input_size).to_vec();
        let mut in_l = input_size;
        let mut wih_cursor = 0usize;

        for l in 0..num_layers {
            let out_width = dirs * hidden;
            let mut layer_out = vec![0f32; batch * seq * out_width];
            let lo_ptr = layer_out.as_mut_ptr() as usize;
            let li_ref: &[f32] = &layer_in;
            let wih_block = three_h * in_l;

            for dir in 0..dirs {
                let ld = l * dirs + dir;
                let wih = f32s((w_ih / 4 + wih_cursor + dir * wih_block) * 4, wih_block);
                let whh = f32s(w_hh + ld * three_h * hidden * 4, three_h * hidden);
                let bih = f32s(b_ih + ld * three_h * 4, three_h);
                let bhh = f32s(b_hh + ld * three_h * 4, three_h);
                let h0p = bptr + h0 + ld * batch * hidden * 4;

                crate::pool::par_range(batch, |b| {
                    let lo = lo_ptr as *mut f32;
                    let mut h = vec![0f32; hidden];
                    if carry {
                        let hin = std::slice::from_raw_parts(
                            (h0p + b * hidden * 4) as *const f32,
                            hidden,
                        );
                        h.copy_from_slice(hin);
                    }
                    let mut xi = vec![0f32; three_h]; // x·W_ihᵀ + b_ih
                    let mut hi = vec![0f32; three_h]; // h·W_hhᵀ + b_hh
                    for step in 0..seq {
                        let t = if dir == 0 { step } else { seq - 1 - step };
                        let x_t = &li_ref[(b * seq + t) * in_l..(b * seq + t + 1) * in_l];
                        for r in 0..three_h {
                            let wr = &wih[r * in_l..(r + 1) * in_l];
                            let mut a = bih[r];
                            for j in 0..in_l {
                                a += wr[j] * x_t[j];
                            }
                            xi[r] = a;
                            let hr = &whh[r * hidden..(r + 1) * hidden];
                            let mut bb = bhh[r];
                            for (j, &hj) in h.iter().enumerate() {
                                bb += hr[j] * hj;
                            }
                            hi[r] = bb;
                        }
                        for k in 0..hidden {
                            let rg = sigmoid(xi[k] + hi[k]);
                            let zg = sigmoid(xi[hidden + k] + hi[hidden + k]);
                            // Reset applied to hidden term after its bias.
                            let ng = (xi[2 * hidden + k] + rg * hi[2 * hidden + k]).tanh();
                            let h_new = (1.0 - zg) * ng + zg * h[k];
                            h[k] = h_new;
                            *lo.add((b * seq + t) * out_width + dir * hidden + k) = h_new;
                        }
                    }
                    if carry {
                        let hout = std::slice::from_raw_parts_mut(
                            (h0p + b * hidden * 4) as *mut f32,
                            hidden,
                        );
                        hout.copy_from_slice(&h);
                    }
                });
            }

            wih_cursor += dirs * wih_block;
            layer_in = layer_out;
            in_l = out_width;
        }

        let dst_slice = std::slice::from_raw_parts_mut((bptr + dst) as *mut f32, layer_in.len());
        dst_slice.copy_from_slice(&layer_in);
    }
}

/// Reference / host-fallback for `Op::Rnn` (Elman; `act = relu` when `relu`
/// else `tanh`; multi-layer / bidirectional / carry). Single merged bias.
/// `h' = act(x·W_ihᵀ + h·W_hhᵀ + bias)`. Packing mirrors `Op::Lstm` with
/// `hidden` gate rows.
#[allow(clippy::too_many_arguments)]
pub unsafe fn execute_rnn_f32(
    x: usize,
    w_ih: usize,
    w_hh: usize,
    bias: usize,
    h0: usize,
    dst: usize,
    batch: usize,
    seq: usize,
    input_size: usize,
    hidden: usize,
    num_layers: usize,
    bidirectional: bool,
    carry: bool,
    relu: bool,
    base: *mut u8,
) {
    let bptr = base as usize;
    let dirs = if bidirectional { 2 } else { 1 };

    unsafe {
        let f32s = |off: usize, n: usize| -> &[f32] {
            std::slice::from_raw_parts((bptr + off) as *const f32, n)
        };

        let mut layer_in: Vec<f32> = f32s(x, batch * seq * input_size).to_vec();
        let mut in_l = input_size;
        let mut wih_cursor = 0usize;

        for l in 0..num_layers {
            let out_width = dirs * hidden;
            let mut layer_out = vec![0f32; batch * seq * out_width];
            let lo_ptr = layer_out.as_mut_ptr() as usize;
            let li_ref: &[f32] = &layer_in;
            let wih_block = hidden * in_l;

            for dir in 0..dirs {
                let ld = l * dirs + dir;
                let wih = f32s((w_ih / 4 + wih_cursor + dir * wih_block) * 4, wih_block);
                let whh = f32s(w_hh + ld * hidden * hidden * 4, hidden * hidden);
                let bs = f32s(bias + ld * hidden * 4, hidden);
                let h0p = bptr + h0 + ld * batch * hidden * 4;

                crate::pool::par_range(batch, |b| {
                    let lo = lo_ptr as *mut f32;
                    let mut h = vec![0f32; hidden];
                    if carry {
                        let hin = std::slice::from_raw_parts(
                            (h0p + b * hidden * 4) as *const f32,
                            hidden,
                        );
                        h.copy_from_slice(hin);
                    }
                    for step in 0..seq {
                        let t = if dir == 0 { step } else { seq - 1 - step };
                        let x_t = &li_ref[(b * seq + t) * in_l..(b * seq + t + 1) * in_l];
                        let mut h_new = vec![0f32; hidden];
                        for k in 0..hidden {
                            let wr = &wih[k * in_l..(k + 1) * in_l];
                            let mut acc = bs[k];
                            for j in 0..in_l {
                                acc += wr[j] * x_t[j];
                            }
                            let hr = &whh[k * hidden..(k + 1) * hidden];
                            for (j, &hj) in h.iter().enumerate() {
                                acc += hr[j] * hj;
                            }
                            h_new[k] = if relu { acc.max(0.0) } else { acc.tanh() };
                        }
                        for k in 0..hidden {
                            h[k] = h_new[k];
                            *lo.add((b * seq + t) * out_width + dir * hidden + k) = h_new[k];
                        }
                    }
                    if carry {
                        let hout = std::slice::from_raw_parts_mut(
                            (h0p + b * hidden * 4) as *mut f32,
                            hidden,
                        );
                        hout.copy_from_slice(&h);
                    }
                });
            }

            wih_cursor += dirs * wih_block;
            layer_in = layer_out;
            in_l = out_width;
        }

        let dst_slice = std::slice::from_raw_parts_mut((bptr + dst) as *mut f32, layer_in.len());
        dst_slice.copy_from_slice(&layer_in);
    }
}

/// Reference for `Op::ArgMax`/`Op::ArgMin` (f32-encoded indices, first-hit
/// tie-break). Reduces the middle `reduced` axis: input is logically
/// `[outer, reduced, inner]`, output `[outer, inner]`. Shared by the CPU thunk
/// and the Metal/WGPU host paths.
pub unsafe fn execute_argreduce_f32(
    src: usize,
    dst: usize,
    outer: usize,
    reduced: usize,
    inner: usize,
    is_max: bool,
    base: *mut u8,
) {
    let bptr = base as usize;
    unsafe {
        let inp = std::slice::from_raw_parts((bptr + src) as *const f32, outer * reduced * inner);
        let out = std::slice::from_raw_parts_mut((bptr + dst) as *mut f32, outer * inner);
        for o in 0..outer {
            for i in 0..inner {
                let mut best = inp[o * reduced * inner + i];
                let mut best_idx = 0usize;
                for r in 1..reduced {
                    let v = inp[o * reduced * inner + r * inner + i];
                    let better = if is_max { v > best } else { v < best };
                    if better {
                        best = v;
                        best_idx = r;
                    }
                }
                out[o * inner + i] = best_idx as f32;
            }
        }
    }
}

/// Reference for `Op::Mamba2` — SSD scalar-decay SSM scan. Inputs (f32):
/// `x [B,S,H,P]`, `dt [B,S,H]`, `a [H]`, `b/c [B,S,H,N]`. Per `(batch, head)`
/// the state `S[P,N]` is zero-init: `dA=exp(dt·a)`, `S=dA·S + (dt·x)⊗b`,
/// `y=Σ_n S[:,n]·c[n]`. Output `[B,S,H,P]`.
#[allow(clippy::too_many_arguments)]
pub unsafe fn execute_mamba2_f32(
    x: usize,
    dt: usize,
    a: usize,
    b: usize,
    c: usize,
    dst: usize,
    batch: usize,
    seq: usize,
    heads: usize,
    head_dim: usize,
    state_size: usize,
    base: *mut u8,
) {
    let (bn, s, h, p, n) = (batch, seq, heads, head_dim, state_size);
    let bptr = base as usize;
    unsafe {
        let f32s = |off: usize, len: usize| -> &[f32] {
            std::slice::from_raw_parts((bptr + off) as *const f32, len)
        };
        let xs = f32s(x, bn * s * h * p);
        let dts = f32s(dt, bn * s * h);
        let am = f32s(a, h);
        let bm = f32s(b, bn * s * h * n);
        let cm = f32s(c, bn * s * h * n);
        let out_ptr = bptr + dst;

        // Independent per (batch, head).
        crate::pool::par_range(bn * h, |bh| {
            let bi = bh / h;
            let hi = bh % h;
            let out = out_ptr as *mut f32;
            let mut state = vec![0f32; p * n];
            for t in 0..s {
                let dt_t = dts[(bi * s + t) * h + hi];
                let da = (dt_t * am[hi]).exp();
                let x_off = ((bi * s + t) * h + hi) * p;
                let bc_off = ((bi * s + t) * h + hi) * n;
                for pi in 0..p {
                    let dtx = dt_t * xs[x_off + pi];
                    for ni in 0..n {
                        state[pi * n + ni] = da * state[pi * n + ni] + dtx * bm[bc_off + ni];
                    }
                }
                for pi in 0..p {
                    let mut acc = 0f32;
                    for ni in 0..n {
                        acc += state[pi * n + ni] * cm[bc_off + ni];
                    }
                    *out.add(x_off + pi) = acc;
                }
            }
        });
    }
}

/// Host-fallback entry for `Op::GatedDeltaNet` (Metal / unified memory).
/// When `state == 0`, uses a zero-initialized scratch state per batch item.
/// Batch-general reverse/flip (dtype-agnostic, byte-copy). Shared by the CPU
/// `Thunk::Reverse` arm and the Metal/WGPU host paths. `dims` is the row-major
/// input shape; `rev_mask[a]` flips axis `a`. `src`/`dst` are byte offsets.
pub unsafe fn execute_reverse(
    src: usize,
    dst: usize,
    dims: &[u32],
    rev_mask: &[bool],
    elem_bytes: usize,
    base: *mut u8,
) {
    let rank = dims.len();
    let total: usize = dims.iter().map(|&d| d as usize).product::<usize>().max(1);
    let mut strides = vec![1usize; rank];
    for i in (0..rank.saturating_sub(1)).rev() {
        strides[i] = strides[i + 1] * dims[i + 1] as usize;
    }
    unsafe {
        let src_base = base.add(src);
        let dst_base = base.add(dst);
        for o in 0..total {
            let mut rem = o;
            let mut in_flat = 0usize;
            for ax in 0..rank {
                let idx = rem / strides[ax];
                rem %= strides[ax];
                let in_idx = if rev_mask[ax] {
                    dims[ax] as usize - 1 - idx
                } else {
                    idx
                };
                in_flat += in_idx * strides[ax];
            }
            std::ptr::copy_nonoverlapping(
                src_base.add(in_flat * elem_bytes),
                dst_base.add(o * elem_bytes),
                elem_bytes,
            );
        }
    }
}

/// Token-sampling reference. Shared by the CPU `Thunk::Sample` arm and the
/// Metal unified-memory host fallback. `logits` is `[batch, vocab]` f32;
/// writes one f32-encoded index per batch row to `dst`. With a fixed `seed`
/// (and same top_k/top_p/temperature) the result is deterministic.
pub unsafe fn execute_sample_f32(
    logits: usize,
    dst: usize,
    batch: usize,
    vocab: usize,
    top_k: usize,
    top_p: f32,
    temperature: f32,
    seed: u64,
    base: *mut u8,
) {
    let (b, v) = (batch, vocab);
    let k = top_k.min(v);
    unsafe {
        let lg = sl(logits, base, b * v);
        let out = sl_mut(dst, base, b);
        let mut rng = rlx_ir::Philox4x32::new(if seed == 0 { 0xDEADBEEF } else { seed });
        for bi in 0..b {
            let row = &lg[bi * v..(bi + 1) * v];
            out[bi] = sample_row(row, k, top_p, temperature, &mut rng) as f32;
        }
    }
}

/// Mamba selective-scan reference (f32). Shared by the CPU `Thunk::SelectiveScan`
/// arm and the Metal unified-memory host fallback. `base` is the arena byte
/// pointer; the five inputs (`x`, `delta`, `a`, `b`, `c`) and `dst` are byte
/// offsets into it. Recurrence per `(batch, seq, channel, state)`:
/// `h[t] = exp(Δ·A)·h[t-1] + Δ·B·x;  y = Σ_n C·h`. State resets per batch row.
pub unsafe fn execute_selective_scan_f32(
    x: usize,
    delta: usize,
    a: usize,
    b: usize,
    c: usize,
    dst: usize,
    batch: usize,
    seq: usize,
    hidden: usize,
    state_size: usize,
    base: *mut u8,
) {
    let (bn, s, h, n) = (batch, seq, hidden, state_size);
    unsafe {
        let xs = sl(x, base, bn * s * h);
        let dt = sl(delta, base, bn * s * h);
        let am = sl(a, base, h * n);
        let bm = sl(b, base, bn * s * n);
        let cm = sl(c, base, bn * s * n);
        let out = sl_mut(dst, base, bn * s * h);

        // State buffer per-batch: h channels × n state. Sequential along
        // the seq dimension; reset at the start of each batch row.
        let mut state = vec![0f32; h * n];
        for bi in 0..bn {
            for v in state.iter_mut() {
                *v = 0.0;
            }
            for si in 0..s {
                let x_row = &xs[bi * s * h + si * h..bi * s * h + (si + 1) * h];
                let dt_row = &dt[bi * s * h + si * h..bi * s * h + (si + 1) * h];
                let b_row = &bm[bi * s * n + si * n..bi * s * n + (si + 1) * n];
                let c_row = &cm[bi * s * n + si * n..bi * s * n + (si + 1) * n];
                let out_row = &mut out[bi * s * h + si * h..bi * s * h + (si + 1) * h];

                for ci in 0..h {
                    let d = dt_row[ci];
                    let xv = x_row[ci];
                    let mut acc = 0f32;
                    for ni in 0..n {
                        // Discretize: exp(d * a) and d * b.
                        let da = (d * am[ci * n + ni]).exp();
                        state[ci * n + ni] = da * state[ci * n + ni] + d * b_row[ni] * xv;
                        acc += c_row[ni] * state[ci * n + ni];
                    }
                    out_row[ci] = acc;
                }
            }
        }
    }
}

pub unsafe fn execute_gated_delta_net_f32(
    q: usize,
    k: usize,
    v: usize,
    g: usize,
    beta: usize,
    state: usize,
    dst: usize,
    batch: usize,
    seq: usize,
    heads: usize,
    state_size: usize,
    base: *mut u8,
) {
    #[derive(Copy, Clone)]
    struct ArenaPtr(usize);
    unsafe impl Send for ArenaPtr {}
    unsafe impl Sync for ArenaPtr {}
    impl ArenaPtr {
        #[inline]
        fn get(self) -> *mut u8 {
            self.0 as *mut u8
        }
    }

    unsafe {
        let arena = ArenaPtr(base as usize);
        let (b, s, h, n) = (batch, seq, heads, state_size);
        let scale = 1.0f32 / (n as f32).sqrt();
        let use_external = state != 0;
        let mut owned_state = vec![0f32; h * n * n];

        crate::pool::num_threads();

        assert!(
            n <= crate::gdn::GDN_MAX_STATE,
            "GatedDeltaNet state_size={n} exceeds stack scratch ({})",
            crate::gdn::GDN_MAX_STATE
        );

        let qs = sl(q, arena.get(), b * s * h * n);
        let ks = sl(k, arena.get(), b * s * h * n);
        let vs = sl(v, arena.get(), b * s * h * n);
        let gs = sl(g, arena.get(), b * s * h);
        let betas = sl(beta, arena.get(), b * s * h);
        let _out = sl_mut(dst, arena.get(), b * s * h * n);
        let hs_n = h * n;

        let run_head = |bi: usize, hi: usize, s_mat: &mut [f32], sk: &mut [f32]| {
            for ti in 0..s {
                let qkv_step = bi * s * hs_n + ti * hs_n + hi * n;
                let gb_step = bi * s * h + ti * h + hi;
                let out_row = sl_mut(dst + qkv_step * std::mem::size_of::<f32>(), arena.get(), n);
                crate::gdn::gdn_step_blas(
                    s_mat,
                    &qs[qkv_step..qkv_step + n],
                    &ks[qkv_step..qkv_step + n],
                    &vs[qkv_step..qkv_step + n],
                    gs[gb_step],
                    betas[gb_step],
                    out_row,
                    sk,
                    n,
                    scale,
                );
            }
        };

        // Prefill (seq>1, ephemeral state): time-outer, parallel over heads —
        // better occupancy than head-outer when prompt length dominates.
        if !use_external && s > 1 {
            for bi in 0..b {
                crate::pool::par_range(h, |hi| {
                    let mut sk_buf = [0f32; crate::gdn::GDN_MAX_STATE];
                    let sk = &mut sk_buf[..n];
                    let mut local_state =
                        [0f32; crate::gdn::GDN_MAX_STATE * crate::gdn::GDN_MAX_STATE];
                    let s_mat = &mut local_state[..n * n];
                    s_mat.fill(0.0);
                    run_head(bi, hi, s_mat, sk);
                });
            }
            return;
        }

        if use_external {
            let state_bytes = state;
            crate::pool::par_range(b * h, |bhi| {
                let bi = bhi / h;
                let hi = bhi % h;
                let elem_off = bi * h * n * n + hi * n * n;
                let s_mat = sl_mut(
                    state_bytes + elem_off * std::mem::size_of::<f32>(),
                    arena.get(),
                    n * n,
                );
                let mut sk_buf = [0f32; crate::gdn::GDN_MAX_STATE];
                run_head(bi, hi, s_mat, &mut sk_buf[..n]);
            });
        } else {
            for bi in 0..b {
                owned_state.fill(0.0);
                #[cfg(not(target_arch = "wasm32"))]
                {
                    use rayon::prelude::*;
                    owned_state
                        .par_chunks_mut(n * n)
                        .enumerate()
                        .for_each(|(hi, s_mat)| {
                            let mut sk_buf = [0f32; crate::gdn::GDN_MAX_STATE];
                            run_head(bi, hi, s_mat, &mut sk_buf[..n]);
                        });
                }
                #[cfg(target_arch = "wasm32")]
                {
                    owned_state
                        .chunks_mut(n * n)
                        .enumerate()
                        .for_each(|(hi, s_mat)| {
                            let mut sk_buf = [0f32; crate::gdn::GDN_MAX_STATE];
                            run_head(bi, hi, s_mat, &mut sk_buf[..n]);
                        });
                }
            }
        }
    }
}

/// Host-fallback: `Op::RmsNormBackwardInput` (GPU unified-memory / D2H arenas).
pub unsafe fn execute_rms_norm_backward_input_f32(
    x: usize,
    gamma: usize,
    beta: usize,
    dy: usize,
    dx: usize,
    rows: u32,
    h: u32,
    eps: f32,
    base: *mut u8,
) {
    let (rows, h) = (rows as usize, h as usize);
    let mut dg = vec![0f32; h];
    let mut db = vec![0f32; h];
    let xs = sl(x, base, rows * h);
    let dys = sl(dy, base, rows * h);
    let g = sl(gamma, base, h);
    let b = sl(beta, base, h);
    let out = sl_mut(dx, base, rows * h);
    for r in 0..rows {
        crate::training_bwd::rms_norm_backward_row(
            &xs[r * h..(r + 1) * h],
            g,
            b,
            &dys[r * h..(r + 1) * h],
            &mut out[r * h..(r + 1) * h],
            &mut dg,
            &mut db,
            eps,
        );
    }
}

pub unsafe fn execute_rms_norm_backward_gamma_f32(
    x: usize,
    gamma: usize,
    beta: usize,
    dy: usize,
    dgamma: usize,
    rows: u32,
    h: u32,
    eps: f32,
    base: *mut u8,
) {
    let (rows, h) = (rows as usize, h as usize);
    let out = sl_mut(dgamma, base, h);
    out.fill(0.0);
    let mut dx = vec![0f32; h];
    let mut db = vec![0f32; h];
    let xs = sl(x, base, rows * h);
    let dys = sl(dy, base, rows * h);
    let g = sl(gamma, base, h);
    let b = sl(beta, base, h);
    for r in 0..rows {
        crate::training_bwd::rms_norm_backward_row(
            &xs[r * h..(r + 1) * h],
            g,
            b,
            &dys[r * h..(r + 1) * h],
            &mut dx,
            out,
            &mut db,
            eps,
        );
    }
}

pub unsafe fn execute_rms_norm_backward_beta_f32(
    x: usize,
    gamma: usize,
    beta: usize,
    dy: usize,
    dbeta: usize,
    rows: u32,
    h: u32,
    eps: f32,
    base: *mut u8,
) {
    let (rows, h) = (rows as usize, h as usize);
    let out = sl_mut(dbeta, base, h);
    out.fill(0.0);
    let mut dx = vec![0f32; h];
    let mut dg = vec![0f32; h];
    let xs = sl(x, base, rows * h);
    let dys = sl(dy, base, rows * h);
    let g = sl(gamma, base, h);
    let b = sl(beta, base, h);
    for r in 0..rows {
        crate::training_bwd::rms_norm_backward_row(
            &xs[r * h..(r + 1) * h],
            g,
            b,
            &dys[r * h..(r + 1) * h],
            &mut dx,
            &mut dg,
            out,
            eps,
        );
    }
}

#[allow(clippy::too_many_arguments)]
pub unsafe fn execute_conv2d_forward_f32(
    src: usize,
    weight: usize,
    dst: usize,
    n: u32,
    c_in: u32,
    h: u32,
    w: u32,
    c_out: u32,
    h_out: u32,
    w_out: u32,
    kh: u32,
    kw: u32,
    sh: u32,
    sw: u32,
    ph: u32,
    pw: u32,
    dh: u32,
    dw: u32,
    groups: u32,
    base: *mut u8,
) {
    let n = n as usize;
    let c_in = c_in as usize;
    let h = h as usize;
    let w = w as usize;
    let c_out = c_out as usize;
    let h_out = h_out as usize;
    let w_out = w_out as usize;
    let kh = kh as usize;
    let kw = kw as usize;
    let sh = sh as usize;
    let sw = sw as usize;
    let ph = ph as usize;
    let pw = pw as usize;
    let dh = dh as usize;
    let dw = dw as usize;
    let groups = groups as usize;
    let c_in_per_g = c_in / groups;
    let inp = sl(src, base, n * c_in * h * w);
    let wt = sl(weight, base, c_out * c_in_per_g * kh * kw);
    let out = sl_mut(dst, base, n * c_out * h_out * w_out);
    crate::conv_fwd::conv2d_forward_nchw_f32(
        inp, wt, out, n, c_in, h, w, c_out, h_out, w_out, kh, kw, sh, sw, ph, pw, dh, dw, groups,
    );
}

pub unsafe fn execute_maxpool2d_backward_f32(
    x: usize,
    dy: usize,
    dx: usize,
    n: u32,
    c: u32,
    h: u32,
    w: u32,
    h_out: u32,
    w_out: u32,
    kh: u32,
    kw: u32,
    sh: u32,
    sw: u32,
    ph: u32,
    pw: u32,
    base: *mut u8,
) {
    let (n, c, h, w) = (n as usize, c as usize, h as usize, w as usize);
    let (h_out, w_out) = (h_out as usize, w_out as usize);
    let (kh, kw) = (kh as usize, kw as usize);
    let (sh, sw) = (sh as usize, sw as usize);
    let (ph, pw) = (ph as usize, pw as usize);
    let xs = sl(x, base, n * c * h * w);
    let dys = sl(dy, base, n * c * h_out * w_out);
    let dxs = sl_mut(dx, base, n * c * h * w);
    // Each (n, c) plane is independent — under RLX_FAST_CONV, fan the existing
    // per-plane kernel out over the channel-batch (disjoint dx windows).
    if fast_conv_enabled() && crate::pool::should_parallelize(n * c * h * w) {
        let (in_plane, out_plane) = (h * w, h_out * w_out);
        let x_addr = xs.as_ptr() as usize;
        let dy_addr = dys.as_ptr() as usize;
        let dx_addr = dxs.as_mut_ptr() as usize;
        crate::pool::par_for(n * c, crate::pool::outer_chunk(n * c), &|off, cnt| {
            for nc in off..off + cnt {
                let xp =
                    std::slice::from_raw_parts((x_addr as *const f32).add(nc * in_plane), in_plane);
                let dyp = std::slice::from_raw_parts(
                    (dy_addr as *const f32).add(nc * out_plane),
                    out_plane,
                );
                let dxp = std::slice::from_raw_parts_mut(
                    (dx_addr as *mut f32).add(nc * in_plane),
                    in_plane,
                );
                crate::training_bwd::maxpool2d_backward_nchw(
                    xp, dyp, dxp, 1, 1, h, w, h_out, w_out, kh, kw, sh, sw, ph, pw,
                );
            }
        });
    } else {
        crate::training_bwd::maxpool2d_backward_nchw(
            xs, dys, dxs, n, c, h, w, h_out, w_out, kh, kw, sh, sw, ph, pw,
        );
    }
}

pub unsafe fn execute_rope_backward_f32(
    dy: usize,
    cos: usize,
    sin: usize,
    dx: usize,
    batch: u32,
    seq: u32,
    hidden: u32,
    head_dim: u32,
    n_rot: u32,
    cos_len: u32,
    base: *mut u8,
) {
    let (b, s, hs, dh, nr, cl) = (
        batch as usize,
        seq as usize,
        hidden as usize,
        head_dim as usize,
        n_rot as usize,
        cos_len as usize,
    );
    let nh = hs / dh;
    let tab_half = dh / 2;
    let dys = sl(dy, base, b * s * hs);
    let cos_tab = sl(cos, base, cl);
    let sin_tab = sl(sin, base, cl);
    let out = sl_mut(dx, base, b * s * hs);
    for bi in 0..b {
        for si in 0..s {
            let tab_off = si.saturating_mul(tab_half) % cl.max(1);
            let cp = &cos_tab[tab_off..tab_off + tab_half.min(cl)];
            let sp = &sin_tab[tab_off..tab_off + tab_half.min(cl)];
            for hi in 0..nh {
                let base_idx = bi * s * hs + si * hs + hi * dh;
                crate::training_bwd::rope_backward_row(
                    &dys[base_idx..base_idx + dh],
                    cp,
                    sp,
                    &mut out[base_idx..base_idx + dh],
                    dh,
                    nr,
                );
            }
        }
    }
}

pub unsafe fn execute_cumsum_backward_f32(
    dy: usize,
    dx: usize,
    rows: u32,
    cols: u32,
    exclusive: bool,
    base: *mut u8,
) {
    let (rows, cols) = (rows as usize, cols as usize);
    let dys = sl(dy, base, rows * cols);
    let out = sl_mut(dx, base, rows * cols);
    for r in 0..rows {
        crate::training_bwd::cumsum_backward_row(
            &dys[r * cols..(r + 1) * cols],
            &mut out[r * cols..(r + 1) * cols],
            exclusive,
        );
    }
}

pub unsafe fn execute_gather_backward_f32(
    dy: usize,
    indices: usize,
    dst: usize,
    outer: u32,
    axis_dim: u32,
    num_idx: u32,
    trailing: u32,
    base: *mut u8,
) {
    let (outer, axis_dim, num_idx, trailing) = (
        outer as usize,
        axis_dim as usize,
        num_idx as usize,
        trailing as usize,
    );
    let out = sl_mut(dst, base, outer * axis_dim * trailing);
    out.fill(0.0);
    crate::training_bwd::gather_axis_backward(
        sl(dy, base, outer * num_idx * trailing),
        sl(indices, base, num_idx),
        out,
        outer,
        axis_dim,
        num_idx,
        trailing,
    );
}

/// Host-fallback entry for GGUF `Op::DequantMatMul` (Metal unified memory).
pub unsafe fn execute_dequant_matmul_gguf_f32(
    x: usize,
    w_q: usize,
    dst: usize,
    m: usize,
    k: usize,
    n: usize,
    scheme: rlx_ir::quant::QuantScheme,
    base: *mut u8,
) {
    unsafe {
        let block_bytes = scheme.gguf_block_bytes() as usize;
        let block_elems = scheme.gguf_block_size() as usize;
        let total_bytes = (k * n) / block_elems * block_bytes;
        let xs = sl(x, base, m * k);
        let w_bytes = std::slice::from_raw_parts(base.add(w_q) as *const u8, total_bytes);
        let out = sl_mut(dst, base, m * n);
        crate::gguf_matmul::gguf_matmul_bt(xs, w_bytes, out, m, k, n, scheme);
    }
}

/// Host-fallback entry for GGUF `Op::DequantGroupedMatMul` (MoE expert stack).
pub unsafe fn execute_dequant_grouped_matmul_gguf_f32(
    input: usize,
    w_q: usize,
    expert_idx: usize,
    dst: usize,
    m: usize,
    k: usize,
    n: usize,
    num_experts: usize,
    scheme: rlx_ir::quant::QuantScheme,
    base: *mut u8,
) {
    unsafe {
        let block_bytes = scheme.gguf_block_bytes() as usize;
        let block_elems = scheme.gguf_block_size() as usize;
        let slab_bytes = (k * n) / block_elems * block_bytes;
        let xs = sl(input, base, m * k);
        let w_bytes =
            std::slice::from_raw_parts(base.add(w_q) as *const u8, num_experts * slab_bytes);
        let ids = sl(expert_idx, base, m);
        let out = sl_mut(dst, base, m * n);
        crate::gguf_matmul::gguf_grouped_matmul_bt(
            xs,
            w_bytes,
            ids,
            out,
            m,
            k,
            n,
            num_experts,
            scheme,
        );
    }
}

/// Host-fallback entry for Int8 `Op::DequantMatMul` (Metal unified memory).
pub unsafe fn execute_dequant_matmul_int8_f32(
    x: usize,
    w_q: usize,
    scale: usize,
    zp: usize,
    dst: usize,
    m: usize,
    k: usize,
    n: usize,
    block_size: u32,
    is_asymmetric: bool,
    base: *mut u8,
) {
    let bs = block_size as usize;
    let n_blocks = k.div_ceil(bs);
    unsafe {
        let xs = sl(x, base, m * k);
        let w_bytes = std::slice::from_raw_parts(base.add(w_q) as *const i8, k * n);
        let scales = sl(scale, base, n_blocks * n);
        let zps = if is_asymmetric {
            sl(zp, base, n_blocks * n)
        } else {
            &[][..]
        };
        let out = sl_mut(dst, base, m * n);
        dequant_matmul_int8(xs, w_bytes, scales, zps, out, m, k, n, bs, is_asymmetric);
    }
}

/// Host-fallback entry for Int4 `Op::DequantMatMul` (Metal unified memory).
pub unsafe fn execute_dequant_matmul_int4_f32(
    x: usize,
    w_q: usize,
    scale: usize,
    zp: usize,
    dst: usize,
    m: usize,
    k: usize,
    n: usize,
    block_size: u32,
    is_asymmetric: bool,
    base: *mut u8,
) {
    let bs = block_size as usize;
    let n_blocks = k.div_ceil(bs);
    unsafe {
        let xs = sl(x, base, m * k);
        let w_bytes = std::slice::from_raw_parts(base.add(w_q) as *const u8, (k * n).div_ceil(2));
        let scales = sl(scale, base, n_blocks * n);
        let zps = if is_asymmetric {
            sl(zp, base, n_blocks * n)
        } else {
            &[][..]
        };
        let out = sl_mut(dst, base, m * n);
        dequant_matmul_int4(xs, w_bytes, scales, zps, out, m, k, n, bs, is_asymmetric);
    }
}

/// Host-fallback entry for FP8 `Op::DequantMatMul` (Metal unified memory).
pub unsafe fn execute_dequant_matmul_fp8_f32(
    x: usize,
    w_q: usize,
    scale: usize,
    dst: usize,
    m: usize,
    k: usize,
    n: usize,
    e5m2: bool,
    base: *mut u8,
) {
    unsafe {
        let xs = sl(x, base, m * k);
        let w_bytes = std::slice::from_raw_parts(base.add(w_q) as *const u8, k * n);
        let scales = sl(scale, base, n);
        let out = sl_mut(dst, base, m * n);
        dequant_matmul_fp8(xs, w_bytes, scales, out, m, k, n, e5m2);
    }
}

/// Host-fallback entry for NVFP4 `Op::DequantMatMul` (Metal unified memory).
pub unsafe fn execute_dequant_matmul_nvfp4_f32(
    x: usize,
    w_q: usize,
    scale: usize,
    global_scale: usize,
    dst: usize,
    m: usize,
    k: usize,
    n: usize,
    base: *mut u8,
) {
    let n_scale = k.div_ceil(rlx_ir::NVFP4_GROUP_SIZE) * n;
    unsafe {
        let xs = sl(x, base, m * k);
        let w_bytes = std::slice::from_raw_parts(base.add(w_q) as *const u8, (k * n).div_ceil(2));
        let scale_bytes = std::slice::from_raw_parts(base.add(scale) as *const u8, n_scale);
        let gs = sl(global_scale, base, 1)[0];
        let out = sl_mut(dst, base, m * n);
        dequant_matmul_nvfp4(xs, w_bytes, scale_bytes, gs, out, m, k, n);
    }
}

// ── Native low-precision ScaledMatMul host fallbacks (unified-memory) ──
// Reuse the CPU oracle kernels so Metal (no FP8 matrix HW) runs the exact same
// decode-and-accumulate reference. TN layout: lhs [m,k], rhs [n,k].

/// Host fallback for `Op::ScaledQuantScale`. Byte offsets into `base`.
pub unsafe fn execute_scaled_quant_scale_f32(
    x: usize,
    dst: usize,
    rows: usize,
    cols: usize,
    fmt: rlx_ir::ScaledFormat,
    layout: rlx_ir::ScaleLayout,
    base: *mut u8,
) {
    unsafe {
        let xs = sl(x, base, rows * cols);
        let scales = lowp_compute_scales(xs, fmt, layout, rows, cols);
        let nblk = lowp_nblk(cols, layout);
        match layout {
            rlx_ir::ScaleLayout::PerTensor => {
                sl_mut(dst, base, 1)[0] = scales[0];
            }
            rlx_ir::ScaleLayout::BlockMxE8M0 { .. } => {
                let out = std::slice::from_raw_parts_mut(base.add(dst), rows * nblk);
                for (o, &s) in out.iter_mut().zip(&scales) {
                    *o = rlx_ir::lowp_codec::f32_to_e8m0(s);
                }
            }
            rlx_ir::ScaleLayout::Nvfp4 { .. } => {
                let out = std::slice::from_raw_parts_mut(base.add(dst), rows * nblk);
                for (o, &s) in out.iter_mut().zip(&scales) {
                    *o = rlx_ir::lowp_codec::encode(rlx_ir::ScaledFormat::F8E4M3, s);
                }
            }
        }
    }
}

/// Host fallback for `Op::ScaledQuantize`. Byte offsets into `base`.
#[allow(clippy::too_many_arguments)]
pub unsafe fn execute_scaled_quantize_f32(
    x: usize,
    scale: usize,
    dst: usize,
    rows: usize,
    cols: usize,
    fmt: rlx_ir::ScaledFormat,
    layout: rlx_ir::ScaleLayout,
    base: *mut u8,
) {
    unsafe {
        let xs = sl(x, base, rows * cols);
        let nblk = lowp_nblk(cols, layout);
        let n_scale = if matches!(layout, rlx_ir::ScaleLayout::PerTensor) {
            1
        } else {
            rows * nblk
        };
        let scales = lowp_read_scales(layout, base, scale, n_scale);
        let out = std::slice::from_raw_parts_mut(base.add(dst), rows * cols);
        lowp_quantize(xs, &scales, fmt, layout, rows, cols, out);
    }
}

/// Host fallback for `Op::ScaledDequantize` — packed codes (`U8`) → f32, the
/// inverse of [`execute_scaled_quantize_f32`]. Byte offsets into `base`.
#[allow(clippy::too_many_arguments)]
pub unsafe fn execute_scaled_dequantize_f32(
    codes: usize,
    scale: usize,
    dst: usize,
    rows: usize,
    cols: usize,
    fmt: rlx_ir::ScaledFormat,
    layout: rlx_ir::ScaleLayout,
    base: *mut u8,
) {
    unsafe {
        let nblk = lowp_nblk(cols, layout);
        let n_scale = if matches!(layout, rlx_ir::ScaleLayout::PerTensor) {
            1
        } else {
            rows * nblk
        };
        let cs = std::slice::from_raw_parts(base.add(codes), rows * cols);
        let scales = lowp_read_scales(layout, base, scale, n_scale);
        let out = std::slice::from_raw_parts_mut(base.add(dst) as *mut f32, rows * cols);
        lowp_dequantize(cs, &scales, fmt, layout, rows, cols, out);
    }
}

/// Host fallback for `Op::ScaledMatMul` (TN). Byte offsets into `base`.
#[allow(clippy::too_many_arguments)]
pub unsafe fn execute_scaled_matmul_f32(
    lhs: usize,
    rhs: usize,
    lhs_scale: usize,
    rhs_scale: usize,
    bias: usize,
    dst: usize,
    m: usize,
    k: usize,
    n: usize,
    has_bias: bool,
    lhs_fmt: rlx_ir::ScaledFormat,
    rhs_fmt: rlx_ir::ScaledFormat,
    layout: rlx_ir::ScaleLayout,
    base: *mut u8,
) {
    unsafe {
        let lhs_b = std::slice::from_raw_parts(base.add(lhs), m * k);
        let rhs_b = std::slice::from_raw_parts(base.add(rhs), n * k);
        let nblk = lowp_nblk(k, layout);
        let per_tensor = matches!(layout, rlx_ir::ScaleLayout::PerTensor);
        let n_l = if per_tensor { 1 } else { m * nblk };
        let n_r = if per_tensor { 1 } else { n * nblk };
        let ls = lowp_read_scales(layout, base, lhs_scale, n_l);
        let rs = lowp_read_scales(layout, base, rhs_scale, n_r);
        let bias_s = if has_bias {
            Some(sl(bias, base, n))
        } else {
            None
        };
        let out = sl_mut(dst, base, m * n);
        lowp_scaled_matmul(
            lhs_b, rhs_b, &ls, &rs, bias_s, out, m, n, k, layout, lhs_fmt, rhs_fmt,
        );
    }
}

/// Host-fallback entry for f16 `Op::GatedDeltaNet` tensors on Metal.
pub unsafe fn execute_gated_delta_net_f16(
    q: usize,
    k: usize,
    v: usize,
    g: usize,
    beta: usize,
    state: usize,
    dst: usize,
    batch: usize,
    seq: usize,
    heads: usize,
    state_size: usize,
    base: *mut u8,
) {
    use half::f16;
    unsafe {
        let read_f16 = |off: usize, len: usize| -> Vec<f32> {
            let raw = std::slice::from_raw_parts(base.add(off) as *const u8, len * 2);
            raw.chunks_exact(2)
                .map(|c| f16::from_le_bytes([c[0], c[1]]).to_f32())
                .collect()
        };
        let write_f16 = |off: usize, data: &[f32]| {
            let out = std::slice::from_raw_parts_mut(base.add(off), data.len() * 2);
            for (i, &v) in data.iter().enumerate() {
                let le = f16::from_f32(v).to_le_bytes();
                out[i * 2] = le[0];
                out[i * 2 + 1] = le[1];
            }
        };

        let (b, s, h, n) = (batch, seq, heads, state_size);
        let q_f = read_f16(q, b * s * h * n);
        let k_f = read_f16(k, b * s * h * n);
        let v_f = read_f16(v, b * s * h * n);
        let g_f = read_f16(g, b * s * h);
        let b_f = read_f16(beta, b * s * h);
        let mut state_f = if state != 0 {
            read_f16(state, b * h * n * n)
        } else {
            vec![0f32; b * h * n * n]
        };
        let mut out_f = vec![0f32; b * s * h * n];
        let scale = 1.0f32 / (n as f32).sqrt();
        let mut sk_buf = vec![0f32; n];
        let mut owned_state = vec![0f32; h * n * n];

        for bi in 0..b {
            let state_slice: &mut [f32] = if state != 0 {
                let start = bi * h * n * n;
                &mut state_f[start..start + h * n * n]
            } else {
                owned_state.fill(0.0);
                &mut owned_state
            };

            for ti in 0..s {
                let qkv_step_base = bi * s * h * n + ti * h * n;
                let gb_step_base = bi * s * h + ti * h;

                for hi in 0..h {
                    let q_row = &q_f[qkv_step_base + hi * n..qkv_step_base + (hi + 1) * n];
                    let k_row = &k_f[qkv_step_base + hi * n..qkv_step_base + (hi + 1) * n];
                    let v_row = &v_f[qkv_step_base + hi * n..qkv_step_base + (hi + 1) * n];
                    let g_t = g_f[gb_step_base + hi];
                    let beta_t = b_f[gb_step_base + hi];

                    let s_base = hi * n * n;
                    let s_mat = &mut state_slice[s_base..s_base + n * n];

                    let g_exp = g_t.exp();
                    for st in s_mat.iter_mut() {
                        *st *= g_exp;
                    }

                    for j in 0..n {
                        let mut acc = 0f32;
                        for i in 0..n {
                            acc += s_mat[i * n + j] * k_row[i];
                        }
                        sk_buf[j] = acc;
                    }

                    for j in 0..n {
                        sk_buf[j] = (v_row[j] - sk_buf[j]) * beta_t;
                    }

                    for i in 0..n {
                        let ki = k_row[i];
                        for j in 0..n {
                            s_mat[i * n + j] += ki * sk_buf[j];
                        }
                    }

                    let out_row = &mut out_f[qkv_step_base + hi * n..qkv_step_base + (hi + 1) * n];
                    for j in 0..n {
                        let mut acc = 0f32;
                        for i in 0..n {
                            acc += s_mat[i * n + j] * q_row[i];
                        }
                        out_row[j] = acc * scale;
                    }
                }
            }
        }

        write_f16(dst, &out_f);
        if state != 0 {
            write_f16(state, &state_f);
        }
    }
}

/// Host fallback for NCHW group norm (Metal unified-memory arena).
pub unsafe fn execute_group_norm_nchw_f32(
    src: usize,
    g: usize,
    b: usize,
    dst: usize,
    n: usize,
    c: usize,
    h: usize,
    w: usize,
    num_groups: usize,
    eps: f32,
    base: *mut u8,
) {
    let plane = c * h * w;
    for ni in 0..n {
        let input = unsafe { sl(src + ni * plane * std::mem::size_of::<f32>(), base, plane) };
        let gamma = unsafe { sl(g, base, c) };
        let beta = unsafe { sl(b, base, c) };
        let output = unsafe { sl_mut(dst + ni * plane * std::mem::size_of::<f32>(), base, plane) };
        crate::kernels::group_norm_nchw(input, gamma, beta, output, 1, c, h, w, num_groups, eps);
    }
}

