laddu_runtime/

cpu.rs

use std::{
    collections::HashMap,
    mem::size_of,
    sync::{Arc, OnceLock},
};

use laddu_autodiff::{AutodiffMode, AutodiffPlan, AutodiffResult, gradient_ir};
use laddu_compile::{
    CachePlan, CompiledModel, ExecutablePlan, ReductionPlan, ReductionTransform,
    SolveComponentPlan, SolveRowMatrixPlan,
};
#[cfg(test)]
use laddu_data::data::accurate::AccurateComplex64;
use laddu_data::{
    BatchLayout,
    data::accurate::AccurateF64,
    data::{CacheStorage, Dataset, EventBatch, MemoryPolicy},
    schema::{Precision as DataPrecision, Schema},
};
use laddu_expr::{
    BinaryOp, ExprGraph, ExprId, ExprNode, P4Component, UnaryOp, ValueKind,
    parameters::{ParamId, ParamLayout, ParamValues},
};
use laddu_kernel::ir::{
    GradientKernelIr, KernelInstruction, KernelValue, KernelValueClass, KernelValueId,
    KernelValueKind, OutputComponent, ScalarKernelIr,
};
use laddu_memory::{FootprintOverflow, MemoryFitRequest, MemoryFootprint};
use nalgebra::{DMatrix, DVector, Dyn, LU};
use num::{
    complex::{Complex, Complex32, Complex64},
    traits::Float,
};
use rayon::prelude::*;

use crate::{JitPolicy, MemoryLease, Precision, RuntimeError, RuntimeResult, execution::Execution};

mod gradient_interpreter;
use gradient_interpreter::GradientInterpreter;

#[cfg(feature = "jit")]
use crate::jit::{JitCacheView, JitGradientKernel, JitPrecision, JitScalarKernel};

const SCALAR_BLOCK_SIZE: usize = 32;

/// Supplies event-dependent scalar values for direct CPU evaluation.
pub trait EventLookup {
    /// Returns the scalar named `name`, or `None` when it is unavailable.
    fn scalar(&self, name: &str) -> Option<f64>;

    /// Returns one component of a named four-momentum.
    fn p4_component(&self, name: &str, component: P4Component) -> Option<f64> {
        let key = format!("{}.{}", name, component.label());
        self.scalar(&key)
    }
}

impl<F> EventLookup for F
where
    F: for<'a> Fn(&'a str) -> Option<f64>,
{
    fn scalar(&self, name: &str) -> Option<f64> {
        self(name)
    }
}

impl EventLookup for HashMap<String, f64> {
    fn scalar(&self, name: &str) -> Option<f64> {
        self.get(name).copied()
    }
}

/// Prepares compiled models for CPU execution.
#[derive(Clone, Debug, Default)]
pub struct CpuBackend;

/// CPU scalar-kernel execution strategy.
#[derive(Copy, Clone, Debug, Default, PartialEq, Eq)]
pub enum CpuExecutionMode {
    /// Prefer JIT execution when available and fall back to interpretation.
    #[default]
    Auto,
    /// Always interpret the scalar kernel.
    Interpreter,
}

/// A compiled model prepared for CPU evaluation.
#[derive(Clone, Debug)]
pub struct CpuPlan {
    precision: Precision,
    graph: ExprGraph,
    params: ParamLayout,
    parameter_slots: Vec<Option<ParamId>>,
    autodiff: AutodiffPlan,
    cache_plan: CachePlan,
    cache_slots: Vec<Option<usize>>,
    cached_evaluation_nodes: Vec<ExprId>,
    cached_value_slots: Vec<Option<usize>>,
    scalar_kernel: Option<ScalarKernelIr>,
    scalar_executor: Option<ScalarExecutor>,
    #[cfg_attr(not(feature = "jit"), allow(dead_code))]
    gradient_executor: GradientExecutor,
    // Direct EventLookup evaluation cannot use block JIT kernels, so f32 plans retain
    // an interpreter fallback even when cached and invariant gradients use the JIT.
    f32_gradient_fallback_real: Option<GradientKernelIr>,
    f32_gradient_fallback_imag: Option<GradientKernelIr>,
    cache_materialization_nodes: Vec<ExprId>,
    solve_components: Vec<Option<SolveComponentPlan>>,
    solve_rhs_elements: Vec<Option<Vec<ExprId>>>,
    solve_row_matrices: Vec<SolveRowMatrixPlan>,
    solve_row_keys: Vec<(ExprId, usize, usize)>,
    factor_matrix_slots: Vec<Option<usize>>,
    factor_matrices: Vec<(ExprId, usize)>,
    constant_factor_slots: Vec<Option<usize>>,
    constant_factors: Vec<Arc<OnceLock<DynamicLu>>>,
}