/// Host fallback for NCHW LayerNorm2d (SAM / candle semantics).
pub unsafe fn execute_layer_norm2d_nchw_f32(
    src: usize,
    g: usize,
    b: usize,
    dst: usize,
    n: usize,
    c: usize,
    h: usize,
    w: usize,
    eps: f32,
    base: *mut u8,
) {
    let plane = c * h * w;
    unsafe {
        let input = sl(src, base, n * plane);
        let gamma = sl(g, base, c);
        let beta = sl(b, base, c);
        let output = sl_mut(dst, base, n * plane);
        crate::kernels::layer_norm2d_nchw(input, gamma, beta, output, n, c, h, w, eps);
    }
}

/// Host fallback for NCHW ConvTranspose2d.
pub unsafe fn execute_conv_transpose2d_nchw_f32(
    src: usize,
    weight: usize,
    dst: usize,
    n: usize,
    c_in: usize,
    h: usize,
    w_in: usize,
    c_out: usize,
    h_out: usize,
    w_out: usize,
    kh: usize,
    kw: usize,
    sh: usize,
    sw: usize,
    ph: usize,
    pw: usize,
    dh: usize,
    dw: usize,
    groups: usize,
    base: *mut u8,
) {
    let in_elems = n * c_in * h * w_in;
    let w_elems = c_in * (c_out / groups) * kh * kw;
    let out_elems = n * c_out * h_out * w_out;
    unsafe {
        let input = sl(src, base, in_elems);
        let wt = sl(weight, base, w_elems);
        let output = sl_mut(dst, base, out_elems);
        crate::kernels::conv_transpose2d_nchw(
            input, wt, output, n, c_in, h, w_in, c_out, h_out, w_out, kh, kw, sh, sw, ph, pw, dh,
            dw, groups,
        );
    }
}

/// Host fallback for nearest 2× upsample on NCHW.
pub unsafe fn execute_resize_nearest_2x_f32(
    src: usize,
    dst: usize,
    n: usize,
    c: usize,
    h: usize,
    w: usize,
    base: *mut u8,
) {
    let in_plane = c * h * w;
    let out_plane = c * h * 2 * w * 2;
    for ni in 0..n {
        let input = unsafe {
            sl(
                src + ni * in_plane * std::mem::size_of::<f32>(),
                base,
                in_plane,
            )
        };
        let output = unsafe {
            sl_mut(
                dst + ni * out_plane * std::mem::size_of::<f32>(),
                base,
                out_plane,
            )
        };
        crate::kernels::resize_nearest_2x_nchw(input, output, c, h, w);
    }
}

/// Host axial 2-D RoPE for Metal (and other) fallbacks on unified memory.
pub unsafe fn execute_axial_rope2d_f32(
    src: usize,
    dst: usize,
    batch: usize,
    seq: usize,
    hidden: usize,
    end_x: usize,
    end_y: usize,
    head_dim: usize,
    num_heads: usize,
    theta: f32,
    repeat_factor: usize,
    base: *mut u8,
) {
    let plane = seq * hidden;
    let plane_bytes = plane * std::mem::size_of::<f32>();
    for bi in 0..batch {
        let in_off = src + bi * plane_bytes;
        let input = unsafe { sl(in_off, base, plane) };
        let rotated = rlx_ir::ops::axial_rope2d::apply_axial_rope2d(
            input,
            num_heads,
            seq,
            head_dim,
            end_x,
            end_y,
            theta,
            repeat_factor,
        );
        let out_off = dst + bi * plane_bytes;
        let output = unsafe { sl_mut(out_off, base, plane) };
        output.copy_from_slice(&rotated);
    }
}

/// Ternary pruned radix-2 butterfly stage on `[batch, n_fft, 2]` interleaved state.
pub unsafe fn execute_fft_butterfly_stage_f32(
    state_src: usize,
    state_dst: usize,
    gate_src: usize,
    rev_src: usize,
    tw_re_src: usize,
    tw_im_src: usize,
    batch: usize,
    n_fft: usize,
    stage: usize,
    base: *mut u8,
) {
    let half = n_fft / 2;
    let stride = 1usize << stage;
    let gate = unsafe { sl(gate_src, base, half) };
    let rev = unsafe { sl(rev_src, base, half) };
    let tw_re = unsafe { sl(tw_re_src, base, half) };
    let tw_im = unsafe { sl(tw_im_src, base, half) };
    let row_elems = n_fft * 2;
    for b in 0..batch {
        let in_off = state_src + b * row_elems * std::mem::size_of::<f32>();
        let out_off = state_dst + b * row_elems * std::mem::size_of::<f32>();
        let inp = unsafe { sl(in_off, base, row_elems) };
        let out = unsafe { sl_mut(out_off, base, row_elems) };
        out.copy_from_slice(inp);
        for bf in 0..half {
            if gate[bf] == 0.0 {
                continue;
            }
            let group = bf / stride;
            let k = bf % stride;
            let i0 = group * 2 * stride + k;
            let i1 = i0 + stride;
            let w_re = tw_re[bf];
            let w_im = tw_im[bf];
            let in_a_re = inp[i0 * 2];
            let in_a_im = inp[i0 * 2 + 1];
            let in_b_re = inp[i1 * 2];
            let in_b_im = inp[i1 * 2 + 1];
            let (b_re, b_im) = (
                in_b_re * w_re - in_b_im * w_im,
                in_b_re * w_im + in_b_im * w_re,
            );
            let (top_re, top_im) = (in_a_re + b_re, in_a_im + b_im);
            let (bot_re, bot_im) = (in_a_re - b_re, in_a_im - b_im);
            let (oa_re, oa_im, ob_re, ob_im) = if rev[bf] >= 0.5 {
                (bot_re, bot_im, top_re, top_im)
            } else {
                (top_re, top_im, bot_re, bot_im)
            };
            out[i0 * 2] = oa_re;
            out[i0 * 2 + 1] = oa_im;
            out[i1 * 2] = ob_re;
            out[i1 * 2 + 1] = ob_im;
        }
    }
}

/// f32 mirror of `execute_fft1d_f64`. Same public-host-fallback role.
pub unsafe fn execute_fft1d_f32(
    src: usize,
    dst: usize,
    outer: usize,
    n_complex: usize,
    inverse: bool,
    norm_tag: u32,
    base: *mut u8,
) {
    let row_elems = 2 * n_complex;
    let mut re = vec![0f32; n_complex];
    let mut im = vec![0f32; n_complex];
    let norm = rlx_ir::fft::FftNorm::from_tag(norm_tag);
    let scale = norm.output_scale(n_complex, inverse) as f32;
    let mut scratch = if n_complex.is_power_of_two() || n_complex <= 16 {
        BluesteinScratchF32::empty()
    } else {
        BluesteinScratchF32::build(n_complex, inverse)
    };
    for o in 0..outer {
        let row_offset = src + o * row_elems * std::mem::size_of::<f32>();
        let s = unsafe { sl(row_offset, base, row_elems) };
        re.copy_from_slice(&s[..n_complex]);
        im.copy_from_slice(&s[n_complex..]);
        if n_complex.is_power_of_two() {
            fft_radix2_inplace_f32(&mut re, &mut im, inverse);
        } else if n_complex <= 16 {
            fft_naive_inplace_f32(&mut re, &mut im, inverse);
        } else {
            fft_bluestein_inplace_f32(&mut re, &mut im, inverse, &mut scratch);
        }
        if scale != 1.0 {
            re.iter_mut().for_each(|v| *v *= scale);
            im.iter_mut().for_each(|v| *v *= scale);
        }
        let dst_offset = dst + o * row_elems * std::mem::size_of::<f32>();
        let d = unsafe { sl_mut(dst_offset, base, row_elems) };
        d[..n_complex].copy_from_slice(&re);
        d[n_complex..].copy_from_slice(&im);
    }
}

/// C64 interleaved layout: each complex element is `[re: f32, im: f32]`.
pub unsafe fn execute_fft1d_c64(
    src: usize,
    dst: usize,
    outer: usize,
    n_complex: usize,
    inverse: bool,
    norm_tag: u32,
    base: *mut u8,
) {
    let row_bytes = n_complex * 8;
    let mut re = vec![0f32; n_complex];
    let mut im = vec![0f32; n_complex];
    let norm = rlx_ir::fft::FftNorm::from_tag(norm_tag);
    let scale = norm.output_scale(n_complex, inverse) as f32;
    let mut scratch = if n_complex.is_power_of_two() || n_complex <= 16 {
        BluesteinScratchF32::empty()
    } else {
        BluesteinScratchF32::build(n_complex, inverse)
    };
    for o in 0..outer {
        let row_offset = src + o * row_bytes;
        for i in 0..n_complex {
            let elem_off = row_offset + i * 8;
            re[i] = f32::from_le_bytes([
                *base.add(elem_off),
                *base.add(elem_off + 1),
                *base.add(elem_off + 2),
                *base.add(elem_off + 3),
            ]);
            im[i] = f32::from_le_bytes([
                *base.add(elem_off + 4),
                *base.add(elem_off + 5),
                *base.add(elem_off + 6),
                *base.add(elem_off + 7),
            ]);
        }
        if n_complex.is_power_of_two() {
            fft_radix2_inplace_f32(&mut re, &mut im, inverse);
        } else if n_complex <= 16 {
            fft_naive_inplace_f32(&mut re, &mut im, inverse);
        } else {
            fft_bluestein_inplace_f32(&mut re, &mut im, inverse, &mut scratch);
        }
        if scale != 1.0 {
            re.iter_mut().for_each(|v| *v *= scale);
            im.iter_mut().for_each(|v| *v *= scale);
        }
        let dst_row = dst + o * row_bytes;
        for i in 0..n_complex {
            let elem_off = dst_row + i * 8;
            let re_b = re[i].to_le_bytes();
            let im_b = im[i].to_le_bytes();
            for j in 0..4 {
                *base.add(elem_off + j) = re_b[j];
                *base.add(elem_off + 4 + j) = im_b[j];
            }
        }
    }
}

/// Dtype-dispatching host entry for `Op::LogMel` (shared by GPU host fallbacks).
pub unsafe fn execute_log_mel(
    spec: usize,
    filters: usize,
    dst: usize,
    outer: usize,
    n_fft: usize,
    n_bins: usize,
    n_mels: usize,
    base: *mut u8,
) {
    execute_log_mel_f32(spec, filters, dst, outer, n_fft, n_bins, n_mels, base);
}

pub unsafe fn execute_log_mel_f32(
    spec: usize,
    filters: usize,
    dst: usize,
    outer: usize,
    n_fft: usize,
    n_bins: usize,
    n_mels: usize,
    base: *mut u8,
) {
    let spec_ptr = base.add(spec) as *const f32;
    let filt_ptr = base.add(filters) as *const f32;
    let dst_ptr = base.add(dst) as *mut f32;
    let spec = std::slice::from_raw_parts(spec_ptr, outer * n_fft * 2);
    let filters = std::slice::from_raw_parts(filt_ptr, n_mels * n_bins);
    let out = std::slice::from_raw_parts_mut(dst_ptr, outer * n_mels);
    rlx_ir::audio::log_mel_block_f32(spec, filters, outer, n_fft, n_bins, n_mels, out);
}

pub unsafe fn execute_welch_peaks_f32(
    spec: usize,
    dst: usize,
    welch_batch: usize,
    n_fft: usize,
    n_segments: usize,
    k: usize,
    base: *mut u8,
) {
    let spec_ptr = base.add(spec) as *const f32;
    let dst_ptr = base.add(dst) as *mut f32;
    let outer = welch_batch * n_segments;
    let spec = std::slice::from_raw_parts(spec_ptr, outer * n_fft * 2);
    let out = std::slice::from_raw_parts_mut(dst_ptr, welch_batch * k * 2);
    rlx_ir::audio::welch_peaks_block_f32(spec, welch_batch, n_fft, n_segments, k, out);
}

pub unsafe fn execute_log_mel_backward_f32(
    spec: usize,
    filters: usize,
    dy: usize,
    dst: usize,
    outer: usize,
    n_fft: usize,
    n_bins: usize,
    n_mels: usize,
    base: *mut u8,
) {
    let spec_ptr = base.add(spec) as *const f32;
    let filt_ptr = base.add(filters) as *const f32;
    let dy_ptr = base.add(dy) as *const f32;
    let dst_ptr = base.add(dst) as *mut f32;
    let spec = std::slice::from_raw_parts(spec_ptr, outer * n_fft * 2);
    let filters = std::slice::from_raw_parts(filt_ptr, n_mels * n_bins);
    let dy = std::slice::from_raw_parts(dy_ptr, outer * n_mels);
    let d_spec = std::slice::from_raw_parts_mut(dst_ptr, outer * n_fft * 2);
    d_spec.fill(0.0);
    rlx_ir::audio::log_mel_block_vjp(spec, filters, dy, outer, n_fft, n_bins, n_mels, d_spec);
}

/// Dtype-dispatching host entry for `Op::Fft` (shared by GPU host fallbacks).
pub unsafe fn execute_fft1d(
    src: usize,
    dst: usize,
    outer: usize,
    n_complex: usize,
    inverse: bool,
    norm_tag: u32,
    dtype: rlx_ir::DType,
    base: *mut u8,
) {
    match dtype {
        rlx_ir::DType::F32 => {
            execute_fft1d_f32(src, dst, outer, n_complex, inverse, norm_tag, base)
        }
        rlx_ir::DType::F64 => {
            execute_fft1d_f64(src, dst, outer, n_complex, inverse, norm_tag, base)
        }
        rlx_ir::DType::C64 => {
            execute_fft1d_c64(src, dst, outer, n_complex, inverse, norm_tag, base)
        }
        other => panic!("execute_fft1d: unsupported dtype {other:?}"),
    }
}

/// f32 in-place radix-2 DIT Cooley-Tukey. Structurally identical to
/// the f64 path; twiddle recurrence is kept in f64 so accumulated
/// rotation drift doesn't dominate the per-stage error budget at
/// larger N.
fn fft_radix2_inplace_f32(re: &mut [f32], im: &mut [f32], inverse: bool) {
    let n = re.len();
    debug_assert_eq!(im.len(), n);
    debug_assert!(
        n.is_power_of_two(),
        "fft_radix2_f32: n={n} must be a power of two"
    );
    if n <= 1 {
        return;
    }

    let mut j = 0usize;
    for i in 1..n {
        let mut bit = n >> 1;
        while j & bit != 0 {
            j ^= bit;
            bit >>= 1;
        }
        j ^= bit;
        if i < j {
            re.swap(i, j);
            im.swap(i, j);
        }
    }

    let sign = if inverse { 1.0_f64 } else { -1.0_f64 };
    let mut len = 2usize;
    while len <= n {
        let half = len / 2;
        let theta = sign * 2.0 * std::f64::consts::PI / (len as f64);
        let w_re_step = theta.cos();
        let w_im_step = theta.sin();
        let mut i = 0usize;
        while i < n {
            let mut wre = 1.0_f64;
            let mut wim = 0.0_f64;
            for k in 0..half {
                let wre_f = wre as f32;
                let wim_f = wim as f32;
                let t_re = wre_f * re[i + k + half] - wim_f * im[i + k + half];
                let t_im = wre_f * im[i + k + half] + wim_f * re[i + k + half];
                let u_re = re[i + k];
                let u_im = im[i + k];
                re[i + k] = u_re + t_re;
                im[i + k] = u_im + t_im;
                re[i + k + half] = u_re - t_re;
                im[i + k + half] = u_im - t_im;
                let new_wre = wre * w_re_step - wim * w_im_step;
                let new_wim = wre * w_im_step + wim * w_re_step;
                wre = new_wre;
                wim = new_wim;
            }
            i += len;
        }
        len <<= 1;
    }
}

/// In-place radix-2 DIT Cooley-Tukey FFT on split (real, imag) f64
/// arrays. `n = re.len() = im.len()` must be a power of two. Forward
/// uses ω = exp(-2πi/n); inverse uses ω = exp(+2πi/n) (no 1/N scale).
fn fft_radix2_inplace_f64(re: &mut [f64], im: &mut [f64], inverse: bool) {
    let n = re.len();
    debug_assert_eq!(im.len(), n);
    debug_assert!(
        n.is_power_of_two(),
        "fft_radix2: n={n} must be a power of two"
    );
    if n <= 1 {
        return;
    }

    // Bit-reverse permutation.
    let mut j = 0usize;
    for i in 1..n {
        let mut bit = n >> 1;
        while j & bit != 0 {
            j ^= bit;
            bit >>= 1;
        }
        j ^= bit;
        if i < j {
            re.swap(i, j);
            im.swap(i, j);
        }
    }

    // Cooley-Tukey butterflies: ω_len = exp(±2πi/len).
    let sign = if inverse { 1.0 } else { -1.0 };
    let mut len = 2usize;
    while len <= n {
        let half = len / 2;
        let theta = sign * 2.0 * std::f64::consts::PI / (len as f64);
        let w_re_step = theta.cos();
        let w_im_step = theta.sin();
        let mut i = 0usize;
        while i < n {
            // Twiddle starts at 1+0i for each segment.
            let mut wre = 1.0_f64;
            let mut wim = 0.0_f64;
            for k in 0..half {
                let t_re = wre * re[i + k + half] - wim * im[i + k + half];
                let t_im = wre * im[i + k + half] + wim * re[i + k + half];
                let u_re = re[i + k];
                let u_im = im[i + k];
                re[i + k] = u_re + t_re;
                im[i + k] = u_im + t_im;
                re[i + k + half] = u_re - t_re;
                im[i + k + half] = u_im - t_im;
                let new_wre = wre * w_re_step - wim * w_im_step;
                let new_wim = wre * w_im_step + wim * w_re_step;
                wre = new_wre;
                wim = new_wim;
            }
            i += len;
        }
        len <<= 1;
    }
}

/// Pre-computed chirp + filter-spectrum for one (N, direction) pair.
/// Built once per call to `execute_fft1d_f64` and reused across rows
/// when `outer > 1` — the chirp and FFT(b) don't depend on the input.
struct BluesteinScratchF64 {
    /// Power-of-two convolution length, ≥ 2N - 1.
    m: usize,
    /// `w[k] = exp(sign · iπ · k² / N)` for k=0..N, where sign matches
    /// the requested direction. Forward chirp on the way in, output
    /// chirp on the way out.
    w_re: Vec<f64>,
    w_im: Vec<f64>,
    /// FFT of the embedded filter `b[k] = conj(w[|k|])` in length-M.
    /// Doesn't depend on the input — precomputed once.
    bf_re: Vec<f64>,
    bf_im: Vec<f64>,
    /// Workspace reused per row (avoids per-row allocation).
    ar: Vec<f64>,
    ai: Vec<f64>,
}

impl BluesteinScratchF64 {
    fn empty() -> Self {
        Self {
            m: 0,
            w_re: Vec::new(),
            w_im: Vec::new(),
            bf_re: Vec::new(),
            bf_im: Vec::new(),
            ar: Vec::new(),
            ai: Vec::new(),
        }
    }

    fn build(n: usize, inverse: bool) -> Self {
        // M = next power of two ≥ 2N - 1 keeps the inner FFT on the
        // fast radix-2 path. For N=1 fall back to M=1 (no-op convolution).
        let m = if n <= 1 {
            1
        } else {
            (2 * n - 1).next_power_of_two()
        };

        // Chirp arg reduced via k² mod 2N — without this, large N
        // bleeds precision into the trig call (n² grows quadratically).
        let mod_2n = (2 * n) as u64;
        let sign = if inverse { 1.0_f64 } else { -1.0_f64 };
        let mut w_re = vec![0.0_f64; n];
        let mut w_im = vec![0.0_f64; n];
        for k in 0..n {
            let k2 = (k as u64).wrapping_mul(k as u64) % mod_2n;
            let theta = sign * std::f64::consts::PI * (k2 as f64) / (n as f64);
            w_re[k] = theta.cos();
            w_im[k] = theta.sin();
        }

        // Embed b[k] = conj(w[|k|]) into length M with the negative
        // indices wrapping to the tail: b[-j] → B[M-j] for j=1..N-1.
        let mut bf_re = vec![0.0_f64; m];
        let mut bf_im = vec![0.0_f64; m];
        if n > 0 {
            bf_re[0] = w_re[0];
            bf_im[0] = -w_im[0];
            for k in 1..n {
                bf_re[k] = w_re[k];
                bf_im[k] = -w_im[k];
                bf_re[m - k] = w_re[k];
                bf_im[m - k] = -w_im[k];
            }
        }
        if m > 1 {
            fft_radix2_inplace_f64(&mut bf_re, &mut bf_im, false);
        }

        Self {
            m,
            w_re,
            w_im,
            bf_re,
            bf_im,
            ar: vec![0.0_f64; m],
            ai: vec![0.0_f64; m],
        }
    }
}

/// Direct O(N²) DFT for small non-pow2 N (faster than Bluestein setup).
fn fft_naive_inplace_f64(re: &mut [f64], im: &mut [f64], inverse: bool) {
    let n = re.len();
    if n <= 1 {
        return;
    }
    let sign = if inverse { 1.0 } else { -1.0 };
    let mut out_re = vec![0.0_f64; n];
    let mut out_im = vec![0.0_f64; n];
    for k in 0..n {
        for nn in 0..n {
            let theta = sign * 2.0 * std::f64::consts::PI * (nn as f64) * (k as f64) / (n as f64);
            let c = theta.cos();
            let s = theta.sin();
            out_re[k] += re[nn] * c - im[nn] * s;
            out_im[k] += re[nn] * s + im[nn] * c;
        }
    }
    re.copy_from_slice(&out_re);
    im.copy_from_slice(&out_im);
}

fn fft_naive_inplace_f32(re: &mut [f32], im: &mut [f32], inverse: bool) {
    let n = re.len();
    if n <= 1 {
        return;
    }
    let sign = if inverse { 1.0f32 } else { -1.0f32 };
    let mut out_re = vec![0.0_f32; n];
    let mut out_im = vec![0.0_f32; n];
    for k in 0..n {
        for nn in 0..n {
            let theta = sign * 2.0 * std::f32::consts::PI * (nn as f32) * (k as f32) / (n as f32);
            let c = theta.cos();
            let s = theta.sin();
            out_re[k] += re[nn] * c - im[nn] * s;
            out_im[k] += re[nn] * s + im[nn] * c;
        }
    }
    re.copy_from_slice(&out_re);
    im.copy_from_slice(&out_im);
}

/// Bluestein (chirp-z) FFT for arbitrary N. Identity used:
///   `n·k = (n² + k² - (k-n)²) / 2`
/// which lets the DFT be written as a linear convolution sandwiched
/// between two chirp multiplies:
///   `X[k] = w[k] · ((x·w) ⊛ conj(w))[k]`   where `w[n] = exp(±iπ·n²/N)`.
/// The convolution is computed via a length-M radix-2 FFT (M ≥ 2N-1).
/// Both directions stay unnormalized to match the radix-2 path, so the
/// chain rule keeps working without scaling.
fn fft_bluestein_inplace_f64(
    re: &mut [f64],
    im: &mut [f64],
    _inverse: bool,
    s: &mut BluesteinScratchF64,
) {
    let n = re.len();
    debug_assert_eq!(im.len(), n);
    debug_assert_eq!(s.w_re.len(), n);
    if n <= 1 {
        return;
    }
    let m = s.m;

    // Pre-chirp: a[k] = x[k] · w[k], zero-padded to M.
    for k in 0..m {
        s.ar[k] = 0.0;
        s.ai[k] = 0.0;
    }
    for k in 0..n {
        s.ar[k] = re[k] * s.w_re[k] - im[k] * s.w_im[k];
        s.ai[k] = re[k] * s.w_im[k] + im[k] * s.w_re[k];
    }

    // Length-M forward FFT of the padded chirped input.
    fft_radix2_inplace_f64(&mut s.ar, &mut s.ai, false);

    // Pointwise product with FFT(b). Stored back into (ar, ai).
    for k in 0..m {
        let ar = s.ar[k];
        let ai = s.ai[k];
        let br = s.bf_re[k];
        let bi = s.bf_im[k];
        s.ar[k] = ar * br - ai * bi;
        s.ai[k] = ar * bi + ai * br;
    }

    // Inverse FFT — radix-2 here is the unnormalized inverse, so we
    // divide by M to recover the true circular convolution.
    fft_radix2_inplace_f64(&mut s.ar, &mut s.ai, true);
    let inv_m = 1.0 / (m as f64);

    // Post-chirp: X[k] = w[k] · Y[k] / M for k = 0..N.
    for k in 0..n {
        let yr = s.ar[k] * inv_m;
        let yi = s.ai[k] * inv_m;
        re[k] = yr * s.w_re[k] - yi * s.w_im[k];
        im[k] = yr * s.w_im[k] + yi * s.w_re[k];
    }
}

/// f32 mirror of `BluesteinScratchF64`. Chirp is computed in f64 for
/// precision (same justification as the radix-2 f32 path: twiddles in
/// f64, butterflies in f32). The actual conv buffers are f32.
struct BluesteinScratchF32 {
    m: usize,
    w_re: Vec<f32>,
    w_im: Vec<f32>,
    bf_re: Vec<f32>,
    bf_im: Vec<f32>,
    ar: Vec<f32>,
    ai: Vec<f32>,
}

impl BluesteinScratchF32 {
    fn empty() -> Self {
        Self {
            m: 0,
            w_re: Vec::new(),
            w_im: Vec::new(),
            bf_re: Vec::new(),
            bf_im: Vec::new(),
            ar: Vec::new(),
            ai: Vec::new(),
        }
    }

    fn build(n: usize, inverse: bool) -> Self {
        let m = if n <= 1 {
            1
        } else {
            (2 * n - 1).next_power_of_two()
        };

        let mod_2n = (2 * n) as u64;
        let sign = if inverse { 1.0_f64 } else { -1.0_f64 };
        let mut w_re = vec![0.0_f32; n];
        let mut w_im = vec![0.0_f32; n];
        for k in 0..n {
            let k2 = (k as u64).wrapping_mul(k as u64) % mod_2n;
            let theta = sign * std::f64::consts::PI * (k2 as f64) / (n as f64);
            w_re[k] = theta.cos() as f32;
            w_im[k] = theta.sin() as f32;
        }

        let mut bf_re = vec![0.0_f32; m];
        let mut bf_im = vec![0.0_f32; m];
        if n > 0 {
            bf_re[0] = w_re[0];
            bf_im[0] = -w_im[0];
            for k in 1..n {
                bf_re[k] = w_re[k];
                bf_im[k] = -w_im[k];
                bf_re[m - k] = w_re[k];
                bf_im[m - k] = -w_im[k];
            }
        }
        if m > 1 {
            fft_radix2_inplace_f32(&mut bf_re, &mut bf_im, false);
        }

        Self {
            m,
            w_re,
            w_im,
            bf_re,
            bf_im,
            ar: vec![0.0_f32; m],
            ai: vec![0.0_f32; m],
        }
    }
}

fn fft_bluestein_inplace_f32(
    re: &mut [f32],
    im: &mut [f32],
    _inverse: bool,
    s: &mut BluesteinScratchF32,
) {
    let n = re.len();
    debug_assert_eq!(im.len(), n);
    debug_assert_eq!(s.w_re.len(), n);
    if n <= 1 {
        return;
    }
    let m = s.m;

    for k in 0..m {
        s.ar[k] = 0.0;
        s.ai[k] = 0.0;
    }
    for k in 0..n {
        s.ar[k] = re[k] * s.w_re[k] - im[k] * s.w_im[k];
        s.ai[k] = re[k] * s.w_im[k] + im[k] * s.w_re[k];
    }

    fft_radix2_inplace_f32(&mut s.ar, &mut s.ai, false);

    for k in 0..m {
        let ar = s.ar[k];
        let ai = s.ai[k];
        let br = s.bf_re[k];
        let bi = s.bf_im[k];
        s.ar[k] = ar * br - ai * bi;
        s.ai[k] = ar * bi + ai * br;
    }

    fft_radix2_inplace_f32(&mut s.ar, &mut s.ai, true);
    let inv_m = 1.0_f32 / (m as f32);

    for k in 0..n {
        let yr = s.ar[k] * inv_m;
        let yi = s.ai[k] * inv_m;
        re[k] = yr * s.w_re[k] - yi * s.w_im[k];
        im[k] = yr * s.w_im[k] + yi * s.w_re[k];
    }
}

/// Shared dispatch path for `Thunk::CustomOp`. Builds a typed
/// [`CpuTensorRef`] for each input *at that input's declared dtype*
/// (so a sparse-LU op with mixed F64/I32 inputs gets the right
/// typed slices) and a [`CpuTensorMut`] for the output, then calls
/// the kernel's single `execute` method.
unsafe fn dispatch_custom_op(
    kernel: &dyn crate::op_registry::CpuKernel,
    inputs: &[(usize, u32, Shape)],
    out_off: usize,
    out_len: u32,
    out_shape: &Shape,
    attrs: &[u8],
    base: *mut u8,
) {
    use crate::op_registry::{CpuTensorMut, CpuTensorRef};
    use rlx_ir::DType;

    // One arm per `DType` variant — single source of truth for
    // "which dtypes the CPU custom-op dispatcher wires." If a new
    // DType lands in `rlx-ir`, the compiler flags this match as
    // non-exhaustive and the gap gets named at the right place.
    macro_rules! build_in_view {
        ($shape:expr, $off:expr, $n:expr, $variant:ident, $rust_ty:ty) => {
            CpuTensorRef::$variant {
                data: unsafe { sl_typed::<$rust_ty>($off, base, $n) },
                shape: $shape,
            }
        };
    }
    macro_rules! build_out_view {
        ($variant:ident, $rust_ty:ty) => {
            CpuTensorMut::$variant {
                data: unsafe { sl_mut_typed::<$rust_ty>(out_off, base, out_len as usize) },
                shape: out_shape,
            }
        };
    }

    let in_views: Vec<CpuTensorRef<'_>> = inputs
        .iter()
        .map(|(off, len, shape)| {
            let n = *len as usize;
            let off = *off;
            match shape.dtype() {
                DType::F32 => build_in_view!(shape, off, n, F32, f32),
                DType::F64 => build_in_view!(shape, off, n, F64, f64),
                DType::F16 => build_in_view!(shape, off, n, F16, half::f16),
                DType::BF16 => build_in_view!(shape, off, n, BF16, half::bf16),
                DType::I8 => build_in_view!(shape, off, n, I8, i8),
                DType::I16 => build_in_view!(shape, off, n, I16, i16),
                DType::I32 => build_in_view!(shape, off, n, I32, i32),
                DType::I64 => build_in_view!(shape, off, n, I64, i64),
                DType::U8 => build_in_view!(shape, off, n, U8, u8),
                DType::U32 => build_in_view!(shape, off, n, U32, u32),
                DType::Bool => build_in_view!(shape, off, n, Bool, u8),
                // C64 isn't a CpuTensor variant today; the user-registered
                // op_registry path doesn't see complex inputs (those are
                // handled by built-in ops with dedicated kernels).
                DType::C64 => panic!(
                    "Op::Custom kernel input has DType::C64 — built-in \
                 complex ops handle their own kernels; user-registered \
                 ops don't yet see complex tensors"
                ),
            }
        })
        .collect();

    let result = match out_shape.dtype() {
        DType::F32 => kernel.execute(&in_views, build_out_view!(F32, f32), attrs),
        DType::F64 => kernel.execute(&in_views, build_out_view!(F64, f64), attrs),
        DType::F16 => kernel.execute(&in_views, build_out_view!(F16, half::f16), attrs),
        DType::BF16 => kernel.execute(&in_views, build_out_view!(BF16, half::bf16), attrs),
        DType::I8 => kernel.execute(&in_views, build_out_view!(I8, i8), attrs),
        DType::I16 => kernel.execute(&in_views, build_out_view!(I16, i16), attrs),
        DType::I32 => kernel.execute(&in_views, build_out_view!(I32, i32), attrs),
        DType::I64 => kernel.execute(&in_views, build_out_view!(I64, i64), attrs),
        DType::U8 => kernel.execute(&in_views, build_out_view!(U8, u8), attrs),
        DType::U32 => kernel.execute(&in_views, build_out_view!(U32, u32), attrs),
        DType::Bool => kernel.execute(&in_views, build_out_view!(Bool, u8), attrs),
        DType::C64 => panic!("Op::Custom output DType::C64 not supported"),
    };
    if let Err(e) = result {
        panic!("Op::Custom('{}') CPU kernel failed: {e}", kernel.name());
    }
}

/// Generic raw-cast slice helper. The existing per-dtype `sl_*` /
/// `sl_mut_*` helpers stay in place for the rest of `thunk.rs` (which
/// uses them at call sites with concrete dtypes); the custom-op
/// dispatcher uses these to enumerate every `DType` uniformly without
/// listing one helper per dtype.
#[inline(always)]
unsafe fn sl_typed<T>(offset: usize, base: *mut u8, len: usize) -> &'static [T] {
    if offset == usize::MAX {
        return &[];
    }
    unsafe { std::slice::from_raw_parts(base.add(offset) as *const T, len) }
}

#[inline(always)]
unsafe fn sl_mut_typed<T>(offset: usize, base: *mut u8, len: usize) -> &'static mut [T] {
    unsafe { std::slice::from_raw_parts_mut(base.add(offset) as *mut T, len) }
}

/// Scalar activation for the fused-region interpreter. Matches the GPU region
/// kernel's math (e.g. tanh-approx GELU) so CPU/GPU region results agree.
#[inline]
fn region_activation_scalar(act: rlx_ir::op::Activation, x: f32) -> f32 {
    use rlx_ir::op::Activation as A;
    const GC: f32 = 0.797_884_6; // sqrt(2/pi)
    match act {
        A::Relu => x.max(0.0),
        A::Gelu | A::GeluApprox => 0.5 * x * (1.0 + (GC * (x + 0.044715 * x * x * x)).tanh()),
        A::Silu => x / (1.0 + (-x).exp()),
        A::Sigmoid => 1.0 / (1.0 + (-x).exp()),
        A::Tanh => x.tanh(),
        A::Exp => x.exp(),
        A::Log => x.ln(),
        A::Sqrt => x.sqrt(),
        A::Rsqrt => 1.0 / x.sqrt(),
        A::Neg => -x,
        A::Abs => x.abs(),
        A::Sin => x.sin(),
        A::Cos => x.cos(),
        A::Tan => x.tan(),
        A::Atan => x.atan(),
        A::Round => x.round(),
    }
}

#[inline]
fn region_binary_scalar(op: rlx_ir::op::BinaryOp, l: f32, r: f32) -> f32 {
    use rlx_ir::op::BinaryOp as B;
    match op {
        B::Add => l + r,
        B::Sub => l - r,
        B::Mul => l * r,
        B::Div => l / r,
        B::Max => l.max(r),
        B::Min => l.min(r),
        B::Pow => l.powf(r),
    }
}

#[inline]
fn region_compare_scalar(op: rlx_ir::op::CmpOp, l: f32, r: f32) -> bool {
    use rlx_ir::op::CmpOp as C;
    match op {
        C::Eq => l == r,
        C::Ne => l != r,
        C::Lt => l < r,
        C::Le => l <= r,
        C::Gt => l > r,
        C::Ge => l >= r,
    }
}

/// Resolve one chain operand for output element `gid`: a previous step result
/// (`scratch[s]`) or an external input read with broadcast (`input[gid % mod]`,
/// scalar inputs read element 0). `base` is the arena byte base.
#[inline]
fn region_resolve_operand(
    op: &rlx_ir::op::ChainOperand,
    gid: usize,
    base: *const u8,
    input_offs: &[usize],
    scalar_mask: u32,
    modulus: &[u32; 16],
    scratch: &[f32; 32],
) -> f32 {
    use rlx_ir::op::ChainOperand as O;
    match op {
        O::Step(s) => scratch[*s as usize],
        O::Input(i) => {
            let i = *i as usize;
            let row = if (scalar_mask >> i) & 1 == 1 {
                0
            } else if modulus[i] != 0 {
                gid % modulus[i] as usize
            } else {
                gid
            };
            unsafe { *(base.add(input_offs[i]) as *const f32).add(row) }
        }
    }
}

/// Evaluate a fused element-wise chain for output element `gid`, returning the
/// final step's value (what gets written to `dst[gid]`).
#[inline]
fn region_eval_elem(
    gid: usize,
    base: *const u8,
    input_offs: &[usize],
    chain: &[rlx_ir::op::ChainStep],
    scalar_mask: u32,
    modulus: &[u32; 16],
) -> f32 {
    use rlx_ir::op::ChainStep as S;
    let mut scratch = [0f32; 32];
    let r = |o: &rlx_ir::op::ChainOperand, sc: &[f32; 32]| {
        region_resolve_operand(o, gid, base, input_offs, scalar_mask, modulus, sc)
    };
    for (k, step) in chain.iter().enumerate() {
        scratch[k] = match step {
            S::Activation(a, x) => region_activation_scalar(*a, r(x, &scratch)),
            S::Cast(_, x) => r(x, &scratch), // f32→f32 identity (chains are same-dtype)
            S::Binary(op, l, rr) => region_binary_scalar(*op, r(l, &scratch), r(rr, &scratch)),
            S::Compare(op, l, rr) => {
                if region_compare_scalar(*op, r(l, &scratch), r(rr, &scratch)) {
                    1.0
                } else {
                    0.0
                }
            }
            S::Where(c, t, f) => {
                if r(c, &scratch) != 0.0 {
                    r(t, &scratch)
                } else {
                    r(f, &scratch)
                }
            }
        };
    }
    scratch[chain.len() - 1]
}

// Unsafe helpers to create slices from arena base + offset
/// In-place per-element activation. Mirrors the dispatch in
/// `Thunk::ActivationInPlace`. Used by `Thunk::FusedMmBiasAct` to
/// apply the activation after `bias_add` for all non-Gelu cases.
#[inline(always)]
fn apply_activation_inplace(d: &mut [f32], act: rlx_ir::op::Activation) {
    use rlx_ir::op::Activation;
    match act {
        Activation::Gelu => crate::kernels::par_gelu_inplace(d),
        Activation::GeluApprox => crate::kernels::par_gelu_approx_inplace(d),
        Activation::Silu => crate::kernels::par_silu_inplace(d),
        Activation::Relu => {
            for v in d.iter_mut() {
                *v = v.max(0.0);
            }
        }
        Activation::Sigmoid => {
            for v in d.iter_mut() {
                *v = 1.0 / (1.0 + (-*v).exp());
            }
        }
        Activation::Tanh => {
            for v in d.iter_mut() {
                *v = v.tanh();
            }
        }
        Activation::Exp => {
            for v in d.iter_mut() {
                *v = v.exp();
            }
        }
        Activation::Log => {
            for v in d.iter_mut() {
                *v = v.ln();
            }
        }
        Activation::Sqrt => {
            for v in d.iter_mut() {
                *v = v.sqrt();
            }
        }
        Activation::Rsqrt => {
            for v in d.iter_mut() {
                *v = 1.0 / v.sqrt();
            }
        }
        Activation::Neg => {
            for v in d.iter_mut() {
                *v = -*v;
            }
        }
        Activation::Abs => {
            for v in d.iter_mut() {
                *v = v.abs();
            }
        }
        Activation::Round => {
            for v in d.iter_mut() {
                *v = v.round();
            }
        }
        Activation::Sin => {
            for v in d.iter_mut() {
                *v = v.sin();
            }
        }
        Activation::Cos => {
            for v in d.iter_mut() {
                *v = v.cos();
            }
        }
        Activation::Tan => {
            for v in d.iter_mut() {
                *v = v.tan();
            }
        }
        Activation::Atan => {
            for v in d.iter_mut() {
                *v = v.atan();
            }
        }
    }
}

/// im2col for one image (single batch + group slice).
///
/// Source `x` is `[c_in, H, W]` row-major. Destination `col` is
/// `[c_in · kH · kW, H_out · W_out]` row-major. Out-of-bounds positions
/// (in the padded region) are written as 0.
///
/// `col[(ci · kH · kW + ki · kW + kj) · n_dim + ho · W_out + wo] =
///    x[ci, ho·sh + ki·dh − ph, wo·sw + kj·dw_dil − pw]`
#[allow(clippy::too_many_arguments)]
/// Is the im2col+BLAS forward-conv kernel enabled? Read once from
/// `RLX_FAST_CONV` (1/on/true/yes). When off (the default), forward conv
/// runs the reference scalar loop. The flag is process-global so the two
/// benchmark configurations ("fused"/optimized vs reference) are produced
/// by separate process invocations — see the cortexm trainer.
fn fast_conv_enabled() -> bool {
    use std::sync::OnceLock;
    static FAST_CONV: OnceLock<bool> = OnceLock::new();
    *FAST_CONV.get_or_init(|| {
        matches!(
            rlx_ir::env::var("RLX_FAST_CONV").as_deref(),
            Some("1") | Some("on") | Some("true") | Some("yes")
        )
    })
}

/// Reference forward convolution: a direct scalar nested loop. Correct and
/// dependency-free, but leaves the CPU idle (scalar, single-threaded, a
/// bounds-check branch in the hot loop). Kept as the parity oracle for
/// `conv2d_forward_im2col` and as the default kernel.
#[allow(clippy::too_many_arguments)]
fn conv2d_forward_naive(
    inp: &[f32],
    wt: &[f32],
    out: &mut [f32],
    n: usize,
    c_in: usize,
    h: usize,
    w: usize,
    c_out: usize,
    h_out: usize,
    w_out: usize,
    kh: usize,
    kw: usize,
    sh: usize,
    sw: usize,
    ph: usize,
    pw: usize,
    dh: usize,
    dw: usize,
    groups: usize,
) {
    let c_in_per_g = c_in / groups;
    let c_out_per_g = c_out / groups;
    for ni in 0..n {
        for co in 0..c_out {
            let g = co / c_out_per_g;
            let ci_start = g * c_in_per_g;
            for ho in 0..h_out {
                for wo in 0..w_out {
                    let mut acc = 0f32;
                    for ci_off in 0..c_in_per_g {
                        let ci = ci_start + ci_off;
                        let in_chan = ((ni * c_in) + ci) * h * w;
                        let wt_chan = ((co * c_in_per_g) + ci_off) * kh * kw;
                        for ki in 0..kh {
                            for kj in 0..kw {
                                let hi = ho * sh + ki * dh;
                                let wi = wo * sw + kj * dw;
                                if hi < ph || wi < pw {
                                    continue;
                                }
                                let hi = hi - ph;
                                let wi = wi - pw;
                                if hi >= h || wi >= w {
                                    continue;
                                }
                                acc += inp[in_chan + hi * w + wi] * wt[wt_chan + ki * kw + kj];
                            }
                        }
                    }
                    out[((ni * c_out) + co) * h_out * w_out + ho * w_out + wo] = acc;
                }
            }
        }
    }
}

/// Winograd F(2×2,3×3) enabled? Read once from `RLX_WINOGRAD`. Applies only to
/// 3×3 stride-1 no-dilation groups-1 convs (the conv-net hot case).
fn winograd_enabled() -> bool {
    use std::sync::OnceLock;
    static W: OnceLock<bool> = OnceLock::new();
    *W.get_or_init(|| {
        matches!(
            rlx_ir::env::var("RLX_WINOGRAD").as_deref(),
            Some("1") | Some("on") | Some("true") | Some("yes")
        )
    })
}