#[derive(Clone, Debug)]
enum ScalarExecutor {
    Interpreter(ScalarEvaluationPlan),
    #[cfg(feature = "jit")]
    Jit(JitScalarKernel),
}

impl ScalarExecutor {
    fn prepare(
        plan: &ScalarKernelIr,
        mode: CpuExecutionMode,
        precision: Precision,
    ) -> Option<Self> {
        #[cfg(not(feature = "jit"))]
        let _ = precision;
        match mode {
            CpuExecutionMode::Auto => {
                #[cfg(feature = "jit")]
                {
                    let jit_precision = match precision {
                        Precision::F32 => JitPrecision::F32,
                        Precision::Auto | Precision::F64 => JitPrecision::F64,
                    };
                    if let Ok(Some(kernel)) =
                        JitScalarKernel::compile_with_precision(plan, jit_precision)
                    {
                        return Some(Self::Jit(kernel));
                    }
                }
                ScalarEvaluationPlan::from_kernel_ir(plan).map(Self::Interpreter)
            }
            CpuExecutionMode::Interpreter => {
                ScalarEvaluationPlan::from_kernel_ir(plan).map(Self::Interpreter)
            }
        }
    }
}

#[derive(Clone, Debug)]
enum GradientExecutor {
    Interpreter(Option<GradientInterpreter>),
    #[cfg(feature = "jit")]
    Jit(JitGradientKernel),
}

impl GradientExecutor {
    fn prepare(
        plan: Option<&ScalarKernelIr>,
        params: &ParamLayout,
        mode: CpuExecutionMode,
        precision: Precision,
        gradient_ir: Option<(&GradientKernelIr, Option<&GradientKernelIr>)>,
    ) -> AutodiffResult<Self> {
        #[cfg(not(feature = "jit"))]
        let _ = (plan, params, mode, precision, gradient_ir);
        #[cfg(feature = "jit")]
        if mode == CpuExecutionMode::Auto
            && let Some(plan) = plan
            && let Ok(kernel) = (if let Some((real, imag)) = gradient_ir {
                JitGradientKernel::compile_gradient_ir(real, imag, JitPrecision::F32)
            } else {
                JitGradientKernel::compile_with_precision(
                    plan,
                    params.free_params(),
                    match precision {
                        Precision::F32 => JitPrecision::F32,
                        Precision::Auto | Precision::F64 => JitPrecision::F64,
                    },
                )
                .and_then(|kernel| kernel.ok_or_else(|| "missing gradient kernel".into()))
            })
        {
            return Ok(Self::Jit(kernel));
        }
        Ok(Self::Interpreter(
            plan.map(|plan| GradientInterpreter::new(plan, params.free_params()))
                .transpose()?,
        ))
    }
}

#[derive(Clone, Debug)]
struct ScalarEvaluationPlan {
    invariant_instructions: Vec<ScalarInvariantInstruction>,
    invariant_real_slot_count: usize,
    invariant_complex_slot_count: usize,
    event_instructions: Vec<ScalarEventInstruction>,
    event_real_slot_count: usize,
    event_complex_slot_count: usize,
    outputs: Vec<ScalarOperand>,
}

impl ScalarEvaluationPlan {
    fn from_kernel_ir(ir: &ScalarKernelIr) -> Option<Self> {
        Self::from_kernel_values(ir.values(), &[ir.root()])
    }

    fn from_kernel_values(values: &[KernelValue], outputs: &[KernelValueId]) -> Option<Self> {
        let mut required = vec![false; values.len()];
        let mut pending = outputs.to_vec();
        while let Some(id) = pending.pop() {
            if required[id.index()] {
                continue;
            }
            required[id.index()] = true;
            pending.extend(values[id.index()].instruction.operands());
        }
        let mut operands = Vec::with_capacity(values.len());
        let mut invariant_instructions = Vec::new();
        let mut invariant_real_slots = 0;
        let mut invariant_complex_slots = 0;
        let mut event_instructions = Vec::new();