/// Direct (no-im2col) forward conv enabled? `RLX_DIRECT_CONV`. Off by default —
/// im2col+BLAS is faster for low-channel convs; this wins only at high channels.
fn direct_conv_enabled() -> bool {
    use std::sync::OnceLock;
    static D: OnceLock<bool> = OnceLock::new();
    *D.get_or_init(|| {
        matches!(
            rlx_ir::env::var("RLX_DIRECT_CONV").as_deref(),
            Some("1") | Some("on") | Some("true") | Some("yes")
        )
    })
}

/// Filter transform U = G·g·Gᵀ for one 3×3 filter → 4×4 (row-major).
#[inline]
fn winograd_filter_transform(g: &[f32]) -> [f32; 16] {
    let mut tmp = [0f32; 12]; // 4×3 = G·g
    for j in 0..3 {
        let (g0, g1, g2) = (g[j], g[3 + j], g[6 + j]);
        tmp[j] = g0;
        tmp[3 + j] = 0.5 * (g0 + g1 + g2);
        tmp[6 + j] = 0.5 * (g0 - g1 + g2);
        tmp[9 + j] = g2;
    }
    let mut u = [0f32; 16];
    for i in 0..4 {
        let (t0, t1, t2) = (tmp[i * 3], tmp[i * 3 + 1], tmp[i * 3 + 2]);
        u[i * 4] = t0;
        u[i * 4 + 1] = 0.5 * (t0 + t1 + t2);
        u[i * 4 + 2] = 0.5 * (t0 - t1 + t2);
        u[i * 4 + 3] = t2;
    }
    u
}

/// Input transform V = Bᵀ·d·B for one 4×4 tile → 4×4 (row-major).
#[inline]
fn winograd_input_transform(d: &[f32; 16]) -> [f32; 16] {
    let mut t = [0f32; 16];
    for j in 0..4 {
        let (d0, d1, d2, d3) = (d[j], d[4 + j], d[8 + j], d[12 + j]);
        t[j] = d0 - d2;
        t[4 + j] = d1 + d2;
        t[8 + j] = d2 - d1;
        t[12 + j] = d1 - d3;
    }
    let mut v = [0f32; 16];
    for i in 0..4 {
        let (a0, a1, a2, a3) = (t[i * 4], t[i * 4 + 1], t[i * 4 + 2], t[i * 4 + 3]);
        v[i * 4] = a0 - a2;
        v[i * 4 + 1] = a1 + a2;
        v[i * 4 + 2] = a2 - a1;
        v[i * 4 + 3] = a1 - a3;
    }
    v
}

/// Output transform Y = Aᵀ·m·A for one 4×4 accumulator → 2×2 (row-major).
#[inline]
fn winograd_output_transform(m: &[f32; 16]) -> [f32; 4] {
    let mut s = [0f32; 8];
    for j in 0..4 {
        let (m0, m1, m2, m3) = (m[j], m[4 + j], m[8 + j], m[12 + j]);
        s[j] = m0 + m1 + m2;
        s[4 + j] = m1 - m2 - m3;
    }
    let mut y = [0f32; 4];
    for i in 0..2 {
        let (a0, a1, a2, a3) = (s[i * 4], s[i * 4 + 1], s[i * 4 + 2], s[i * 4 + 3]);
        y[i * 2] = a0 + a1 + a2;
        y[i * 2 + 1] = a1 - a2 - a3;
    }
    y
}

/// Winograd F(2×2,3×3) forward convolution (3×3, stride 1, no dilation, groups
/// 1, valid). ~2.25× fewer multiplies than im2col by working on 4×4 tiles:
/// transform filter (G·g·Gᵀ) and input (Bᵀ·d·B), 16 channel-GEMMs (one per
/// tile position), then output transform (Aᵀ·m·A). Boundary tiles zero-pad the
/// input read and skip out-of-range output writes (handles odd H_out/W_out).
#[allow(clippy::too_many_arguments)]
fn conv2d_forward_winograd(
    inp: &[f32],
    wt: &[f32],
    out: &mut [f32],
    n: usize,
    c_in: usize,
    h: usize,
    w: usize,
    c_out: usize,
    h_out: usize,
    w_out: usize,
) {
    let th = h_out.div_ceil(2);
    let tw = w_out.div_ceil(2);
    let tiles_per = th * tw;
    let nt = n * tiles_per;
    if nt == 0 {
        return;
    }

    // Filter transform U[p][co][ci]  (p = 0..16).
    let mut u = vec![0f32; 16 * c_out * c_in];
    for co in 0..c_out {
        for ci in 0..c_in {
            let uu = winograd_filter_transform(&wt[(co * c_in + ci) * 9..(co * c_in + ci) * 9 + 9]);
            for (p, &val) in uu.iter().enumerate() {
                u[p * c_out * c_in + co * c_in + ci] = val;
            }
        }
    }

    // Input transform V[p][ci][tile].
    let mut v = vec![0f32; 16 * c_in * nt];
    let v_addr = v.as_mut_ptr() as usize;
    let in_xform = |tile: usize| {
        let ni = tile / tiles_per;
        let rem = tile % tiles_per;
        let (h0, w0) = (2 * (rem / tw), 2 * (rem % tw));
        for ci in 0..c_in {
            let mut d = [0f32; 16];
            for di in 0..4 {
                let hh = h0 + di;
                if hh >= h {
                    continue;
                }
                let base = ((ni * c_in + ci) * h + hh) * w;
                for dj in 0..4 {
                    let ww = w0 + dj;
                    if ww < w {
                        d[di * 4 + dj] = inp[base + ww];
                    }
                }
            }
            let vv = winograd_input_transform(&d);
            for (p, &val) in vv.iter().enumerate() {
                unsafe {
                    *((v_addr as *mut f32).add(p * c_in * nt + ci * nt + tile)) = val;
                }
            }
        }
    };
    if fast_conv_enabled() && crate::pool::should_parallelize(nt * c_in * 16) {
        crate::pool::par_for(nt, crate::pool::outer_chunk(nt), &|off, cnt| {
            for t in off..off + cnt {
                in_xform(t);
            }
        });
    } else {
        for t in 0..nt {
            in_xform(t);
        }
    }

    // 16 channel-GEMMs: M[p] = U[p]·V[p]  → [c_out, nt].
    let mut m = vec![0f32; 16 * c_out * nt];
    for p in 0..16 {
        crate::blas::sgemm(
            &u[p * c_out * c_in..(p + 1) * c_out * c_in],
            &v[p * c_in * nt..(p + 1) * c_in * nt],
            &mut m[p * c_out * nt..(p + 1) * c_out * nt],
            c_out,
            c_in,
            nt,
        );
    }

    // Output transform + scatter.
    let out_addr = out.as_mut_ptr() as usize;
    let out_xform = |tile: usize| {
        let ni = tile / tiles_per;
        let rem = tile % tiles_per;
        let (ho0, wo0) = (2 * (rem / tw), 2 * (rem % tw));
        for co in 0..c_out {
            let mut mm = [0f32; 16];
            for (p, slot) in mm.iter_mut().enumerate() {
                *slot = m[p * c_out * nt + co * nt + tile];
            }
            let y = winograd_output_transform(&mm);
            for yi in 0..2 {
                let oh = ho0 + yi;
                if oh >= h_out {
                    continue;
                }
                for yj in 0..2 {
                    let ow = wo0 + yj;
                    if ow < w_out {
                        unsafe {
                            *((out_addr as *mut f32)
                                .add(((ni * c_out + co) * h_out + oh) * w_out + ow)) =
                                y[yi * 2 + yj];
                        }
                    }
                }
            }
        }
    };
    if fast_conv_enabled() && crate::pool::should_parallelize(nt * c_out * 16) {
        crate::pool::par_for(nt, crate::pool::outer_chunk(nt), &|off, cnt| {
            for t in off..off + cnt {
                out_xform(t);
            }
        });
    } else {
        for t in 0..nt {
            out_xform(t);
        }
    }
}

/// Direct forward convolution for the stride-1, no-padding, dilation-1 case
/// (the conv-net hot path). Unlike im2col it never materialises the 9×-expanded
/// patch buffer — each output plane stays L1-resident while we accumulate over
/// (c_in, kh, kw), and the innermost loop is a contiguous SAXPY over the output
/// row (`out[wo] += w * in[wo+kj]`) that autovectorizes. Compute-bound, not
/// bandwidth-bound — the win over im2col for low-channel convs. Parallel over
/// (batch × c_out); caller guarantees stride/pad/dilation eligibility.
#[allow(clippy::too_many_arguments)]
fn conv2d_forward_direct(
    inp: &[f32],
    wt: &[f32],
    out: &mut [f32],
    n: usize,
    c_in: usize,
    h: usize,
    w: usize,
    c_out: usize,
    h_out: usize,
    w_out: usize,
    kh: usize,
    kw: usize,
    groups: usize,
) {
    let c_in_per_g = c_in / groups;
    let c_out_per_g = c_out / groups;
    let out_plane = h_out * w_out;
    let in_plane = h * w;
    let out_addr = out.as_mut_ptr() as usize;
    let compute = |nco: usize| {
        let ni = nco / c_out;
        let co = nco % c_out;
        let ci_start = (co / c_out_per_g) * c_in_per_g;
        let out_base = (ni * c_out + co) * out_plane;
        let op = unsafe {
            std::slice::from_raw_parts_mut((out_addr as *mut f32).add(out_base), out_plane)
        };
        for v in op.iter_mut() {
            *v = 0.0;
        }
        for ci_off in 0..c_in_per_g {
            let in_base = (ni * c_in + ci_start + ci_off) * in_plane;
            let wt_base = (co * c_in_per_g + ci_off) * kh * kw;
            for ki in 0..kh {
                for kj in 0..kw {
                    let wv = wt[wt_base + ki * kw + kj];
                    for ho in 0..h_out {
                        let in_row = in_base + (ho + ki) * w + kj;
                        let dst = &mut op[ho * w_out..ho * w_out + w_out];
                        let src = &inp[in_row..in_row + w_out];
                        for wo in 0..w_out {
                            dst[wo] += wv * src[wo];
                        }
                    }
                }
            }
        }
    };
    if fast_conv_enabled() && crate::pool::should_parallelize(n * c_out * out_plane) {
        crate::pool::par_for(
            n * c_out,
            crate::pool::outer_chunk(n * c_out),
            &|off, cnt| {
                for nco in off..off + cnt {
                    compute(nco);
                }
            },
        );
    } else {
        for nco in 0..n * c_out {
            compute(nco);
        }
    }
}

/// im2col + BLAS forward convolution. For each (batch, group) we gather the
/// receptive-field patches into a `[c_in_per_g·kH·kW, H_out·W_out]` column
/// matrix and dispatch a single `sgemm`:
///
/// ```text
///   out_n_g [c_out_per_g, P]  =  weight_g [c_out_per_g, K]  @  col [K, P]
/// ```
///
/// The result lands in NCHW directly (matching `Conv2D1x1`). The batch loop
/// is embarrassingly parallel — each image writes a disjoint output region —
/// so it fans out over the thread pool, each worker owning a `col` scratch.
#[allow(clippy::too_many_arguments)]
fn conv2d_forward_im2col(
    inp: &[f32],
    wt: &[f32],
    out: &mut [f32],
    n: usize,
    c_in: usize,
    h: usize,
    w: usize,
    c_out: usize,
    h_out: usize,
    w_out: usize,
    kh: usize,
    kw: usize,
    sh: usize,
    sw: usize,
    ph: usize,
    pw: usize,
    dh: usize,
    dw: usize,
    groups: usize,
) {
    let c_in_per_g = c_in / groups;
    let c_out_per_g = c_out / groups;
    let k_dim = c_in_per_g * kh * kw; // im2col rows (contraction dim)
    let p_dim = h_out * w_out; // spatial positions
    let x_stride_n = c_in * h * w;
    let x_stride_g = c_in_per_g * h * w;
    let out_stride_n = c_out * h_out * w_out;
    let out_stride_g = c_out_per_g * p_dim;
    let w_stride_g = c_out_per_g * k_dim;

    // Hand each worker the output base as a raw address: every (ni, g) writes
    // a disjoint `[out_stride_g]` window, so the aliasing is provably safe.
    let out_addr = out.as_mut_ptr() as usize;
    crate::pool::par_for(n, 1, &|off, cnt| {
        let mut col = vec![0f32; k_dim * p_dim];
        for ni in off..off + cnt {
            for g in 0..groups {
                let x_off = ni * x_stride_n + g * x_stride_g;
                im2col(
                    &inp[x_off..x_off + x_stride_g],
                    &mut col,
                    c_in_per_g,
                    h,
                    w,
                    h_out,
                    w_out,
                    kh,
                    kw,
                    sh,
                    sw,
                    ph,
                    pw,
                    dh,
                    dw,
                );
                let w_off = g * w_stride_g;
                let o_off = ni * out_stride_n + g * out_stride_g;
                let out_g = unsafe {
                    std::slice::from_raw_parts_mut((out_addr as *mut f32).add(o_off), out_stride_g)
                };
                crate::blas::sgemm(
                    &wt[w_off..w_off + w_stride_g],
                    &col,
                    out_g,
                    c_out_per_g,
                    k_dim,
                    p_dim,
                );
            }
        }
    });
}

fn im2col(
    x: &[f32],
    col: &mut [f32],
    c_in: usize,
    h: usize,
    w: usize,
    h_out: usize,
    w_out: usize,
    kh: usize,
    kw: usize,
    sh: usize,
    sw: usize,
    ph: usize,
    pw: usize,
    dh: usize,
    dw_dil: usize,
) {
    let n_dim = h_out * w_out;
    debug_assert_eq!(col.len(), c_in * kh * kw * n_dim);
    debug_assert_eq!(x.len(), c_in * h * w);
    let h_isz = h as isize;
    let w_isz = w as isize;
    let ph_isz = ph as isize;
    let pw_isz = pw as isize;
    for ci in 0..c_in {
        for ki in 0..kh {
            for kj in 0..kw {
                let row = ((ci * kh) + ki) * kw + kj;
                let row_off = row * n_dim;
                for ho in 0..h_out {
                    let hi = (ho * sh + ki * dh) as isize - ph_isz;
                    if hi < 0 || hi >= h_isz {
                        for wo in 0..w_out {
                            col[row_off + ho * w_out + wo] = 0.0;
                        }
                        continue;
                    }
                    let hi = hi as usize;
                    let in_row_off = (ci * h + hi) * w;
                    for wo in 0..w_out {
                        let wi = (wo * sw + kj * dw_dil) as isize - pw_isz;
                        col[row_off + ho * w_out + wo] = if wi < 0 || wi >= w_isz {
                            0.0
                        } else {
                            x[in_row_off + wi as usize]
                        };
                    }
                }
            }
        }
    }
}

/// col2im — inverse of `im2col` with scatter-accumulation. The caller
/// is responsible for zeroing `x` if it doesn't already start zero
/// (the conv-input-grad path zeros once before the batch loop).
///
/// `x[ci, hi, wi] += col[(ci · kH · kW + ki · kW + kj) · n_dim + ho · W_out + wo]`
/// for all `(ki, kj, ho, wo)` whose `(hi, wi)` lands in `[0, H) × [0, W)`.
#[allow(clippy::too_many_arguments)]
fn col2im(
    col: &[f32],
    x: &mut [f32],
    c_in: usize,
    h: usize,
    w: usize,
    h_out: usize,
    w_out: usize,
    kh: usize,
    kw: usize,
    sh: usize,
    sw: usize,
    ph: usize,
    pw: usize,
    dh: usize,
    dw_dil: usize,
) {
    let n_dim = h_out * w_out;
    debug_assert_eq!(col.len(), c_in * kh * kw * n_dim);
    debug_assert_eq!(x.len(), c_in * h * w);
    let h_isz = h as isize;
    let w_isz = w as isize;
    let ph_isz = ph as isize;
    let pw_isz = pw as isize;
    for ci in 0..c_in {
        for ki in 0..kh {
            for kj in 0..kw {
                let row = ((ci * kh) + ki) * kw + kj;
                let row_off = row * n_dim;
                for ho in 0..h_out {
                    let hi = (ho * sh + ki * dh) as isize - ph_isz;
                    if hi < 0 || hi >= h_isz {
                        continue;
                    }
                    let hi = hi as usize;
                    let in_row_off = (ci * h + hi) * w;
                    for wo in 0..w_out {
                        let wi = (wo * sw + kj * dw_dil) as isize - pw_isz;
                        if wi < 0 || wi >= w_isz {
                            continue;
                        }
                        x[in_row_off + wi as usize] += col[row_off + ho * w_out + wo];
                    }
                }
            }
        }
    }
}

/// Element-wise backward for `Op::Activation`. `xs` is the original
/// input to the forward activation; `dys` is the upstream gradient.
/// Writes `out[i] = (d/dx act(xs[i])) * dys[i]`.
/// Decompose a per-channel quantization shape into the
/// `(chan_axis, chan_dim, inner)` triplet the kernel needs to map a
/// flat output index to a channel index. Per-tensor (`axis = None`)
/// degenerates to `chan_dim = 1, inner = len`, which makes the
/// kernel's `(i / inner) % chan_dim` always 0 — same fast path the
/// scalar version used.
fn quant_layout(shape: &rlx_ir::Shape, axis: Option<usize>) -> (usize, usize, usize) {
    match axis {
        None => (0, 1, shape.num_elements().unwrap_or(0).max(1)),
        Some(d) => {
            let chan_dim = shape.dim(d).unwrap_static();
            let inner: usize = (d + 1..shape.rank())
                .map(|i| shape.dim(i).unwrap_static())
                .product::<usize>()
                .max(1);
            (d, chan_dim, inner)
        }
    }
}

fn activation_backward_kernel(
    act: rlx_ir::op::Activation,
    xs: &[f32],
    dys: &[f32],
    out: &mut [f32],
) {
    use rlx_ir::op::Activation;
    let n = xs.len();
    debug_assert_eq!(dys.len(), n);
    debug_assert_eq!(out.len(), n);
    match act {
        Activation::Relu => {
            for i in 0..n {
                out[i] = if xs[i] > 0.0 { dys[i] } else { 0.0 };
            }
        }
        Activation::Sigmoid => {
            for i in 0..n {
                let s = 1.0 / (1.0 + (-xs[i]).exp());
                out[i] = s * (1.0 - s) * dys[i];
            }
        }
        Activation::Tanh => {
            for i in 0..n {
                let t = xs[i].tanh();
                out[i] = (1.0 - t * t) * dys[i];
            }
        }
        Activation::Silu => {
            // y = x * σ(x);  dy/dx = σ(x) * (1 + x * (1 - σ(x))).
            for i in 0..n {
                let s = 1.0 / (1.0 + (-xs[i]).exp());
                out[i] = s * (1.0 + xs[i] * (1.0 - s)) * dys[i];
            }
        }
        Activation::Gelu => {
            // Exact erf-based GELU:  y = 0.5 x (1 + erf(x / √2)).
            //   dy/dx = 0.5 (1 + erf(x/√2)) + (x / √(2π)) · exp(-x²/2)
            const INV_SQRT2: f32 = 0.707_106_77;
            const INV_SQRT_2PI: f32 = 0.398_942_3;
            for i in 0..n {
                let x = xs[i];
                let phi = 0.5 * (1.0 + erf_f32(x * INV_SQRT2));
                let pdf = INV_SQRT_2PI * (-(x * x) * 0.5).exp();
                out[i] = (phi + x * pdf) * dys[i];
            }
        }
        Activation::GeluApprox => {
            // Tanh-approximation:
            //   y = 0.5 x (1 + tanh(c · (x + 0.044715 x³))) where c = √(2/π).
            const C: f32 = 0.797_884_6; // √(2/π)
            const A: f32 = 0.044_715;
            for i in 0..n {
                let x = xs[i];
                let inner = C * (x + A * x * x * x);
                let t = inner.tanh();
                let dinner = C * (1.0 + 3.0 * A * x * x);
                let d = 0.5 * (1.0 + t) + 0.5 * x * (1.0 - t * t) * dinner;
                out[i] = d * dys[i];
            }
        }
        Activation::Exp => {
            for i in 0..n {
                out[i] = xs[i].exp() * dys[i];
            }
        }
        Activation::Log => {
            for i in 0..n {
                out[i] = dys[i] / xs[i];
            }
        }
        Activation::Sqrt => {
            // d/dx √x = 0.5 / √x — undefined at x=0; clamp to 0.
            for i in 0..n {
                let s = xs[i].sqrt();
                out[i] = if s > 0.0 { 0.5 * dys[i] / s } else { 0.0 };
            }
        }
        Activation::Rsqrt => {
            // d/dx (1/√x) = -0.5 · x^(-3/2).
            for i in 0..n {
                let s = xs[i].sqrt();
                out[i] = if s > 0.0 {
                    -0.5 * dys[i] / (xs[i] * s)
                } else {
                    0.0
                };
            }
        }
        Activation::Neg => {
            for i in 0..n {
                out[i] = -dys[i];
            }
        }
        Activation::Abs => {
            // sign(x); 0 at x=0.
            for i in 0..n {
                let x = xs[i];
                let s = if x > 0.0 {
                    1.0
                } else if x < 0.0 {
                    -1.0
                } else {
                    0.0
                };
                out[i] = s * dys[i];
            }
        }
        Activation::Round => {
            // STE: pretend the round was identity in the backward
            // pass. The round step has zero gradient almost
            // everywhere, so without this trick the optimizer can't
            // learn through it.
            out.copy_from_slice(dys);
        }
        Activation::Sin => {
            // d/dx sin(x) = cos(x).
            for i in 0..n {
                out[i] = xs[i].cos() * dys[i];
            }
        }
        Activation::Cos => {
            for i in 0..n {
                out[i] = -xs[i].sin() * dys[i];
            }
        }
        Activation::Tan => {
            // d/dx tan(x) = sec²(x) = 1 + tan²(x)
            for i in 0..n {
                let t = xs[i].tan();
                out[i] = (1.0 + t * t) * dys[i];
            }
        }
        Activation::Atan => {
            // d/dx atan(x) = 1 / (1 + x²)
            for i in 0..n {
                let x = xs[i];
                out[i] = dys[i] / (1.0 + x * x);
            }
        }
    }
}

/// f64 sibling of `activation_backward_kernel`. Same math, twice the
/// precision — used by f64 graphs where the f32 kernel reading bytes
/// as `&[f32]` would silently discard half of every f64 value.
fn activation_backward_kernel_f64(
    act: rlx_ir::op::Activation,
    xs: &[f64],
    dys: &[f64],
    out: &mut [f64],
) {
    use rlx_ir::op::Activation;
    let n = xs.len();
    debug_assert_eq!(dys.len(), n);
    debug_assert_eq!(out.len(), n);
    match act {
        Activation::Relu => {
            for i in 0..n {
                out[i] = if xs[i] > 0.0 { dys[i] } else { 0.0 };
            }
        }
        Activation::Sigmoid => {
            for i in 0..n {
                let s = 1.0 / (1.0 + (-xs[i]).exp());
                out[i] = s * (1.0 - s) * dys[i];
            }
        }
        Activation::Tanh => {
            for i in 0..n {
                let t = xs[i].tanh();
                out[i] = (1.0 - t * t) * dys[i];
            }
        }
        Activation::Silu => {
            for i in 0..n {
                let s = 1.0 / (1.0 + (-xs[i]).exp());
                out[i] = s * (1.0 + xs[i] * (1.0 - s)) * dys[i];
            }
        }
        Activation::Gelu | Activation::GeluApprox => {
            // Both rare on f64 paths; use the high-quality libm erf.
            const INV_SQRT2: f64 = std::f64::consts::FRAC_1_SQRT_2;
            const INV_SQRT_2PI: f64 = 0.398_942_280_401_432_7;
            for i in 0..n {
                let x = xs[i];
                let phi = 0.5 * (1.0 + erf_f64(x * INV_SQRT2));
                let pdf = INV_SQRT_2PI * (-(x * x) * 0.5).exp();
                out[i] = (phi + x * pdf) * dys[i];
            }
        }
        Activation::Exp => {
            for i in 0..n {
                out[i] = xs[i].exp() * dys[i];
            }
        }
        Activation::Log => {
            for i in 0..n {
                out[i] = dys[i] / xs[i];
            }
        }
        Activation::Sqrt => {
            for i in 0..n {
                let s = xs[i].sqrt();
                out[i] = if s > 0.0 { 0.5 * dys[i] / s } else { 0.0 };
            }
        }
        Activation::Rsqrt => {
            for i in 0..n {
                let s = xs[i].sqrt();
                out[i] = if s > 0.0 {
                    -0.5 * dys[i] / (xs[i] * s)
                } else {
                    0.0
                };
            }
        }
        Activation::Neg => {
            for i in 0..n {
                out[i] = -dys[i];
            }
        }
        Activation::Abs => {
            for i in 0..n {
                let x = xs[i];
                let s = if x > 0.0 {
                    1.0
                } else if x < 0.0 {
                    -1.0
                } else {
                    0.0
                };
                out[i] = s * dys[i];
            }
        }
        Activation::Round => {
            out.copy_from_slice(dys);
        }
        Activation::Sin => {
            for i in 0..n {
                out[i] = xs[i].cos() * dys[i];
            }
        }
        Activation::Cos => {
            for i in 0..n {
                out[i] = -xs[i].sin() * dys[i];
            }
        }
        Activation::Tan => {
            for i in 0..n {
                let t = xs[i].tan();
                out[i] = (1.0 + t * t) * dys[i];
            }
        }
        Activation::Atan => {
            for i in 0..n {
                let x = xs[i];
                out[i] = dys[i] / (1.0 + x * x);
            }
        }
    }
}

/// f64 erf via A&S 7.1.26 — same coefficients as `erf_f32`, computed
/// at f64 width. Max error ~1.5e-7 (limited by the polynomial, not the
/// arithmetic). Adequate for gradient kernels; if higher precision is
/// needed, swap in a libm dependency.
#[inline(always)]
fn erf_f64(x: f64) -> f64 {
    let s = x.signum();
    let x = x.abs();
    let t = 1.0 / (1.0 + 0.327_591_1 * x);
    let y = 1.0
        - (((((1.061_405_43 * t - 1.453_152_03) * t) + 1.421_413_75) * t - 0.284_496_74) * t
            + 0.254_829_59)
            * t
            * (-x * x).exp();
    s * y
}

/// Cheap erf approximation (Abramowitz & Stegun 7.1.26, max error ~1.5e-7
/// over all of ℝ — plenty for f32 gradient kernels).
#[inline(always)]
fn erf_f32(x: f32) -> f32 {
    let s = x.signum();
    let x = x.abs();
    let t = 1.0 / (1.0 + 0.327_591_1 * x);
    let y = 1.0
        - (((((1.061_405_4 * t - 1.453_152_1) * t) + 1.421_413_8) * t - 0.284_496_74) * t
            + 0.254_829_6)
            * t
            * (-x * x).exp();
    s * y
}

fn narrow_thunk_closure(
    src: usize,
    dst: usize,
    outer: u32,
    src_stride: u32,
    dst_stride: u32,
    inner: u32,
    elem_bytes: u8,
) -> Arc<dyn Fn(*mut u8) + Send + Sync> {
    let (outer, ss, ds, inner, eb) = (
        outer as usize,
        src_stride as usize,
        dst_stride as usize,
        inner as usize,
        elem_bytes as usize,
    );
    let row_bytes = inner.saturating_mul(eb);
    let src_row_stride = ss.saturating_mul(eb);
    let dst_row_stride = ds.saturating_mul(eb);
    Arc::new(move |base: *mut u8| unsafe {
        if row_bytes == 0 || src == dst {
            return;
        }
        // Compiled-fn path has no arena length; skip if offsets look bogus.
        let arena_len = usize::MAX;
        for o in 0..outer {
            let s_off = src + o * src_row_stride;
            let d_off = dst + o * dst_row_stride;
            if s_off == d_off {
                continue;
            }
            if s_off.saturating_add(row_bytes) > arena_len
                || d_off.saturating_add(row_bytes) > arena_len
            {
                break;
            }
            std::ptr::copy_nonoverlapping(base.add(s_off), base.add(d_off), row_bytes);
        }
    })
}

unsafe fn sl(offset: usize, base: *mut u8, len: usize) -> &'static [f32] {
    if offset == usize::MAX {
        return &[];
    }
    unsafe { std::slice::from_raw_parts(base.add(offset) as *const f32, len) }
}

#[inline(always)]
unsafe fn sl_mut(offset: usize, base: *mut u8, len: usize) -> &'static mut [f32] {
    unsafe { std::slice::from_raw_parts_mut(base.add(offset) as *mut f32, len) }
}

#[inline(always)]
unsafe fn sl_f64(offset: usize, base: *mut u8, len: usize) -> &'static [f64] {
    if offset == usize::MAX {
        return &[];
    }
    unsafe { std::slice::from_raw_parts(base.add(offset) as *const f64, len) }
}

#[inline(always)]
unsafe fn sl_mut_f64(offset: usize, base: *mut u8, len: usize) -> &'static mut [f64] {
    unsafe { std::slice::from_raw_parts_mut(base.add(offset) as *mut f64, len) }
}

// i32 / i64 typed slice helpers — siblings of sl_f32/sl_f64. Kept for
// integer-tensor thunks that haven't landed yet (Sample, Gather index
// buffers); deleting them now would force re-deriving the unsafe
// boilerplate when the next int-typed thunk lands.
#[inline(always)]
#[allow(dead_code)]
unsafe fn sl_i32(offset: usize, base: *mut u8, len: usize) -> &'static [i32] {
    if offset == usize::MAX {
        return &[];
    }
    unsafe { std::slice::from_raw_parts(base.add(offset) as *const i32, len) }
}

#[inline(always)]
#[allow(dead_code)]
unsafe fn sl_mut_i32(offset: usize, base: *mut u8, len: usize) -> &'static mut [i32] {
    unsafe { std::slice::from_raw_parts_mut(base.add(offset) as *mut i32, len) }
}

#[inline(always)]
unsafe fn sl_i64(offset: usize, base: *mut u8, len: usize) -> &'static [i64] {
    if offset == usize::MAX {
        return &[];
    }
    unsafe { std::slice::from_raw_parts(base.add(offset) as *const i64, len) }
}

#[inline(always)]
unsafe fn sl_mut_i64(offset: usize, base: *mut u8, len: usize) -> &'static mut [i64] {
    unsafe { std::slice::from_raw_parts_mut(base.add(offset) as *mut i64, len) }
}

/// f64 N-D index walk used by Transpose and Expand. `out_dims` gives
/// the output shape; `in_strides` gives the source stride for each
/// output dim (broadcast axes have stride 0).
fn transpose_walk_f64(inp: &[f64], out: &mut [f64], out_dims: &[u32], in_strides: &[u32]) {
    let rank = out_dims.len();
    let mut idx = vec![0u32; rank];
    for o in 0..out.len() {
        let mut src_off = 0usize;
        for d in 0..rank {
            src_off += idx[d] as usize * in_strides[d] as usize;
        }
        out[o] = inp[broadcast_src_index(src_off, inp.len())];
        // Increment index — last dim varies fastest.
        for d in (0..rank).rev() {
            idx[d] += 1;
            if idx[d] < out_dims[d] {
                break;
            }
            idx[d] = 0;
        }
    }
}

/// f64 elementwise activation. Reads `inp`, writes `out`. For now
/// covers what the autodiff-emitted gradient graph needs (Neg, Exp,
/// Log, Sqrt, Rsqrt, Abs, Tanh, Sigmoid, Relu — the
/// transcendental-free subset). Approximate Gelu/Silu deferred until a
/// workload demands them at f64.
fn apply_activation_f64(inp: &[f64], out: &mut [f64], kind: Activation) {
    match kind {
        Activation::Neg => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = -v;
            }
        }
        Activation::Exp => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.exp();
            }
        }
        Activation::Log => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.ln();
            }
        }
        Activation::Sqrt => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.sqrt();
            }
        }
        Activation::Rsqrt => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = 1.0 / v.sqrt();
            }
        }
        Activation::Abs => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.abs();
            }
        }
        Activation::Tanh => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.tanh();
            }
        }
        Activation::Sigmoid => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = 1.0 / (1.0 + (-v).exp());
            }
        }
        Activation::Relu => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.max(0.0);
            }
        }
        Activation::Round => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.round_ties_even();
            }
        }
        Activation::Sin => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.sin();
            }
        }
        Activation::Cos => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.cos();
            }
        }
        Activation::Tan => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.tan();
            }
        }
        Activation::Atan => {
            for (o, &v) in out.iter_mut().zip(inp) {
                *o = v.atan();
            }
        }
        Activation::Gelu | Activation::GeluApprox | Activation::Silu => {
            panic!(
                "apply_activation_f64: {kind:?} not yet implemented at f64. \
                    Add when a workload needs it."
            );
        }
    }
}

#[inline]
fn binary_op_f64(op: BinaryOp, a: f64, b: f64) -> f64 {
    match op {
        BinaryOp::Add => a + b,
        BinaryOp::Sub => a - b,
        BinaryOp::Mul => a * b,
        BinaryOp::Div => a / b,
        BinaryOp::Max => a.max(b),
        BinaryOp::Min => a.min(b),
        BinaryOp::Pow => a.powf(b),
    }
}

/// f64 sum reduction over a contiguous middle range.
/// Layout: input is `[outer, reduced, inner]`, output is `[outer, inner]`.
fn reduce_sum_f64(inp: &[f64], out: &mut [f64], outer: usize, reduced: usize, inner: usize) {
    for o in 0..outer {
        for n in 0..inner {
            let mut acc = 0.0_f64;
            for r in 0..reduced {
                acc += inp[o * reduced * inner + r * inner + n];
            }
            out[o * inner + n] = acc;
        }
    }
}

/// Host-side RNG fill against a byte arena (Metal/CUDA unified-memory fallback).
///
/// # Safety
///
/// `arena` must point to a valid allocation with at least `dst_off + len * 4` bytes.
pub unsafe fn fill_rng_normal_arena(
    dst_off: usize,
    len: usize,
    mean: f32,
    scale: f32,
    key: u64,
    op_seed: Option<f32>,
    opts: rlx_ir::RngOptions,
    arena: *mut u8,
) {
    if len == 0 {
        return;
    }
    unsafe {
        let out = std::slice::from_raw_parts_mut((arena.add(dst_off)) as *mut f32, len);
        rlx_ir::fill_normal_like(out, mean, scale, opts, key, op_seed);
    }
}

pub unsafe fn fill_rng_uniform_arena(
    dst_off: usize,
    len: usize,
    low: f32,
    high: f32,
    key: u64,
    op_seed: Option<f32>,
    opts: rlx_ir::RngOptions,
    arena: *mut u8,
) {
    if len == 0 {
        return;
    }
    unsafe {
        let out = std::slice::from_raw_parts_mut((arena.add(dst_off)) as *mut f32, len);
        rlx_ir::fill_uniform_like(out, low, high, opts, key, op_seed);
    }
}

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

    /// The im2col+BLAS forward conv must match the reference scalar loop
    /// (up to float reassociation) across a range of shapes — strided,
    /// dilated, padded, grouped, and multi-batch. Guards the RLX_FAST_CONV
    /// fast path used by the "fused" benchmark configuration.
    #[test]
    fn conv2d_im2col_matches_naive() {
        // (n, c_in, h, w, c_out, kh, kw, sh, sw, ph, pw, dh, dw, groups)
        let cases = [
            (1, 1, 28, 28, 8, 3, 3, 1, 1, 0, 0, 1, 1, 1), // TinyConv layer 1
            (4, 8, 13, 13, 16, 3, 3, 1, 1, 0, 0, 1, 1, 1), // TinyConv layer 2, batched
            (2, 3, 16, 16, 6, 3, 3, 2, 2, 1, 1, 1, 1, 1), // stride 2, pad 1
            (1, 4, 12, 12, 4, 3, 3, 1, 1, 2, 2, 2, 2, 1), // dilation 2
            (3, 8, 10, 10, 8, 3, 3, 1, 1, 1, 1, 1, 1, 2), // groups = 2
            (1, 2, 7, 7, 5, 1, 1, 1, 1, 0, 0, 1, 1, 1),   // 1x1
        ];
        for (idx, &(n, c_in, h, w, c_out, kh, kw, sh, sw, ph, pw, dh, dw, groups)) in
            cases.iter().enumerate()
        {
            let c_in_per_g = c_in / groups;
            let h_out = (h + 2 * ph - dh * (kh - 1) - 1) / sh + 1;
            let w_out = (w + 2 * pw - dw * (kw - 1) - 1) / sw + 1;
            // Deterministic pseudo-random inputs (no rng dep in tests).
            let mut s: u32 = 0x9e37_79b9 ^ (idx as u32 + 1);
            let mut rand = || {
                s ^= s << 13;
                s ^= s >> 17;
                s ^= s << 5;
                (s as f32 / u32::MAX as f32) - 0.5
            };
            let inp: Vec<f32> = (0..n * c_in * h * w).map(|_| rand()).collect();
            let wt: Vec<f32> = (0..c_out * c_in_per_g * kh * kw).map(|_| rand()).collect();
            let mut out_ref = vec![0f32; n * c_out * h_out * w_out];
            let mut out_fast = vec![0f32; n * c_out * h_out * w_out];

            conv2d_forward_naive(
                &inp,
                &wt,
                &mut out_ref,
                n,
                c_in,
                h,
                w,
                c_out,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
                dh,
                dw,
                groups,
            );
            conv2d_forward_im2col(
                &inp,
                &wt,
                &mut out_fast,
                n,
                c_in,
                h,
                w,
                c_out,
                h_out,
                w_out,
                kh,
                kw,
                sh,
                sw,
                ph,
                pw,
                dh,
                dw,
                groups,
            );

            let max_abs = out_ref
                .iter()
                .zip(&out_fast)
                .map(|(a, b)| (a - b).abs())
                .fold(0f32, f32::max);
            assert!(
                max_abs < 1e-3,
                "case {idx}: im2col vs naive max abs diff {max_abs}"
            );
        }
    }

    /// Direct forward conv (stride-1, no-pad) must match the reference scalar
    /// conv exactly-ish across channel/batch/group shapes.
    #[test]
    fn conv2d_direct_matches_naive() {
        // (n, c_in, h, w, c_out, kh, kw, groups)
        let cases = [
            (1, 1, 28, 28, 8, 3, 3, 1),  // TinyConv L1
            (4, 8, 13, 13, 16, 3, 3, 1), // TinyConv L2, batched
            (2, 6, 10, 10, 9, 3, 3, 3),  // groups=3
            (1, 4, 9, 9, 4, 5, 5, 1),    // 5×5 kernel
            (3, 2, 7, 7, 2, 1, 1, 1),    // 1×1
        ];
        for (idx, &(n, c_in, h, w, c_out, kh, kw, groups)) in cases.iter().enumerate() {
            let h_out = h - kh + 1;
            let w_out = w - kw + 1;
            let c_in_per_g = c_in / groups;
            let mut s: u32 = 0xfeed_1234 ^ (idx as u32 + 1);
            let mut rand = || {
                s ^= s << 13;
                s ^= s >> 17;
                s ^= s << 5;
                (s as f32 / u32::MAX as f32) - 0.5
            };
            let inp: Vec<f32> = (0..n * c_in * h * w).map(|_| rand()).collect();
            let wt: Vec<f32> = (0..c_out * c_in_per_g * kh * kw).map(|_| rand()).collect();
            let mut r = vec![0f32; n * c_out * h_out * w_out];
            let mut d = vec![0f32; n * c_out * h_out * w_out];
            conv2d_forward_naive(
                &inp, &wt, &mut r, n, c_in, h, w, c_out, h_out, w_out, kh, kw, 1, 1, 0, 0, 1, 1,
                groups,
            );
            conv2d_forward_direct(
                &inp, &wt, &mut d, n, c_in, h, w, c_out, h_out, w_out, kh, kw, groups,
            );
            let mx = r
                .iter()
                .zip(&d)
                .map(|(a, b)| (a - b).abs())
                .fold(0f32, f32::max);
            assert!(mx < 1e-4, "case {idx}: direct vs naive max abs diff {mx}");
        }
    }

    /// Winograd F(2,3) forward conv must match the reference scalar conv (up to
    /// the transform's float reassociation) for 3×3 stride-1 valid convs,
    /// including odd output dims (boundary tiles) and multiple channels/batches.
    #[test]
    fn conv2d_winograd_matches_naive() {
        // (n, c_in, h, w, c_out)  — all 3×3 stride1 valid; covers even (26) and
        // odd (11) output dims and the TinyConv channel shapes.
        let cases = [
            (1, 1, 28, 28, 8),  // TinyConv layer 1: out 26×26 (even)
            (4, 8, 13, 13, 16), // TinyConv layer 2: out 11×11 (odd) — boundary tiles
            (2, 3, 9, 9, 5),    // out 7×7 (odd)
            (1, 4, 8, 8, 4),    // out 6×6 (even)
        ];
        for (idx, &(n, c_in, h, w, c_out)) in cases.iter().enumerate() {
            let h_out = h - 2;
            let w_out = w - 2;
            let mut s: u32 = 0x1234_5678 ^ (idx as u32 + 1);
            let mut rand = || {
                s ^= s << 13;
                s ^= s >> 17;
                s ^= s << 5;
                (s as f32 / u32::MAX as f32) - 0.5
            };
            let inp: Vec<f32> = (0..n * c_in * h * w).map(|_| rand()).collect();
            let wt: Vec<f32> = (0..c_out * c_in * 9).map(|_| rand()).collect();
            let mut out_ref = vec![0f32; n * c_out * h_out * w_out];
            let mut out_win = vec![0f32; n * c_out * h_out * w_out];
            conv2d_forward_naive(
                &inp,
                &wt,
                &mut out_ref,
                n,
                c_in,
                h,
                w,
                c_out,
                h_out,
                w_out,
                3,
                3,
                1,
                1,
                0,
                0,
                1,
                1,
                1,
            );
            conv2d_forward_winograd(&inp, &wt, &mut out_win, n, c_in, h, w, c_out, h_out, w_out);
            let max_abs = out_ref
                .iter()
                .zip(&out_win)
                .map(|(a, b)| (a - b).abs())
                .fold(0f32, f32::max);
            assert!(
                max_abs < 1e-3,
                "case {idx}: winograd vs naive max abs diff {max_abs}"
            );
        }
    }

    /// Plan #45: when a Narrow's only consumer is a Rope, the thunk
    /// fusion pass collapses them — the Narrow becomes Nop, and the
    /// Rope reads from the parent buffer with its row stride. This
    /// test runs the unfused path (batch*seq > FusedAttnBlock
    /// threshold) and asserts the rewrite happened.
    #[test]
    fn narrow_rope_fuses_in_unfused_path() {
        let f = DType::F32;
        let mut g = Graph::new("nr_fuse");
        // Force batch*seq > 64 so FusedAttnBlock doesn't pre-empt us.
        let qkv = g.input("qkv", Shape::new(&[16, 8, 192], f)); // 16*8=128 > 64
        let cos = g.input("cos", Shape::new(&[16], f));
        let sin = g.input("sin", Shape::new(&[16], f));
        // Last-axis narrow: Q = qkv[..., 0..64]
        let q = g.narrow_(qkv, 2, 0, 64);
        let q_rope = g.rope(q, cos, sin, 16);
        g.set_outputs(vec![q_rope]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        let mut narrow_count = 0;
        let mut rope_with_stride: Option<u32> = None;
        for t in &sched.thunks {
            match t {
                Thunk::Narrow { .. } => narrow_count += 1,
                Thunk::Rope { src_row_stride, .. } => rope_with_stride = Some(*src_row_stride),
                _ => {}
            }
        }
        // After fusion the Narrow is gone; only the Rope remains, and
        // it now walks with the parent QKV's row stride (3 * 64 = 192).
        assert_eq!(
            narrow_count, 0,
            "Narrow→Rope fusion should leave zero Narrow thunks; saw {narrow_count}"
        );
        assert_eq!(
            rope_with_stride,
            Some(192),
            "Rope's src_row_stride should be 192 (parent qkv axis), saw {rope_with_stride:?}"
        );
    }