        for (index, value) in values.iter().enumerate() {
            if !required[index] {
                operands.push(None);
                continue;
            }
            if !matches!(value.kind, KernelValueKind::Real | KernelValueKind::Complex) {
                return None;
            }
            let instruction = ScalarInstruction::from_kernel(&value.instruction, &operands);
            let operand = match value.class {
                KernelValueClass::Invariant => match value.kind {
                    KernelValueKind::Real => {
                        let slot = invariant_real_slots;
                        invariant_real_slots += 1;
                        invariant_instructions.push((ScalarSlot::Real(slot), instruction));
                        ScalarOperand::InvariantReal(slot)
                    }
                    KernelValueKind::Complex => {
                        let slot = invariant_complex_slots;
                        invariant_complex_slots += 1;
                        invariant_instructions.push((ScalarSlot::Complex(slot), instruction));
                        ScalarOperand::InvariantComplex(slot)
                    }
                    KernelValueKind::Vector { .. } | KernelValueKind::Matrix { .. } => {
                        unreachable!("aggregate values were rejected before scalar lowering")
                    }
                },
                KernelValueClass::Event => {
                    let slot = event_instructions.len();
                    let output = match value.kind {
                        KernelValueKind::Real => ScalarSlot::Real(slot),
                        KernelValueKind::Complex => ScalarSlot::Complex(slot),
                        KernelValueKind::Vector { .. } | KernelValueKind::Matrix { .. } => {
                            unreachable!("aggregate values were rejected before scalar lowering")
                        }
                    };
                    event_instructions.push((output, instruction));
                    match value.kind {
                        KernelValueKind::Real => ScalarOperand::EventReal(slot),
                        KernelValueKind::Complex => ScalarOperand::EventComplex(slot),
                        KernelValueKind::Vector { .. } | KernelValueKind::Matrix { .. } => {
                            unreachable!("aggregate values were rejected before scalar lowering")
                        }
                    }
                }
            };
            operands.push(Some(operand));
        }

        Some(Self::new(
            invariant_instructions,
            event_instructions,
            outputs
                .iter()
                .map(|output| operands[output.index()].expect("kernel output is required"))
                .collect(),
            invariant_real_slots,
            invariant_complex_slots,
        ))
    }

    fn new(
        invariant_instructions: Vec<(ScalarSlot, ScalarInstruction)>,
        event_instructions: Vec<(ScalarSlot, ScalarInstruction)>,
        outputs: Vec<ScalarOperand>,
        invariant_real_slot_count: usize,
        invariant_complex_slot_count: usize,
    ) -> Self {
        let mut last_use = vec![0; event_instructions.len()];
        for (index, (_, instruction)) in event_instructions.iter().enumerate() {
            instruction.record_event_uses(&mut last_use, index);
        }
        for output in &outputs {
            output.record_event_use(&mut last_use, event_instructions.len());
        }

        let mut logical_to_physical = vec![usize::MAX; event_instructions.len()];
        let mut free_real_slots = Vec::new();
        let mut free_complex_slots = Vec::new();
        let mut next_real_slot = 0;
        let mut next_complex_slot = 0;
        let mut slotted_instructions = Vec::with_capacity(event_instructions.len());

        for (index, (output, instruction)) in event_instructions.into_iter().enumerate() {
            let output_slot = match output {
                ScalarSlot::Real(_) => {
                    let slot = if let Some(slot) = free_real_slots.pop() {
                        slot
                    } else {
                        let slot = next_real_slot;
                        next_real_slot += 1;
                        slot
                    };
                    ScalarSlot::Real(slot)
                }
                ScalarSlot::Complex(_) => {
                    let slot = if let Some(slot) = free_complex_slots.pop() {
                        slot
                    } else {
                        let slot = next_complex_slot;
                        next_complex_slot += 1;
                        slot
                    };
                    ScalarSlot::Complex(slot)
                }
            };
            logical_to_physical[index] = output_slot.index();

            let mut event_inputs = Vec::new();
            instruction.collect_event_slots(&mut event_inputs);
            event_inputs.sort_unstable();
            event_inputs.dedup();

            slotted_instructions.push(ScalarEventInstruction {
                output_slot,
                instruction: instruction.remap_event_operands(&logical_to_physical),
            });

            for input in event_inputs {
                if last_use[input] == index {
                    match output_slot_for_event(&slotted_instructions, input) {
                        ScalarSlot::Real(slot) => free_real_slots.push(slot),
                        ScalarSlot::Complex(slot) => free_complex_slots.push(slot),
                    }
                }
            }
        }

        Self {
            invariant_instructions: invariant_instructions
                .into_iter()
                .map(|(output_slot, instruction)| ScalarInvariantInstruction {
                    output_slot,
                    instruction,
                })
                .collect(),
            invariant_real_slot_count,
            invariant_complex_slot_count,
            event_instructions: slotted_instructions,
            event_real_slot_count: next_real_slot,
            event_complex_slot_count: next_complex_slot,
            outputs: outputs
                .into_iter()
                .map(|output| output.remap_event(&logical_to_physical))
                .collect(),
        }
    }

    fn root(&self) -> ScalarOperand {
        self.outputs[0]
    }
}

fn output_slot_for_event(
    instructions: &[ScalarEventInstruction],
    logical_slot: usize,
) -> ScalarSlot {
    instructions
        .get(logical_slot)
        .map(|instruction| instruction.output_slot)
        .expect("event input is produced by an earlier event instruction")
}