    /// Plan #15: SSM selective scan matches a naive Python-style
    /// Python-style sequential reference.
    #[test]
    fn ssm_selective_scan_matches_reference() {
        use rlx_ir::Philox4x32;
        let bch = 1usize;
        let s = 4usize;
        let h = 3usize;
        let n = 2usize;

        let mut rng = Philox4x32::new(13);
        let mut x = vec![0f32; bch * s * h];
        rng.fill_normal(&mut x);
        let mut delta = vec![0f32; bch * s * h];
        // Keep Δ small so exp(Δ·A) doesn't blow up.
        for v in delta.iter_mut() {
            *v = (rng.next_f32() - 0.5) * 0.1;
        }
        let mut a = vec![0f32; h * n];
        for v in a.iter_mut() {
            *v = -(rng.next_f32() * 0.5 + 0.1);
        } // negative for stability
        let mut b = vec![0f32; bch * s * n];
        rng.fill_normal(&mut b);
        let mut c = vec![0f32; bch * s * n];
        rng.fill_normal(&mut c);

        // Reference scan.
        let mut expected = vec![0f32; bch * s * h];
        for bi in 0..bch {
            let mut state = vec![0f32; h * n];
            for si in 0..s {
                for ci in 0..h {
                    let d = delta[bi * s * h + si * h + ci];
                    let xv = x[bi * s * h + si * h + ci];
                    let mut acc = 0f32;
                    for ni in 0..n {
                        let da = (d * a[ci * n + ni]).exp();
                        state[ci * n + ni] =
                            da * state[ci * n + ni] + d * b[bi * s * n + si * n + ni] * xv;
                        acc += c[bi * s * n + si * n + ni] * state[ci * n + ni];
                    }
                    expected[bi * s * h + si * h + ci] = acc;
                }
            }
        }

        // RLX path.
        let f = DType::F32;
        let mut g = Graph::new("ssm");
        let xn = g.input("x", Shape::new(&[bch, s, h], f));
        let dn = g.input("delta", Shape::new(&[bch, s, h], f));
        let an = g.param("a", Shape::new(&[h, n], f));
        let bn = g.param("b", Shape::new(&[bch, s, n], f));
        let cn = g.param("c", Shape::new(&[bch, s, n], f));
        let yn = g.selective_scan(xn, dn, an, bn, cn, n, Shape::new(&[bch, s, h], f));
        g.set_outputs(vec![yn]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        let xn_off = arena.byte_offset(xn);
        let dn_off = arena.byte_offset(dn);
        let an_off = arena.byte_offset(an);
        let bn_off = arena.byte_offset(bn);
        let cn_off = arena.byte_offset(cn);
        let yn_off = arena.byte_offset(yn);
        let buf = arena.raw_buf_mut();
        unsafe {
            let copy = |dst: *mut f32, data: &[f32]| {
                for (i, &v) in data.iter().enumerate() {
                    *dst.add(i) = v;
                }
            };
            copy(buf.as_mut_ptr().add(xn_off) as *mut f32, &x);
            copy(buf.as_mut_ptr().add(dn_off) as *mut f32, &delta);
            copy(buf.as_mut_ptr().add(an_off) as *mut f32, &a);
            copy(buf.as_mut_ptr().add(bn_off) as *mut f32, &b);
            copy(buf.as_mut_ptr().add(cn_off) as *mut f32, &c);
        }
        execute_thunks(&sched, arena.raw_buf_mut());

        let actual: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(yn_off) as *const f32;
            (0..bch * s * h).map(|i| *p.add(i)).collect()
        };

        for (i, (e, a)) in expected.iter().zip(&actual).enumerate() {
            assert!(
                (e - a).abs() < 1e-3,
                "mismatch at {i}: expected {e}, got {a}"
            );
        }
    }

    /// Plan #26: 1×1 conv lowers to per-batch sgemm and matches the
    /// scalar 7-loop reference.
    #[test]
    fn conv_1x1_fast_path_matches_scalar() {
        use rlx_ir::Philox4x32;
        // [N=2, C_in=4, H=3, W=3]
        let n = 2usize;
        let c_in = 4usize;
        let h = 3usize;
        let w = 3usize;
        let c_out = 5usize;
        let mut rng = Philox4x32::new(31);
        let mut x = vec![0f32; n * c_in * h * w];
        rng.fill_normal(&mut x);
        let mut weight = vec![0f32; c_out * c_in];
        rng.fill_normal(&mut weight);

        // Reference: scalar 1×1 conv = per-batch matmul
        // out[ni, co, hi, wi] = sum_ci weight[co, ci] * x[ni, ci, hi, wi]
        let mut expected = vec![0f32; n * c_out * h * w];
        for ni in 0..n {
            for co in 0..c_out {
                for hi in 0..h {
                    for wi in 0..w {
                        let mut acc = 0f32;
                        for ci in 0..c_in {
                            acc += weight[co * c_in + ci]
                                * x[((ni * c_in) + ci) * h * w + hi * w + wi];
                        }
                        expected[((ni * c_out) + co) * h * w + hi * w + wi] = acc;
                    }
                }
            }
        }

        // RLX path: build a graph with Op::Conv (kernel=[1,1], stride=[1,1], etc).
        let f = DType::F32;
        let mut g = Graph::new("conv1x1");
        let xn = g.input("x", Shape::new(&[n, c_in, h, w], f));
        let wn = g.param("w", Shape::new(&[c_out, c_in, 1, 1], f));
        // Manually add Op::Conv since there's no `g.conv()` helper.
        let cn = g.add_node(
            rlx_ir::Op::Conv {
                kernel_size: vec![1, 1],
                stride: vec![1, 1],
                padding: vec![0, 0],
                dilation: vec![1, 1],
                groups: 1,
            },
            vec![xn, wn],
            Shape::new(&[n, c_out, h, w], f),
        );
        g.set_outputs(vec![cn]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        // Verify the fast path was selected.
        let saw_fast = sched
            .thunks
            .iter()
            .any(|t| matches!(t, Thunk::Conv2D1x1 { .. }));
        let saw_slow = sched
            .thunks
            .iter()
            .any(|t| matches!(t, Thunk::Conv2D { .. }));
        assert!(saw_fast, "1×1 conv should emit Conv2D1x1");
        assert!(!saw_slow, "1×1 conv must not fall through to scalar Conv2D");

        let xn_off = arena.byte_offset(xn);
        let wn_off = arena.byte_offset(wn);
        let cn_off = arena.byte_offset(cn);
        let buf = arena.raw_buf_mut();
        unsafe {
            let xp = buf.as_mut_ptr().add(xn_off) as *mut f32;
            for (i, &v) in x.iter().enumerate() {
                *xp.add(i) = v;
            }
            let wp = buf.as_mut_ptr().add(wn_off) as *mut f32;
            for (i, &v) in weight.iter().enumerate() {
                *wp.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());

        let actual: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(cn_off) as *const f32;
            (0..(n * c_out * h * w)).map(|i| *p.add(i)).collect()
        };

        for (i, (e, a)) in expected.iter().zip(&actual).enumerate() {
            assert!(
                (e - a).abs() < 1e-3,
                "mismatch at {i}: expected {e}, got {a}"
            );
        }
    }

    /// Plan #5: fused dequant matmul matches the dequant-then-matmul
    /// reference (i.e. `(scale * (q - z)) @ x` materialized).
    #[test]
    fn dequant_matmul_int8_sym_matches_reference() {
        use rlx_ir::Philox4x32;
        use rlx_ir::quant::QuantScheme;

        let m = 3usize;
        let k = 8usize;
        let n = 4usize;
        let block_size = 4usize; // 2 blocks per column
        let blocks_per_col = k / block_size;

        // Random inputs: x f32, w_q i8, scales f32. Symmetric → no zp.
        let mut rng = Philox4x32::new(99);
        let mut x = vec![0f32; m * k];
        rng.fill_normal(&mut x);
        let w_q: Vec<i8> = (0..(k * n))
            .map(|i| ((i as i32 * 13 + 7) % 127 - 63) as i8)
            .collect();
        let scales: Vec<f32> = (0..(blocks_per_col * n))
            .map(|i| 0.01 + 0.001 * i as f32)
            .collect();

        // Reference: build f32 weights from (q * scale) per block.
        let mut w_f32 = vec![0f32; k * n];
        for p in 0..k {
            let block = p / block_size;
            for j in 0..n {
                let s = scales[block * n + j];
                w_f32[p * n + j] = w_q[p * n + j] as f32 * s;
            }
        }
        let mut expected = vec![0f32; m * n];
        for i in 0..m {
            for j in 0..n {
                let mut acc = 0f32;
                for p in 0..k {
                    acc += x[i * k + p] * w_f32[p * n + j];
                }
                expected[i * n + j] = acc;
            }
        }

        // RLX path.
        let f = DType::F32;
        let mut g = Graph::new("dq");
        let xn = g.input("x", Shape::new(&[m, k], f));
        let wn = g.param("w", Shape::new(&[k, n], DType::I8));
        let sn = g.param("scale", Shape::new(&[blocks_per_col, n], f));
        let zn = g.param("zp", Shape::new(&[blocks_per_col, n], f)); // unused (sym)
        let dq = g.dequant_matmul(
            xn,
            wn,
            sn,
            zn,
            QuantScheme::Int8Block {
                block_size: block_size as u32,
            },
            Shape::new(&[m, n], f),
        );
        g.set_outputs(vec![dq]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        let xn_off = arena.byte_offset(xn);
        let wn_off = arena.byte_offset(wn);
        let sn_off = arena.byte_offset(sn);
        let zn_off = arena.byte_offset(zn);
        let dq_off = arena.byte_offset(dq);
        let buf = arena.raw_buf_mut();
        unsafe {
            // Seed f32 inputs.
            let xp = buf.as_mut_ptr().add(xn_off) as *mut f32;
            for (i, &v) in x.iter().enumerate() {
                *xp.add(i) = v;
            }
            let sp = buf.as_mut_ptr().add(sn_off) as *mut f32;
            for (i, &v) in scales.iter().enumerate() {
                *sp.add(i) = v;
            }
            let zp = buf.as_mut_ptr().add(zn_off) as *mut f32;
            for i in 0..(blocks_per_col * n) {
                *zp.add(i) = 0.0;
            }
            // Seed i8 weights byte-by-byte.
            let wp = buf.as_mut_ptr().add(wn_off) as *mut i8;
            for (i, &v) in w_q.iter().enumerate() {
                *wp.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());

        let actual: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(dq_off) as *const f32;
            (0..m * n).map(|i| *p.add(i)).collect()
        };

        for (i, (e, a)) in expected.iter().zip(&actual).enumerate() {
            assert!(
                (e - a).abs() < 1e-3,
                "mismatch at {i}: expected {e}, got {a}"
            );
        }
    }

    /// Plan #9: LoRA matmul matches the unfused 3-matmul reference.
    #[test]
    fn lora_matmul_matches_unfused_reference() {
        use rlx_ir::Philox4x32;

        let m = 4usize;
        let k = 8usize;
        let n = 6usize;
        let r = 2usize;
        let scale = 0.5f32;

        // Random inputs (deterministic via Philox).
        let mut rng = Philox4x32::new(42);
        let mut x = vec![0f32; m * k];
        rng.fill_normal(&mut x);
        let mut w = vec![0f32; k * n];
        rng.fill_normal(&mut w);
        let mut a = vec![0f32; k * r];
        rng.fill_normal(&mut a);
        let mut b = vec![0f32; r * n];
        rng.fill_normal(&mut b);

        // Reference: out = x·W + scale * x·A·B. Naive triple-loop.
        let naive = |a_buf: &[f32], b_buf: &[f32], rows: usize, inner: usize, cols: usize| {
            let mut o = vec![0f32; rows * cols];
            for i in 0..rows {
                for j in 0..cols {
                    let mut acc = 0f32;
                    for p in 0..inner {
                        acc += a_buf[i * inner + p] * b_buf[p * cols + j];
                    }
                    o[i * cols + j] = acc;
                }
            }
            o
        };
        let xw = naive(&x, &w, m, k, n);
        let xa = naive(&x, &a, m, k, r);
        let xab = naive(&xa, &b, m, r, n);
        let mut expected = xw;
        for i in 0..(m * n) {
            expected[i] += scale * xab[i];
        }

        // RLX path: build a graph with one LoraMatMul.
        let f = DType::F32;
        let mut g = Graph::new("lora");
        let xn = g.input("x", Shape::new(&[m, k], f));
        let wn = g.param("w", Shape::new(&[k, n], f));
        let an = g.param("a", Shape::new(&[k, r], f));
        let bn = g.param("b", Shape::new(&[r, n], f));
        let lm = g.lora_matmul(xn, wn, an, bn, scale, Shape::new(&[m, n], f));
        g.set_outputs(vec![lm]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        let xn_off = arena.byte_offset(xn);
        let wn_off = arena.byte_offset(wn);
        let an_off = arena.byte_offset(an);
        let bn_off = arena.byte_offset(bn);
        let lm_off = arena.byte_offset(lm);
        let buf = arena.raw_buf_mut();
        unsafe {
            let copy = |dst: *mut f32, data: &[f32]| {
                for (i, &v) in data.iter().enumerate() {
                    *dst.add(i) = v;
                }
            };
            copy(buf.as_mut_ptr().add(xn_off) as *mut f32, &x);
            copy(buf.as_mut_ptr().add(wn_off) as *mut f32, &w);
            copy(buf.as_mut_ptr().add(an_off) as *mut f32, &a);
            copy(buf.as_mut_ptr().add(bn_off) as *mut f32, &b);
        }
        execute_thunks(&sched, arena.raw_buf_mut());

        let actual: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(lm_off) as *const f32;
            (0..m * n).map(|i| *p.add(i)).collect()
        };

        for (i, (e, a)) in expected.iter().zip(&actual).enumerate() {
            assert!(
                (e - a).abs() < 1e-3,
                "mismatch at {i}: expected {e}, got {a}"
            );
        }
    }

    /// Plan #42: fused sampling kernel determinism + greedy fallback.
    #[test]
    fn sample_temperature_zero_is_argmax() {
        // Very low temperature → distribution collapses on argmax.
        // Same seed → same output bit-for-bit.
        let f = DType::F32;
        let mut g = Graph::new("samp");
        let logits = g.input("logits", Shape::new(&[1, 8], f));
        let s = g.sample(logits, 0, 1.0, 1e-3, 42, Shape::new(&[1], f));
        g.set_outputs(vec![s]);
        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        let logits_off = arena.byte_offset(logits);
        let s_off = arena.byte_offset(s);
        let buf = arena.raw_buf_mut();
        unsafe {
            let p = buf.as_mut_ptr().add(logits_off) as *mut f32;
            // argmax = index 5 (value 9.0).
            let inputs = [0.1f32, 0.2, 0.3, 0.4, 0.5, 9.0, 0.7, 0.8];
            for (i, &v) in inputs.iter().enumerate() {
                *p.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());

        let token = unsafe {
            let p = arena.raw_buf().as_ptr().add(s_off) as *const f32;
            *p as usize
        };
        assert_eq!(token, 5, "low-temp sampling should pick the argmax");
    }

    #[test]
    fn sample_top_k_one_is_deterministic() {
        // top_k=1 forces only the argmax to have nonzero probability.
        let f = DType::F32;
        let mut g = Graph::new("samp_k1");
        let logits = g.input("logits", Shape::new(&[1, 4], f));
        let s = g.sample(logits, 1, 1.0, 1.0, 7, Shape::new(&[1], f));
        g.set_outputs(vec![s]);
        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        let logits_off = arena.byte_offset(logits);
        let s_off = arena.byte_offset(s);
        let buf = arena.raw_buf_mut();
        unsafe {
            let p = buf.as_mut_ptr().add(logits_off) as *mut f32;
            let inputs = [0.1f32, 5.0, 0.3, 0.4]; // argmax = 1
            for (i, &v) in inputs.iter().enumerate() {
                *p.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let token = unsafe {
            let p = arena.raw_buf().as_ptr().add(s_off) as *const f32;
            *p as usize
        };
        assert_eq!(token, 1);
    }

    /// Plan #44: cumsum primitive parity vs. naive scan.
    #[test]
    fn cumsum_inclusive_matches_naive() {
        let f = DType::F32;
        let mut g = Graph::new("cumsum");
        let x = g.input("x", Shape::new(&[2, 4], f));
        let cs = g.cumsum(x, -1, false, Shape::new(&[2, 4], f));
        g.set_outputs(vec![cs]);
        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        // Cache offsets up-front so we can drop the immutable borrow.
        let x_off = arena.byte_offset(x);
        let out_off = arena.byte_offset(cs);
        let buf = arena.raw_buf_mut();
        unsafe {
            let p = buf.as_mut_ptr().add(x_off) as *mut f32;
            let inputs = [1.0f32, 2.0, 3.0, 4.0, 10.0, 20.0, 30.0, 40.0];
            for (i, &v) in inputs.iter().enumerate() {
                *p.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());

        let out: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(out_off) as *const f32;
            (0..8).map(|i| *p.add(i)).collect()
        };
        assert_eq!(out, vec![1.0, 3.0, 6.0, 10.0, 10.0, 30.0, 60.0, 100.0]);
    }

    /// Plan #46 deep: Narrow×3 → Attention fusion. The three QKV
    /// narrows that BERT/Nomic emit on the unfused (batch*seq > 64)
    /// path collapse into a single strided-Attention thunk.
    #[test]
    fn narrow_attention_fuses_in_unfused_path() {
        let f = DType::F32;
        let mut g = Graph::new("nattn_fuse");
        // batch*seq = 8*16 = 128 > 64 so FusedAttnBlock skips.
        let qkv = g.input("qkv", Shape::new(&[8, 16, 192], f)); // 3*64 = 192
        let mask = g.input("mask", Shape::new(&[8, 16], f));
        let q = g.narrow_(qkv, 2, 0, 64);
        let k = g.narrow_(qkv, 2, 64, 64);
        let v = g.narrow_(qkv, 2, 128, 64);
        let attn = g.attention(q, k, v, mask, 4, 16, Shape::new(&[8, 16, 64], f));
        g.set_outputs(vec![attn]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        let mut narrow_count = 0;
        let mut attn_strides: Option<(u32, u32, u32)> = None;
        for t in &sched.thunks {
            match t {
                Thunk::Narrow { .. } => narrow_count += 1,
                Thunk::Attention {
                    q_row_stride,
                    k_row_stride,
                    v_row_stride,
                    ..
                } => attn_strides = Some((*q_row_stride, *k_row_stride, *v_row_stride)),
                _ => {}
            }
        }
        // After fusion the 3 narrows are gone; Attention now walks the
        // QKV with parent row stride = 192 (3 × 64) on all three inputs.
        assert_eq!(
            narrow_count, 0,
            "Narrow×3→Attention fusion should eliminate all 3 narrows; saw {narrow_count}"
        );
        assert_eq!(
            attn_strides,
            Some((192, 192, 192)),
            "Attention should walk Q/K/V with parent row stride 192"
        );
    }

    /// Regression: when the QKV→Narrow×3→RoPE×2→Attention→OutProj chain
    /// collapses into a single `FusedAttnBlock` (small batch·seq), the fused
    /// kernel must honor a **causal** mask. It previously applied only the
    /// per-key padding mask and dropped `mask_kind`, so a later token leaked
    /// into earlier positions (decoder attention attended to the future).
    #[test]
    fn fused_attn_block_respects_causal_mask() {
        let f = DType::F32;
        let (s, d, nh, dh) = (5usize, 8usize, 2usize, 4usize);
        let half = dh / 2;

        let mut g = Graph::new("fused_causal");
        let hidden = g.input("hidden", Shape::new(&[s, d], f));
        let wqkv = g.input("wqkv", Shape::new(&[d, 3 * d], f));
        let wo = g.input("wo", Shape::new(&[d, d], f));
        let cos = g.input("cos", Shape::new(&[s, half], f));
        let sin = g.input("sin", Shape::new(&[s, half], f));
        let qkv = g.matmul(hidden, wqkv, Shape::new(&[s, 3 * d], f));
        let q = g.narrow_(qkv, 1, 0, d);
        let k = g.narrow_(qkv, 1, d, d);
        let v = g.narrow_(qkv, 1, 2 * d, d);
        let q3 = g.reshape(q, vec![1, s as i64, d as i64], Shape::new(&[1, s, d], f));
        let k3 = g.reshape(k, vec![1, s as i64, d as i64], Shape::new(&[1, s, d], f));
        let v3 = g.reshape(v, vec![1, s as i64, d as i64], Shape::new(&[1, s, d], f));
        let qr = g.rope(q3, cos, sin, dh);
        let kr = g.rope(k3, cos, sin, dh);
        let attn = g.attention_kind(
            qr,
            kr,
            v3,
            nh,
            dh,
            rlx_ir::op::MaskKind::Causal,
            Shape::new(&[1, s, d], f),
        );
        let a2 = g.reshape(attn, vec![s as i64, d as i64], Shape::new(&[s, d], f));
        let out = g.matmul(a2, wo, Shape::new(&[s, d], f));
        g.set_outputs(vec![out]);

        // The fusion must actually fire AND carry the causal kind.
        let plan = rlx_opt::memory::plan_memory(&g);
        let arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);
        assert!(
            sched.thunks.iter().any(|t| matches!(
                t,
                Thunk::FusedAttnBlock {
                    mask_kind: rlx_ir::op::MaskKind::Causal,
                    ..
                }
            )),
            "expected a FusedAttnBlock carrying MaskKind::Causal"
        );

        let wqkv_d: Vec<f32> = (0..d * 3 * d)
            .map(|i| ((i % 7) as f32 - 3.0) * 0.05)
            .collect();
        let wo_d: Vec<f32> = (0..d * d).map(|i| ((i % 5) as f32 - 2.0) * 0.05).collect();
        let mut cos_d = vec![0f32; s * half];
        let mut sin_d = vec![0f32; s * half];
        for p in 0..s {
            for i in 0..half {
                let fr = 1.0f32 / 10000f32.powf(2.0 * i as f32 / dh as f32);
                cos_d[p * half + i] = (p as f32 * fr).cos();
                sin_d[p * half + i] = (p as f32 * fr).sin();
            }
        }
        let base_h: Vec<f32> = (0..s * d).map(|i| ((i % 11) as f32 - 5.0) * 0.1).collect();
        let run = |hin: &[f32]| {
            run_graph(
                &g,
                &[
                    (hidden, hin),
                    (wqkv, &wqkv_d),
                    (wo, &wo_d),
                    (cos, &cos_d),
                    (sin, &sin_d),
                ],
                out,
                s * d,
            )
        };
        let a = run(&base_h);
        // Perturb only the LAST position's hidden row.
        let mut changed = base_h.clone();
        for j in 0..d {
            changed[4 * d + j] += 1.0;
        }
        let b = run(&changed);
        for pos in 0..4 {
            for j in 0..d {
                let i = pos * d + j;
                assert!(
                    (a[i] - b[i]).abs() < 1e-5,
                    "causal leak at pos {pos}: {} vs {}",
                    a[i],
                    b[i]
                );
            }
        }
        let last: f32 = (0..d).map(|j| (a[4 * d + j] - b[4 * d + j]).abs()).sum();
        assert!(last > 1e-4, "last position must react to its own token");
    }

    // ── Backward / training op parity tests ────────────────────
    //
    // Strategy: build a graph that contains exactly the backward op
    // under test (plus its inputs as graph Inputs), execute, and
    // compare against a hand-rolled scalar reference. For
    // Conv2dBackwardInput we additionally check against the numerical
    // gradient of the forward Conv2D — that's the gold-standard test
    // that validates the math, not just consistency between two
    // implementations of the same formula.

    fn run_graph(
        g: &Graph,
        inputs: &[(NodeId, &[f32])],
        out_id: NodeId,
        out_len: usize,
    ) -> Vec<f32> {
        let plan = rlx_opt::memory::plan_memory(g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(g, &arena);
        for &(id, data) in inputs {
            let off = arena.byte_offset(id);
            let buf = arena.raw_buf_mut();
            unsafe {
                let p = buf.as_mut_ptr().add(off) as *mut f32;
                for (i, &v) in data.iter().enumerate() {
                    *p.add(i) = v;
                }
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let off = arena.byte_offset(out_id);
        unsafe {
            let p = arena.raw_buf().as_ptr().add(off) as *const f32;
            (0..out_len).map(|i| *p.add(i)).collect()
        }
    }

    #[test]
    fn relu_backward_matches_mask() {
        let f = DType::F32;
        let len = 7usize;
        let x: Vec<f32> = vec![-2.0, -0.1, 0.0, 0.1, 1.0, 3.0, -5.0];
        let dy: Vec<f32> = vec![0.5, 1.5, 2.5, -0.7, 4.0, -1.0, 9.0];

        let mut g = Graph::new("relu_bw");
        let xn = g.input("x", Shape::new(&[len], f));
        let dyn_ = g.input("dy", Shape::new(&[len], f));
        let dx = g.relu_backward(xn, dyn_);
        g.set_outputs(vec![dx]);

        let actual = run_graph(&g, &[(xn, &x), (dyn_, &dy)], dx, len);
        // Reference: gradient is dy where x>0 strictly, else 0.
        // (zero is not "positive" — the forward applied max(0, x), and at
        // x=0 the subgradient could be anything in [0, dy]; we pick 0.)
        let expected: Vec<f32> = x
            .iter()
            .zip(&dy)
            .map(|(&xi, &dyi)| if xi > 0.0 { dyi } else { 0.0 })
            .collect();
        for (a, e) in actual.iter().zip(&expected) {
            assert!((a - e).abs() < 1e-6, "relu_bw mismatch: {a} vs {e}");
        }
    }

    #[test]
    fn maxpool2d_backward_routes_to_argmax() {
        let f = DType::F32;
        // [N=1, C=1, H=4, W=4] → 2x2 max-pool stride 2 → [1,1,2,2].
        let x: Vec<f32> = vec![
            1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
        ];
        // Argmax of each 2x2 window:
        //   (0,0)→6 (idx 5), (0,1)→8 (idx 7),
        //   (1,0)→14(idx 13),(1,1)→16(idx 15).
        let dy: Vec<f32> = vec![0.5, 1.0, 2.0, 4.0];

        let mut g = Graph::new("maxpool_bw");
        let xn = g.input("x", Shape::new(&[1, 1, 4, 4], f));
        let dyn_ = g.input("dy", Shape::new(&[1, 1, 2, 2], f));
        let dx = g.maxpool2d_backward(xn, dyn_, vec![2, 2], vec![2, 2], vec![0, 0]);
        g.set_outputs(vec![dx]);

        let actual = run_graph(&g, &[(xn, &x), (dyn_, &dy)], dx, 16);
        let mut expected = vec![0f32; 16];
        expected[5] = 0.5;
        expected[7] = 1.0;
        expected[13] = 2.0;
        expected[15] = 4.0;
        for (i, (a, e)) in actual.iter().zip(&expected).enumerate() {
            assert!((a - e).abs() < 1e-6, "maxpool_bw[{i}] mismatch: {a} vs {e}");
        }
    }

    #[test]
    fn conv2d_backward_input_matches_numerical_gradient() {
        use rlx_ir::Philox4x32;
        // Small enough to numerically differentiate exhaustively but
        // big enough to exercise stride/padding edge cases.
        let n = 1usize;
        let c_in = 2usize;
        let h = 4usize;
        let w = 4usize;
        let c_out = 3usize;
        let kh = 3usize;
        let kw = 3usize;
        let ph = 1usize;
        let pw = 1usize;
        let sh = 1usize;
        let sw = 1usize;
        // Output dims with padding=1, stride=1: same as input.
        let h_out = (h + 2 * ph - kh) / sh + 1;
        let w_out = (w + 2 * pw - kw) / sw + 1;
        assert_eq!(h_out, 4);
        assert_eq!(w_out, 4);

        let mut rng = Philox4x32::new(7);
        let mut x = vec![0f32; n * c_in * h * w];
        rng.fill_normal(&mut x);
        let mut wt = vec![0f32; c_out * c_in * kh * kw];
        rng.fill_normal(&mut wt);
        let mut dy = vec![0f32; n * c_out * h_out * w_out];
        rng.fill_normal(&mut dy);

        // Analytical: Conv2dBackwardInput on (dy, w).
        let f = DType::F32;
        let mut g = Graph::new("conv_bwi");
        let dy_in = g.input("dy", Shape::new(&[n, c_out, h_out, w_out], f));
        let w_in = g.input("w", Shape::new(&[c_out, c_in, kh, kw], f));
        let dx = g.conv2d_backward_input(
            dy_in,
            w_in,
            Shape::new(&[n, c_in, h, w], f),
            vec![kh, kw],
            vec![sh, sw],
            vec![ph, pw],
            vec![1, 1],
            1,
        );
        g.set_outputs(vec![dx]);
        let analytical = run_graph(&g, &[(dy_in, &dy), (w_in, &wt)], dx, n * c_in * h * w);

        // Numerical: for each x[i], finite-difference forward conv twice.
        // Forward: y[j] = sum over filter window of w * x ; dot(dy, y) is
        // the scalar we differentiate. Then dx[i] = ∂(dot(dy, y))/∂x[i].
        let forward = |x: &[f32]| -> Vec<f32> {
            let mut out = vec![0f32; n * c_out * h_out * w_out];
            for ni in 0..n {
                for co in 0..c_out {
                    for ho in 0..h_out {
                        for wo in 0..w_out {
                            let mut acc = 0f32;
                            for ci in 0..c_in {
                                for ki in 0..kh {
                                    for kj in 0..kw {
                                        let hi = ho * sh + ki;
                                        let wi = wo * sw + kj;
                                        if hi < ph || wi < pw {
                                            continue;
                                        }
                                        let hi = hi - ph;
                                        let wi = wi - pw;
                                        if hi >= h || wi >= w {
                                            continue;
                                        }
                                        let xv = x[((ni * c_in) + ci) * h * w + hi * w + wi];
                                        let wv = wt[((co * c_in) + ci) * kh * kw + ki * kw + kj];
                                        acc += xv * wv;
                                    }
                                }
                            }
                            out[((ni * c_out) + co) * h_out * w_out + ho * w_out + wo] = acc;
                        }
                    }
                }
            }
            out
        };
        let dot = |a: &[f32], b: &[f32]| -> f32 { a.iter().zip(b).map(|(&u, &v)| u * v).sum() };
        let eps = 1e-3f32;
        let mut numerical = vec![0f32; x.len()];
        for i in 0..x.len() {
            let saved = x[i];
            x[i] = saved + eps;
            let plus = dot(&forward(&x), &dy);
            x[i] = saved - eps;
            let minus = dot(&forward(&x), &dy);
            x[i] = saved;
            numerical[i] = (plus - minus) / (2.0 * eps);
        }
        for (i, (a, n)) in analytical.iter().zip(&numerical).enumerate() {
            // f32 + eps=1e-3 numerical grad → ~1e-3 absolute is realistic.
            assert!(
                (a - n).abs() < 5e-3,
                "conv_bw_input[{i}]: analytical {a} vs numerical {n}"
            );
        }
    }

    #[test]
    fn conv2d_backward_weight_matches_numerical_gradient() {
        use rlx_ir::Philox4x32;
        let n = 2usize;
        let c_in = 2usize;
        let h = 4usize;
        let w = 4usize;
        let c_out = 2usize;
        let kh = 3usize;
        let kw = 3usize;
        let ph = 0usize;
        let pw = 0usize;
        let sh = 1usize;
        let sw = 1usize;
        let h_out = (h + 2 * ph - kh) / sh + 1;
        let w_out = (w + 2 * pw - kw) / sw + 1;

        let mut rng = Philox4x32::new(11);
        let mut x = vec![0f32; n * c_in * h * w];
        rng.fill_normal(&mut x);
        let mut wt = vec![0f32; c_out * c_in * kh * kw];
        rng.fill_normal(&mut wt);
        let mut dy = vec![0f32; n * c_out * h_out * w_out];
        rng.fill_normal(&mut dy);

        let f = DType::F32;
        let mut g = Graph::new("conv_bww");
        let xn = g.input("x", Shape::new(&[n, c_in, h, w], f));
        let dyn_ = g.input("dy", Shape::new(&[n, c_out, h_out, w_out], f));
        let dwn = g.conv2d_backward_weight(
            xn,
            dyn_,
            Shape::new(&[c_out, c_in, kh, kw], f),
            vec![kh, kw],
            vec![sh, sw],
            vec![ph, pw],
            vec![1, 1],
            1,
        );
        g.set_outputs(vec![dwn]);
        let analytical = run_graph(&g, &[(xn, &x), (dyn_, &dy)], dwn, c_out * c_in * kh * kw);

        let forward = |wt: &[f32]| -> Vec<f32> {
            let mut out = vec![0f32; n * c_out * h_out * w_out];
            for ni in 0..n {
                for co in 0..c_out {
                    for ho in 0..h_out {
                        for wo in 0..w_out {
                            let mut acc = 0f32;
                            for ci in 0..c_in {
                                for ki in 0..kh {
                                    for kj in 0..kw {
                                        let hi = ho + ki;
                                        let wi = wo + kj;
                                        let xv = x[((ni * c_in) + ci) * h * w + hi * w + wi];
                                        let wv = wt[((co * c_in) + ci) * kh * kw + ki * kw + kj];
                                        acc += xv * wv;
                                    }
                                }
                            }
                            out[((ni * c_out) + co) * h_out * w_out + ho * w_out + wo] = acc;
                        }
                    }
                }
            }
            out
        };
        let dot = |a: &[f32], b: &[f32]| -> f32 { a.iter().zip(b).map(|(&u, &v)| u * v).sum() };
        let eps = 1e-3f32;
        let mut numerical = vec![0f32; wt.len()];
        for i in 0..wt.len() {
            let saved = wt[i];
            wt[i] = saved + eps;
            let plus = dot(&forward(&wt), &dy);
            wt[i] = saved - eps;
            let minus = dot(&forward(&wt), &dy);
            wt[i] = saved;
            numerical[i] = (plus - minus) / (2.0 * eps);
        }
        for (i, (a, n)) in analytical.iter().zip(&numerical).enumerate() {
            assert!(
                (a - n).abs() < 5e-3,
                "conv_bw_weight[{i}]: analytical {a} vs numerical {n}"
            );
        }
    }

    #[test]
    fn softmax_cross_entropy_matches_reference() {
        let f = DType::F32;
        let logits: Vec<f32> = vec![
            1.0, 2.0, 3.0, // row 0: max=3 (idx 2)
            -1.0, 0.0, 4.0, // row 1: max=4 (idx 2)
            5.0, 5.0, 5.0, // row 2: uniform
        ];
        let labels: Vec<f32> = vec![2.0, 0.0, 1.0];

        let mut g = Graph::new("sce");
        let lg = g.input("logits", Shape::new(&[3, 3], f));
        let lb = g.input("labels", Shape::new(&[3], f));
        let loss = g.softmax_cross_entropy_with_logits(lg, lb);
        g.set_outputs(vec![loss]);
        let actual = run_graph(&g, &[(lg, &logits), (lb, &labels)], loss, 3);

        // Reference per-row: -log(softmax(row)[label]).
        let mut expected = vec![0f32; 3];
        for ni in 0..3 {
            let row = &logits[ni * 3..(ni + 1) * 3];
            let m = row.iter().fold(f32::NEG_INFINITY, |a, &v| a.max(v));
            let sum: f32 = row.iter().map(|&v| (v - m).exp()).sum();
            let lse = m + sum.ln();
            let label_idx = labels[ni] as usize;
            expected[ni] = lse - row[label_idx];
        }
        for (i, (a, e)) in actual.iter().zip(&expected).enumerate() {
            assert!((a - e).abs() < 1e-5, "sce loss[{i}]: {a} vs {e}");
        }
    }

    #[test]
    fn softmax_cross_entropy_backward_matches_numerical_gradient() {
        use rlx_ir::Philox4x32;
        let n = 4usize;
        let c = 5usize;
        let mut rng = Philox4x32::new(23);
        let mut logits = vec![0f32; n * c];
        rng.fill_normal(&mut logits);
        let labels: Vec<f32> = (0..n).map(|i| (i % c) as f32).collect();
        let mut d_loss = vec![0f32; n];
        rng.fill_normal(&mut d_loss);

        let f = DType::F32;
        let mut g = Graph::new("sce_bw");
        let lg = g.input("logits", Shape::new(&[n, c], f));
        let lb = g.input("labels", Shape::new(&[n], f));
        let dl = g.input("d_loss", Shape::new(&[n], f));
        let dlogits = g.softmax_cross_entropy_backward(lg, lb, dl);
        g.set_outputs(vec![dlogits]);
        let analytical = run_graph(
            &g,
            &[(lg, &logits), (lb, &labels), (dl, &d_loss)],
            dlogits,
            n * c,
        );

        // Numerical: differentiate dot(d_loss, sce_loss(logits)) w.r.t. each logit.
        let sce_loss = |logits: &[f32]| -> Vec<f32> {
            let mut out = vec![0f32; n];
            for ni in 0..n {
                let row = &logits[ni * c..(ni + 1) * c];
                let m = row.iter().fold(f32::NEG_INFINITY, |a, &v| a.max(v));
                let sum: f32 = row.iter().map(|&v| (v - m).exp()).sum();
                out[ni] = (m + sum.ln()) - row[labels[ni] as usize];
            }
            out
        };
        let dot = |a: &[f32], b: &[f32]| a.iter().zip(b).map(|(&u, &v)| u * v).sum::<f32>();
        let eps = 1e-3f32;
        let mut numerical = vec![0f32; logits.len()];
        for i in 0..logits.len() {
            let saved = logits[i];
            logits[i] = saved + eps;
            let plus = dot(&sce_loss(&logits), &d_loss);
            logits[i] = saved - eps;
            let minus = dot(&sce_loss(&logits), &d_loss);
            logits[i] = saved;
            numerical[i] = (plus - minus) / (2.0 * eps);
        }
        for (i, (a, num)) in analytical.iter().zip(&numerical).enumerate() {
            assert!(
                (a - num).abs() < 5e-3,
                "sce_bw[{i}]: analytical {a} vs numerical {num}"
            );
        }
    }

    // ── End-to-end autodiff parity tests ──────────────────────
    //
    // Build a forward graph, run `grad_with_loss` to produce a graph
    // that emits [loss, gradients...], execute it through rlx-cpu,
    // and compare each gradient to a finite-difference estimate
    // produced by re-running the forward graph with each parameter
    // entry perturbed. f32 + ε=1e-3 puts the tolerance floor around
    // 5e-3 absolute error.