#[derive(Copy, Clone, Debug)]
enum ScalarSlot {
    Real(usize),
    Complex(usize),
}

impl ScalarSlot {
    fn index(self) -> usize {
        match self {
            Self::Real(slot) | Self::Complex(slot) => slot,
        }
    }
}

#[derive(Clone, Default)]
struct ScalarInvariantValues {
    real: Vec<f64>,
    complex: Vec<Complex64>,
}

#[derive(Clone, Default)]
struct ScalarEventWorkspace {
    real: Vec<[f64; SCALAR_BLOCK_SIZE]>,
    complex: Vec<[Complex64; SCALAR_BLOCK_SIZE]>,
}

#[derive(Copy, Clone, Debug)]
enum ScalarOperand {
    InvariantReal(usize),
    InvariantComplex(usize),
    EventReal(usize),
    EventComplex(usize),
}

impl ScalarOperand {
    fn complex_value(
        self,
        invariant: &ScalarInvariantValues,
        event: &ScalarEventWorkspace,
    ) -> Complex64 {
        match self {
            Self::InvariantReal(slot) => Complex64::from(invariant.real[slot]),
            Self::InvariantComplex(slot) => invariant.complex[slot],
            Self::EventReal(slot) => Complex64::from(event.real[slot][0]),
            Self::EventComplex(slot) => event.complex[slot][0],
        }
    }

    fn real_value(self, invariant: &ScalarInvariantValues, event: &ScalarEventWorkspace) -> f64 {
        match self {
            Self::InvariantReal(slot) => invariant.real[slot],
            Self::InvariantComplex(slot) => invariant.complex[slot].re,
            Self::EventReal(slot) => event.real[slot][0],
            Self::EventComplex(slot) => event.complex[slot][0].re,
        }
    }

    fn block_complex_value(
        self,
        invariant: &ScalarInvariantValues,
        event: &ScalarEventWorkspace,
        lane: usize,
    ) -> Complex64 {
        match self {
            Self::InvariantReal(slot) => Complex64::from(invariant.real[slot]),
            Self::InvariantComplex(slot) => invariant.complex[slot],
            Self::EventReal(slot) => Complex64::from(event.real[slot][lane]),
            Self::EventComplex(slot) => event.complex[slot][lane],
        }
    }

    fn block_real_value(
        self,
        invariant: &ScalarInvariantValues,
        event: &ScalarEventWorkspace,
        lane: usize,
    ) -> f64 {
        match self {
            Self::InvariantReal(slot) => invariant.real[slot],
            Self::InvariantComplex(slot) => invariant.complex[slot].re,
            Self::EventReal(slot) => event.real[slot][lane],
            Self::EventComplex(slot) => event.complex[slot][lane].re,
        }
    }

    fn collect_event_slot(self, slots: &mut Vec<usize>) {
        if let Self::EventReal(slot) | Self::EventComplex(slot) = self {
            slots.push(slot);
        }
    }

    fn record_event_use(self, last_use: &mut [usize], instruction_index: usize) {
        if let Self::EventReal(slot) | Self::EventComplex(slot) = self {
            last_use[slot] = instruction_index;
        }
    }

    fn remap_event(self, logical_to_physical: &[usize]) -> Self {
        match self {
            Self::InvariantReal(slot) => Self::InvariantReal(slot),
            Self::InvariantComplex(slot) => Self::InvariantComplex(slot),
            Self::EventReal(slot) => Self::EventReal(logical_to_physical[slot]),
            Self::EventComplex(slot) => Self::EventComplex(logical_to_physical[slot]),
        }
    }
}