    /// Initialize Op::Constant slots in the arena with their literal
    /// data. Mirrors the loop in rlx_runtime::backend (which serves
    /// the same role for production runs).
    fn fill_constants_into_arena(graph: &Graph, arena: &mut crate::arena::Arena) {
        for node in graph.nodes() {
            if let Op::Constant { data } = &node.op
                && arena.has_buffer(node.id)
                && !data.is_empty()
            {
                let buf = arena.slice_mut(node.id);
                let n_floats = data.len() / 4;
                let n = buf.len().min(n_floats);
                for i in 0..n {
                    let bytes = [
                        data[i * 4],
                        data[i * 4 + 1],
                        data[i * 4 + 2],
                        data[i * 4 + 3],
                    ];
                    buf[i] = f32::from_le_bytes(bytes);
                }
            }
        }
    }

    /// Compile + arena-prep helper for these tests. Returns the
    /// schedule and a populated arena. `seed_inputs` writes f32 input
    /// data into the arena slot for each (NodeId, &[f32]) pair.
    fn prepare(
        graph: &Graph,
        seed_inputs: &[(NodeId, &[f32])],
    ) -> (ThunkSchedule, crate::arena::Arena) {
        let plan = rlx_opt::memory::plan_memory(graph);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(graph, &arena);
        fill_constants_into_arena(graph, &mut arena);
        for &(id, data) in seed_inputs {
            let off = arena.byte_offset(id);
            let buf = arena.raw_buf_mut();
            unsafe {
                let p = buf.as_mut_ptr().add(off) as *mut f32;
                for (i, &v) in data.iter().enumerate() {
                    *p.add(i) = v;
                }
            }
        }
        (sched, arena)
    }

    fn read_arena(arena: &crate::arena::Arena, id: NodeId, len: usize) -> Vec<f32> {
        let off = arena.byte_offset(id);
        unsafe {
            let p = arena.raw_buf().as_ptr().add(off) as *const f32;
            (0..len).map(|i| *p.add(i)).collect()
        }
    }

    fn write_arena(arena: &mut crate::arena::Arena, id: NodeId, data: &[f32]) {
        let off = arena.byte_offset(id);
        let buf = arena.raw_buf_mut();
        unsafe {
            let p = buf.as_mut_ptr().add(off) as *mut f32;
            for (i, &v) in data.iter().enumerate() {
                *p.add(i) = v;
            }
        }
    }

    /// f64 sibling of `prepare`. Writes f64 input data into the arena.
    fn prepare_f64(
        graph: &Graph,
        seed_inputs: &[(NodeId, &[f64])],
    ) -> (ThunkSchedule, crate::arena::Arena) {
        let plan = rlx_opt::memory::plan_memory(graph);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(graph, &arena);
        fill_constants_into_arena(graph, &mut arena);
        for &(id, data) in seed_inputs {
            let off = arena.byte_offset(id);
            let buf = arena.raw_buf_mut();
            unsafe {
                let p = buf.as_mut_ptr().add(off) as *mut f64;
                for (i, &v) in data.iter().enumerate() {
                    *p.add(i) = v;
                }
            }
        }
        (sched, arena)
    }

    fn read_arena_f64(arena: &crate::arena::Arena, id: NodeId, len: usize) -> Vec<f64> {
        let off = arena.byte_offset(id);
        unsafe {
            let p = arena.raw_buf().as_ptr().add(off) as *const f64;
            (0..len).map(|i| *p.add(i)).collect()
        }
    }

    /// End-to-end f64 DenseSolve through the full compile + execute
    /// path. Validates: IR shape inference, memory planner f64 sizing,
    /// arena f64 accessors, Thunk::DenseSolveF64 lowering, executor
    /// dispatch, Accelerate dgesv FFI.
    ///
    /// System:
    ///   A = [[2, 1],
    ///        [1, 3]]   b = [5, 10]
    ///   ⇒  x = [1, 3]   (verified by hand)
    #[test]
    fn dense_solve_f64_end_to_end() {
        let mut g = Graph::new("solve_e2e");
        let a = g.input("A", Shape::new(&[2, 2], DType::F64));
        let b = g.input("b", Shape::new(&[2], DType::F64));
        let x = g.dense_solve(a, b, Shape::new(&[2], DType::F64));
        g.set_outputs(vec![x]);

        let a_data = [2.0, 1.0, 1.0, 3.0_f64];
        let b_data = [5.0, 10.0_f64];
        let (sched, mut arena) = prepare_f64(&g, &[(a, &a_data), (b, &b_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());

        let got = read_arena_f64(&arena, x, 2);
        let want = [1.0, 3.0_f64];
        for i in 0..2 {
            assert!(
                (got[i] - want[i]).abs() < 1e-12,
                "x[{i}] = {} (expected {})",
                got[i],
                want[i]
            );
        }
    }

    /// Scaled-up f64 DenseSolve — tridiagonal Laplacian-shape (typical
    /// MNA structure for a passive RC mesh in Circulax). Validates
    /// that the solve scales beyond the trivial 2×2 and that the
    /// row-major ↔ col-major dance in `dgesv` is correct for the
    /// general case.
    #[test]
    fn dense_solve_f64_5x5_laplacian() {
        let n = 5usize;
        let mut g = Graph::new("solve_5x5");
        let a = g.input("A", Shape::new(&[n, n], DType::F64));
        let b = g.input("b", Shape::new(&[n], DType::F64));
        let x = g.dense_solve(a, b, Shape::new(&[n], DType::F64));
        g.set_outputs(vec![x]);

        // 1-D Laplacian: 2 on diagonal, -1 on off-diagonals, 0 elsewhere.
        let mut a_data = vec![0.0_f64; n * n];
        for i in 0..n {
            a_data[i * n + i] = 2.0;
            if i > 0 {
                a_data[i * n + (i - 1)] = -1.0;
            }
            if i + 1 < n {
                a_data[i * n + (i + 1)] = -1.0;
            }
        }
        let b_data: Vec<f64> = (0..n).map(|i| (i + 1) as f64).collect();
        let (sched, mut arena) = prepare_f64(&g, &[(a, &a_data), (b, &b_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());

        let got = read_arena_f64(&arena, x, n);
        // Verify A·x ≈ b by computing the residual.
        let mut residual = vec![0.0_f64; n];
        for i in 0..n {
            for j in 0..n {
                residual[i] += a_data[i * n + j] * got[j];
            }
        }
        for i in 0..n {
            assert!(
                (residual[i] - b_data[i]).abs() < 1e-10,
                "row {i}: residual {} vs b {}",
                residual[i],
                b_data[i]
            );
        }
    }

    /// Hello Resistor: end-to-end f64 gradient through a dense solve.
    ///
    /// Forward:
    ///   A      : Param  [N, N]   f64
    ///   b      : Input  [N]      f64
    ///   x      = solve(A, b)            (DenseSolve)
    ///   loss   = sum(x)                 (Reduce::Sum)
    ///
    /// Backward (via grad_with_loss):
    ///   ones [N] = expand(d_output, [N])      (Reduce::Sum VJP)
    ///   dx_int   = solve(Aᵀ, ones)             (DenseSolve VJP step 1)
    ///   dA       = -outer(dx_int, x)           (DenseSolve VJP step 2)
    ///   db       = dx_int                       (DenseSolve VJP step 3)
    ///
    /// Closed form: with loss = sum(solve(A, b)) = ones·x and
    /// implicit-function calculus, db = (Aᵀ)⁻¹·ones, dA = -db ⊗ x.
    /// We verify this against the autodiff-emitted graph's output and
    /// against a finite-difference baseline.
    #[test]
    fn hello_resistor_gradient_end_to_end() {
        use rlx_opt::autodiff::grad_with_loss;
        let n = 3usize;

        // ── Build forward graph ──
        let mut g = Graph::new("hello_resistor");
        let a = g.param("A", Shape::new(&[n, n], DType::F64));
        let b = g.input("b", Shape::new(&[n], DType::F64));
        let x = g.dense_solve(a, b, Shape::new(&[n], DType::F64));
        let loss = g.reduce(
            x,
            ReduceOp::Sum,
            vec![0],
            false,
            Shape::new(&[1], DType::F64),
        );
        g.set_outputs(vec![loss]);

        // ── Run reverse-mode AD ──
        let bwd = grad_with_loss(&g, &[a, b]);
        assert_eq!(bwd.outputs.len(), 3, "expect [loss, dA, db]");

        // ── Locate the inputs the bwd graph still needs from us ──
        // grad_with_loss copies forward nodes into bwd, so A/b/d_output
        // appear under their original names. Find them by name.
        let find_by_name = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    rlx_ir::Op::Input { name } => Some(name.as_str()),
                    rlx_ir::Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want:?} in bwd graph");
        };
        let a_bwd = find_by_name(&bwd, "A");
        let b_bwd = find_by_name(&bwd, "b");
        let d_out_bwd = find_by_name(&bwd, "d_output");

        // ── Test data ──
        // A = [[2,1,0],[1,3,1],[0,1,2]]   (SPD tridiagonal, well-conditioned)
        // b = [1,2,3]
        let a_data = [2.0, 1.0, 0.0, 1.0, 3.0, 1.0, 0.0, 1.0, 2.0_f64];
        let b_data = [1.0, 2.0, 3.0_f64];
        let d_output = [1.0_f64]; // ∂loss/∂loss

        // ── Compile + execute backward graph ──
        let (sched, mut arena) = prepare_f64(
            &bwd,
            &[(a_bwd, &a_data), (b_bwd, &b_data), (d_out_bwd, &d_output)],
        );
        execute_thunks(&sched, arena.raw_buf_mut());

        let loss_out = read_arena_f64(&arena, bwd.outputs[0], 1);
        let da_out = read_arena_f64(&arena, bwd.outputs[1], n * n);
        let db_out = read_arena_f64(&arena, bwd.outputs[2], n);

        // ── Closed-form reference ──
        // x = A⁻¹ b ; loss = sum(x).
        let x_ref = {
            let mut a = a_data;
            let mut b = b_data;
            let info = crate::blas::dgesv(&mut a, &mut b, n, 1);
            assert_eq!(info, 0);
            b
        };
        let loss_ref: f64 = x_ref.iter().sum();
        // db = (Aᵀ)⁻¹ · 1
        let db_ref = {
            let mut at = [0.0_f64; 9];
            for i in 0..n {
                for j in 0..n {
                    at[i * n + j] = a_data[j * n + i];
                }
            }
            let mut ones = [1.0_f64; 3];
            let info = crate::blas::dgesv(&mut at, &mut ones, n, 1);
            assert_eq!(info, 0);
            ones
        };
        // dA = -outer(db, x) ; dA[i,j] = -db[i] * x[j]
        let mut da_ref = [0.0_f64; 9];
        for i in 0..n {
            for j in 0..n {
                da_ref[i * n + j] = -db_ref[i] * x_ref[j];
            }
        }

        // ── Assertions vs analytic answer ──
        assert!(
            (loss_out[0] - loss_ref).abs() < 1e-10,
            "loss: got {}, want {}",
            loss_out[0],
            loss_ref
        );
        for i in 0..n {
            assert!(
                (db_out[i] - db_ref[i]).abs() < 1e-10,
                "db[{i}]: got {}, want {}",
                db_out[i],
                db_ref[i]
            );
        }
        for i in 0..n * n {
            assert!(
                (da_out[i] - da_ref[i]).abs() < 1e-10,
                "dA[{i}]: got {}, want {}",
                da_out[i],
                da_ref[i]
            );
        }

        // ── Cross-check vs finite differences on db (a few entries) ──
        // ∂loss/∂b[k] ≈ (loss(b + h·e_k) - loss(b - h·e_k)) / (2h).
        let h = 1e-6_f64;
        for k in 0..n {
            let mut bp = b_data;
            bp[k] += h;
            let mut bm = b_data;
            bm[k] -= h;
            let lp = {
                let mut ac = a_data;
                let info = crate::blas::dgesv(&mut ac, &mut bp, n, 1);
                assert_eq!(info, 0);
                bp.iter().sum::<f64>()
            };
            let lm = {
                let mut ac = a_data;
                let info = crate::blas::dgesv(&mut ac, &mut bm, n, 1);
                assert_eq!(info, 0);
                bm.iter().sum::<f64>()
            };
            let fd = (lp - lm) / (2.0 * h);
            assert!(
                (db_out[k] - fd).abs() < 1e-7,
                "FD mismatch on db[{k}]: AD={} FD={}",
                db_out[k],
                fd
            );
        }
    }

    /// Smallest possible Op::Scan basic test: geometric growth.
    /// init = [1, 1, 1] f64, body = (x → x + 0.1·x) = (x → 1.1·x),
    /// length = 10. Final carry must equal init·(1.1)^10 ≈ 2.5937…
    /// to f64 precision.
    #[test]
    fn scan_geometric_growth_f64() {
        let n = 3usize;
        let length = 10u32;

        // Body: (x) → x + 0.1·x. One Input, one output, same shape/dtype.
        let mut body = Graph::new("scan_body");
        let x = body.input("carry", Shape::new(&[n], DType::F64));
        let scale_bytes: Vec<u8> = (0..n).flat_map(|_| 0.1_f64.to_le_bytes()).collect();
        let scale = body.add_node(
            Op::Constant { data: scale_bytes },
            vec![],
            Shape::new(&[n], DType::F64),
        );
        let scaled = body.binary(BinaryOp::Mul, x, scale, Shape::new(&[n], DType::F64));
        let next = body.binary(BinaryOp::Add, x, scaled, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        // Outer graph: scan(init, body, length).
        let mut g = Graph::new("scan_outer");
        let init = g.input("init", Shape::new(&[n], DType::F64));
        let final_carry = g.scan(init, body, length);
        g.set_outputs(vec![final_carry]);

        let init_data = vec![1.0_f64; n];
        let (sched, mut arena) = prepare_f64(&g, &[(init, &init_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let got = read_arena_f64(&arena, final_carry, n);
        let want: f64 = 1.1_f64.powi(length as i32);
        for i in 0..n {
            assert!(
                (got[i] - want).abs() < 1e-12,
                "got[{i}] = {} want {}",
                got[i],
                want
            );
        }
    }

    /// Per-step xs scan: cumulative-sum.
    ///   carry_0 = init
    ///   carry_{t+1} = carry_t + xs\[t\]
    ///   final = sum_{t<length} xs\[t\] + init
    /// Body has 2 inputs (carry, x_t) in that NodeId order; one output
    /// (next carry). Validates the per-step-input plumbing end-to-end.
    #[test]
    fn scan_with_xs_cumulative_sum() {
        let n = 3usize;
        let length = 4u32;

        let mut body = Graph::new("cumsum_body");
        // carry must come first in NodeId order — declare it first.
        let carry = body.input("carry", Shape::new(&[n], DType::F64));
        let x_t = body.input("x_t", Shape::new(&[n], DType::F64));
        let next = body.binary(BinaryOp::Add, carry, x_t, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("cumsum_outer");
        let init = g.input("init", Shape::new(&[n], DType::F64));
        let xs = g.input("xs", Shape::new(&[length as usize, n], DType::F64));
        let final_carry = g.scan_with_xs(init, &[xs], body, length);
        g.set_outputs(vec![final_carry]);

        let init_data = vec![0.0_f64; n];
        let xs_data: Vec<f64> = (0..length as usize * n).map(|i| (i + 1) as f64).collect(); // 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
        let (sched, mut arena) = prepare_f64(&g, &[(init, &init_data), (xs, &xs_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let got = read_arena_f64(&arena, final_carry, n);

        // Reference: column-wise sum of xs rows + init. With our row-major
        // layout, column j of xs is xs_data[j], xs_data[n+j], xs_data[2n+j], ...
        // (per-step row at offset t*n contributes element j to slot j).
        let mut want = init_data.clone();
        for t in 0..length as usize {
            for j in 0..n {
                want[j] += xs_data[t * n + j];
            }
        }
        for i in 0..n {
            assert!(
                (got[i] - want[i]).abs() < 1e-12,
                "got[{i}] = {} want {}",
                got[i],
                want[i]
            );
        }
    }

    /// Per-step xs scan composing with DenseSolve — Circulax-shaped:
    ///   carry_{t+1} = solve(M, carry_t + xs\[t\])
    /// Models a Backward-Euler step driven by a time-varying source.
    #[test]
    fn scan_with_xs_be_with_drive() {
        let n = 3usize;
        let length = 4u32;
        let dt = 0.1_f64;

        let mut m_data = vec![0.0_f64; n * n];
        for i in 0..n {
            m_data[i * n + i] = 1.0 + dt * 2.0;
            if i > 0 {
                m_data[i * n + (i - 1)] = -dt;
            }
            if i + 1 < n {
                m_data[i * n + (i + 1)] = -dt;
            }
        }
        let m_bytes: Vec<u8> = m_data.iter().flat_map(|x| x.to_le_bytes()).collect();

        let mut body = Graph::new("be_drive_body");
        let carry = body.input("carry", Shape::new(&[n], DType::F64));
        let drive = body.input("drive", Shape::new(&[n], DType::F64));
        let m = body.add_node(
            Op::Constant { data: m_bytes },
            vec![],
            Shape::new(&[n, n], DType::F64),
        );
        let driven = body.binary(BinaryOp::Add, carry, drive, Shape::new(&[n], DType::F64));
        let next = body.dense_solve(m, driven, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("be_drive_outer");
        let init = g.input("init", Shape::new(&[n], DType::F64));
        let xs = g.input("xs", Shape::new(&[length as usize, n], DType::F64));
        let final_carry = g.scan_with_xs(init, &[xs], body, length);
        g.set_outputs(vec![final_carry]);

        let init_data = vec![0.0_f64; n];
        // Drive the system with a unit pulse on element 0 at t=0,
        // zeros after.
        let mut xs_data = vec![0.0_f64; length as usize * n];
        xs_data[0] = 1.0;

        let (sched, mut arena) = prepare_f64(&g, &[(init, &init_data), (xs, &xs_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let got = read_arena_f64(&arena, final_carry, n);

        // Reference: per-step in pure Rust.
        let mut x = init_data.clone();
        for t in 0..length as usize {
            for j in 0..n {
                x[j] += xs_data[t * n + j];
            }
            let mut a_copy = m_data.clone();
            crate::blas::dgesv(&mut a_copy, &mut x, n, 1);
        }
        for i in 0..n {
            assert!(
                (got[i] - x[i]).abs() < 1e-12,
                "got[{i}] = {} ref {}",
                got[i],
                x[i]
            );
        }
    }

    /// Reverse-mode AD through Op::BatchedDenseSolve. Forward solves
    /// `[B, N, N] · x = [B, N]`; loss = sum of all entries. Closed
    /// form: dB = (Aᵀ)⁻¹·1, dA = -(Aᵀ)⁻¹·1 ⊗ x. Verified analytically
    /// per batch (each slice matches what the unbatched DenseSolve VJP
    /// would compute).
    #[test]
    fn batched_dense_solve_gradient_matches_per_batch_analytic() {
        use rlx_opt::autodiff::grad_with_loss;
        let n = 3usize;
        let batch = 4usize;

        let mut g = Graph::new("bds_grad");
        let a = g.param("A", Shape::new(&[batch, n, n], DType::F64));
        let b = g.input("b", Shape::new(&[batch, n], DType::F64));
        let x = g.batched_dense_solve(a, b, Shape::new(&[batch, n], DType::F64));
        let loss = g.reduce(
            x,
            ReduceOp::Sum,
            vec![0, 1],
            false,
            Shape::new(&[1], DType::F64),
        );
        g.set_outputs(vec![loss]);

        let bwd = grad_with_loss(&g, &[a, b]);

        let find = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want}");
        };
        let a_id = find(&bwd, "A");
        let b_id = find(&bwd, "b");
        let d_out_id = find(&bwd, "d_output");

        let mut rng = rlx_ir::Philox4x32::new(0x57e1_u64);
        let mut a_data = vec![0.0_f64; batch * n * n];
        let mut b_data = vec![0.0_f64; batch * n];
        for bi in 0..batch {
            for i in 0..n {
                for j in 0..n {
                    a_data[bi * n * n + i * n + j] = rng.next_f32() as f64 * 0.1;
                }
                a_data[bi * n * n + i * n + i] += 1.0 + n as f64;
            }
            for i in 0..n {
                b_data[bi * n + i] = rng.next_f32() as f64;
            }
        }
        let d_seed = [1.0_f64];

        let (sched, mut arena) = prepare_f64(
            &bwd,
            &[(a_id, &a_data), (b_id, &b_data), (d_out_id, &d_seed)],
        );
        execute_thunks(&sched, arena.raw_buf_mut());
        let da_out = read_arena_f64(&arena, bwd.outputs[1], batch * n * n);
        let db_out = read_arena_f64(&arena, bwd.outputs[2], batch * n);

        // Reference: per-batch analytic solve. dB_i = (A_iᵀ)⁻¹ · 1,
        // dA_i = -dB_i ⊗ x_i.
        for bi in 0..batch {
            let a_slice: Vec<f64> = a_data[bi * n * n..(bi + 1) * n * n].to_vec();
            let mut b_slice: Vec<f64> = b_data[bi * n..(bi + 1) * n].to_vec();
            let mut a_copy = a_slice.clone();
            crate::blas::dgesv(&mut a_copy, &mut b_slice, n, 1);
            let x_ref = b_slice.clone();
            // dB: solve(A^T, ones)
            let mut at = vec![0.0_f64; n * n];
            for i in 0..n {
                for j in 0..n {
                    at[i * n + j] = a_slice[j * n + i];
                }
            }
            let mut ones = vec![1.0_f64; n];
            crate::blas::dgesv(&mut at, &mut ones, n, 1);
            let db_ref = ones;
            for i in 0..n {
                let got = db_out[bi * n + i];
                assert!(
                    (got - db_ref[i]).abs() < 1e-10,
                    "batch {bi}, db[{i}]: got {got} ref {}",
                    db_ref[i]
                );
            }
            // dA: -outer(db, x)
            for i in 0..n {
                for j in 0..n {
                    let got = da_out[bi * n * n + i * n + j];
                    let want = -db_ref[i] * x_ref[j];
                    assert!(
                        (got - want).abs() < 1e-10,
                        "batch {bi}, dA[{i},{j}]: got {got} ref {want}"
                    );
                }
            }
        }
    }

    /// AD knob: gradient through `scan_checkpointed` automatically
    /// uses the recompute backward path. Compares dinit from a plain
    /// scan against the same forward written with `scan_checkpointed`,
    /// both run through `grad_with_loss`. They must match to f64.
    #[test]
    fn scan_checkpointed_grad_matches_plain_scan_grad() {
        use rlx_opt::autodiff::grad_with_loss;
        let n = 2usize;
        let length = 6u32;

        let make_body = || {
            let mut body = Graph::new("ck_body");
            let carry = body.input("carry", Shape::new(&[n], DType::F64));
            let scale_bytes: Vec<u8> = (0..n).flat_map(|_| 1.05_f64.to_le_bytes()).collect();
            let scale = body.add_node(
                Op::Constant { data: scale_bytes },
                vec![],
                Shape::new(&[n], DType::F64),
            );
            let next = body.binary(BinaryOp::Mul, carry, scale, Shape::new(&[n], DType::F64));
            body.set_outputs(vec![next]);
            body
        };

        // Plain scan path.
        let mut g_plain = Graph::new("ck_plain");
        let init_p = g_plain.input("init", Shape::new(&[n], DType::F64));
        let final_p = g_plain.scan(init_p, make_body(), length);
        let loss_p = g_plain.reduce(
            final_p,
            ReduceOp::Sum,
            vec![0],
            false,
            Shape::new(&[1], DType::F64),
        );
        g_plain.set_outputs(vec![loss_p]);
        let bwd_p = grad_with_loss(&g_plain, &[init_p]);

        // Checkpointed scan path with K=2 (length=6).
        let mut g_ck = Graph::new("ck_ckpt");
        let init_c = g_ck.input("init", Shape::new(&[n], DType::F64));
        let final_c = g_ck.scan_checkpointed(init_c, make_body(), length, 2);
        let loss_c = g_ck.reduce(
            final_c,
            ReduceOp::Sum,
            vec![0],
            false,
            Shape::new(&[1], DType::F64),
        );
        g_ck.set_outputs(vec![loss_c]);
        let bwd_c = grad_with_loss(&g_ck, &[init_c]);

        let find = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no {want}");
        };

        let init_data = vec![0.5_f64, -0.5];
        let d_seed = [1.0_f64];

        let (s_p, mut a_p) = prepare_f64(
            &bwd_p,
            &[
                (find(&bwd_p, "init"), &init_data),
                (find(&bwd_p, "d_output"), &d_seed),
            ],
        );
        execute_thunks(&s_p, a_p.raw_buf_mut());
        let dinit_p = read_arena_f64(&a_p, bwd_p.outputs[1], n);

        let (s_c, mut a_c) = prepare_f64(
            &bwd_c,
            &[
                (find(&bwd_c, "init"), &init_data),
                (find(&bwd_c, "d_output"), &d_seed),
            ],
        );
        execute_thunks(&s_c, a_c.raw_buf_mut());
        let dinit_c = read_arena_f64(&a_c, bwd_c.outputs[1], n);

        for i in 0..n {
            assert!(
                (dinit_p[i] - dinit_c[i]).abs() < 1e-12,
                "dinit[{i}]: plain={} checkpointed={}",
                dinit_p[i],
                dinit_c[i]
            );
        }
    }

    /// Recursive checkpointing end-to-end: build a ScanBackward
    /// configured with K=2 checkpoints (for length=4), and compare
    /// dinit against the same backward graph with full trajectory
    /// (K=0). Forward computes a cumulative-sum-style scan; loss = sum.
    /// Both paths must agree to f64 precision.
    #[test]
    fn recursive_checkpointing_matches_full_trajectory() {
        let n = 2usize;
        let length = 4u32;

        // Body: carry + ones (deterministic, no xs)
        let build_body = || -> Graph {
            let mut body = Graph::new("rc_body");
            let carry = body.input("carry", Shape::new(&[n], DType::F64));
            let ones_bytes: Vec<u8> = (0..n).flat_map(|_| 1.0_f64.to_le_bytes()).collect();
            let ones = body.add_node(
                Op::Constant { data: ones_bytes },
                vec![],
                Shape::new(&[n], DType::F64),
            );
            let next = body.binary(BinaryOp::Add, carry, ones, Shape::new(&[n], DType::F64));
            body.set_outputs(vec![next]);
            body
        };

        // body_vjp: same body + d_output, output dcarry. body_vjp is
        // used by ScanBackward to walk the chain rule per step.
        let body_vjp_for = || -> Graph {
            use rlx_opt::autodiff::grad;
            let body = build_body();
            // grad(body, [carry_id]) → graph with dcarry as the output.
            let carry_id = body
                .nodes()
                .iter()
                .find(|n| matches!(n.op, Op::Input { .. }))
                .map(|n| n.id)
                .unwrap();
            grad(&body, &[carry_id])
        };

        // ── Forward (All-strategy): scan with full trajectory ──
        let mut g_full = Graph::new("rc_outer_full");
        let init_full = g_full.input("init", Shape::new(&[n], DType::F64));
        let traj_full_id = g_full.scan_trajectory(init_full, build_body(), length);
        // Hand-build a ScanBackward node that reads the full trajectory.
        let upstream_full = g_full.input("upstream", Shape::new(&[length as usize, n], DType::F64));
        let dinit_full_id = g_full.scan_backward(
            init_full,
            traj_full_id,
            upstream_full,
            &[],
            body_vjp_for(),
            length,
            true,
            Shape::new(&[n], DType::F64),
        );
        g_full.set_outputs(vec![dinit_full_id]);

        // ── Forward (Recursive-2): scan saves only K=2 rows ──
        // Build the trajectory shape [K, *carry] = [2, 2].
        let k = 2u32;
        let mut g_rec = Graph::new("rc_outer_rec");
        let init_rec = g_rec.input("init", Shape::new(&[n], DType::F64));
        let traj_rec_id = g_rec.add_node(
            Op::Scan {
                body: Box::new(build_body()),
                length,
                save_trajectory: true,
                num_bcast: 0,
                num_xs: 0,
                num_checkpoints: k,
            },
            vec![init_rec],
            Shape::new(&[k as usize, n], DType::F64),
        );
        // Same upstream shape as the full version (the upstream is per
        // *forward step*, length rows — independent of K).
        let upstream_rec = g_rec.input("upstream", Shape::new(&[length as usize, n], DType::F64));
        let dinit_rec_id = g_rec.add_node(
            Op::ScanBackward {
                body_vjp: Box::new(body_vjp_for()),
                length,
                save_trajectory: true,
                num_xs: 0,
                num_checkpoints: k,
                forward_body: Some(Box::new(build_body())),
            },
            vec![init_rec, traj_rec_id, upstream_rec],
            Shape::new(&[n], DType::F64),
        );
        g_rec.set_outputs(vec![dinit_rec_id]);

        // ── Run both, same inputs ──
        let init_data = vec![0.5_f64, -0.5];
        let upstream_data: Vec<f64> = (0..length as usize * n).map(|i| (i as f64) * 0.1).collect();

        let find = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                if let Op::Input { name } = &node.op
                    && name == want
                {
                    return node.id;
                }
            }
            panic!("no input {want}");
        };

        let (s_full, mut a_full) = prepare_f64(
            &g_full,
            &[
                (find(&g_full, "init"), &init_data),
                (find(&g_full, "upstream"), &upstream_data),
            ],
        );
        execute_thunks(&s_full, a_full.raw_buf_mut());
        let dinit_full = read_arena_f64(&a_full, g_full.outputs[0], n);

        let (s_rec, mut a_rec) = prepare_f64(
            &g_rec,
            &[
                (find(&g_rec, "init"), &init_data),
                (find(&g_rec, "upstream"), &upstream_data),
            ],
        );
        execute_thunks(&s_rec, a_rec.raw_buf_mut());
        let dinit_rec = read_arena_f64(&a_rec, g_rec.outputs[0], n);

        for i in 0..n {
            assert!(
                (dinit_full[i] - dinit_rec[i]).abs() < 1e-12,
                "i={i}: full={} rec={}",
                dinit_full[i],
                dinit_rec[i]
            );
        }
    }

    /// vmap-of-grad: gradient through Scan, vmap'd over init.
    /// Forward (per row):
    ///   carry_{t+1} = carry_t + ones    (body adds a constant)
    ///   loss = sum(carry_length) = sum(init) + length·n
    /// Closed form: dloss/dinit_i = 1 for every i. vmap over init at
    /// batch=3 → dinit_batched is all-ones [3, n]. Cross-checks
    /// against per-row grad_with_loss runs. Validates the vmap rule
    /// for Op::ScanBackward.
    #[test]
    fn vmap_of_grad_scan_matches_per_row_runs() {
        use rlx_opt::autodiff::grad_with_loss;
        use rlx_opt::vmap::vmap;
        let n = 2usize;
        let length = 3u32;
        let batch = 3usize;

        let mut body = Graph::new("scan_grad_body");
        let carry = body.input("carry", Shape::new(&[n], DType::F64));
        let ones_bytes: Vec<u8> = (0..n).flat_map(|_| 1.0_f64.to_le_bytes()).collect();
        let ones = body.add_node(
            Op::Constant { data: ones_bytes },
            vec![],
            Shape::new(&[n], DType::F64),
        );
        let next = body.binary(BinaryOp::Add, carry, ones, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("scan_grad_outer");
        let init = g.input("init", Shape::new(&[n], DType::F64));
        let final_x = g.scan(init, body, length);
        let loss = g.reduce(
            final_x,
            ReduceOp::Sum,
            vec![0],
            false,
            Shape::new(&[1], DType::F64),
        );
        g.set_outputs(vec![loss]);

        let bwd = grad_with_loss(&g, &[init]);
        let bg = vmap(&bwd, &["init"], batch);

        let find = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want}");
        };
        let init_b = find(&bg, "init");
        let d_out_b = find(&bg, "d_output");

        let init_data: Vec<f64> = (0..batch * n).map(|i| (i as f64) * 0.5).collect();
        let d_seed = [1.0_f64];

        let (sched, mut arena) = prepare_f64(&bg, &[(init_b, &init_data), (d_out_b, &d_seed)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let dinit_b = read_arena_f64(&arena, bg.outputs[1], batch * n);

        for i in 0..batch * n {
            assert!(
                (dinit_b[i] - 1.0).abs() < 1e-12,
                "dinit[{i}] = {} (expected 1.0)",
                dinit_b[i]
            );
        }

        // Cross-check vs per-row grad_with_loss.
        for bi in 0..batch {
            let row = &init_data[bi * n..(bi + 1) * n];
            let mut g2 = Graph::new("per_row_grad");
            let init2 = g2.input("init", Shape::new(&[n], DType::F64));
            let mut body2 = Graph::new("per_row_body");
            let c2 = body2.input("carry", Shape::new(&[n], DType::F64));
            let ones2_bytes: Vec<u8> = (0..n).flat_map(|_| 1.0_f64.to_le_bytes()).collect();
            let ones2 = body2.add_node(
                Op::Constant { data: ones2_bytes },
                vec![],
                Shape::new(&[n], DType::F64),
            );
            let next2 = body2.binary(BinaryOp::Add, c2, ones2, Shape::new(&[n], DType::F64));
            body2.set_outputs(vec![next2]);
            let final2 = g2.scan(init2, body2, length);
            let loss2 = g2.reduce(
                final2,
                ReduceOp::Sum,
                vec![0],
                false,
                Shape::new(&[1], DType::F64),
            );
            g2.set_outputs(vec![loss2]);
            let bwd2 = grad_with_loss(&g2, &[init2]);
            let init2_id = find(&bwd2, "init");
            let d_out2_id = find(&bwd2, "d_output");
            let (s2, mut a2) = prepare_f64(&bwd2, &[(init2_id, row), (d_out2_id, &d_seed)]);
            execute_thunks(&s2, a2.raw_buf_mut());
            let row_dinit = read_arena_f64(&a2, bwd2.outputs[1], n);
            for j in 0..n {
                let got = dinit_b[bi * n + j];
                let want = row_dinit[j];
                assert!(
                    (got - want).abs() < 1e-12,
                    "row {bi}, j {j}: vmap'd={got} per-row={want}"
                );
            }
        }
    }

    /// vmap of Op::Scan: batched cumulative-sum. Forward
    ///   carry_{t+1} = carry_t + xs\[t\]
    ///   final = init + sum(xs)
    /// vmap over both init and xs at batch=3. Each batch row should
    /// equal the scalar run of the same body+xs subset.
    #[test]
    fn vmap_scan_cumulative_sum_matches_scalar_runs() {
        use rlx_opt::vmap::vmap;
        let n = 2usize;
        let length = 4u32;
        let batch = 3usize;

        // Body: (carry, x_t) → carry + x_t
        let mut body = Graph::new("scan_body_cumsum");
        let carry = body.input("carry", Shape::new(&[n], DType::F64));
        let x_t = body.input("x_t", Shape::new(&[n], DType::F64));
        let next = body.binary(BinaryOp::Add, carry, x_t, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("scan_outer_cumsum");
        let init = g.input("init", Shape::new(&[n], DType::F64));
        let xs = g.input("xs", Shape::new(&[length as usize, n], DType::F64));
        let final_carry = g.scan_with_xs(init, &[xs], body, length);
        g.set_outputs(vec![final_carry]);

        // vmap over both init and xs.
        let bg = vmap(&g, &["init", "xs"], batch);

        // Test data — distinct per-batch rows.
        let init_data: Vec<f64> = (0..batch * n).map(|i| (i + 1) as f64).collect();
        // xs has shape [B, length, n] after vmap (the outer's xs is
        // [length, n]; vmap lifts it to [B, length, n]).
        let xs_data: Vec<f64> = (0..batch * length as usize * n)
            .map(|i| 0.1 * (i as f64))
            .collect();

        let find = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                if let Op::Input { name } = &node.op
                    && name == want
                {
                    return node.id;
                }
            }
            panic!("no input {want}");
        };
        let init_b = find(&bg, "init");
        let xs_b = find(&bg, "xs");
        let (sched, mut arena) = prepare_f64(&bg, &[(init_b, &init_data), (xs_b, &xs_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let batched_out = read_arena_f64(&arena, bg.outputs[0], batch * n);

        // Reference: per-batch scalar Scan.
        for bi in 0..batch {
            let init_slice = &init_data[bi * n..(bi + 1) * n];
            let mut x = init_slice.to_vec();
            for t in 0..length as usize {
                for j in 0..n {
                    x[j] += xs_data[bi * length as usize * n + t * n + j];
                }
            }

            for i in 0..n {
                let got = batched_out[bi * n + i];
                assert!(
                    (got - x[i]).abs() < 1e-12,
                    "row {bi}, i {i}: got {got} ref {}",
                    x[i]
                );
            }
        }
    }

    /// vmap of dense solve — Circulax-shaped batched parameter sweep.
    /// Forward: x = solve(A, b). vmap over both A (batched [B,N,N])
    /// and b (batched [B,N]). Run on CPU and compare each batch row
    /// against an independent scalar dgesv.
    #[test]
    fn vmap_dense_solve_matches_scalar_runs() {
        use rlx_opt::vmap::vmap;
        let n = 3usize;
        let batch = 4usize;

        let mut g = Graph::new("solve_forward");
        let a = g.input("A", Shape::new(&[n, n], DType::F64));
        let b = g.input("b", Shape::new(&[n], DType::F64));
        let x = g.dense_solve(a, b, Shape::new(&[n], DType::F64));
        g.set_outputs(vec![x]);

        // vmap both A and b across the batch.
        let bg = vmap(&g, &["A", "b"], batch);

        // Independent A and b per batch row.
        let mut rng = rlx_ir::Philox4x32::new(0xb47c_u64);
        let mut a_data = vec![0.0_f64; batch * n * n];
        let mut b_data = vec![0.0_f64; batch * n];
        for bi in 0..batch {
            // Diagonally dominant A — guaranteed non-singular.
            for i in 0..n {
                for j in 0..n {
                    a_data[bi * n * n + i * n + j] = rng.next_f32() as f64 * 0.1;
                }
                a_data[bi * n * n + i * n + i] += 1.0 + n as f64;
            }
            for i in 0..n {
                b_data[bi * n + i] = rng.next_f32() as f64;
            }
        }

        let find = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                if let Op::Input { name } = &node.op
                    && name == want
                {
                    return node.id;
                }
            }
            panic!("no input named {want}");
        };
        let ba = find(&bg, "A");
        let bb = find(&bg, "b");
        let (sched, mut arena) = prepare_f64(&bg, &[(ba, &a_data), (bb, &b_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let batched_x = read_arena_f64(&arena, bg.outputs[0], batch * n);

        // Reference: per-batch dgesv.
        for bi in 0..batch {
            let mut a_slice: Vec<f64> = a_data[bi * n * n..(bi + 1) * n * n].to_vec();
            let mut b_slice: Vec<f64> = b_data[bi * n..(bi + 1) * n].to_vec();
            crate::blas::dgesv(&mut a_slice, &mut b_slice, n, 1);
            for i in 0..n {
                let got = batched_x[bi * n + i];
                let want = b_slice[i];
                assert!(
                    (got - want).abs() < 1e-12,
                    "row {bi}, i {i}: got {got} want {want}"
                );
            }
        }
    }

    /// vmap end-to-end: build a graph that computes y = MatMul(x, w) + b
    /// and reduces to a per-element loss. vmap over x with batch=4.
    /// Run the batched graph and compare each output row against an
    /// independent scalar run of the original graph. Validates the
    /// structural lift + the runtime path for batched MatMul +
    /// batched Binary + batched Reduce.
    #[test]
    fn vmap_matmul_add_reduce_matches_scalar_runs() {
        use rlx_opt::vmap::vmap;
        let n = 3usize;
        let batch = 4usize;

        // Forward graph: y = MatMul(reshape(x, [1,n]), w) + b ; loss = sum(y).
        let mut g = Graph::new("vmap_e2e_forward");
        let x = g.input("x", Shape::new(&[n], DType::F64));
        let w = g.input("w", Shape::new(&[n, n], DType::F64));
        let b = g.input("b", Shape::new(&[n], DType::F64));
        let x_row = g.add_node(
            Op::Reshape {
                new_shape: vec![1, n as i64],
            },
            vec![x],
            Shape::new(&[1, n], DType::F64),
        );
        let mm = g.matmul(x_row, w, Shape::new(&[1, n], DType::F64));
        let mm_flat = g.add_node(
            Op::Reshape {
                new_shape: vec![n as i64],
            },
            vec![mm],
            Shape::new(&[n], DType::F64),
        );
        let yv = g.binary(BinaryOp::Add, mm_flat, b, Shape::new(&[n], DType::F64));
        let loss = g.reduce(
            yv,
            ReduceOp::Sum,
            vec![0],
            false,
            Shape::new(&[1], DType::F64),
        );
        g.set_outputs(vec![loss]);

        // Build the vmap'd version (batch over x; w and b shared).
        let bg = vmap(&g, &["x"], batch);

        // Test data — distinct rows so we can verify the per-row dispatch.
        let mut rng = rlx_ir::Philox4x32::new(0xc1c0_u64);
        let n_w = n * n;
        let w_data: Vec<f64> = (0..n_w).map(|_| rng.next_f32() as f64).collect();
        let b_data: Vec<f64> = (0..n).map(|_| rng.next_f32() as f64).collect();
        let mut x_data_batched: Vec<f64> = Vec::with_capacity(batch * n);
        for _ in 0..batch * n {
            x_data_batched.push(rng.next_f32() as f64);
        }

        // Run the batched graph.
        let find = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                if let Op::Input { name } = &node.op
                    && name == want
                {
                    return node.id;
                }
            }
            panic!("no input named {want}");
        };
        let bx = find(&bg, "x");
        let bw = find(&bg, "w");
        let bb = find(&bg, "b");
        let (sched, mut arena) =
            prepare_f64(&bg, &[(bx, &x_data_batched), (bw, &w_data), (bb, &b_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        // Reduce::Sum on shifted axis 1 with keep_dim=false → output [B, 1]
        // (it preserves the leading batch axis but reduces what was [n] to [].
        // Since the original output was [1] f64 and the reduce was over
        // axis 0, after vmap the leading-axis-shifted reduce keeps the
        // leading 1 from the original output's [1] shape.)
        let batched_out = read_arena_f64(&arena, bg.outputs[0], batch);

        // Reference: run the original (un-batched) graph once per batch row.
        for bi in 0..batch {
            let xs_slice = &x_data_batched[bi * n..(bi + 1) * n];
            let mut g2 = Graph::new("scalar_run");
            let x2 = g2.input("x", Shape::new(&[n], DType::F64));
            let w2 = g2.input("w", Shape::new(&[n, n], DType::F64));
            let b2 = g2.input("b", Shape::new(&[n], DType::F64));
            let xr = g2.add_node(
                Op::Reshape {
                    new_shape: vec![1, n as i64],
                },
                vec![x2],
                Shape::new(&[1, n], DType::F64),
            );
            let m = g2.matmul(xr, w2, Shape::new(&[1, n], DType::F64));
            let mf = g2.add_node(
                Op::Reshape {
                    new_shape: vec![n as i64],
                },
                vec![m],
                Shape::new(&[n], DType::F64),
            );
            let yv2 = g2.binary(BinaryOp::Add, mf, b2, Shape::new(&[n], DType::F64));
            let l2 = g2.reduce(
                yv2,
                ReduceOp::Sum,
                vec![0],
                false,
                Shape::new(&[1], DType::F64),
            );
            g2.set_outputs(vec![l2]);
            let (s2, mut a2) = prepare_f64(&g2, &[(x2, xs_slice), (w2, &w_data), (b2, &b_data)]);
            execute_thunks(&s2, a2.raw_buf_mut());
            let scalar_out = read_arena_f64(&a2, l2, 1);
            assert!(
                (batched_out[bi] - scalar_out[0]).abs() < 1e-12,
                "row {bi}: batched={} scalar={}",
                batched_out[bi],
                scalar_out[0]
            );
        }
    }

    /// Full gradient through scan-with-xs: dinit AND dxs both checked
    /// against finite differences. Forward
    ///   carry_{t+1} = solve(M, carry_t + xs\[t\])
    ///   loss        = sum(carry_length)
    /// Verifies that grad_with_loss returns gradients w.r.t. both
    /// `init` and `xs` and that dxs matches per-element FD.
    #[test]
    fn scan_with_xs_dxs_matches_fd() {
        use rlx_opt::autodiff::grad_with_loss;
        let n = 3usize;
        let length = 3u32;
        let dt = 0.1_f64;

        let mut m_data = vec![0.0_f64; n * n];
        for i in 0..n {
            m_data[i * n + i] = 1.0 + dt * 2.0;
            if i > 0 {
                m_data[i * n + (i - 1)] = -dt;
            }
            if i + 1 < n {
                m_data[i * n + (i + 1)] = -dt;
            }
        }
        let m_bytes: Vec<u8> = m_data.iter().flat_map(|x| x.to_le_bytes()).collect();

        let mut body = Graph::new("be_dxs_body");
        let carry = body.input("carry", Shape::new(&[n], DType::F64));
        let drive = body.input("drive", Shape::new(&[n], DType::F64));
        let m = body.add_node(
            Op::Constant { data: m_bytes },
            vec![],
            Shape::new(&[n, n], DType::F64),
        );
        let driven = body.binary(BinaryOp::Add, carry, drive, Shape::new(&[n], DType::F64));
        let next = body.dense_solve(m, driven, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("be_dxs_outer");
        let init = g.input("init", Shape::new(&[n], DType::F64));
        let xs = g.input("xs", Shape::new(&[length as usize, n], DType::F64));
        let final_carry = g.scan_with_xs(init, &[xs], body, length);
        let loss = g.reduce(
            final_carry,
            ReduceOp::Sum,
            vec![0],
            false,
            Shape::new(&[1], DType::F64),
        );
        g.set_outputs(vec![loss]);

        // wrt = [init, xs] — get both gradients back.
        let bwd = grad_with_loss(&g, &[init, xs]);
        assert_eq!(bwd.outputs.len(), 3, "[loss, dinit, dxs]");

        let find_by_name = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want:?}");
        };
        let init_bwd = find_by_name(&bwd, "init");
        let xs_bwd = find_by_name(&bwd, "xs");
        let d_out_bwd = find_by_name(&bwd, "d_output");

        let init_data = vec![0.5_f64, 0.0, -0.5];
        let xs_data: Vec<f64> = (0..length as usize * n)
            .map(|i| 0.1_f64 * ((i as f64) - 4.0))
            .collect();
        let d_seed = [1.0_f64];

        let (sched, mut arena) = prepare_f64(
            &bwd,
            &[
                (init_bwd, &init_data),
                (xs_bwd, &xs_data),
                (d_out_bwd, &d_seed),
            ],
        );
        execute_thunks(&sched, arena.raw_buf_mut());
        let dinit = read_arena_f64(&arena, bwd.outputs[1], n);
        let dxs = read_arena_f64(&arena, bwd.outputs[2], length as usize * n);

        let h = 1e-6;
        let loss_at = |x0: &[f64], xs_in: &[f64]| -> f64 {
            let mut acc = x0.to_vec();
            for t in 0..length as usize {
                for j in 0..n {
                    acc[j] += xs_in[t * n + j];
                }
                let mut a_copy = m_data.clone();
                crate::blas::dgesv(&mut a_copy, &mut acc, n, 1);
            }
            acc.iter().sum()
        };

        // FD on dinit (sanity).
        for i in 0..n {
            let mut ip = init_data.to_vec();
            ip[i] += h;
            let mut im = init_data.to_vec();
            im[i] -= h;
            let fd = (loss_at(&ip, &xs_data) - loss_at(&im, &xs_data)) / (2.0 * h);
            assert!(
                (dinit[i] - fd).abs() < 1e-7,
                "FD dinit[{i}]: AD={} FD={}",
                dinit[i],
                fd
            );
        }

        // FD on every dxs entry — full per-step gradient check.
        for t in 0..length as usize {
            for j in 0..n {
                let idx = t * n + j;
                let mut xp = xs_data.clone();
                xp[idx] += h;
                let mut xm = xs_data.clone();
                xm[idx] -= h;
                let fd = (loss_at(&init_data, &xp) - loss_at(&init_data, &xm)) / (2.0 * h);
                assert!(
                    (dxs[idx] - fd).abs() < 1e-7,
                    "FD dxs[t={t},j={j}]: AD={} FD={}",
                    dxs[idx],
                    fd
                );
            }
        }
    }

    /// Gradient through a scan with per-step xs (Circulax-shaped).
    /// Forward:
    ///   carry_{t+1} = solve(M, carry_t + xs\[t\])
    ///   loss = sum(carry_length)
    /// dxs is out of MVP (asserted in the VJP rule's body_vjp `wrt`),
    /// but `dinit` flows correctly through the body's reverse Jacobian
    /// even with xs in the chain. Verify dinit against finite differences.
    #[test]
    fn scan_with_xs_gradient_dinit_matches_fd() {
        use rlx_opt::autodiff::grad_with_loss;
        let n = 3usize;
        let length = 3u32;
        let dt = 0.1_f64;

        let mut m_data = vec![0.0_f64; n * n];
        for i in 0..n {
            m_data[i * n + i] = 1.0 + dt * 2.0;
            if i > 0 {
                m_data[i * n + (i - 1)] = -dt;
            }
            if i + 1 < n {
                m_data[i * n + (i + 1)] = -dt;
            }
        }
        let m_bytes: Vec<u8> = m_data.iter().flat_map(|x| x.to_le_bytes()).collect();

        let mut body = Graph::new("be_xs_grad_body");
        let carry = body.input("carry", Shape::new(&[n], DType::F64));
        let drive = body.input("drive", Shape::new(&[n], DType::F64));
        let m = body.add_node(
            Op::Constant { data: m_bytes },
            vec![],
            Shape::new(&[n, n], DType::F64),
        );
        let driven = body.binary(BinaryOp::Add, carry, drive, Shape::new(&[n], DType::F64));
        let next = body.dense_solve(m, driven, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("be_xs_grad_outer");
        let init = g.input("init", Shape::new(&[n], DType::F64));
        let xs = g.input("xs", Shape::new(&[length as usize, n], DType::F64));
        let final_carry = g.scan_with_xs(init, &[xs], body, length);
        let loss = g.reduce(
            final_carry,
            ReduceOp::Sum,
            vec![0],
            false,
            Shape::new(&[1], DType::F64),
        );
        g.set_outputs(vec![loss]);

        let bwd = grad_with_loss(&g, &[init]);

        let find_by_name = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want:?}");
        };
        let init_bwd = find_by_name(&bwd, "init");
        let xs_bwd = find_by_name(&bwd, "xs");
        let d_out_bwd = find_by_name(&bwd, "d_output");

        let init_data = vec![0.5_f64, 0.0, -0.5];
        // Drive: small per-step pulse, varying per element.
        let xs_data: Vec<f64> = (0..length as usize * n)
            .map(|i| 0.1_f64 * ((i as f64) - 4.0))
            .collect();
        let d_seed = [1.0_f64];

        let (sched, mut arena) = prepare_f64(
            &bwd,
            &[
                (init_bwd, &init_data),
                (xs_bwd, &xs_data),
                (d_out_bwd, &d_seed),
            ],
        );
        execute_thunks(&sched, arena.raw_buf_mut());
        let dinit = read_arena_f64(&arena, bwd.outputs[1], n);

        let h = 1e-6;
        let loss_at = |x0: &[f64]| -> f64 {
            let mut acc = x0.to_vec();
            for t in 0..length as usize {
                for j in 0..n {
                    acc[j] += xs_data[t * n + j];
                }
                let mut a_copy = m_data.clone();
                crate::blas::dgesv(&mut a_copy, &mut acc, n, 1);
            }
            acc.iter().sum()
        };
        for i in 0..n {
            let mut ip = init_data.to_vec();
            ip[i] += h;
            let mut im = init_data.to_vec();
            im[i] -= h;
            let fd = (loss_at(&ip) - loss_at(&im)) / (2.0 * h);
            assert!(
                (dinit[i] - fd).abs() < 1e-7,
                "FD dinit[{i}]: AD={} FD={}",
                dinit[i],
                fd
            );
        }
    }

    /// Gradient through a geometric-growth scan: forward
    ///   x_{t+1} = 1.1 · x_t,    x_0 = init
    ///   final   = x_length     = init · 1.1^length
    ///   loss    = sum(final)
    /// closed-form ∂loss/∂init\[i\] = 1.1^length for every i.
    /// Validates the VJP path: AD pre-pass rewrites save_trajectory=false
    /// to true, autodiff emits Op::ScanBackward, executor walks t back.
    #[test]
    fn scan_gradient_geometric_matches_closed_form() {
        use rlx_opt::autodiff::grad_with_loss;
        let n = 3usize;
        let length = 5u32;

        let mut body = Graph::new("scan_grad_body");
        let x = body.input("carry", Shape::new(&[n], DType::F64));
        let scale_bytes: Vec<u8> = (0..n).flat_map(|_| 1.1_f64.to_le_bytes()).collect();
        let scale = body.add_node(
            Op::Constant { data: scale_bytes },
            vec![],
            Shape::new(&[n], DType::F64),
        );
        let next = body.binary(BinaryOp::Mul, x, scale, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("scan_grad_outer");
        let init = g.input("init", Shape::new(&[n], DType::F64));
        let final_x = g.scan(init, body, length);
        let loss = g.reduce(
            final_x,
            ReduceOp::Sum,
            vec![0],
            false,
            Shape::new(&[1], DType::F64),
        );
        g.set_outputs(vec![loss]);

        let bwd = grad_with_loss(&g, &[init]);
        assert_eq!(bwd.outputs.len(), 2);

        let find_by_name = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want:?}");
        };
        let init_bwd = find_by_name(&bwd, "init");
        let d_out_bwd = find_by_name(&bwd, "d_output");

        let init_data = vec![1.0_f64; n];
        let d_seed = [1.0_f64];
        let (sched, mut arena) = prepare_f64(&bwd, &[(init_bwd, &init_data), (d_out_bwd, &d_seed)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let dinit = read_arena_f64(&arena, bwd.outputs[1], n);

        let want = 1.1_f64.powi(length as i32);
        for i in 0..n {
            assert!(
                (dinit[i] - want).abs() < 1e-12,
                "dinit[{i}] = {} want {}",
                dinit[i],
                want
            );
        }

        // Finite-difference cross-check on init[0].
        let h = 1e-6;
        let loss_at = |x: &[f64]| -> f64 {
            let mut acc = x.to_vec();
            for _ in 0..length {
                for v in acc.iter_mut() {
                    *v *= 1.1;
                }
            }
            acc.iter().sum()
        };
        let mut ip = init_data.clone();
        ip[0] += h;
        let mut im = init_data.clone();
        im[0] -= h;
        let fd = (loss_at(&ip) - loss_at(&im)) / (2.0 * h);
        assert!(
            (dinit[0] - fd).abs() < 1e-7,
            "FD dinit[0]: AD={} FD={}",
            dinit[0],
            fd
        );
    }

    /// Gradient through Backward Euler scan composing with DenseSolve.
    /// Asserts dinit matches finite-difference per coordinate.
    #[test]
    fn scan_gradient_backward_euler_matches_fd() {
        use rlx_opt::autodiff::grad_with_loss;
        let n = 4usize;
        let length = 3u32;
        let dt = 0.05_f64;

        let mut m_data = vec![0.0_f64; n * n];
        for i in 0..n {
            m_data[i * n + i] = 1.0 + dt * 2.0;
            if i > 0 {
                m_data[i * n + (i - 1)] = -dt;
            }
            if i + 1 < n {
                m_data[i * n + (i + 1)] = -dt;
            }
        }
        let m_bytes: Vec<u8> = m_data.iter().flat_map(|x| x.to_le_bytes()).collect();

        let mut body = Graph::new("be_grad_body");
        let x = body.input("x", Shape::new(&[n], DType::F64));
        let m = body.add_node(
            Op::Constant { data: m_bytes },
            vec![],
            Shape::new(&[n, n], DType::F64),
        );
        let next = body.dense_solve(m, x, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("be_grad_outer");
        let init = g.input("x0", Shape::new(&[n], DType::F64));
        let final_x = g.scan(init, body, length);
        let loss = g.reduce(
            final_x,
            ReduceOp::Sum,
            vec![0],
            false,
            Shape::new(&[1], DType::F64),
        );
        g.set_outputs(vec![loss]);

        let bwd = grad_with_loss(&g, &[init]);

        let find_by_name = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want:?}");
        };
        let init_bwd = find_by_name(&bwd, "x0");
        let d_out_bwd = find_by_name(&bwd, "d_output");

        let init_data: [f64; 4] = [0.0, 1.0, 0.0, 0.0];
        let d_seed = [1.0_f64];
        let (sched, mut arena) = prepare_f64(&bwd, &[(init_bwd, &init_data), (d_out_bwd, &d_seed)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let dinit = read_arena_f64(&arena, bwd.outputs[1], n);

        let h = 1e-6;
        let loss_at = |x0: &[f64]| -> f64 {
            let mut acc = x0.to_vec();
            for _ in 0..length {
                let mut a_copy = m_data.clone();
                crate::blas::dgesv(&mut a_copy, &mut acc, n, 1);
            }
            acc.iter().sum()
        };
        for i in 0..n {
            let mut ip = init_data.to_vec();
            ip[i] += h;
            let mut im = init_data.to_vec();
            im[i] -= h;
            let fd = (loss_at(&ip) - loss_at(&im)) / (2.0 * h);
            assert!(
                (dinit[i] - fd).abs() < 1e-7,
                "FD dinit[{i}]: AD={} FD={}",
                dinit[i],
                fd
            );
        }
    }

    /// Trajectory-mode scan: same Backward Euler body, but record the
    /// carry at every step. Output is `[length, n]` — row `t` is the
    /// state after step `t+1`. Validates the SaveAt-style waveform
    /// recording end-to-end, including that the last row equals what
    /// the no-trajectory variant would have returned.
    #[test]
    fn scan_trajectory_backward_euler_records_waveform() {
        let n = 4usize;
        let length = 5u32;
        let dt = 0.05_f64;

        let mut m_data = vec![0.0_f64; n * n];
        for i in 0..n {
            m_data[i * n + i] = 1.0 + dt * 2.0;
            if i > 0 {
                m_data[i * n + (i - 1)] = -dt;
            }
            if i + 1 < n {
                m_data[i * n + (i + 1)] = -dt;
            }
        }
        let m_bytes: Vec<u8> = m_data.iter().flat_map(|x| x.to_le_bytes()).collect();

        let mut body = Graph::new("be_traj_body");
        let x = body.input("x", Shape::new(&[n], DType::F64));
        let m = body.add_node(
            Op::Constant { data: m_bytes },
            vec![],
            Shape::new(&[n, n], DType::F64),
        );
        let next = body.dense_solve(m, x, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("be_traj_outer");
        let init = g.input("x0", Shape::new(&[n], DType::F64));
        let traj = g.scan_trajectory(init, body, length);
        g.set_outputs(vec![traj]);

        let init_data: [f64; 4] = [0.0, 1.0, 0.0, 0.0];
        let (sched, mut arena) = prepare_f64(&g, &[(init, &init_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let got = read_arena_f64(&arena, traj, length as usize * n);

        // Reference: each step's solve, recorded.
        let mut want = Vec::<f64>::with_capacity(length as usize * n);
        let mut x_ref = init_data.to_vec();
        for _ in 0..length {
            let mut a_copy = m_data.clone();
            crate::blas::dgesv(&mut a_copy, &mut x_ref, n, 1);
            want.extend_from_slice(&x_ref);
        }
        for i in 0..length as usize * n {
            assert!(
                (got[i] - want[i]).abs() < 1e-12,
                "got[{i}] = {} ref {}",
                got[i],
                want[i]
            );
        }

        // Sanity: trajectory rows are monotone-decreasing in mass
        // (Backward Euler diffuses; boundary leak removes mass).
        for t in 1..length as usize {
            let prev: f64 = got[(t - 1) * n..t * n].iter().sum();
            let curr: f64 = got[t * n..(t + 1) * n].iter().sum();
            assert!(
                curr <= prev + 1e-15,
                "mass should decay: row {} sum {prev}, row {t} sum {curr}",
                t - 1
            );
        }

        // Last row of the trajectory equals what a non-trajectory
        // scan returns — verify by running the same forward through
        // the simpler API and comparing.
        let mut body2 = Graph::new("be_final_body");
        let x2 = body2.input("x", Shape::new(&[n], DType::F64));
        let m_bytes2: Vec<u8> = m_data.iter().flat_map(|x| x.to_le_bytes()).collect();
        let m2 = body2.add_node(
            Op::Constant { data: m_bytes2 },
            vec![],
            Shape::new(&[n, n], DType::F64),
        );
        let next2 = body2.dense_solve(m2, x2, Shape::new(&[n], DType::F64));
        body2.set_outputs(vec![next2]);

        let mut g2 = Graph::new("be_final_outer");
        let init2 = g2.input("x0", Shape::new(&[n], DType::F64));
        let final_x = g2.scan(init2, body2, length);
        g2.set_outputs(vec![final_x]);
        let (sched2, mut arena2) = prepare_f64(&g2, &[(init2, &init_data)]);
        execute_thunks(&sched2, arena2.raw_buf_mut());
        let final_got = read_arena_f64(&arena2, final_x, n);

        let last_row = &got[(length as usize - 1) * n..length as usize * n];
        for i in 0..n {
            assert!(
                (last_row[i] - final_got[i]).abs() < 1e-15,
                "last trajectory row[{i}] = {} vs final-scan = {}",
                last_row[i],
                final_got[i]
            );
        }
    }

    /// Op::Scan composing with Op::DenseSolve — the Circulax-shaped
    /// pattern for Backward Euler.
    /// Body: x_{t+1} = solve(I + dt·A, x_t).
    /// 1-D heat-equation Laplacian A; analytic ground truth from
    /// composing the same per-step solve in Rust.
    #[test]
    fn scan_backward_euler_heat_f64() {
        let n = 4usize;
        let length = 5u32;
        let dt = 0.05_f64;

        // Construct M = I + dt · L  where L is the Laplacian (-1, 2, -1).
        // M is constant across iterations; embed it in the body via Op::Constant.
        let mut m_data = vec![0.0_f64; n * n];
        for i in 0..n {
            m_data[i * n + i] = 1.0 + dt * 2.0;
            if i > 0 {
                m_data[i * n + (i - 1)] = -dt;
            }
            if i + 1 < n {
                m_data[i * n + (i + 1)] = -dt;
            }
        }
        let m_bytes: Vec<u8> = m_data.iter().flat_map(|x| x.to_le_bytes()).collect();

        let mut body = Graph::new("be_body");
        let x = body.input("x", Shape::new(&[n], DType::F64));
        let m = body.add_node(
            Op::Constant { data: m_bytes },
            vec![],
            Shape::new(&[n, n], DType::F64),
        );
        let next = body.dense_solve(m, x, Shape::new(&[n], DType::F64));
        body.set_outputs(vec![next]);

        let mut g = Graph::new("be_outer");
        let init = g.input("x0", Shape::new(&[n], DType::F64));
        let final_x = g.scan(init, body, length);
        g.set_outputs(vec![final_x]);

        // Initial: a sharp pulse at index 1.
        let init_data: [f64; 4] = [0.0, 1.0, 0.0, 0.0];
        let (sched, mut arena) = prepare_f64(&g, &[(init, &init_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let got = read_arena_f64(&arena, final_x, n);

        // Reference: apply the same M-solve `length` times in pure Rust.
        let mut ref_x = init_data.to_vec();
        for _ in 0..length {
            let mut a_copy = m_data.clone();
            crate::blas::dgesv(&mut a_copy, &mut ref_x, n, 1);
        }
        for i in 0..n {
            assert!(
                (got[i] - ref_x[i]).abs() < 1e-12,
                "got[{i}] = {} ref {}",
                got[i],
                ref_x[i]
            );
        }
        // Sanity: pulse should diffuse, mass should be conserved-ish
        // (Backward Euler is mass-conserving for this stencil with
        // zero-flux boundaries — but our boundaries leak, so check
        // that mass strictly decreases instead).
        let mass: f64 = got.iter().sum();
        assert!(mass > 0.0 && mass < 1.0, "diffusion mass: {mass}");
    }

    /// Multi-RHS forward DenseSolve: X = solve(A, B) with B [N, K]
    /// stays correct end-to-end. Verifies the executor/lowering and
    /// the LAPACK column-major dance both honour `nrhs > 1`.
    #[test]
    fn dense_solve_f64_multi_rhs_forward() {
        let n = 3usize;
        let k = 2usize;
        let mut g = Graph::new("solve_multi_rhs");
        let a = g.input("A", Shape::new(&[n, n], DType::F64));
        let b = g.input("B", Shape::new(&[n, k], DType::F64));
        let x = g.dense_solve(a, b, Shape::new(&[n, k], DType::F64));
        g.set_outputs(vec![x]);

        let a_data = [2.0, 1.0, 0.0, 1.0, 3.0, 1.0, 0.0, 1.0, 2.0_f64];
        let b_data = [1.0, 4.0, 2.0, -1.0, 3.0, 2.0_f64];
        let (sched, mut arena) = prepare_f64(&g, &[(a, &a_data), (b, &b_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let x_got = read_arena_f64(&arena, x, n * k);
        for c in 0..k {
            for i in 0..n {
                let mut acc = 0.0_f64;
                for j in 0..n {
                    acc += a_data[i * n + j] * x_got[j * k + c];
                }
                let want = b_data[i * k + c];
                assert!(
                    (acc - want).abs() < 1e-10,
                    "col {c} row {i}: got {acc} want {want}"
                );
            }
        }
    }

    /// Multi-RHS reverse-mode VJP: dB = (Aᵀ)⁻¹·1, dA = -dB · Xᵀ.
    /// Verified analytically + finite differences on dB[0,0].
    #[test]
    fn dense_solve_f64_multi_rhs_gradient() {
        use rlx_opt::autodiff::grad_with_loss;
        let n = 3usize;
        let k = 2usize;
        let mut g = Graph::new("solve_mrhs_grad");
        let a = g.param("A", Shape::new(&[n, n], DType::F64));
        let b = g.input("B", Shape::new(&[n, k], DType::F64));
        let x = g.dense_solve(a, b, Shape::new(&[n, k], DType::F64));
        let loss = g.reduce(
            x,
            ReduceOp::Sum,
            vec![0, 1],
            false,
            Shape::new(&[1], DType::F64),
        );
        g.set_outputs(vec![loss]);

        let bwd = grad_with_loss(&g, &[a, b]);
        let find_by_name = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want:?}");
        };
        let a_bwd = find_by_name(&bwd, "A");
        let b_bwd = find_by_name(&bwd, "B");
        let d_out = find_by_name(&bwd, "d_output");

        let a_data = [2.0, 1.0, 0.0, 1.0, 3.0, 1.0, 0.0, 1.0, 2.0_f64];
        let b_data = [1.0, 4.0, 2.0, -1.0, 3.0, 2.0_f64];
        let d_seed = [1.0_f64];

        let (sched, mut arena) = prepare_f64(
            &bwd,
            &[(a_bwd, &a_data), (b_bwd, &b_data), (d_out, &d_seed)],
        );
        execute_thunks(&sched, arena.raw_buf_mut());
        let da_got = read_arena_f64(&arena, bwd.outputs[1], n * n);
        let db_got = read_arena_f64(&arena, bwd.outputs[2], n * k);

        // Reference.
        let mut x_ref = b_data;
        {
            let mut a_copy = a_data;
            crate::blas::dgesv(&mut a_copy, &mut x_ref, n, k);
        }
        let mut at = [0.0_f64; 9];
        for i in 0..n {
            for j in 0..n {
                at[i * n + j] = a_data[j * n + i];
            }
        }
        let mut ones_nk = vec![1.0_f64; n * k];
        crate::blas::dgesv(&mut at, &mut ones_nk, n, k);
        let db_ref = ones_nk;
        let mut da_ref = [0.0_f64; 9];
        for i in 0..n {
            for j in 0..n {
                let mut acc = 0.0_f64;
                for c in 0..k {
                    acc += db_ref[i * k + c] * x_ref[j * k + c];
                }
                da_ref[i * n + j] = -acc;
            }
        }
        for i in 0..n * k {
            assert!(
                (db_got[i] - db_ref[i]).abs() < 1e-10,
                "dB[{i}]: got {} want {}",
                db_got[i],
                db_ref[i]
            );
        }
        for i in 0..n * n {
            assert!(
                (da_got[i] - da_ref[i]).abs() < 1e-10,
                "dA[{i}]: got {} want {}",
                da_got[i],
                da_ref[i]
            );
        }

        // FD on dB[0,0].
        let h = 1e-6;
        let mut bp = b_data;
        bp[0] += h;
        let mut bm = b_data;
        bm[0] -= h;
        let xp = {
            let mut a_copy = a_data;
            crate::blas::dgesv(&mut a_copy, &mut bp, n, k);
            bp
        };
        let xm = {
            let mut a_copy = a_data;
            crate::blas::dgesv(&mut a_copy, &mut bm, n, k);
            bm
        };
        let lp: f64 = xp.iter().sum();
        let lm: f64 = xm.iter().sum();
        let fd = (lp - lm) / (2.0 * h);
        assert!(
            (db_got[0] - fd).abs() < 1e-7,
            "FD dB[0,0]: AD={} FD={}",
            db_got[0],
            fd
        );
    }

    /// Multi-RHS forward-mode JVP w.r.t. B. Closed form: t_X = solve(A, t_B).
    #[test]
    fn dense_solve_f64_multi_rhs_jvp() {
        use rlx_opt::autodiff_fwd::jvp;
        let n = 3usize;
        let k = 2usize;
        let mut g = Graph::new("solve_mrhs_jvp");
        let a = g.input("A", Shape::new(&[n, n], DType::F64));
        let b = g.input("B", Shape::new(&[n, k], DType::F64));
        let x = g.dense_solve(a, b, Shape::new(&[n, k], DType::F64));
        g.set_outputs(vec![x]);

        let jg = jvp(&g, &[b]);
        let find_by_name = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want:?}");
        };
        let a_id = find_by_name(&jg, "A");
        let b_id = find_by_name(&jg, "B");
        let tb_id = find_by_name(&jg, "tangent_B");

        let a_data = [2.0, 1.0, 0.0, 1.0, 3.0, 1.0, 0.0, 1.0, 2.0_f64];
        let b_data = [1.0, 4.0, 2.0, -1.0, 3.0, 2.0_f64];
        let tb_data = [0.5, 0.0, -0.25, 1.0, 1.0, -0.5_f64];

        let (sched, mut arena) =
            prepare_f64(&jg, &[(a_id, &a_data), (b_id, &b_data), (tb_id, &tb_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let tangent_x = read_arena_f64(&arena, jg.outputs[1], n * k);

        let mut a_copy = a_data;
        let mut tb_copy = tb_data;
        crate::blas::dgesv(&mut a_copy, &mut tb_copy, n, k);
        for i in 0..n * k {
            assert!(
                (tangent_x[i] - tb_copy[i]).abs() < 1e-10,
                "t_X[{i}]: AD={} ref={}",
                tangent_x[i],
                tb_copy[i]
            );
        }

        let h = 1e-6;
        let mut bp = b_data;
        let mut bm = b_data;
        for i in 0..n * k {
            bp[i] += h * tb_data[i];
            bm[i] -= h * tb_data[i];
        }
        let xp = {
            let mut a_copy = a_data;
            crate::blas::dgesv(&mut a_copy, &mut bp, n, k);
            bp
        };
        let xm = {
            let mut a_copy = a_data;
            crate::blas::dgesv(&mut a_copy, &mut bm, n, k);
            bm
        };
        for i in 0..n * k {
            let fd = (xp[i] - xm[i]) / (2.0 * h);
            assert!(
                (tangent_x[i] - fd).abs() < 1e-7,
                "FD t_X[{i}]: AD={} FD={}",
                tangent_x[i],
                fd
            );
        }
    }

    /// Forward-mode JVP through DenseSolve, end-to-end at f64.
    ///
    /// Build forward x = solve(A, b), call `jvp(forward, [b])`,
    /// compile + run, and check the tangent output matches the
    /// closed form `t_x = solve(A, t_b)` plus a finite-difference
    /// cross-check `(solve(A, b + h·t_b) − solve(A, b − h·t_b)) / 2h`.
    #[test]
    fn jvp_dense_solve_b_runs_and_matches_fd() {
        use rlx_opt::autodiff_fwd::jvp;
        let n = 3usize;

        // Forward.
        let mut g = Graph::new("jvp_b_e2e");
        let a = g.input("A", Shape::new(&[n, n], DType::F64));
        let b = g.input("b", Shape::new(&[n], DType::F64));
        let x = g.dense_solve(a, b, Shape::new(&[n], DType::F64));
        g.set_outputs(vec![x]);

        // JVP graph perturbing b only.
        let jg = jvp(&g, &[b]);
        // The JVP graph holds a fresh "tangent_b" Input on top of A and b.
        let find_by_name = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want:?}");
        };
        let a_id = find_by_name(&jg, "A");
        let b_id = find_by_name(&jg, "b");
        let tb_id = find_by_name(&jg, "tangent_b");

        let a_data: [f64; 9] = [2.0, 1.0, 0.0, 1.0, 3.0, 1.0, 0.0, 1.0, 2.0];
        let b_data: [f64; 3] = [1.0, 2.0, 3.0];
        // Pick an arbitrary perturbation direction.
        let tb_data: [f64; 3] = [0.5, -0.25, 1.0];

        let (sched, mut arena) =
            prepare_f64(&jg, &[(a_id, &a_data), (b_id, &b_data), (tb_id, &tb_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());

        // Outputs: [primal_x, tangent_x].
        let primal_x = read_arena_f64(&arena, jg.outputs[0], n);
        let tangent_x = read_arena_f64(&arena, jg.outputs[1], n);

        // Closed form: t_x = solve(A, t_b).
        let t_x_ref = {
            let mut a = a_data;
            let mut tb = tb_data;
            let info = crate::blas::dgesv(&mut a, &mut tb, n, 1);
            assert_eq!(info, 0);
            tb
        };
        for i in 0..n {
            assert!(
                (tangent_x[i] - t_x_ref[i]).abs() < 1e-10,
                "t_x[{i}]: got {} want {}",
                tangent_x[i],
                t_x_ref[i]
            );
        }

        // FD: x(b + h·tb) − x(b − h·tb)) / 2h
        let h = 1e-6;
        let mut bp = b_data;
        let mut bm = b_data;
        for i in 0..n {
            bp[i] += h * tb_data[i];
            bm[i] -= h * tb_data[i];
        }
        let xp = {
            let mut a = a_data;
            let info = crate::blas::dgesv(&mut a, &mut bp, n, 1);
            assert_eq!(info, 0);
            bp
        };
        let xm = {
            let mut a = a_data;
            let info = crate::blas::dgesv(&mut a, &mut bm, n, 1);
            assert_eq!(info, 0);
            bm
        };
        let fd: Vec<f64> = (0..n).map(|i| (xp[i] - xm[i]) / (2.0 * h)).collect();
        for i in 0..n {
            assert!(
                (tangent_x[i] - fd[i]).abs() < 1e-7,
                "FD mismatch t_x[{i}]: AD={} FD={}",
                tangent_x[i],
                fd[i]
            );
        }
        // Sanity: primal output is the actual solve.
        let primal_ref = {
            let mut a = a_data;
            let mut b = b_data;
            crate::blas::dgesv(&mut a, &mut b, n, 1);
            b
        };
        for i in 0..n {
            assert!((primal_x[i] - primal_ref[i]).abs() < 1e-10);
        }
    }

    /// Forward-mode JVP through DenseSolve perturbing A. The tangent
    /// path includes the −t_A·x correction term.
    /// `t_x = −solve(A, t_A · x)` should match a finite-difference
    /// directional derivative of `solve(A, b)` w.r.t. A in the
    /// `t_A` direction.
    #[test]
    fn jvp_dense_solve_a_runs_and_matches_fd() {
        use rlx_opt::autodiff_fwd::jvp;
        let n = 3usize;

        let mut g = Graph::new("jvp_a_e2e");
        let a = g.input("A", Shape::new(&[n, n], DType::F64));
        let b = g.input("b", Shape::new(&[n], DType::F64));
        let x = g.dense_solve(a, b, Shape::new(&[n], DType::F64));
        g.set_outputs(vec![x]);

        let jg = jvp(&g, &[a]);
        let find_by_name = |graph: &Graph, want: &str| -> NodeId {
            for node in graph.nodes() {
                let name = match &node.op {
                    Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                    _ => None,
                };
                if name == Some(want) {
                    return node.id;
                }
            }
            panic!("no node named {want:?}");
        };
        let a_id = find_by_name(&jg, "A");
        let b_id = find_by_name(&jg, "b");
        let ta_id = find_by_name(&jg, "tangent_A");

        let a_data: [f64; 9] = [2.0, 1.0, 0.0, 1.0, 3.0, 1.0, 0.0, 1.0, 2.0];
        let b_data: [f64; 3] = [1.0, 2.0, 3.0];
        // Asymmetric perturbation direction for A.
        let ta_data: [f64; 9] = [0.10, -0.05, 0.02, 0.03, 0.20, -0.04, -0.01, 0.07, 0.15];

        let (sched, mut arena) =
            prepare_f64(&jg, &[(a_id, &a_data), (b_id, &b_data), (ta_id, &ta_data)]);
        execute_thunks(&sched, arena.raw_buf_mut());

        let tangent_x = read_arena_f64(&arena, jg.outputs[1], n);

        // Closed form: x = solve(A, b); t_x = −solve(A, t_A · x).
        let x_ref = {
            let mut a = a_data;
            let mut b = b_data;
            crate::blas::dgesv(&mut a, &mut b, n, 1);
            b
        };
        let mut prod = [0.0_f64; 3];
        for i in 0..n {
            for j in 0..n {
                prod[i] += ta_data[i * n + j] * x_ref[j];
            }
        }
        let t_x_ref = {
            let mut a = a_data;
            let mut p = prod;
            crate::blas::dgesv(&mut a, &mut p, n, 1);
            [-p[0], -p[1], -p[2]]
        };
        for i in 0..n {
            assert!(
                (tangent_x[i] - t_x_ref[i]).abs() < 1e-10,
                "closed-form t_x[{i}]: AD={} ref={}",
                tangent_x[i],
                t_x_ref[i]
            );
        }

        // FD: solve(A + h·t_A, b) and solve(A − h·t_A, b).
        let h = 1e-6;
        let mut ap = a_data;
        let mut am = a_data;
        for i in 0..n * n {
            ap[i] += h * ta_data[i];
            am[i] -= h * ta_data[i];
        }
        let xp = {
            let mut a = ap;
            let mut b = b_data;
            crate::blas::dgesv(&mut a, &mut b, n, 1);
            b
        };
        let xm = {
            let mut a = am;
            let mut b = b_data;
            crate::blas::dgesv(&mut a, &mut b, n, 1);
            b
        };
        for i in 0..n {
            let fd = (xp[i] - xm[i]) / (2.0 * h);
            assert!(
                (tangent_x[i] - fd).abs() < 1e-7,
                "FD t_x[{i}]: AD={} FD={}",
                tangent_x[i],
                fd
            );
        }
    }

    /// Real INT8 conv2d parity. Same setup as QMatMul: pre-quantize
    /// f32 inputs to i8, run `Op::QConv2d`, compare against an
    /// in-test reference loop that does the same i32 accumulation
    /// and requantize math. Symmetric quant (zp=0) to keep the math
    /// head-to-head.
    #[test]
    fn q_conv2d_matches_reference() {
        use rlx_ir::Philox4x32;
        // Small NCHW shape — enough to exercise stride/padding edges.
        let n = 1usize;
        let c_in = 2usize;
        let h = 5usize;
        let w_in = 5usize;
        let c_out = 3usize;
        let kh = 3usize;
        let kw = 3usize;
        let ph = 1usize;
        let pw = 1usize;
        let sh = 1usize;
        let sw = 1usize;
        let h_out = (h + 2 * ph - kh) / sh + 1;
        let w_out = (w_in + 2 * pw - kw) / sw + 1;

        let x_scale = 0.04f32;
        let w_scale = 0.02f32;
        let out_scale = 0.5f32;
        let mult = x_scale * w_scale / out_scale;

        let mut rng = Philox4x32::new(2099);
        let mut xf = vec![0f32; n * c_in * h * w_in];
        rng.fill_normal(&mut xf);
        let mut wf = vec![0f32; c_out * c_in * kh * kw];
        rng.fill_normal(&mut wf);
        let xq: Vec<i8> = xf
            .iter()
            .map(|&v| ((v / x_scale).round() as i32).clamp(-128, 127) as i8)
            .collect();
        let wq: Vec<i8> = wf
            .iter()
            .map(|&v| ((v / w_scale).round() as i32).clamp(-128, 127) as i8)
            .collect();
        let bias: Vec<i32> = vec![0i32; c_out];

        let mut g = Graph::new("qconv");
        let xn = g.input("x", Shape::new(&[n, c_in, h, w_in], DType::I8));
        let wn = g.input("w", Shape::new(&[c_out, c_in, kh, kw], DType::I8));
        let bn = g.input("b", Shape::new(&[c_out], DType::I32));
        let out = g.q_conv2d(
            xn,
            wn,
            bn,
            vec![kh, kw],
            vec![sh, sw],
            vec![ph, pw],
            vec![1, 1],
            1,
            0,
            0,
            0,
            mult,
            Shape::new(&[n, c_out, h_out, w_out], DType::I8),
        );
        g.set_outputs(vec![out]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);
        // Capture offsets before borrowing the buf mutably (avoids
        // overlap between &mut and the &arena.byte_offset reads).
        let xn_off = arena.byte_offset(xn);
        let wn_off = arena.byte_offset(wn);
        let bn_off = arena.byte_offset(bn);
        let out_off = arena.byte_offset(out);
        let buf = arena.raw_buf_mut();
        unsafe {
            let p = buf.as_mut_ptr().add(xn_off) as *mut i8;
            for (i, &v) in xq.iter().enumerate() {
                *p.add(i) = v;
            }
            let p = buf.as_mut_ptr().add(wn_off) as *mut i8;
            for (i, &v) in wq.iter().enumerate() {
                *p.add(i) = v;
            }
            let p = buf.as_mut_ptr().add(bn_off) as *mut i32;
            for (i, &v) in bias.iter().enumerate() {
                *p.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let out_q: Vec<i8> = unsafe {
            let p = arena.raw_buf().as_ptr().add(out_off) as *const i8;
            (0..n * c_out * h_out * w_out).map(|i| *p.add(i)).collect()
        };

        // Reference: scalar loop in NCHW with the same requantize.
        let mut out_ref = vec![0i8; n * c_out * h_out * w_out];
        for ni in 0..n {
            for co in 0..c_out {
                for ho in 0..h_out {
                    for wo in 0..w_out {
                        let mut acc: i32 = 0;
                        for ci in 0..c_in {
                            for ki in 0..kh {
                                for kj in 0..kw {
                                    let hi = ho * sh + ki;
                                    let wi = wo * sw + kj;
                                    if hi < ph || wi < pw {
                                        continue;
                                    }
                                    let hi = hi - ph;
                                    let wi = wi - pw;
                                    if hi >= h || wi >= w_in {
                                        continue;
                                    }
                                    let xv =
                                        xq[((ni * c_in) + ci) * h * w_in + hi * w_in + wi] as i32;
                                    let wv = wq[((co * c_in) + ci) * kh * kw + ki * kw + kj] as i32;
                                    acc += xv * wv;
                                }
                            }
                        }
                        let r = (acc as f32 * mult).round() as i32;
                        let r = r.clamp(-128, 127) as i8;
                        out_ref[((ni * c_out) + co) * h_out * w_out + ho * w_out + wo] = r;
                    }
                }
            }
        }

        for (i, (a, r)) in out_q.iter().zip(&out_ref).enumerate() {
            assert_eq!(a, r, "q_conv2d[{i}]: kernel {a} vs reference {r}");
        }
    }

    /// Real INT8 matmul parity: compare `Op::QMatMul` against the
    /// fake-quant reference `Dequantize → MatMul → Quantize` that
    /// would produce the same output if we round-tripped through
    /// f32. Both should agree element-for-element (or within ±1 i8
    /// step, since rounding in the requantize uses different code
    /// paths). Symmetric quantization (zp=0) for both paths to keep
    /// the math head-to-head.
    #[test]
    fn q_matmul_matches_fake_quant_reference() {
        use rlx_ir::Philox4x32;
        let m = 3usize;
        let k = 8usize;
        let n = 5usize;
        let mut rng = Philox4x32::new(2031);

        // Pick scales and quantize random f32 inputs to i8.
        let x_scale = 0.05f32;
        let w_scale = 0.03f32;
        let out_scale = 0.4f32;
        let mult = x_scale * w_scale / out_scale;
        let mut xf = vec![0f32; m * k];
        rng.fill_normal(&mut xf);
        let mut wf = vec![0f32; k * n];
        rng.fill_normal(&mut wf);
        let xq: Vec<i8> = xf
            .iter()
            .map(|&v| ((v / x_scale).round() as i32).clamp(-128, 127) as i8)
            .collect();
        let wq: Vec<i8> = wf
            .iter()
            .map(|&v| ((v / w_scale).round() as i32).clamp(-128, 127) as i8)
            .collect();
        let bias: Vec<i32> = vec![0i32; n];

        // ── Direct INT8 path ──
        let _f = DType::F32;
        let mut g_q = Graph::new("qmm_direct");
        let xn = g_q.input("x", Shape::new(&[m, k], DType::I8));
        let wn = g_q.input("w", Shape::new(&[k, n], DType::I8));
        let bn = g_q.input("b", Shape::new(&[n], DType::I32));
        let out = g_q.q_matmul(xn, wn, bn, 0, 0, 0, mult, Shape::new(&[m, n], DType::I8));
        g_q.set_outputs(vec![out]);
        let plan = rlx_opt::memory::plan_memory(&g_q);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g_q, &arena);

        // Fill inputs.
        let xn_off = arena.byte_offset(xn);
        let wn_off = arena.byte_offset(wn);
        let bn_off = arena.byte_offset(bn);
        let out_off = arena.byte_offset(out);
        let buf = arena.raw_buf_mut();
        unsafe {
            let p = buf.as_mut_ptr().add(xn_off) as *mut i8;
            for (i, &v) in xq.iter().enumerate() {
                *p.add(i) = v;
            }
            let p = buf.as_mut_ptr().add(wn_off) as *mut i8;
            for (i, &v) in wq.iter().enumerate() {
                *p.add(i) = v;
            }
            let p = buf.as_mut_ptr().add(bn_off) as *mut i32;
            for (i, &v) in bias.iter().enumerate() {
                *p.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let out_q: Vec<i8> = unsafe {
            let p = arena.raw_buf().as_ptr().add(out_off) as *const i8;
            (0..m * n).map(|i| *p.add(i)).collect()
        };

        // ── Fake-quant reference: scalar emulation in plain Rust ──
        // Same arithmetic the kernel does, but in a verifier loop:
        //   acc = Σ (x[m,k]) · (w[k,n]),  // zps are 0
        //   out[m,n] = saturate_i8(round(acc · mult) + 0)
        let mut out_ref = vec![0i8; m * n];
        for mi in 0..m {
            for ni in 0..n {
                let mut acc: i32 = 0;
                for ki in 0..k {
                    acc += (xq[mi * k + ki] as i32) * (wq[ki * n + ni] as i32);
                }
                let r = (acc as f32 * mult).round() as i32;
                out_ref[mi * n + ni] = r.clamp(-128, 127) as i8;
            }
        }

        for (i, (a, r)) in out_q.iter().zip(&out_ref).enumerate() {
            assert_eq!(a, r, "q_matmul[{i}]: kernel {a} vs reference {r}");
        }
    }

    /// Quantize/Dequantize round-trip — quantize an f32 tensor, then
    /// dequantize back, and confirm the result tracks the input
    /// within the per-element scale (the inevitable rounding error).
    /// Also pins the kernel's saturation behavior at the i8 limits.
    #[test]
    fn quantize_dequantize_round_trip() {
        use rlx_ir::Philox4x32;
        let len = 64;
        let mut rng = Philox4x32::new(2027);
        let mut x = vec![0f32; len];
        rng.fill_normal(&mut x);
        // Stretch a couple values past the +/- saturation cliff so
        // the saturate_i8 path is exercised.
        x[0] = 999.0;
        x[1] = -999.0;

        let scale = 0.05f32;
        let zp = 3i32;

        let f = DType::F32;
        let mut g = Graph::new("qdq");
        let xn = g.input("x", Shape::new(&[len], f));
        let q = g.quantize(xn, scale, zp);
        let dq = g.dequantize(q, scale, zp);
        g.set_outputs(vec![dq]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);
        let xn_off = arena.byte_offset(xn);
        let dq_off = arena.byte_offset(dq);
        let buf = arena.raw_buf_mut();
        unsafe {
            let p = buf.as_mut_ptr().add(xn_off) as *mut f32;
            for (i, &v) in x.iter().enumerate() {
                *p.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let out: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(dq_off) as *const f32;
            (0..len).map(|i| *p.add(i)).collect()
        };

        // Saturated values at i=0,1 should clamp to ±127's dequant
        // range (= (±127 - zp) · scale).
        let sat_pos = (127 - zp) as f32 * scale;
        let sat_neg = (-128 - zp) as f32 * scale;
        assert!((out[0] - sat_pos).abs() < 1e-6, "+sat: {}", out[0]);
        assert!((out[1] - sat_neg).abs() < 1e-6, "-sat: {}", out[1]);

        // Everything else should round-trip within `scale` (one quant
        // step = the worst-case rounding error).
        for i in 2..len {
            assert!(
                (out[i] - x[i]).abs() <= scale + 1e-5,
                "qdq[{i}]: {} → {}, scale={scale}",
                x[i],
                out[i]
            );
        }
    }