#[derive(Clone, Debug)]
enum OperandRun {
    InvariantReal(Vec<usize>),
    InvariantComplex(Vec<usize>),
    EventReal(Vec<usize>),
    EventComplex(Vec<usize>),
}

impl OperandRun {
    fn from_operands(operands: impl IntoIterator<Item = ScalarOperand>) -> Vec<Self> {
        let mut runs = Vec::new();
        for operand in operands {
            match (runs.last_mut(), operand) {
                (Some(Self::InvariantReal(slots)), ScalarOperand::InvariantReal(slot))
                | (Some(Self::InvariantComplex(slots)), ScalarOperand::InvariantComplex(slot))
                | (Some(Self::EventReal(slots)), ScalarOperand::EventReal(slot))
                | (Some(Self::EventComplex(slots)), ScalarOperand::EventComplex(slot)) => {
                    slots.push(slot)
                }
                (_, ScalarOperand::InvariantReal(slot)) => {
                    runs.push(Self::InvariantReal(vec![slot]))
                }
                (_, ScalarOperand::InvariantComplex(slot)) => {
                    runs.push(Self::InvariantComplex(vec![slot]))
                }
                (_, ScalarOperand::EventReal(slot)) => runs.push(Self::EventReal(vec![slot])),
                (_, ScalarOperand::EventComplex(slot)) => runs.push(Self::EventComplex(vec![slot])),
            }
        }
        runs
    }

    fn add_to_complex(
        &self,
        value: &mut Complex64,
        invariant: &ScalarInvariantValues,
        event: &ScalarEventWorkspace,
    ) {
        match self {
            Self::InvariantReal(slots) => {
                for slot in slots {
                    *value += invariant.real[*slot];
                }
            }
            Self::InvariantComplex(slots) => {
                for slot in slots {
                    *value += invariant.complex[*slot];
                }
            }
            Self::EventReal(slots) => {
                for slot in slots {
                    *value += event.real[*slot][0];
                }
            }
            Self::EventComplex(slots) => {
                for slot in slots {
                    *value += event.complex[*slot][0];
                }
            }
        }
    }

    fn add_to_real(
        &self,
        value: &mut f64,
        invariant: &ScalarInvariantValues,
        event: &ScalarEventWorkspace,
    ) {
        match self {
            Self::InvariantReal(slots) => {
                for slot in slots {
                    *value += invariant.real[*slot];
                }
            }
            Self::EventReal(slots) => {
                for slot in slots {
                    *value += event.real[*slot][0];
                }
            }
            Self::InvariantComplex(_) | Self::EventComplex(_) => {
                unreachable!("complex operand appeared in real add instruction")
            }
        }
    }

    fn multiply_into_complex(
        &self,
        value: &mut Complex64,
        invariant: &ScalarInvariantValues,
        event: &ScalarEventWorkspace,
    ) {
        match self {
            Self::InvariantReal(slots) => {
                for slot in slots {
                    *value *= invariant.real[*slot];
                }
            }
            Self::InvariantComplex(slots) => {
                for slot in slots {
                    *value *= invariant.complex[*slot];
                }
            }
            Self::EventReal(slots) => {
                for slot in slots {
                    *value *= event.real[*slot][0];
                }
            }
            Self::EventComplex(slots) => {
                for slot in slots {
                    *value *= event.complex[*slot][0];
                }
            }
        }
    }

    fn multiply_into_real(
        &self,
        value: &mut f64,
        invariant: &ScalarInvariantValues,
        event: &ScalarEventWorkspace,
    ) {
        match self {
            Self::InvariantReal(slots) => {
                for slot in slots {
                    *value *= invariant.real[*slot];
                }
            }
            Self::EventReal(slots) => {
                for slot in slots {
                    *value *= event.real[*slot][0];
                }
            }
            Self::InvariantComplex(_) | Self::EventComplex(_) => {
                unreachable!("complex operand appeared in real multiply instruction")
            }
        }
    }

    fn collect_event_slots(&self, slots: &mut Vec<usize>) {
        if let Self::EventReal(event_slots) | Self::EventComplex(event_slots) = self {
            slots.extend(event_slots);
        }
    }

    fn record_event_uses(&self, last_use: &mut [usize], instruction_index: usize) {
        if let Self::EventReal(event_slots) | Self::EventComplex(event_slots) = self {
            for slot in event_slots {
                last_use[*slot] = instruction_index;
            }
        }
    }

    fn remap_events(&self, logical_to_physical: &[usize]) -> Self {
        match self {
            Self::InvariantReal(slots) => Self::InvariantReal(slots.clone()),
            Self::InvariantComplex(slots) => Self::InvariantComplex(slots.clone()),
            Self::EventReal(slots) => Self::EventReal(
                slots
                    .iter()
                    .map(|slot| logical_to_physical[*slot])
                    .collect(),
            ),
            Self::EventComplex(slots) => Self::EventComplex(
                slots
                    .iter()
                    .map(|slot| logical_to_physical[*slot])
                    .collect(),
            ),
        }
    }
}

#[derive(Clone, Debug)]
enum ScalarInstruction {
    Cached(usize),
    Constant(Complex64),
    Parameter(ParamId),
    Unary {
        op: UnaryOp,
        input: ScalarOperand,
    },
    Binary {
        op: BinaryOp,
        lhs: ScalarOperand,
        rhs: ScalarOperand,
    },
    Add(Vec<OperandRun>),
    Mul(Vec<OperandRun>),
    Complex {
        re: ScalarOperand,
        im: ScalarOperand,
    },
    SolveRow {
        row_slot: usize,
        rhs: Vec<ScalarOperand>,
    },
    SolveRowAdjointElement {
        row_slot: usize,
        index: usize,
        len: usize,
        adjoint: ScalarOperand,
    },
}

impl ScalarInstruction {
    fn from_kernel(instruction: &KernelInstruction, operands: &[Option<ScalarOperand>]) -> Self {
        let operand = |id: KernelValueId| {
            operands[id.index()].expect("required instruction operand was lowered")
        };
        match instruction {
            KernelInstruction::Cached(slot) => Self::Cached(*slot),
            KernelInstruction::RealConstant(value) => Self::Constant(Complex64::from(*value)),
            KernelInstruction::ComplexConstant(value) => Self::Constant(*value),
            KernelInstruction::Parameter(id) => Self::Parameter(*id),
            KernelInstruction::Unary { op, input } => Self::Unary {
                op: *op,
                input: operand(*input),
            },
            KernelInstruction::Binary { op, lhs, rhs } => Self::Binary {
                op: *op,
                lhs: operand(*lhs),
                rhs: operand(*rhs),
            },
            KernelInstruction::Add(terms) => Self::Add(OperandRun::from_operands(
                terms.iter().map(|id| operand(*id)),
            )),
            KernelInstruction::Mul(factors) => Self::Mul(OperandRun::from_operands(
                factors.iter().map(|id| operand(*id)),
            )),
            KernelInstruction::Complex { re, im } => Self::Complex {
                re: operand(*re),
                im: operand(*im),
            },
            KernelInstruction::SolveRow { row_slot, rhs } => Self::SolveRow {
                row_slot: *row_slot,
                rhs: rhs.iter().map(|id| operand(*id)).collect(),
            },
            KernelInstruction::SolveRowAdjointElement {
                row_slot,
                index,
                len,
                adjoint,
            } => Self::SolveRowAdjointElement {
                row_slot: *row_slot,
                index: *index,
                len: *len,
                adjoint: operand(*adjoint),
            },
            KernelInstruction::Vector(_)
            | KernelInstruction::Matrix { .. }
            | KernelInstruction::Component { .. }
            | KernelInstruction::MatrixElement { .. }
            | KernelInstruction::MatMul { .. }
            | KernelInstruction::MatVec { .. }
            | KernelInstruction::Dot { .. }
            | KernelInstruction::Solve { .. } => {
                unreachable!("aggregate instruction cannot enter the scalar interpreter")
            }
        }
    }

    fn collect_event_slots(&self, slots: &mut Vec<usize>) {
        match self {
            Self::Cached(_) | Self::Constant(_) | Self::Parameter(_) => {}
            Self::Unary { input, .. } => input.collect_event_slot(slots),
            Self::Binary { lhs, rhs, .. } => {
                lhs.collect_event_slot(slots);
                rhs.collect_event_slot(slots);
            }
            Self::Add(runs) | Self::Mul(runs) => {
                for run in runs {
                    run.collect_event_slots(slots);
                }
            }
            Self::Complex { re, im } => {
                re.collect_event_slot(slots);
                im.collect_event_slot(slots);
            }
            Self::SolveRow { rhs, .. } => {
                for operand in rhs {
                    operand.collect_event_slot(slots);
                }
            }
            Self::SolveRowAdjointElement { adjoint, .. } => {
                adjoint.collect_event_slot(slots);
            }
        }
    }

    fn record_event_uses(&self, last_use: &mut [usize], instruction_index: usize) {
        match self {
            Self::Cached(_) | Self::Constant(_) | Self::Parameter(_) => {}
            Self::Unary { input, .. } => input.record_event_use(last_use, instruction_index),
            Self::Binary { lhs, rhs, .. } => {
                lhs.record_event_use(last_use, instruction_index);
                rhs.record_event_use(last_use, instruction_index);
            }
            Self::Add(runs) | Self::Mul(runs) => {
                for run in runs {
                    run.record_event_uses(last_use, instruction_index);
                }
            }
            Self::Complex { re, im } => {
                re.record_event_use(last_use, instruction_index);
                im.record_event_use(last_use, instruction_index);
            }
            Self::SolveRow { rhs, .. } => {
                for operand in rhs {
                    operand.record_event_use(last_use, instruction_index);
                }
            }
            Self::SolveRowAdjointElement { adjoint, .. } => {
                adjoint.record_event_use(last_use, instruction_index);
            }
        }
    }