    /// Per-channel quantize / dequantize: independent scale and zp
    /// per slice along an axis. Verifies (a) each channel uses its
    /// own scale (not a shared one), (b) saturation still respects
    /// the i8 range, (c) channel data layout decomposition is
    /// correct (no cross-channel leakage).
    #[test]
    fn quantize_per_channel_round_trip() {
        let c = 4usize;
        let inner = 5usize;
        // Different magnitudes per channel — proves the per-channel
        // scale is actually being read for each row.
        let mags = [0.01f32, 0.5, 5.0, 50.0];
        let mut x = vec![0f32; c * inner];
        for ci in 0..c {
            for ii in 0..inner {
                // Sweep through values that span [-max_abs, +max_abs]
                // for each channel, plus one value past the cliff to
                // trigger saturation.
                x[ci * inner + ii] = match ii {
                    0 => -mags[ci],
                    1 => 0.0,
                    2 => mags[ci],
                    3 => mags[ci] * 1000.0,  // saturates +
                    _ => -mags[ci] * 1000.0, // saturates -
                };
            }
        }
        let scales: Vec<f32> = mags.iter().map(|&m| m / 127.0).collect();
        let zps: Vec<i32> = vec![0, 0, 0, 0];

        let f = DType::F32;
        let mut g = Graph::new("qdq_pc");
        let xn = g.input("x", Shape::new(&[c, inner], f));
        let q = g.quantize_per_channel(xn, 0, scales.clone(), zps.clone());
        let dq = g.dequantize_per_channel(q, 0, scales.clone(), zps);
        g.set_outputs(vec![dq]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);
        let xn_off = arena.byte_offset(xn);
        let dq_off = arena.byte_offset(dq);
        let buf = arena.raw_buf_mut();
        unsafe {
            let p = buf.as_mut_ptr().add(xn_off) as *mut f32;
            for (i, &v) in x.iter().enumerate() {
                *p.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let out: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(dq_off) as *const f32;
            (0..c * inner).map(|i| *p.add(i)).collect()
        };

        for ci in 0..c {
            // Within-range entries (positions 0, 1, 2) must round-trip
            // within one quant step of *that channel's* scale.
            for ii in 0..3 {
                let idx = ci * inner + ii;
                assert!(
                    (out[idx] - x[idx]).abs() <= scales[ci] + 1e-5,
                    "ch {ci} idx {ii}: {} vs {}",
                    x[idx],
                    out[idx]
                );
            }
            // Saturated positions clamp to ±127 · scale[ci].
            let sat_pos = 127.0 * scales[ci];
            let sat_neg = -128.0 * scales[ci];
            assert!(
                (out[ci * inner + 3] - sat_pos).abs() < 1e-5,
                "ch {ci} +sat: {}",
                out[ci * inner + 3]
            );
            assert!(
                (out[ci * inner + 4] - sat_neg).abs() < 1e-5,
                "ch {ci} -sat: {}",
                out[ci * inner + 4]
            );
        }
    }

    /// `Op::ActivationBackward` parity for every supported kind.
    /// Builds a single-op graph `dx = activation_backward(x, dy)` and
    /// compares each `dx[i]` to the central-difference `(act(x+ε) -
    /// act(x-ε)) / (2ε) · dy\[i\]`. Sweeps the closed-form covered by
    /// the kernel.
    #[test]
    fn activation_backward_matches_numerical_per_kind() {
        use rlx_ir::Philox4x32;
        use rlx_ir::op::Activation;
        let mut rng = Philox4x32::new(91);
        let len = 32;
        // x sampled away from kink/branch points: shifted positive
        // (exp/sqrt/log domain) for the unary-positive activations;
        // wide range otherwise. Two parallel tests would be cleaner
        // but this is concise enough.
        let mut x_pos = vec![0f32; len];
        rng.fill_normal(&mut x_pos);
        for v in x_pos.iter_mut() {
            *v = v.abs() + 0.5;
        }
        let mut x_any = vec![0f32; len];
        rng.fill_normal(&mut x_any);
        let mut dy = vec![0f32; len];
        rng.fill_normal(&mut dy);

        for &(kind, x_data, eps, tol) in &[
            (Activation::Sigmoid, &x_any[..], 1e-3, 5e-3),
            (Activation::Tanh, &x_any[..], 1e-3, 5e-3),
            (Activation::Silu, &x_any[..], 1e-3, 5e-3),
            (Activation::Gelu, &x_any[..], 1e-3, 5e-3),
            (Activation::GeluApprox, &x_any[..], 1e-3, 5e-3),
            (Activation::Exp, &x_any[..], 1e-4, 5e-3),
            (Activation::Log, &x_pos[..], 1e-4, 5e-3),
            (Activation::Sqrt, &x_pos[..], 1e-4, 5e-3),
            (Activation::Rsqrt, &x_pos[..], 1e-4, 5e-3),
            (Activation::Neg, &x_any[..], 1e-3, 5e-4),
        ] {
            let f = DType::F32;
            let mut g = Graph::new("act_bw");
            let xn = g.input("x", Shape::new(&[len], f));
            let dyn_ = g.input("dy", Shape::new(&[len], f));
            let dx = g.activation_backward(kind, xn, dyn_);
            g.set_outputs(vec![dx]);

            let plan = rlx_opt::memory::plan_memory(&g);
            let mut arena = crate::arena::Arena::from_plan(plan);
            let sched = compile_thunks(&g, &arena);

            let xn_off = arena.byte_offset(xn);
            let dyn_off = arena.byte_offset(dyn_);
            let dx_off = arena.byte_offset(dx);
            let buf = arena.raw_buf_mut();
            unsafe {
                let p = buf.as_mut_ptr().add(xn_off) as *mut f32;
                for (i, &v) in x_data.iter().enumerate() {
                    *p.add(i) = v;
                }
                let p = buf.as_mut_ptr().add(dyn_off) as *mut f32;
                for (i, &v) in dy.iter().enumerate() {
                    *p.add(i) = v;
                }
            }
            execute_thunks(&sched, arena.raw_buf_mut());
            let analytical: Vec<f32> = unsafe {
                let p = arena.raw_buf().as_ptr().add(dx_off) as *const f32;
                (0..len).map(|i| *p.add(i)).collect()
            };

            // Apply the forward activation manually; finite-difference
            // each element.
            let act_apply = |kind: Activation, x: f32| -> f32 {
                match kind {
                    Activation::Sigmoid => 1.0 / (1.0 + (-x).exp()),
                    Activation::Tanh => x.tanh(),
                    Activation::Silu => x / (1.0 + (-x).exp()),
                    Activation::Gelu => {
                        // Match the kernel's exact erf form.
                        const INV_SQRT2: f32 = 0.707_106_77;
                        0.5 * x * (1.0 + erf_f32(x * INV_SQRT2))
                    }
                    Activation::GeluApprox => {
                        const C: f32 = 0.797_884_6;
                        const A: f32 = 0.044_715;
                        let inner = C * (x + A * x * x * x);
                        0.5 * x * (1.0 + inner.tanh())
                    }
                    Activation::Exp => x.exp(),
                    Activation::Log => x.ln(),
                    Activation::Sqrt => x.sqrt(),
                    Activation::Rsqrt => 1.0 / x.sqrt(),
                    Activation::Neg => -x,
                    Activation::Relu => x.max(0.0),
                    Activation::Abs => x.abs(),
                    Activation::Round => x.round(),
                    Activation::Sin => x.sin(),
                    Activation::Cos => x.cos(),
                    Activation::Tan => x.tan(),
                    Activation::Atan => x.atan(),
                }
            };
            for i in 0..len {
                let xv = x_data[i];
                let plus = act_apply(kind, xv + eps);
                let minus = act_apply(kind, xv - eps);
                let num = (plus - minus) / (2.0 * eps) * dy[i];
                assert!(
                    (analytical[i] - num).abs() < tol,
                    "{kind:?}[{i}]: analytical {} vs numerical {num}",
                    analytical[i]
                );
            }
        }
    }

    /// Batched 3-D MatMul VJP — the transformer-attention shape
    /// `[B, M, K] @ [B, K, N] = [B, M, N]`. Both gradients flow through
    /// `Op::Transpose` with a perm that swaps the last two dims.
    #[test]
    fn matmul_3d_gradient_matches_numerical() {
        use rlx_ir::Philox4x32;
        let batch = 2usize;
        let m = 3usize;
        let k = 4usize;
        let n = 5usize;
        let mut rng = Philox4x32::new(101);
        let mut a_data = vec![0f32; batch * m * k];
        rng.fill_normal(&mut a_data);
        let mut b_data = vec![0f32; batch * k * n];
        rng.fill_normal(&mut b_data);

        let f = DType::F32;
        let mut fwd = Graph::new("matmul_3d");
        let an = fwd.input("a", Shape::new(&[batch, m, k], f));
        let bp = fwd.param("b", Shape::new(&[batch, k, n], f));
        let mm = fwd.matmul(an, bp, Shape::new(&[batch, m, n], f));
        let loss = fwd.add_node(
            Op::Reduce {
                op: ReduceOp::Sum,
                axes: vec![0, 1, 2],
                keep_dim: false,
            },
            vec![mm],
            Shape::from_dims(&[], f),
        );
        fwd.set_outputs(vec![loss]);

        let bwd_graph = rlx_opt::autodiff::grad_with_loss(&fwd, &[bp]);
        let d_out = bwd_graph
            .nodes()
            .iter()
            .find(|n| matches!(&n.op, Op::Input { name } if name == "d_output"))
            .map(|n| n.id)
            .unwrap();

        let plan = rlx_opt::memory::plan_memory(&bwd_graph);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&bwd_graph, &arena);
        for &(id, data) in &[(an, &a_data), (bp, &b_data), (d_out, &vec![1.0f32])] {
            let off = arena.byte_offset(id);
            let buf = arena.raw_buf_mut();
            unsafe {
                let p = buf.as_mut_ptr().add(off) as *mut f32;
                for (i, &v) in data.iter().enumerate() {
                    *p.add(i) = v;
                }
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let gb_id = bwd_graph.outputs[1];
        let g_b: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(arena.byte_offset(gb_id)) as *const f32;
            (0..batch * k * n).map(|i| *p.add(i)).collect()
        };

        // Numerical gradient: differentiate sum(a @ b) w.r.t. each b entry.
        let forward_loss = |b_vals: &[f32]| -> f32 {
            let mut out = vec![0f32; batch * m * n];
            for bi in 0..batch {
                for mi in 0..m {
                    for ni in 0..n {
                        let mut acc = 0f32;
                        for ki in 0..k {
                            acc +=
                                a_data[bi * m * k + mi * k + ki] * b_vals[bi * k * n + ki * n + ni];
                        }
                        out[bi * m * n + mi * n + ni] = acc;
                    }
                }
            }
            out.iter().sum()
        };
        let eps = 1e-3f32;
        let mut bp_p = b_data.clone();
        let mut g_b_num = vec![0f32; b_data.len()];
        for i in 0..b_data.len() {
            let s = bp_p[i];
            bp_p[i] = s + eps;
            let lp = forward_loss(&bp_p);
            bp_p[i] = s - eps;
            let lm = forward_loss(&bp_p);
            bp_p[i] = s;
            g_b_num[i] = (lp - lm) / (2.0 * eps);
        }
        for (i, (a, n)) in g_b.iter().zip(&g_b_num).enumerate() {
            assert!(
                (a - n).abs() < 5e-3,
                "matmul_3d g_b[{i}]: analytical {a} vs numerical {n}"
            );
        }
    }

    /// Composed `Op::Softmax` VJP — the gradient is built from
    /// `mul + reduce_sum + expand + sub + mul`, no dedicated
    /// SoftmaxBackward kernel. Verifies the closed-form
    /// `dx = y · (g - Σ y·g)` matches the FD gradient over a small
    /// 2-D logits tensor.
    #[test]
    fn softmax_gradient_matches_numerical() {
        use rlx_ir::Philox4x32;
        let n = 3usize;
        let c = 5usize;
        let mut rng = Philox4x32::new(57);
        let mut x_data = vec![0f32; n * c];
        rng.fill_normal(&mut x_data);

        let f = DType::F32;
        let mut fwd = Graph::new("softmax_only");
        let xn = fwd.input("x", Shape::new(&[n, c], f));
        let sm = fwd.add_node(Op::Softmax { axis: -1 }, vec![xn], Shape::new(&[n, c], f));
        // Loss = sum(softmax · target) for some random fixed target —
        // any linear loss will do; sum-of-all is the simplest and gives
        // a uniform gradient flow into the softmax.
        let loss = fwd.add_node(
            Op::Reduce {
                op: ReduceOp::Sum,
                axes: vec![0, 1],
                keep_dim: false,
            },
            vec![sm],
            Shape::from_dims(&[], f),
        );
        fwd.set_outputs(vec![loss]);

        // `wrt = [xn]` — autodiff exposes the gradient w.r.t. the
        // input so we can compare it directly. The forward NodeId for
        // `xn` doubles as its bwd-graph mirror.
        let bwd_graph = rlx_opt::autodiff::grad_with_loss(&fwd, &[xn]);
        let d_out = bwd_graph
            .nodes()
            .iter()
            .find(|n| matches!(&n.op, Op::Input { name } if name == "d_output"))
            .map(|n| n.id)
            .unwrap();

        let plan = rlx_opt::memory::plan_memory(&bwd_graph);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&bwd_graph, &arena);
        for &(id, data) in &[(xn, &x_data), (d_out, &vec![1.0f32])] {
            let off = arena.byte_offset(id);
            let buf = arena.raw_buf_mut();
            unsafe {
                let p = buf.as_mut_ptr().add(off) as *mut f32;
                for (i, &v) in data.iter().enumerate() {
                    *p.add(i) = v;
                }
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let g_x_id = bwd_graph.outputs[1];
        let g_x: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(arena.byte_offset(g_x_id)) as *const f32;
            (0..n * c).map(|i| *p.add(i)).collect()
        };

        // Loss derivative: softmax sums to 1 per row → d/dx_i sum(softmax) = 0
        // analytically. So expect g_x ≈ 0 within FD precision. (This
        // doubles as a strong sanity check for the composition.)
        let forward_loss = |x: &[f32]| -> f32 {
            let mut total = 0f32;
            for ni in 0..n {
                let row = &x[ni * c..(ni + 1) * c];
                let m = row.iter().fold(f32::NEG_INFINITY, |a, &v| a.max(v));
                let denom: f32 = row.iter().map(|&v| (v - m).exp()).sum();
                for &v in row {
                    total += (v - m).exp() / denom;
                }
            }
            total
        };
        let eps = 1e-3f32;
        let mut p = x_data.clone();
        for i in 0..x_data.len() {
            let s = p[i];
            p[i] = s + eps;
            let lp = forward_loss(&p);
            p[i] = s - eps;
            let lm = forward_loss(&p);
            p[i] = s;
            let num = (lp - lm) / (2.0 * eps);
            assert!(
                (g_x[i] - num).abs() < 5e-3,
                "softmax g_x[{i}]: analytical {} vs numerical {num}",
                g_x[i]
            );
        }
    }

    /// LayerNorm VJP — three gradients in one pass:
    ///   d_x via `LayerNormBackwardInput`,
    ///   d_gamma via `LayerNormBackwardGamma`,
    ///   d_beta = `unbroadcast(upstream)` to gamma's shape.
    #[test]
    fn layer_norm_gradient_matches_numerical() {
        use rlx_ir::Philox4x32;
        let rows = 3usize;
        let h = 6usize;
        let mut rng = Philox4x32::new(1009);
        let mut x_data = vec![0f32; rows * h];
        rng.fill_normal(&mut x_data);
        let mut g_data = vec![0f32; h];
        rng.fill_normal(&mut g_data);
        for v in g_data.iter_mut() {
            *v = v.abs() + 0.5;
        }
        let mut b_data = vec![0f32; h];
        rng.fill_normal(&mut b_data);
        let eps = 1e-5f32;

        let f = DType::F32;
        let mut fwd = Graph::new("ln_only");
        let xn = fwd.input("x", Shape::new(&[rows, h], f));
        let gp = fwd.param("gamma", Shape::new(&[h], f));
        let bp = fwd.param("beta", Shape::new(&[h], f));
        let ln = fwd.add_node(
            Op::LayerNorm { axis: -1, eps },
            vec![xn, gp, bp],
            Shape::new(&[rows, h], f),
        );
        let loss = fwd.add_node(
            Op::Reduce {
                op: ReduceOp::Sum,
                axes: vec![0, 1],
                keep_dim: false,
            },
            vec![ln],
            Shape::from_dims(&[], f),
        );
        fwd.set_outputs(vec![loss]);

        let bwd_graph = rlx_opt::autodiff::grad_with_loss(&fwd, &[xn, gp, bp]);
        let d_out = bwd_graph
            .nodes()
            .iter()
            .find(|n| matches!(&n.op, Op::Input { name } if name == "d_output"))
            .map(|n| n.id)
            .unwrap();

        let plan = rlx_opt::memory::plan_memory(&bwd_graph);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&bwd_graph, &arena);
        for &(id, data) in &[
            (xn, &x_data),
            (gp, &g_data),
            (bp, &b_data),
            (d_out, &vec![1.0f32]),
        ] {
            let off = arena.byte_offset(id);
            let buf = arena.raw_buf_mut();
            unsafe {
                let p = buf.as_mut_ptr().add(off) as *mut f32;
                for (i, &v) in data.iter().enumerate() {
                    *p.add(i) = v;
                }
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let read = |id: NodeId, n: usize| -> Vec<f32> {
            let off = arena.byte_offset(id);
            unsafe {
                let p = arena.raw_buf().as_ptr().add(off) as *const f32;
                (0..n).map(|i| *p.add(i)).collect()
            }
        };
        let dx_a = read(bwd_graph.outputs[1], rows * h);
        let dg_a = read(bwd_graph.outputs[2], h);
        let db_a = read(bwd_graph.outputs[3], h);

        let forward_loss = |x: &[f32], g: &[f32], b: &[f32]| -> f32 {
            let mut total = 0f32;
            for r in 0..rows {
                let row = &x[r * h..(r + 1) * h];
                let mean = row.iter().sum::<f32>() / h as f32;
                let var = row.iter().map(|&v| (v - mean) * (v - mean)).sum::<f32>() / h as f32;
                let inv_std = 1.0 / (var + eps).sqrt();
                for d in 0..h {
                    total += ((row[d] - mean) * inv_std) * g[d] + b[d];
                }
            }
            total
        };
        let h_eps = 1e-3f32;

        let mut x_p = x_data.clone();
        for i in 0..x_p.len() {
            let s = x_p[i];
            x_p[i] = s + h_eps;
            let lp = forward_loss(&x_p, &g_data, &b_data);
            x_p[i] = s - h_eps;
            let lm = forward_loss(&x_p, &g_data, &b_data);
            x_p[i] = s;
            let num = (lp - lm) / (2.0 * h_eps);
            assert!(
                (dx_a[i] - num).abs() < 5e-3,
                "ln dx[{i}]: analytical {} vs numerical {num}",
                dx_a[i]
            );
        }
        let mut g_p = g_data.clone();
        for i in 0..g_p.len() {
            let s = g_p[i];
            g_p[i] = s + h_eps;
            let lp = forward_loss(&x_data, &g_p, &b_data);
            g_p[i] = s - h_eps;
            let lm = forward_loss(&x_data, &g_p, &b_data);
            g_p[i] = s;
            let num = (lp - lm) / (2.0 * h_eps);
            assert!(
                (dg_a[i] - num).abs() < 5e-3,
                "ln dg[{i}]: analytical {} vs numerical {num}",
                dg_a[i]
            );
        }
        let mut b_p = b_data.clone();
        for i in 0..b_p.len() {
            let s = b_p[i];
            b_p[i] = s + h_eps;
            let lp = forward_loss(&x_data, &g_data, &b_p);
            b_p[i] = s - h_eps;
            let lm = forward_loss(&x_data, &g_data, &b_p);
            b_p[i] = s;
            let num = (lp - lm) / (2.0 * h_eps);
            assert!(
                (db_a[i] - num).abs() < 5e-3,
                "ln db[{i}]: analytical {} vs numerical {num}",
                db_a[i]
            );
        }
    }

    /// Single dense layer + softmax-cross-entropy + mean reduce —
    /// the simplest non-trivial training graph. Validates MatMul,
    /// broadcast Add, SCE, Reduce(Mean) VJPs and the grad_with_loss
    /// plumbing all at once.
    #[test]
    fn dense_sce_mean_gradient_matches_numerical() {
        use rlx_ir::Philox4x32;
        let bs = 4usize;
        let k_in = 3usize;
        let c = 5usize;
        let mut rng = Philox4x32::new(7);
        let mut x = vec![0f32; bs * k_in];
        rng.fill_normal(&mut x);
        let mut w_init = vec![0f32; k_in * c];
        rng.fill_normal(&mut w_init);
        let mut b_init = vec![0f32; c];
        rng.fill_normal(&mut b_init);
        let labels: Vec<f32> = (0..bs).map(|i| (i % c) as f32).collect();

        // ── Forward graph: loss = mean(sce(x @ w + b, labels)) ──
        let f = DType::F32;
        let mut fwd = Graph::new("dense_sce");
        let xn = fwd.input("x", Shape::new(&[bs, k_in], f));
        let lb = fwd.input("labels", Shape::new(&[bs], f));
        let wp = fwd.param("w", Shape::new(&[k_in, c], f));
        let bp = fwd.param("b", Shape::new(&[c], f));
        let mm = fwd.matmul(xn, wp, Shape::new(&[bs, c], f));
        let logits = fwd.binary(BinaryOp::Add, mm, bp, Shape::new(&[bs, c], f));
        let loss_per = fwd.softmax_cross_entropy_with_logits(logits, lb);
        let loss = fwd.add_node(
            Op::Reduce {
                op: ReduceOp::Sum,
                axes: vec![0],
                keep_dim: false,
            },
            vec![loss_per],
            // Reduce sum of [bs] with axes=[0] keep_dim=false → scalar [].
            Shape::from_dims(&[], f),
        );
        // Use Sum + manual /bs scalar mul — also exercises BinaryOp::Mul VJP path
        // less aggressively than Mean would, and gives us a closed-form
        // reference for the loss we expect.
        // For simplicity though, switch to Mean which the tests should also cover.
        // (Re-using `loss` with Sum here for now; the mean factor cancels in
        // the gradient comparison since both analytical and numerical use the
        // same forward.)
        fwd.set_outputs(vec![loss]);

        // ── Backward graph ──
        let bwd_graph = rlx_opt::autodiff::grad_with_loss(&fwd, &[wp, bp]);
        // Outputs: [loss, grad_w, grad_b]. NodeIds for x/labels/w/b/loss
        // in bwd_graph match their fwd ids (the mirror keeps order).
        let d_out = bwd_graph
            .nodes()
            .iter()
            .find(|n| matches!(&n.op, Op::Input { name } if name == "d_output"))
            .map(|n| n.id)
            .expect("d_output input");

        let (sched, mut arena) = prepare(
            &bwd_graph,
            &[
                (xn, &x),
                (lb, &labels),
                (wp, &w_init),
                (bp, &b_init),
                (d_out, &[1.0]),
            ],
        );
        execute_thunks(&sched, arena.raw_buf_mut());

        let outs = &bwd_graph.outputs;
        let loss_id = outs[0];
        let gw_id = outs[1];
        let gb_id = outs[2];
        let loss_actual = read_arena(&arena, loss_id, 1)[0];
        let gw_actual = read_arena(&arena, gw_id, k_in * c);
        let gb_actual = read_arena(&arena, gb_id, c);

        // ── Forward-only graph for finite differences ──
        // Re-use the same `fwd` graph; set up its own arena and rerun
        // for each perturbed parameter.
        let plan = rlx_opt::memory::plan_memory(&fwd);
        let mut fwd_arena = crate::arena::Arena::from_plan(plan);
        let fwd_sched = compile_thunks(&fwd, &fwd_arena);
        write_arena(&mut fwd_arena, xn, &x);
        write_arena(&mut fwd_arena, lb, &labels);

        let run_loss = |arena: &mut crate::arena::Arena, w: &[f32], b: &[f32]| -> f32 {
            write_arena(arena, wp, w);
            write_arena(arena, bp, b);
            execute_thunks(&fwd_sched, arena.raw_buf_mut());
            read_arena(arena, loss, 1)[0]
        };

        // Sanity: the loss reported by the bwd graph matches the
        // forward-only graph on the unperturbed inputs.
        let loss_check = run_loss(&mut fwd_arena, &w_init, &b_init);
        assert!(
            (loss_actual - loss_check).abs() < 1e-4,
            "loss mismatch: bwd graph {loss_actual} vs fwd-only {loss_check}"
        );

        let eps = 1e-3f32;
        let mut w_perturbed = w_init.clone();
        let mut gw_numerical = vec![0f32; w_init.len()];
        for i in 0..w_init.len() {
            let saved = w_perturbed[i];
            w_perturbed[i] = saved + eps;
            let lp = run_loss(&mut fwd_arena, &w_perturbed, &b_init);
            w_perturbed[i] = saved - eps;
            let lm = run_loss(&mut fwd_arena, &w_perturbed, &b_init);
            w_perturbed[i] = saved;
            gw_numerical[i] = (lp - lm) / (2.0 * eps);
        }
        for (i, (a, n)) in gw_actual.iter().zip(&gw_numerical).enumerate() {
            assert!(
                (a - n).abs() < 5e-3,
                "grad_w[{i}]: analytical {a} vs numerical {n}"
            );
        }

        let mut b_perturbed = b_init.clone();
        let mut gb_numerical = vec![0f32; b_init.len()];
        for i in 0..b_init.len() {
            let saved = b_perturbed[i];
            b_perturbed[i] = saved + eps;
            let lp = run_loss(&mut fwd_arena, &w_init, &b_perturbed);
            b_perturbed[i] = saved - eps;
            let lm = run_loss(&mut fwd_arena, &w_init, &b_perturbed);
            b_perturbed[i] = saved;
            gb_numerical[i] = (lp - lm) / (2.0 * eps);
        }
        for (i, (a, n)) in gb_actual.iter().zip(&gb_numerical).enumerate() {
            assert!(
                (a - n).abs() < 5e-3,
                "grad_b[{i}]: analytical {a} vs numerical {n}"
            );
        }
    }

    /// Reduce::Mean specifically — verifies the 1/N scaling in the VJP.
    /// The same dense+SCE graph but with Mean instead of Sum on the loss.
    #[test]
    fn dense_sce_mean_reduce_gradient_matches_numerical() {
        use rlx_ir::Philox4x32;
        let bs = 3usize;
        let k_in = 2usize;
        let c = 4usize;
        let mut rng = Philox4x32::new(13);
        let mut x = vec![0f32; bs * k_in];
        rng.fill_normal(&mut x);
        let mut w_init = vec![0f32; k_in * c];
        rng.fill_normal(&mut w_init);
        let labels: Vec<f32> = (0..bs).map(|i| (i % c) as f32).collect();

        let f = DType::F32;
        let mut fwd = Graph::new("dense_sce_mean");
        let xn = fwd.input("x", Shape::new(&[bs, k_in], f));
        let lb = fwd.input("labels", Shape::new(&[bs], f));
        let wp = fwd.param("w", Shape::new(&[k_in, c], f));
        let mm = fwd.matmul(xn, wp, Shape::new(&[bs, c], f));
        let loss_per = fwd.softmax_cross_entropy_with_logits(mm, lb);
        let loss = fwd.add_node(
            Op::Reduce {
                op: ReduceOp::Mean,
                axes: vec![0],
                keep_dim: false,
            },
            vec![loss_per],
            Shape::from_dims(&[], f),
        );
        fwd.set_outputs(vec![loss]);

        let bwd_graph = rlx_opt::autodiff::grad_with_loss(&fwd, &[wp]);
        let d_out = bwd_graph
            .nodes()
            .iter()
            .find(|n| matches!(&n.op, Op::Input { name } if name == "d_output"))
            .map(|n| n.id)
            .unwrap();

        let (sched, mut arena) = prepare(
            &bwd_graph,
            &[(xn, &x), (lb, &labels), (wp, &w_init), (d_out, &[1.0])],
        );
        execute_thunks(&sched, arena.raw_buf_mut());

        let outs = &bwd_graph.outputs;
        let loss_id = outs[0];
        let gw_id = outs[1];
        let _ = read_arena(&arena, loss_id, 1)[0];
        let gw_actual = read_arena(&arena, gw_id, k_in * c);

        let plan = rlx_opt::memory::plan_memory(&fwd);
        let mut fwd_arena = crate::arena::Arena::from_plan(plan);
        let fwd_sched = compile_thunks(&fwd, &fwd_arena);
        write_arena(&mut fwd_arena, xn, &x);
        write_arena(&mut fwd_arena, lb, &labels);

        let run_loss = |arena: &mut crate::arena::Arena, w: &[f32]| -> f32 {
            write_arena(arena, wp, w);
            execute_thunks(&fwd_sched, arena.raw_buf_mut());
            read_arena(arena, loss, 1)[0]
        };

        let eps = 1e-3f32;
        let mut wp_p = w_init.clone();
        let mut gw_num = vec![0f32; w_init.len()];
        for i in 0..w_init.len() {
            let s = wp_p[i];
            wp_p[i] = s + eps;
            let lp = run_loss(&mut fwd_arena, &wp_p);
            wp_p[i] = s - eps;
            let lm = run_loss(&mut fwd_arena, &wp_p);
            wp_p[i] = s;
            gw_num[i] = (lp - lm) / (2.0 * eps);
        }
        for (i, (a, n)) in gw_actual.iter().zip(&gw_num).enumerate() {
            assert!((a - n).abs() < 5e-3, "mean reduce grad_w[{i}]: {a} vs {n}");
        }
    }
    /// The full TinyConv-MNIST forward path (downsized) plumbed
    /// through grad_with_loss. Validates that Conv, Pool(Max), ReLU,
    /// Reshape, MatMul, Add (broadcast), SCE, Reduce(Mean) VJPs all
    /// compose into a graph that produces correct gradients.
    #[test]
    fn tinyconv_full_gradient_matches_numerical() {
        use rlx_ir::Philox4x32;
        // Tiny shapes so finite differences finish in <1s.
        let n = 1usize;
        let c_in = 1usize;
        let h = 6usize;
        let w_in = 6usize;
        let c_mid = 2usize; // first conv output channels
        let kh = 3;
        let kw = 3;
        let h1 = h - kh + 1; // 4
        let w1 = w_in - kw + 1; // 4
        let h2 = h1 / 2;
        let w2 = w1 / 2; // 2 × 2 after 2× pool
        let flat = c_mid * h2 * w2; // 8
        let num_classes = 3usize;

        let mut rng = Philox4x32::new(31);
        let mut x = vec![0f32; n * c_in * h * w_in];
        rng.fill_normal(&mut x);
        let mut wc = vec![0f32; c_mid * c_in * kh * kw];
        rng.fill_normal(&mut wc);
        for v in wc.iter_mut() {
            *v *= 0.2;
        }
        // Shift conv-bias well away from the ReLU zero-boundary. Without
        // this, an ε-perturbation of bc[c] can flip the ReLU mask on a
        // pre-activation that happened to land near zero — making the
        // central-difference numerical gradient discontinuous and
        // diverge from the analytical (which assumes local smoothness).
        // +5.0 keeps every pre-activation positive for any random init
        // produced by Philox seed 31 with the wc/x scales used here, so
        // ReLU acts as an identity and finite differences are exact.
        let bc: Vec<f32> = (0..c_mid).map(|i| 5.0 + 0.1 * i as f32).collect();
        let mut wfc = vec![0f32; flat * num_classes];
        rng.fill_normal(&mut wfc);
        for v in wfc.iter_mut() {
            *v *= 0.5;
        }
        let mut bfc = vec![0f32; num_classes];
        rng.fill_normal(&mut bfc);
        let labels: Vec<f32> = vec![1.0]; // batch=1

        let f = DType::F32;
        let mut fwd = Graph::new("tinyconv");
        let xn = fwd.input("x", Shape::new(&[n, c_in, h, w_in], f));
        let lb = fwd.input("labels", Shape::new(&[n], f));
        let wcp = fwd.param("wc", Shape::new(&[c_mid, c_in, kh, kw], f));
        let bcp = fwd.param("bc", Shape::new(&[c_mid], f));
        let wfp = fwd.param("wfc", Shape::new(&[flat, num_classes], f));
        let bfp = fwd.param("bfc", Shape::new(&[num_classes], f));

        // conv: [n, c_in, h, w] → [n, c_mid, h1, w1]
        let conv = fwd.add_node(
            Op::Conv {
                kernel_size: vec![kh, kw],
                stride: vec![1, 1],
                padding: vec![0, 0],
                dilation: vec![1, 1],
                groups: 1,
            },
            vec![xn, wcp],
            Shape::new(&[n, c_mid, h1, w1], f),
        );
        // Bias add: expand bc[c_mid] up to the full [n, c_mid, h1, w1]
        // shape so the Add becomes a plain element-wise op. Going through
        // an explicit Reshape→Expand instead of relying on the Add to
        // broadcast `[1, C, 1, 1]` → `[N, C, H, W]` works around a known
        // limitation of `rlx-cpu`'s `Op::Binary` lowering: it dispatches
        // on `out_len % rhs_len == 0` and treats `rhs` as a last-axis
        // bias, which produces `bc[0], bc[1], bc[0], bc[1], …` alternating
        // across all positions instead of channel-broadcasting. Going
        // through Expand (a real broadcast thunk) avoids that path
        // entirely. The autodiff still exercises `unbroadcast` because
        // `Op::Expand`'s VJP reduces over the broadcast axes.
        let bc_4d = fwd.add_node(
            Op::Reshape {
                new_shape: vec![1, c_mid as i64, 1, 1],
            },
            vec![bcp],
            Shape::new(&[1, c_mid, 1, 1], f),
        );
        let bc_expanded = fwd.add_node(
            Op::Expand {
                target_shape: vec![n as i64, c_mid as i64, h1 as i64, w1 as i64],
            },
            vec![bc_4d],
            Shape::new(&[n, c_mid, h1, w1], f),
        );
        let conv_b = fwd.binary(
            BinaryOp::Add,
            conv,
            bc_expanded,
            Shape::new(&[n, c_mid, h1, w1], f),
        );
        let relu = fwd.activation(Activation::Relu, conv_b, Shape::new(&[n, c_mid, h1, w1], f));
        let pool = fwd.add_node(
            Op::Pool {
                kind: ReduceOp::Max,
                kernel_size: vec![2, 2],
                stride: vec![2, 2],
                padding: vec![0, 0],
            },
            vec![relu],
            Shape::new(&[n, c_mid, h2, w2], f),
        );
        let flatn = fwd.add_node(
            Op::Reshape {
                new_shape: vec![n as i64, flat as i64],
            },
            vec![pool],
            Shape::new(&[n, flat], f),
        );
        let mm = fwd.matmul(flatn, wfp, Shape::new(&[n, num_classes], f));
        let logits = fwd.binary(BinaryOp::Add, mm, bfp, Shape::new(&[n, num_classes], f));
        let loss_per = fwd.softmax_cross_entropy_with_logits(logits, lb);
        let loss = fwd.add_node(
            Op::Reduce {
                op: ReduceOp::Mean,
                axes: vec![0],
                keep_dim: false,
            },
            vec![loss_per],
            Shape::from_dims(&[], f),
        );
        fwd.set_outputs(vec![loss]);

        let bwd_graph = rlx_opt::autodiff::grad_with_loss(&fwd, &[wcp, bcp, wfp, bfp]);
        let d_out = bwd_graph
            .nodes()
            .iter()
            .find(|n| matches!(&n.op, Op::Input { name } if name == "d_output"))
            .map(|n| n.id)
            .unwrap();

        let (sched, mut arena) = prepare(
            &bwd_graph,
            &[
                (xn, &x),
                (lb, &labels),
                (wcp, &wc),
                (bcp, &bc),
                (wfp, &wfc),
                (bfp, &bfc),
                (d_out, &[1.0]),
            ],
        );
        execute_thunks(&sched, arena.raw_buf_mut());

        let outs = bwd_graph.outputs.clone();
        let loss_id = outs[0];
        let g_wc_id = outs[1];
        let g_bc_id = outs[2];
        let g_wfc_id = outs[3];
        let g_bfc_id = outs[4];
        let loss_actual = read_arena(&arena, loss_id, 1)[0];
        let g_wc = read_arena(&arena, g_wc_id, wc.len());
        let g_bc = read_arena(&arena, g_bc_id, bc.len());
        let g_wfc = read_arena(&arena, g_wfc_id, wfc.len());
        let g_bfc = read_arena(&arena, g_bfc_id, bfc.len());

        // Forward-only arena for finite differences.
        let plan = rlx_opt::memory::plan_memory(&fwd);
        let mut fwd_arena = crate::arena::Arena::from_plan(plan);
        let fwd_sched = compile_thunks(&fwd, &fwd_arena);
        write_arena(&mut fwd_arena, xn, &x);
        write_arena(&mut fwd_arena, lb, &labels);

        // Closure variant: we need to set all four params each call so
        // perturbations to one don't leak between sweeps.
        let run_loss = |arena: &mut crate::arena::Arena,
                        wc: &[f32],
                        bc: &[f32],
                        wfc: &[f32],
                        bfc: &[f32]|
         -> f32 {
            write_arena(arena, wcp, wc);
            write_arena(arena, bcp, bc);
            write_arena(arena, wfp, wfc);
            write_arena(arena, bfp, bfc);
            execute_thunks(&fwd_sched, arena.raw_buf_mut());
            read_arena(arena, loss, 1)[0]
        };

        let loss_check = run_loss(&mut fwd_arena, &wc, &bc, &wfc, &bfc);
        assert!(
            (loss_actual - loss_check).abs() < 1e-4,
            "tinyconv loss mismatch: bwd {loss_actual} vs fwd {loss_check}"
        );

        let eps = 1e-3f32;
        let check_grad = |arena: &mut crate::arena::Arena,
                          name: &str,
                          analytical: &[f32],
                          mut perturb: Box<
            dyn FnMut(&mut [f32], usize, f32, &mut crate::arena::Arena) -> f32 + '_,
        >,
                          n: usize| {
            for i in 0..n {
                let lp = perturb(&mut analytical.to_vec(), i, eps, arena);
                let lm = perturb(&mut analytical.to_vec(), i, -eps, arena);
                let num = (lp - lm) / (2.0 * eps);
                assert!(
                    (analytical[i] - num).abs() < 5e-3,
                    "{name}[{i}]: analytical {} vs numerical {num}",
                    analytical[i]
                );
            }
        };

        // Helper to perturb one param and run forward. Kept as a
        // reference for the explicit per-param sweep pattern below.
        #[allow(unused_macros)]
        macro_rules! sweep {
            ($name:expr, $base:expr, $analytical:expr, $set_param:ident) => {{
                let n = $base.len();
                for i in 0..n {
                    let mut p = $base.clone();
                    let s = p[i];
                    p[i] = s + eps;
                    let lp = {
                        let $set_param = &p;
                        run_loss(&mut fwd_arena, &wc, &bc, &wfc, &bfc).max(f32::NEG_INFINITY);
                        // Reset others, set the one being swept, run.
                        // (the macro receives one of the four params via $set_param)
                        let _ = $set_param;
                        // Fall through to the explicit per-param helper:
                        0.0_f32
                    };
                    let _ = lp;
                }
            }};
        }
        let _ = check_grad; // silence unused (sweep! macro is intentionally\n        // unused — kept as reference for the per-param sweep pattern below)

        // Per-param sweeps (explicit, not macro — clearer).
        for i in 0..wc.len() {
            let mut p = wc.clone();
            let s = p[i];
            p[i] = s + eps;
            let lp = run_loss(&mut fwd_arena, &p, &bc, &wfc, &bfc);
            p[i] = s - eps;
            let lm = run_loss(&mut fwd_arena, &p, &bc, &wfc, &bfc);
            let num = (lp - lm) / (2.0 * eps);
            assert!(
                (g_wc[i] - num).abs() < 5e-3,
                "g_wc[{i}]: {} vs {num}",
                g_wc[i]
            );
        }
        for i in 0..bc.len() {
            let mut p = bc.clone();
            let s = p[i];
            p[i] = s + eps;
            let lp = run_loss(&mut fwd_arena, &wc, &p, &wfc, &bfc);
            p[i] = s - eps;
            let lm = run_loss(&mut fwd_arena, &wc, &p, &wfc, &bfc);
            let num = (lp - lm) / (2.0 * eps);
            assert!(
                (g_bc[i] - num).abs() < 5e-3,
                "g_bc[{i}]: {} vs {num}",
                g_bc[i]
            );
        }
        for i in 0..wfc.len() {
            let mut p = wfc.clone();
            let s = p[i];
            p[i] = s + eps;
            let lp = run_loss(&mut fwd_arena, &wc, &bc, &p, &bfc);
            p[i] = s - eps;
            let lm = run_loss(&mut fwd_arena, &wc, &bc, &p, &bfc);
            let num = (lp - lm) / (2.0 * eps);
            assert!(
                (g_wfc[i] - num).abs() < 5e-3,
                "g_wfc[{i}]: {} vs {num}",
                g_wfc[i]
            );
        }
        for i in 0..bfc.len() {
            let mut p = bfc.clone();
            let s = p[i];
            p[i] = s + eps;
            let lp = run_loss(&mut fwd_arena, &wc, &bc, &wfc, &p);
            p[i] = s - eps;
            let lm = run_loss(&mut fwd_arena, &wc, &bc, &wfc, &p);
            let num = (lp - lm) / (2.0 * eps);
            assert!(
                (g_bfc[i] - num).abs() < 5e-3,
                "g_bfc[{i}]: {} vs {num}",
                g_bfc[i]
            );
        }
    }

    /// Negative case: a Narrow whose output has multiple consumers
    /// must NOT be fused (we can't elide its write — something else
    /// reads it).
    #[test]
    fn narrow_rope_skips_when_narrow_has_multiple_consumers() {
        let f = DType::F32;
        let mut g = Graph::new("nr_skip");
        let qkv = g.input("qkv", Shape::new(&[16, 8, 192], f));
        let cos = g.input("cos", Shape::new(&[16], f));
        let sin = g.input("sin", Shape::new(&[16], f));
        let q = g.narrow_(qkv, 2, 0, 64);
        let q_rope = g.rope(q, cos, sin, 16);
        // Second consumer of `q` blocks the fusion.
        let q_dup = g.activation(rlx_ir::op::Activation::Relu, q, Shape::new(&[16, 8, 64], f));
        g.set_outputs(vec![q_rope, q_dup]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        let narrow_count = sched
            .thunks
            .iter()
            .filter(|t| matches!(t, Thunk::Narrow { .. }))
            .count();
        assert!(
            narrow_count >= 1,
            "Narrow with multiple consumers must NOT be fused away"
        );
    }

    // ── Op::CustomFn (custom_vjp / custom_jvp) tests ──
    //
    // Validates: forward execution inlines fwd_body; VJP rule inlines
    // vjp_body in place of recursing into fwd_body; JVP rule inlines
    // jvp_body. Each test deliberately picks a body whose AD-via-tracing
    // would yield a *different* gradient than the override, so we know
    // the override actually fired.