    fn remap_event_operands(self, logical_to_physical: &[usize]) -> Self {
        match self {
            Self::Cached(slot) => Self::Cached(slot),
            Self::Constant(value) => Self::Constant(value),
            Self::Parameter(id) => Self::Parameter(id),
            Self::Unary { op, input } => Self::Unary {
                op,
                input: input.remap_event(logical_to_physical),
            },
            Self::Binary { op, lhs, rhs } => Self::Binary {
                op,
                lhs: lhs.remap_event(logical_to_physical),
                rhs: rhs.remap_event(logical_to_physical),
            },
            Self::Add(runs) => Self::Add(
                runs.iter()
                    .map(|run| run.remap_events(logical_to_physical))
                    .collect(),
            ),
            Self::Mul(runs) => Self::Mul(
                runs.iter()
                    .map(|run| run.remap_events(logical_to_physical))
                    .collect(),
            ),
            Self::Complex { re, im } => Self::Complex {
                re: re.remap_event(logical_to_physical),
                im: im.remap_event(logical_to_physical),
            },
            Self::SolveRow { row_slot, rhs } => Self::SolveRow {
                row_slot,
                rhs: rhs
                    .iter()
                    .map(|operand| operand.remap_event(logical_to_physical))
                    .collect(),
            },
            Self::SolveRowAdjointElement {
                row_slot,
                index,
                len,
                adjoint,
            } => Self::SolveRowAdjointElement {
                row_slot,
                index,
                len,
                adjoint: adjoint.remap_event(logical_to_physical),
            },
        }
    }

    fn evaluate_real(
        &self,
        params: Option<&ParamValues>,
        cache: Option<(&CpuBatchCache, usize)>,
        invariant: &ScalarInvariantValues,
        event: &ScalarEventWorkspace,
    ) -> RuntimeResult<f64> {
        Ok(match self {
            Self::Cached(slot) => {
                cache
                    .expect("cached instruction requires an event cache")
                    .0
                    .scalar(
                        *slot,
                        cache.expect("cached instruction requires an event cache").1,
                    )?
                    .re
            }
            Self::Constant(value) => value.re,
            Self::Parameter(id) => params
                .expect("parameter instruction requires parameter values")
                .get(*id)
                .map_err(|err| RuntimeError::Parameter(err.to_string()))?,
            Self::Unary { op, input } => match op {
                UnaryOp::Neg => -input.real_value(invariant, event),
                UnaryOp::Real | UnaryOp::Conj => input.complex_value(invariant, event).re,
                UnaryOp::Imag => input.complex_value(invariant, event).im,
                UnaryOp::NormSqr => input.complex_value(invariant, event).norm_sqr(),
                UnaryOp::Sqrt => input.real_value(invariant, event).sqrt(),
                UnaryOp::Exp => input.real_value(invariant, event).exp(),
                UnaryOp::Sin => input.real_value(invariant, event).sin(),
                UnaryOp::Cos => input.real_value(invariant, event).cos(),
                UnaryOp::Log => input.real_value(invariant, event).ln(),
                UnaryOp::PowI(power) => input.real_value(invariant, event).powi(*power),
            },
            Self::Binary { op, lhs, rhs } => {
                let lhs = lhs.real_value(invariant, event);
                let rhs = rhs.real_value(invariant, event);
                match op {
                    BinaryOp::Add => lhs + rhs,
                    BinaryOp::Sub => lhs - rhs,
                    BinaryOp::Mul => lhs * rhs,
                    BinaryOp::Div => lhs / rhs,
                    BinaryOp::Atan2 => lhs.atan2(rhs),
                }
            }
            Self::Add(runs) => {
                let mut value = 0.0;
                for run in runs {
                    run.add_to_real(&mut value, invariant, event);
                }
                value
            }
            Self::Mul(runs) => {
                let mut value = 1.0;
                for run in runs {
                    run.multiply_into_real(&mut value, invariant, event);
                }
                value
            }
            Self::Complex { .. } | Self::SolveRow { .. } | Self::SolveRowAdjointElement { .. } => {
                unreachable!("complex-only instruction appeared in real scalar slot")
            }
        })
    }