    /// Forward only: CustomFn wrapping `f(x) = x + c` (c=1 inside body)
    /// without override AD bodies. Verifies the body is compiled,
    /// constants in the body fill correctly, and the output lands at
    /// the outer node's slot.
    #[test]
    fn custom_fn_forward_inlines_body() {
        let s = Shape::new(&[3], DType::F32);

        // Body: f(x) = x + 1
        let mut body = Graph::new("addone_body");
        let x = body.input("x", s.clone());
        let one_data: Vec<u8> = (0..3).flat_map(|_| 1.0_f32.to_le_bytes()).collect();
        let one = body.add_node(Op::Constant { data: one_data }, vec![], s.clone());
        let y = body.binary(BinaryOp::Add, x, one, s.clone());
        body.set_outputs(vec![y]);

        let mut g = Graph::new("custom_fn_outer");
        let xin = g.input("x_in", s.clone());
        let cf = g.custom_fn(vec![xin], body, None, None);
        g.set_outputs(vec![cf]);

        let xs = vec![10.0_f32, 20.0, 30.0];
        let (sched, mut arena) = prepare(&g, &[(xin, &xs)]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let got = read_arena(&arena, cf, 3);
        assert_eq!(got, vec![11.0, 21.0, 31.0]);
    }

    /// Locate an Op::Input or Op::Param by name in a graph.
    fn find_named(graph: &Graph, want: &str) -> NodeId {
        for n in graph.nodes() {
            let name = match &n.op {
                Op::Input { name } | Op::Param { name } => Some(name.as_str()),
                _ => None,
            };
            if name == Some(want) {
                return n.id;
            }
        }
        panic!("no node named {want:?} in graph");
    }

    /// VJP override: f(x) = x but vjp_body returns 2 * d_output, so the
    /// reported gradient should be 2 — different from the natural 1
    /// you'd get by recursing into the identity body.
    #[test]
    fn custom_fn_vjp_overrides_natural_gradient() {
        use rlx_opt::autodiff::grad_with_loss;
        let s = Shape::new(&[1], DType::F32);

        let mut fwd = Graph::new("id_fwd");
        let x = fwd.input("x", s.clone());
        fwd.set_outputs(vec![x]);

        let mut vjp_g = Graph::new("id_vjp");
        let _x_p = vjp_g.input("x", s.clone());
        let _y_p = vjp_g.input("primal_output", s.clone());
        let dy = vjp_g.input("d_output", s.clone());
        let two_data: Vec<u8> = 2.0_f32.to_le_bytes().to_vec();
        let two = vjp_g.add_node(Op::Constant { data: two_data }, vec![], s.clone());
        let dx = vjp_g.binary(BinaryOp::Mul, dy, two, s.clone());
        vjp_g.set_outputs(vec![dx]);

        let mut g = Graph::new("outer");
        let xp = g.param("x", s.clone());
        let cf = g.custom_fn(vec![xp], fwd, Some(vjp_g), None);
        g.set_outputs(vec![cf]);

        let bwd = grad_with_loss(&g, &[xp]);
        assert_eq!(bwd.outputs.len(), 2, "expect [loss, dx]");

        let xb = find_named(&bwd, "x");
        let dout = find_named(&bwd, "d_output");
        let (sched, mut arena) = prepare(&bwd, &[(xb, &[7.0]), (dout, &[1.0])]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let loss = read_arena(&arena, bwd.outputs[0], 1);
        let dx_v = read_arena(&arena, bwd.outputs[1], 1);
        assert!((loss[0] - 7.0).abs() < 1e-6, "loss should be 7.0");
        assert!(
            (dx_v[0] - 2.0).abs() < 1e-6,
            "vjp override should yield dx=2.0, got {} (natural autodiff would give 1.0)",
            dx_v[0]
        );
    }

    /// VJP override: f(a, b) = a*b with vjp_body returning
    /// (b * d_output, a * d_output). Validates routing of multiple
    /// primals + d_output through the override; matches the natural
    /// autodiff-of-Mul gradient (b, a).
    #[test]
    fn custom_fn_vjp_two_inputs_matches_mul_autodiff() {
        use rlx_opt::autodiff::grad_with_loss;
        let s = Shape::new(&[1], DType::F32);

        let mut fwd = Graph::new("mul_fwd");
        let a_f = fwd.input("a", s.clone());
        let b_f = fwd.input("b", s.clone());
        let y_f = fwd.binary(BinaryOp::Mul, a_f, b_f, s.clone());
        fwd.set_outputs(vec![y_f]);

        let mut vjp_g = Graph::new("mul_vjp");
        let a_v = vjp_g.input("a", s.clone());
        let b_v = vjp_g.input("b", s.clone());
        let _y_v = vjp_g.input("primal_output", s.clone());
        let dy_v = vjp_g.input("d_output", s.clone());
        let da = vjp_g.binary(BinaryOp::Mul, b_v, dy_v, s.clone());
        let db = vjp_g.binary(BinaryOp::Mul, a_v, dy_v, s.clone());
        vjp_g.set_outputs(vec![da, db]);

        let mut g = Graph::new("outer");
        let ap = g.param("a", s.clone());
        let bp = g.param("b", s.clone());
        let cf = g.custom_fn(vec![ap, bp], fwd, Some(vjp_g), None);
        g.set_outputs(vec![cf]);

        let bwd = grad_with_loss(&g, &[ap, bp]);
        assert_eq!(bwd.outputs.len(), 3, "expect [loss, da, db]");

        let ab = find_named(&bwd, "a");
        let bb = find_named(&bwd, "b");
        let dout = find_named(&bwd, "d_output");
        let (sched, mut arena) = prepare(&bwd, &[(ab, &[3.0]), (bb, &[5.0]), (dout, &[1.0])]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let loss = read_arena(&arena, bwd.outputs[0], 1);
        let da_v = read_arena(&arena, bwd.outputs[1], 1);
        let db_v = read_arena(&arena, bwd.outputs[2], 1);
        assert!((loss[0] - 15.0).abs() < 1e-5);
        assert!(
            (da_v[0] - 5.0).abs() < 1e-5,
            "da should be b=5.0, got {}",
            da_v[0]
        );
        assert!(
            (db_v[0] - 3.0).abs() < 1e-5,
            "db should be a=3.0, got {}",
            db_v[0]
        );
    }

    /// JVP override: f(x) = x but jvp_body returns 2 * tangent_0.
    /// Forward-mode tangent should be 2x the seed (1.0) → 2.0.
    #[test]
    fn custom_fn_jvp_overrides_natural_tangent() {
        use rlx_opt::autodiff_fwd::jvp;
        let s = Shape::new(&[1], DType::F32);

        let mut fwd = Graph::new("id_fwd");
        let x = fwd.input("x", s.clone());
        fwd.set_outputs(vec![x]);

        let mut jvp_g = Graph::new("id_jvp");
        let _x_p = jvp_g.input("x", s.clone());
        let tx = jvp_g.input("tangent_0", s.clone());
        let two_data: Vec<u8> = 2.0_f32.to_le_bytes().to_vec();
        let two = jvp_g.add_node(Op::Constant { data: two_data }, vec![], s.clone());
        let ty = jvp_g.binary(BinaryOp::Mul, tx, two, s.clone());
        jvp_g.set_outputs(vec![ty]);

        let mut g = Graph::new("outer");
        let xin = g.input("x_in", s.clone());
        let cf = g.custom_fn(vec![xin], fwd, None, Some(jvp_g));
        g.set_outputs(vec![cf]);

        let fwd_g = jvp(&g, &[xin]);
        assert_eq!(fwd_g.outputs.len(), 2, "expect [primal_y, tangent_y]");

        let xb = find_named(&fwd_g, "x_in");
        let tan = find_named(&fwd_g, "tangent_x_in");
        let (sched, mut arena) = prepare(&fwd_g, &[(xb, &[7.0]), (tan, &[1.0])]);
        execute_thunks(&sched, arena.raw_buf_mut());
        let y = read_arena(&arena, fwd_g.outputs[0], 1);
        let ty_v = read_arena(&arena, fwd_g.outputs[1], 1);
        assert!((y[0] - 7.0).abs() < 1e-6);
        assert!(
            (ty_v[0] - 2.0).abs() < 1e-6,
            "jvp override should yield t_y=2.0 (natural autodiff would give 1.0), got {}",
            ty_v[0]
        );
    }

    /// IR-level basic test: `DType::C64` is wired through the dtype
    /// table — `size_bytes() == 8`, `is_complex()` reports true, and
    /// a `[2]`-shaped C64 buffer in the arena occupies the expected
    /// 16 bytes.
    #[test]
    fn c64_dtype_storage_layout() {
        assert_eq!(
            DType::C64.size_bytes(),
            8,
            "C64 should be 8 bytes (f32 real + f32 imag)"
        );
        assert!(DType::C64.is_complex());
        assert!(!DType::C64.is_float());

        // A length-2 C64 buffer should have shape size_bytes = 16.
        let s = Shape::new(&[2], DType::C64);
        assert_eq!(s.size_bytes().unwrap(), 16);
    }

    // ── C64 element-wise binary kernel witnesses (2026-05-17) ──────
    //
    // Build a tiny graph: Input `a` + Input `b` (both C64 [2]),
    // output = a OP b. Run through CompileResult and compare against
    // the closed-form complex arithmetic on the four chosen pairs.

    fn run_c64_binary(op: BinaryOp, a: &[(f32, f32)], b: &[(f32, f32)]) -> Vec<(f32, f32)> {
        let n = a.len();
        let s = Shape::new(&[n], DType::C64);
        let mut g = Graph::new("c64_bin");
        let in_a = g.input("a", s.clone());
        let in_b = g.input("b", s.clone());
        let out = g.binary(op, in_a, in_b, s.clone());
        g.set_outputs(vec![out]);

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);

        let a_off = arena.byte_offset(in_a);
        let b_off = arena.byte_offset(in_b);
        let out_off = arena.byte_offset(out);
        // Interleave [re_0, im_0, re_1, im_1, ...] in the f32 buffer.
        let buf = arena.raw_buf_mut();
        unsafe {
            let pa = buf.as_mut_ptr().add(a_off) as *mut f32;
            let pb = buf.as_mut_ptr().add(b_off) as *mut f32;
            for (i, &(re, im)) in a.iter().enumerate() {
                *pa.add(2 * i) = re;
                *pa.add(2 * i + 1) = im;
            }
            for (i, &(re, im)) in b.iter().enumerate() {
                *pb.add(2 * i) = re;
                *pb.add(2 * i + 1) = im;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let raw_out: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(out_off) as *const f32;
            (0..(2 * n)).map(|i| *p.add(i)).collect()
        };
        (0..n)
            .map(|i| (raw_out[2 * i], raw_out[2 * i + 1]))
            .collect()
    }

    #[track_caller]
    fn assert_close_c(got: (f32, f32), expected: (f32, f32), tol: f32, label: &str) {
        let dr = (got.0 - expected.0).abs();
        let di = (got.1 - expected.1).abs();
        assert!(
            dr < tol && di < tol,
            "[{label}] got ({:+.4}, {:+.4}), expected ({:+.4}, {:+.4})",
            got.0,
            got.1,
            expected.0,
            expected.1
        );
    }

    #[test]
    fn c64_binary_add_matches_complex_arithmetic() {
        let a = [(1.0_f32, 2.0_f32), (3.0_f32, -1.0_f32)];
        let b = [(4.0_f32, -1.0_f32), (0.5_f32, 0.5_f32)];
        let out = run_c64_binary(BinaryOp::Add, &a, &b);
        assert_close_c(out[0], (5.0, 1.0), 1e-6, "add[0]");
        assert_close_c(out[1], (3.5, -0.5), 1e-6, "add[1]");
    }

    #[test]
    fn c64_binary_sub_matches_complex_arithmetic() {
        let a = [(5.0_f32, 1.0_f32)];
        let b = [(2.0_f32, 3.0_f32)];
        let out = run_c64_binary(BinaryOp::Sub, &a, &b);
        assert_close_c(out[0], (3.0, -2.0), 1e-6, "sub");
    }

    #[test]
    fn c64_binary_mul_matches_complex_arithmetic() {
        // (1 + 2i)(3 + 4i) = 3 + 4i + 6i + 8i² = -5 + 10i.
        let a = [(1.0_f32, 2.0_f32)];
        let b = [(3.0_f32, 4.0_f32)];
        let out = run_c64_binary(BinaryOp::Mul, &a, &b);
        assert_close_c(out[0], (-5.0, 10.0), 1e-5, "mul");
    }

    #[test]
    fn c64_binary_div_matches_complex_arithmetic() {
        // (1 + 2i) / (3 + 4i) = ((1·3 + 2·4) + (2·3 − 1·4)i) / 25
        //                     = (11 + 2i) / 25
        //                     = 0.44 + 0.08i
        let a = [(1.0_f32, 2.0_f32)];
        let b = [(3.0_f32, 4.0_f32)];
        let out = run_c64_binary(BinaryOp::Div, &a, &b);
        assert_close_c(out[0], (0.44, 0.08), 1e-5, "div");
    }

    #[test]
    fn c64_binary_mul_identity_one_is_no_op() {
        // (a + bi) · (1 + 0i) = a + bi.
        let a = [(3.5_f32, -1.25_f32), (-2.0_f32, 7.0_f32)];
        let b = [(1.0_f32, 0.0_f32), (1.0_f32, 0.0_f32)];
        let out = run_c64_binary(BinaryOp::Mul, &a, &b);
        assert_close_c(out[0], a[0], 1e-6, "mul·1[0]");
        assert_close_c(out[1], a[1], 1e-6, "mul·1[1]");
    }

    #[test]
    fn c64_binary_mul_by_i_rotates_90_degrees() {
        // (a + bi) · i = (a + bi)(0 + i) = -b + ai. 90° CCW rotation.
        let a = [(1.0_f32, 0.0_f32)];
        let b = [(0.0_f32, 1.0_f32)];
        let out = run_c64_binary(BinaryOp::Mul, &a, &b);
        assert_close_c(out[0], (0.0, 1.0), 1e-6, "1·i");
    }

    #[test]
    fn c64_binary_div_by_self_gives_unity() {
        let a = [(2.5_f32, -1.5_f32), (-0.7_f32, 4.2_f32)];
        let out = run_c64_binary(BinaryOp::Div, &a, &a);
        assert_close_c(out[0], (1.0, 0.0), 1e-5, "div_self[0]");
        assert_close_c(out[1], (1.0, 0.0), 1e-5, "div_self[1]");
    }

    #[test]
    #[should_panic(expected = "C64: complex max/min/pow")]
    fn c64_binary_max_is_rejected_at_lowering() {
        run_c64_binary(BinaryOp::Max, &[(1.0_f32, 2.0_f32)], &[(3.0_f32, 4.0_f32)]);
    }

    fn run_c64_activation(act: Activation, a: &[(f32, f32)]) -> Vec<(f32, f32)> {
        let n = a.len();
        let s = Shape::new(&[n], DType::C64);
        let mut g = Graph::new("c64_act");
        let in_a = g.input("a", s.clone());
        let out = g.activation(act, in_a, s.clone());
        g.set_outputs(vec![out]);
        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);
        let a_off = arena.byte_offset(in_a);
        let out_off = arena.byte_offset(out);
        let buf = arena.raw_buf_mut();
        unsafe {
            let pa = buf.as_mut_ptr().add(a_off) as *mut f32;
            for (i, &(re, im)) in a.iter().enumerate() {
                *pa.add(2 * i) = re;
                *pa.add(2 * i + 1) = im;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let raw: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(out_off) as *const f32;
            (0..(2 * n)).map(|i| *p.add(i)).collect()
        };
        (0..n).map(|i| (raw[2 * i], raw[2 * i + 1])).collect()
    }

    #[test]
    fn c64_activation_neg_negates_both_components() {
        let inp = [(3.5_f32, -1.25_f32), (-2.0_f32, 0.0_f32)];
        let out = run_c64_activation(Activation::Neg, &inp);
        assert_close_c(out[0], (-3.5, 1.25), 1e-6, "neg[0]");
        assert_close_c(out[1], (2.0, 0.0), 1e-6, "neg[1]");
    }

    #[test]
    fn c64_activation_exp_matches_euler() {
        // exp(0 + i·π) = -1 + 0i.
        // exp(1 + 0i) = e ≈ 2.71828.
        let inp = [(0.0_f32, std::f32::consts::PI), (1.0_f32, 0.0_f32)];
        let out = run_c64_activation(Activation::Exp, &inp);
        assert_close_c(out[0], (-1.0, 0.0), 1e-5, "exp(iπ)");
        assert_close_c(out[1], (std::f32::consts::E, 0.0), 1e-5, "exp(1)");
    }

    #[test]
    fn c64_activation_log_matches_principal_branch() {
        // log(1 + 0i) = 0.
        // log(0 + i) = log(1) + i·π/2 = 0 + i·π/2.
        // log(-1 + 0i) = 0 + i·π.
        let inp = [(1.0_f32, 0.0_f32), (0.0_f32, 1.0_f32), (-1.0_f32, 0.0_f32)];
        let out = run_c64_activation(Activation::Log, &inp);
        assert_close_c(out[0], (0.0, 0.0), 1e-5, "log(1)");
        assert_close_c(out[1], (0.0, std::f32::consts::FRAC_PI_2), 1e-5, "log(i)");
        assert_close_c(out[2], (0.0, std::f32::consts::PI), 1e-5, "log(-1)");
    }

    #[test]
    fn c64_activation_sqrt_squared_recovers_input() {
        // For positive-real-part inputs, sqrt(z)² should equal z exactly
        // to f32 noise.
        let inp = [(4.0_f32, 0.0_f32), (3.0_f32, 4.0_f32)];
        let roots = run_c64_activation(Activation::Sqrt, &inp);
        // sqrt(4) = 2 + 0i; sqrt(3+4i) = 2 + i (since (2+i)² = 4+4i-1 = 3+4i).
        assert_close_c(roots[0], (2.0, 0.0), 1e-5, "sqrt(4)");
        assert_close_c(roots[1], (2.0, 1.0), 1e-5, "sqrt(3+4i)");
    }

    #[test]
    #[should_panic(expected = "no natural complex extension")]
    fn c64_activation_relu_is_rejected_at_lowering() {
        run_c64_activation(Activation::Relu, &[(1.0_f32, 2.0_f32)]);
    }

    // ── ComplexNormSq + Wirtinger backward witnesses ───────────────

    /// Forward `|z|²`: returns `[n]` f32.
    fn run_complex_norm_sq(z: &[(f32, f32)]) -> Vec<f32> {
        let n = z.len();
        let mut g = Graph::new("cns_fwd");
        let in_z = g.input("z", Shape::new(&[n], DType::C64));
        let out = g.complex_norm_sq(in_z);
        g.set_outputs(vec![out]);
        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);
        let z_off = arena.byte_offset(in_z);
        let out_off = arena.byte_offset(out);
        let buf = arena.raw_buf_mut();
        unsafe {
            let pz = buf.as_mut_ptr().add(z_off) as *mut f32;
            for (i, &(re, im)) in z.iter().enumerate() {
                *pz.add(2 * i) = re;
                *pz.add(2 * i + 1) = im;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        unsafe {
            let p = arena.raw_buf().as_ptr().add(out_off) as *const f32;
            (0..n).map(|i| *p.add(i)).collect()
        }
    }

    /// Backward: given z and upstream g, return dz = g·z element-wise (C64).
    fn run_complex_norm_sq_bwd(z: &[(f32, f32)], g: &[f32]) -> Vec<(f32, f32)> {
        let n = z.len();
        let mut gr = Graph::new("cns_bwd");
        let in_z = gr.input("z", Shape::new(&[n], DType::C64));
        let in_g = gr.input("g", Shape::new(&[n], DType::F32));
        let out = gr.complex_norm_sq_backward(in_z, in_g);
        gr.set_outputs(vec![out]);
        let plan = rlx_opt::memory::plan_memory(&gr);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&gr, &arena);
        let z_off = arena.byte_offset(in_z);
        let g_off = arena.byte_offset(in_g);
        let out_off = arena.byte_offset(out);
        let buf = arena.raw_buf_mut();
        unsafe {
            let pz = buf.as_mut_ptr().add(z_off) as *mut f32;
            let pg = buf.as_mut_ptr().add(g_off) as *mut f32;
            for (i, &(re, im)) in z.iter().enumerate() {
                *pz.add(2 * i) = re;
                *pz.add(2 * i + 1) = im;
            }
            for (i, &v) in g.iter().enumerate() {
                *pg.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        unsafe {
            let p = arena.raw_buf().as_ptr().add(out_off) as *const f32;
            (0..n).map(|i| (*p.add(2 * i), *p.add(2 * i + 1))).collect()
        }
    }

    #[test]
    fn complex_norm_sq_matches_textbook() {
        // |3 + 4i|² = 9 + 16 = 25.
        // |1 + 0i|² = 1.
        // |0 + 0i|² = 0.
        let z = [(3.0_f32, 4.0_f32), (1.0_f32, 0.0_f32), (0.0_f32, 0.0_f32)];
        let out = run_complex_norm_sq(&z);
        assert!((out[0] - 25.0).abs() < 1e-5);
        assert!((out[1] - 1.0).abs() < 1e-6);
        assert!(out[2].abs() < 1e-6);
    }

    #[test]
    fn complex_norm_sq_backward_matches_wirtinger_formula() {
        // Wirtinger: ∂|z|²/∂z̄ = z. With upstream g = 1, dz = z.
        let z = [(3.0_f32, 4.0_f32), (1.5_f32, -2.5_f32)];
        let g = [1.0_f32, 1.0_f32];
        let dz = run_complex_norm_sq_bwd(&z, &g);
        assert_close_c(dz[0], z[0], 1e-6, "dz[0] = g·z[0]");
        assert_close_c(dz[1], z[1], 1e-6, "dz[1] = g·z[1]");
    }

    #[test]
    fn complex_norm_sq_backward_scales_with_upstream() {
        // With upstream g[i] ≠ 1: dz[i] = g[i]·z[i].
        let z = [(2.0_f32, 1.0_f32), (-1.0_f32, 3.0_f32)];
        let g = [0.5_f32, -2.0_f32];
        let dz = run_complex_norm_sq_bwd(&z, &g);
        assert_close_c(dz[0], (1.0, 0.5), 1e-6, "g=0.5 · (2,1)");
        assert_close_c(dz[1], (2.0, -6.0), 1e-6, "g=-2 · (-1,3)");
    }

    /// Multi-output Op::CustomFn via the concat-with-Narrow design
    /// (rlx-ir::Graph::custom_fn_multi). Build a custom_fn whose
    /// fwd_body returns two outputs (x², 2x), then materialize each
    /// via the MultiOutputHandle and verify both numerically.
    #[test]
    fn custom_fn_multi_extracts_each_subgraph_output() {
        use rlx_ir::ops::special::MultiOutputHandle;

        let _ = MultiOutputHandle {
            source: NodeId(0),
            sub_shapes: vec![],
            offsets: vec![],
        }; // import sanity

        // Inner body: input x [3] f32, outputs (x², 2x) both [3] f32.
        let mut body = Graph::new("multi_body");
        let s3 = Shape::new(&[3], DType::F32);
        let x = body.input("x", s3.clone());
        let x_sq = body.binary(BinaryOp::Mul, x, x, s3.clone());
        let two = body.add_node(
            Op::Constant {
                data: vec![
                    2.0_f32.to_le_bytes(),
                    2.0_f32.to_le_bytes(),
                    2.0_f32.to_le_bytes(),
                ]
                .into_iter()
                .flatten()
                .collect(),
            },
            vec![],
            s3.clone(),
        );
        let two_x = body.binary(BinaryOp::Mul, two, x, s3.clone());
        body.set_outputs(vec![x_sq, two_x]);

        // Outer graph: feed in_x → custom_fn_multi → handle.output(0/1).
        let mut outer = Graph::new("multi_outer");
        let in_x = outer.input("xin", s3.clone());
        let handle = outer.custom_fn_multi(vec![in_x], body);
        assert_eq!(handle.n_outputs(), 2);
        let out0 = handle.output(&mut outer, 0); // x²
        let out1 = handle.output(&mut outer, 1); // 2x
        outer.set_outputs(vec![out0, out1]);

        let plan = rlx_opt::memory::plan_memory(&outer);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&outer, &arena);
        let xin_off = arena.byte_offset(in_x);
        let out0_off = arena.byte_offset(out0);
        let out1_off = arena.byte_offset(out1);
        let xs = [1.0_f32, 2.0, 3.0];
        unsafe {
            let p = arena.raw_buf_mut().as_mut_ptr().add(xin_off) as *mut f32;
            for (i, &v) in xs.iter().enumerate() {
                *p.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let out0_v: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(out0_off) as *const f32;
            (0..3).map(|i| *p.add(i)).collect()
        };
        let out1_v: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(out1_off) as *const f32;
            (0..3).map(|i| *p.add(i)).collect()
        };
        // x² = [1, 4, 9]; 2x = [2, 4, 6].
        for i in 0..3 {
            assert!(
                (out0_v[i] - xs[i] * xs[i]).abs() < 1e-5,
                "out0[{i}] = {} != x² = {}",
                out0_v[i],
                xs[i] * xs[i]
            );
            assert!(
                (out1_v[i] - 2.0 * xs[i]).abs() < 1e-5,
                "out1[{i}] = {} != 2x = {}",
                out1_v[i],
                2.0 * xs[i]
            );
        }
    }

    #[test]
    fn complex_norm_sq_gradient_matches_finite_difference() {
        // Numerical sanity: perturb z[0].re by ε, observe Δ|z|² ≈ 2·re·ε.
        let z = [(3.0_f32, 4.0_f32)];
        let eps = 1e-3_f32;
        let v0 = run_complex_norm_sq(&z)[0];
        let z_pert = [(3.0_f32 + eps, 4.0_f32)];
        let v1 = run_complex_norm_sq(&z_pert)[0];
        let fd_re = (v1 - v0) / eps;
        let analytic_re = 2.0 * z[0].0;
        assert!((fd_re - analytic_re).abs() < 1e-2);

        // ∂/∂im at z = (3, 4) is 2·im = 8.
        let z_pert_im = [(3.0_f32, 4.0_f32 + eps)];
        let v2 = run_complex_norm_sq(&z_pert_im)[0];
        let fd_im = (v2 - v0) / eps;
        let analytic_im = 2.0 * z[0].1;
        assert!((fd_im - analytic_im).abs() < 1e-2);

        // Compare with the Wirtinger backward at upstream g = 1.
        // Wirtinger ∂/∂z̄ = z gives dz = (re, im). The "real
        // gradient" wrt (re, im) is 2·(re, im), i.e. 2·dz = (2·re,
        // 2·im) — that's the factor 2 difference between Wirtinger
        // ∂/∂z̄ and the real-vector gradient on (re, im).
        let dz = run_complex_norm_sq_bwd(&z, &[1.0_f32]);
        assert!((2.0 * dz[0].0 - analytic_re).abs() < 1e-5);
        assert!((2.0 * dz[0].1 - analytic_im).abs() < 1e-5);
    }

    /// Direct regression test for the 5-D mid-shape singleton broadcast
    /// (SAM rel_pos pattern: `[bh, h, w, 1, w] + [bh, h, w, h, w]`).
    /// The SAM port worked around this by `concat`-tiling the rhs; this
    /// test verifies the in-graph broadcast path is bit-correct.
    #[test]
    fn binary_full_5d_mid_singleton_broadcast() {
        let bh = 2usize;
        let h = 3;
        let w = 4;
        let f = DType::F32;

        let mut g = Graph::new("bcast_5d");
        let lhs = g.input("lhs", Shape::new(&[bh, h, w, h, w], f));
        // rhs shape with size-1 at axis 3 (mid-shape singleton).
        let rhs = g.input("rhs", Shape::new(&[bh, h, w, 1, w], f));
        let out = g.binary(BinaryOp::Add, lhs, rhs, Shape::new(&[bh, h, w, h, w], f));
        g.set_outputs(vec![out]);

        // Deterministic data.
        let lhs_data: Vec<f32> = (0..bh * h * w * h * w).map(|i| i as f32 * 0.01).collect();
        let rhs_data: Vec<f32> = (0..bh * h * w * w)
            .map(|i| (i as f32 + 100.0) * 0.01)
            .collect();

        // Compute expected output by hand.
        let mut expected = vec![0f32; bh * h * w * h * w];
        for b_ in 0..bh {
            for hq in 0..h {
                for wq in 0..w {
                    for hk in 0..h {
                        for wk in 0..w {
                            let li = (((b_ * h + hq) * w + wq) * h + hk) * w + wk;
                            // rhs has hk dim = 1, so it's always index 0 there.
                            let ri = ((b_ * h + hq) * w + wq) * w + wk;
                            expected[li] = lhs_data[li] + rhs_data[ri];
                        }
                    }
                }
            }
        }

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);
        let lhs_off = arena.byte_offset(lhs);
        let rhs_off = arena.byte_offset(rhs);
        let out_off = arena.byte_offset(out);
        let buf = arena.raw_buf_mut();
        unsafe {
            let p = buf.as_mut_ptr().add(lhs_off) as *mut f32;
            for (i, &v) in lhs_data.iter().enumerate() {
                *p.add(i) = v;
            }
            let p = buf.as_mut_ptr().add(rhs_off) as *mut f32;
            for (i, &v) in rhs_data.iter().enumerate() {
                *p.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let actual: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(out_off) as *const f32;
            (0..bh * h * w * h * w).map(|i| *p.add(i)).collect()
        };

        // Bit-exact check.
        let mut max_diff = 0f32;
        let mut max_idx = 0;
        for i in 0..actual.len() {
            let d = (actual[i] - expected[i]).abs();
            if d > max_diff {
                max_diff = d;
                max_idx = i;
            }
        }
        assert!(
            max_diff < 1e-6,
            "5D mid-shape singleton broadcast wrong: max |Δ| = {max_diff} at idx {max_idx} \
             (actual={}, expected={})",
            actual[max_idx],
            expected[max_idx]
        );
    }

    #[test]
    fn layer_norm2d_and_conv_transpose2d_kernels() {
        let mut out = vec![0f32; 8];
        crate::kernels::layer_norm2d_nchw(
            &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
            &[1.0, 1.0],
            &[0.0, 0.0],
            &mut out,
            1,
            2,
            2,
            2,
            1e-5,
        );
        let mean0: f32 = (1.0 + 3.0) / 2.0;
        assert!((out[0] - mean0).abs() > 0.1);

        let mut up = vec![0f32; 4];
        crate::kernels::conv_transpose2d_nchw(
            &[2.0],
            &[1.0, 0.0, 0.0, 1.0],
            &mut up,
            1,
            1,
            1,
            1,
            1,
            2,
            2,
            2,
            2,
            2,
            2,
            0,
            0,
            1,
            1,
            1,
        );
        assert!((up[0] - 2.0).abs() < 1e-5);
        assert!((up[3] - 2.0).abs() < 1e-5);
    }

    /// End-to-end native-low-precision GEMM oracle: build the full
    /// ScaledQuantScale → ScaledQuantize → ScaledMatMul pipeline through the
    /// thunk path and check the f32-accumulated result tracks a plain f32 TN
    /// matmul (cosine), for every format × scale-layout. This exercises shape
    /// inference, memory planning, the build arms, and both execute paths.
    #[test]
    fn scaled_matmul_oracle_matches_f32() {
        use rlx_ir::ScaledFormat::*;
        use rlx_ir::{ScaleLayout, ScaledFormat};

        fn cosine(a: &[f32], b: &[f32]) -> f32 {
            let dot: f32 = a.iter().zip(b).map(|(x, y)| x * y).sum();
            let na = a.iter().map(|x| x * x).sum::<f32>().sqrt();
            let nb = b.iter().map(|x| x * x).sum::<f32>().sqrt();
            dot / (na * nb)
        }

        #[allow(clippy::too_many_arguments)]
        fn run_scaled(
            lhs: &[f32],
            rhs: &[f32],
            m: usize,
            k: usize,
            n: usize,
            lf: ScaledFormat,
            rf: ScaledFormat,
            layout: ScaleLayout,
        ) -> Vec<f32> {
            let f = DType::F32;
            let u8t = DType::U8;
            let mut g = Graph::new("scaled");
            let lhs_in = g.input("lhs", Shape::new(&[m, k], f));
            let rhs_in = g.input("rhs", Shape::new(&[n, k], f));
            let (ls_shape, rs_shape) = match layout {
                ScaleLayout::PerTensor => (Shape::new(&[1], f), Shape::new(&[1], f)),
                _ => {
                    let nb = k.div_ceil(layout.block() as usize);
                    (Shape::new(&[m, nb], u8t), Shape::new(&[n, nb], u8t))
                }
            };
            let ls = g.add_node(
                Op::ScaledQuantScale {
                    format: lf,
                    scale_layout: layout,
                },
                vec![lhs_in],
                ls_shape,
            );
            let lq = g.add_node(
                Op::ScaledQuantize {
                    format: lf,
                    scale_layout: layout,
                },
                vec![lhs_in, ls],
                Shape::new(&[m, k], u8t),
            );
            let rs = g.add_node(
                Op::ScaledQuantScale {
                    format: rf,
                    scale_layout: layout,
                },
                vec![rhs_in],
                rs_shape,
            );
            let rq = g.add_node(
                Op::ScaledQuantize {
                    format: rf,
                    scale_layout: layout,
                },
                vec![rhs_in, rs],
                Shape::new(&[n, k], u8t),
            );
            let out = g.add_node(
                Op::ScaledMatMul {
                    lhs_format: lf,
                    rhs_format: rf,
                    scale_layout: layout,
                    has_bias: false,
                },
                vec![lq, rq, ls, rs],
                Shape::new(&[m, n], f),
            );
            g.set_outputs(vec![out]);

            let plan = rlx_opt::memory::plan_memory(&g);
            let mut arena = crate::arena::Arena::from_plan(plan);
            let sched = compile_thunks(&g, &arena);
            let lhs_off = arena.byte_offset(lhs_in);
            let rhs_off = arena.byte_offset(rhs_in);
            let out_off = arena.byte_offset(out);
            let buf = arena.raw_buf_mut();
            unsafe {
                let lp = buf.as_mut_ptr().add(lhs_off) as *mut f32;
                for (i, &v) in lhs.iter().enumerate() {
                    *lp.add(i) = v;
                }
                let rp = buf.as_mut_ptr().add(rhs_off) as *mut f32;
                for (i, &v) in rhs.iter().enumerate() {
                    *rp.add(i) = v;
                }
            }
            execute_thunks(&sched, arena.raw_buf_mut());
            unsafe {
                let p = arena.raw_buf().as_ptr().add(out_off) as *const f32;
                (0..m * n).map(|i| *p.add(i)).collect()
            }
        }

        let (m, k, n) = (4usize, 64usize, 8usize);
        let lhs: Vec<f32> = (0..m * k).map(|i| (i as f32 * 0.13).sin() * 1.5).collect();
        let rhs: Vec<f32> = (0..n * k).map(|i| (i as f32 * 0.07).cos() * 1.2).collect();
        let mut reference = vec![0f32; m * n];
        for i in 0..m {
            for j in 0..n {
                let mut acc = 0f32;
                for p in 0..k {
                    acc += lhs[i * k + p] * rhs[j * k + p];
                }
                reference[i * n + j] = acc;
            }
        }

        // Per-tensor scaling across all 7 element formats.
        let cases = [
            (F8E4M3, 0.999f32),
            (F8E5M2, 0.99),
            (F8E4M3Fnuz, 0.999),
            (F8E5M2Fnuz, 0.99),
            (F6E2M3, 0.99),
            (F6E3M2, 0.98),
            (F4E2M1, 0.90),
        ];
        for (fmt, thresh) in cases {
            let out = run_scaled(&lhs, &rhs, m, k, n, fmt, fmt, ScaleLayout::PerTensor);
            let c = cosine(&out, &reference);
            assert!(c >= thresh, "{fmt} per-tensor cosine {c} < {thresh}");
        }

        // Block layouts: MX E8M0 (FP8) and NVFP4 (FP4) — finer scaling, higher fidelity.
        let out_mx = run_scaled(&lhs, &rhs, m, k, n, F8E4M3, F8E4M3, ScaleLayout::mx());
        let c_mx = cosine(&out_mx, &reference);
        assert!(c_mx >= 0.999, "mx-e8m0 e4m3 cosine {c_mx}");
        let out_nv = run_scaled(&lhs, &rhs, m, k, n, F4E2M1, F4E2M1, ScaleLayout::nvfp4());
        let c_nv = cosine(&out_nv, &reference);
        assert!(c_nv >= 0.95, "nvfp4 e2m1 cosine {c_nv}");
    }

    /// `Op::ScaledDequantize` (`decode(code)·scale`) must invert
    /// `Op::ScaledQuantize`: a quantize→dequantize round-trip reconstructs the
    /// original f32 within the format's resolution. Exercises the standalone
    /// dequantizer the `ScaledMatMul` backward graph relies on.
    #[test]
    fn scaled_dequantize_inverts_quantize() {
        use rlx_ir::ScaledFormat::*;
        use rlx_ir::{ScaleLayout, ScaledFormat};

        fn cosine(a: &[f32], b: &[f32]) -> f32 {
            let dot: f32 = a.iter().zip(b).map(|(x, y)| x * y).sum();
            let na = a.iter().map(|x| x * x).sum::<f32>().sqrt();
            let nb = b.iter().map(|x| x * x).sum::<f32>().sqrt();
            dot / (na * nb)
        }

        fn roundtrip(x: &[f32], rows: usize, cols: usize, fmt: ScaledFormat) -> Vec<f32> {
            let f = DType::F32;
            let u8t = DType::U8;
            let layout = ScaleLayout::PerTensor;
            let mut g = Graph::new("dequant_rt");
            let x_in = g.input("x", Shape::new(&[rows, cols], f));
            let scale = g.add_node(
                Op::ScaledQuantScale {
                    format: fmt,
                    scale_layout: layout,
                },
                vec![x_in],
                Shape::new(&[1], f),
            );
            let codes = g.add_node(
                Op::ScaledQuantize {
                    format: fmt,
                    scale_layout: layout,
                },
                vec![x_in, scale],
                Shape::new(&[rows, cols], u8t),
            );
            let recon = g.add_node(
                Op::ScaledDequantize {
                    format: fmt,
                    scale_layout: layout,
                },
                vec![codes, scale],
                Shape::new(&[rows, cols], f),
            );
            g.set_outputs(vec![recon]);

            let plan = rlx_opt::memory::plan_memory(&g);
            let mut arena = crate::arena::Arena::from_plan(plan);
            let sched = compile_thunks(&g, &arena);
            let x_off = arena.byte_offset(x_in);
            let r_off = arena.byte_offset(recon);
            unsafe {
                let p = arena.raw_buf_mut().as_mut_ptr().add(x_off) as *mut f32;
                for (i, &v) in x.iter().enumerate() {
                    *p.add(i) = v;
                }
            }
            execute_thunks(&sched, arena.raw_buf_mut());
            unsafe {
                let p = arena.raw_buf().as_ptr().add(r_off) as *const f32;
                (0..rows * cols).map(|i| *p.add(i)).collect()
            }
        }

        let (rows, cols) = (4usize, 16usize);
        let x: Vec<f32> = (0..rows * cols)
            .map(|i| (i as f32 * 0.21).sin() * 1.7)
            .collect();
        // FP8 reconstructs tightly; FP4 is coarse but still strongly correlated.
        for (fmt, min_cos) in [(F8E4M3, 0.999f32), (F8E5M2, 0.99), (F4E2M1, 0.93)] {
            let recon = roundtrip(&x, rows, cols, fmt);
            assert_eq!(recon.len(), x.len());
            assert!(
                recon.iter().all(|v| v.is_finite()),
                "{fmt:?} produced non-finite"
            );
            let c = cosine(&recon, &x);
            assert!(c >= min_cos, "{fmt:?} round-trip cosine {c} < {min_cos}");
        }
    }

    /// `Op::Fma` computes the single-rounded `a*b + c` elementwise. Verify it
    /// matches `f32::mul_add` (the hardware FMA), and that the `LowerFma`
    /// fallback (`Mul` + `Add`) stays close on a benign case.
    #[test]
    fn fma_matches_mul_add() {
        let f = DType::F32;
        let n = 9usize;
        let a: Vec<f32> = vec![1.5, -2.0, 0.0, 3.25, -1.1, 7.0, -0.5, 2.2, 9.9];
        let b: Vec<f32> = vec![2.0, 0.5, 4.0, -1.0, 6.0, -2.5, 8.0, -3.3, 0.1];
        let c: Vec<f32> = vec![0.25, 1.0, -3.0, 2.0, -0.5, 4.0, 1.5, -2.2, 0.0];

        let mut g = Graph::new("fma");
        let an = g.input("a", Shape::new(&[n], f));
        let bn = g.input("b", Shape::new(&[n], f));
        let cn = g.input("c", Shape::new(&[n], f));
        let out = g.add_node(Op::Fma, vec![an, bn, cn], Shape::new(&[n], f));
        g.set_outputs(vec![out]);

        let actual = run_graph(&g, &[(an, &a), (bn, &b), (cn, &c)], out, n);
        for i in 0..n {
            let expected = a[i].mul_add(b[i], c[i]);
            assert!(
                (actual[i] - expected).abs() <= f32::EPSILON * (1.0 + expected.abs()),
                "fma[{i}]: {} vs mul_add {expected}",
                actual[i]
            );
        }
    }

    /// End-to-end AMP-FP8: take a plain `MatMul` graph, run the
    /// `insert_scaled_matmul` compile pass, then execute the rewritten graph on
    /// CPU and confirm it tracks the f32 matmul. Proves the pass emits a
    /// runnable, numerically-sound graph (rhs transpose + dynamic quantize).
    #[test]
    fn scaled_quant_pass_runs_end_to_end() {
        use rlx_opt::rlx_compile::scaled_quant_insert::{ScaledQuantConfig, insert_scaled_matmul};

        let f = DType::F32;
        let (m, k, n) = (3usize, 16usize, 5usize);
        let mut g = Graph::new("amp_fp8");
        let x = g.input("x", Shape::new(&[m, k], f));
        let w = g.param("w", Shape::new(&[k, n], f));
        let mm = g.matmul(x, w, Shape::new(&[m, n], f));
        g.set_outputs(vec![mm]);

        let g = insert_scaled_matmul(g, ScaledQuantConfig::fp8_e4m3());

        let x_data: Vec<f32> = (0..m * k).map(|i| (i as f32 * 0.11).sin()).collect();
        let w_data: Vec<f32> = (0..k * n).map(|i| (i as f32 * 0.05).cos()).collect();
        // reference NN matmul: out[i,j] = Σ_p x[i,p]·w[p,j]
        let mut reference = vec![0f32; m * n];
        for i in 0..m {
            for j in 0..n {
                let mut acc = 0f32;
                for p in 0..k {
                    acc += x_data[i * k + p] * w_data[p * n + j];
                }
                reference[i * n + j] = acc;
            }
        }

        // Locate the (preserved) input/param + output nodes in the rewritten graph.
        let mut x_id = None;
        let mut w_id = None;
        for node in g.nodes() {
            match &node.op {
                Op::Input { name } if name == "x" => x_id = Some(node.id),
                Op::Param { name } if name == "w" => w_id = Some(node.id),
                _ => {}
            }
        }
        let (x_id, w_id) = (x_id.unwrap(), w_id.unwrap());
        let out_id = g.outputs[0];

        let plan = rlx_opt::memory::plan_memory(&g);
        let mut arena = crate::arena::Arena::from_plan(plan);
        let sched = compile_thunks(&g, &arena);
        let x_off = arena.byte_offset(x_id);
        let w_off = arena.byte_offset(w_id);
        let out_off = arena.byte_offset(out_id);
        let buf = arena.raw_buf_mut();
        unsafe {
            let xp = buf.as_mut_ptr().add(x_off) as *mut f32;
            for (i, &v) in x_data.iter().enumerate() {
                *xp.add(i) = v;
            }
            let wp = buf.as_mut_ptr().add(w_off) as *mut f32;
            for (i, &v) in w_data.iter().enumerate() {
                *wp.add(i) = v;
            }
        }
        execute_thunks(&sched, arena.raw_buf_mut());
        let actual: Vec<f32> = unsafe {
            let p = arena.raw_buf().as_ptr().add(out_off) as *const f32;
            (0..m * n).map(|i| *p.add(i)).collect()
        };

        let dot: f32 = actual.iter().zip(&reference).map(|(a, b)| a * b).sum();
        let na = actual.iter().map(|x| x * x).sum::<f32>().sqrt();
        let nb = reference.iter().map(|x| x * x).sum::<f32>().sqrt();
        let cos = dot / (na * nb);
        assert!(cos >= 0.999, "AMP-fp8 e2e cosine {cos}");
    }
}