    fn evaluate_complex(
        &self,
        params: Option<&ParamValues>,
        cache: Option<(&CpuBatchCache, usize)>,
        invariant: &ScalarInvariantValues,
        event: &ScalarEventWorkspace,
    ) -> RuntimeResult<Complex64> {
        Ok(match self {
            Self::Cached(slot) => cache
                .expect("cached instruction requires an event cache")
                .0
                .scalar(
                    *slot,
                    cache.expect("cached instruction requires an event cache").1,
                )?,
            Self::Constant(value) => *value,
            Self::Parameter(id) => Complex64::from(
                params
                    .expect("parameter instruction requires parameter values")
                    .get(*id)
                    .map_err(|err| RuntimeError::Parameter(err.to_string()))?,
            ),
            Self::Unary { op, input } => eval_unary(*op, input.complex_value(invariant, event)),
            Self::Binary { op, lhs, rhs } => eval_binary(
                *op,
                lhs.complex_value(invariant, event),
                rhs.complex_value(invariant, event),
            ),
            Self::Add(runs) => {
                let mut value = Complex64::ZERO;
                for run in runs {
                    run.add_to_complex(&mut value, invariant, event);
                }
                value
            }
            Self::Mul(runs) => {
                let mut value = Complex64::ONE;
                for run in runs {
                    run.multiply_into_complex(&mut value, invariant, event);
                }
                value
            }
            Self::Complex { re, im } => Complex64::new(
                re.real_value(invariant, event),
                im.real_value(invariant, event),
            ),
            Self::SolveRow { row_slot, rhs } => {
                let (cache, row) = cache.expect("solve row instruction requires an event cache");
                let inverse_row = cache.solve_row(*row_slot, row)?;
                if inverse_row.len() != rhs.len() {
                    return Err(RuntimeError::InvalidShape {
                        index: row,
                        message: format!(
                            "specialized solve row has len {}, expected {}",
                            inverse_row.len(),
                            rhs.len()
                        ),
                    });
                }
                inverse_row
                    .iter()
                    .zip(rhs)
                    .map(|(lhs, operand)| lhs * operand.complex_value(invariant, event))
                    .sum()
            }
            Self::SolveRowAdjointElement {
                row_slot,
                index,
                len,
                adjoint,
            } => {
                let (cache, row) =
                    cache.expect("solve-row adjoint instruction requires an event cache");
                let inverse_row = cache.solve_row(*row_slot, row)?;
                if inverse_row.len() != *len {
                    return Err(RuntimeError::InvalidShape {
                        index: row,
                        message: format!(
                            "specialized solve row has len {}, expected {len}",
                            inverse_row.len()
                        ),
                    });
                }
                adjoint.complex_value(invariant, event) * inverse_row[*index].conj()
            }
        })
    }
}

#[derive(Clone, Debug)]
struct ScalarEventInstruction {
    output_slot: ScalarSlot,
    instruction: ScalarInstruction,
}

#[derive(Clone, Debug)]
struct ScalarInvariantInstruction {
    output_slot: ScalarSlot,
    instruction: ScalarInstruction,
}

impl CpuBackend {
    /// Prepares a model using the policies resolved by an execution context.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when model lowering or differentiation fails,
    /// or the requested precision is unsupported for the model.
    pub fn prepare_for_execution(
        &self,
        model: &CompiledModel,
        execution: &Execution,
    ) -> RuntimeResult<CpuPlan> {
        let mode = match execution.jit_policy() {
            JitPolicy::Auto | JitPolicy::Enabled => CpuExecutionMode::Auto,
            JitPolicy::Disabled => CpuExecutionMode::Interpreter,
        };
        let plan = self
            .prepare_with_modes_precision(
                model,
                execution.autodiff_mode(),
                mode,
                execution.precision(),
            )
            .map_err(|error| RuntimeError::Data(error.to_string()))?;
        if execution.precision() == Precision::F32 && !plan.supports_f32_scalar_execution() {
            return Err(crate::ExecutionError::UnsupportedCpuF32Model.into());
        }
        Ok(plan)
    }

    /// Prepares a model with forward autodiff and automatic execution-mode selection.
    ///
    /// # Panics
    ///
    /// Panics if forward differentiation or executable-plan construction fails
    /// for the compiled model.
    pub fn prepare(&self, model: &CompiledModel) -> CpuPlan {
        self.prepare_with_modes(model, AutodiffMode::Forward, CpuExecutionMode::Auto)
            .expect("forward autodiff supports every compiled expression node")
    }

    /// Prepares a model with an explicit scalar-kernel execution mode.
    ///
    /// # Panics
    ///
    /// Panics if forward differentiation or executable-plan construction fails
    /// for the compiled model.
    pub fn prepare_with_execution_mode(
        &self,
        model: &CompiledModel,
        execution_mode: CpuExecutionMode,
    ) -> CpuPlan {
        self.prepare_with_modes(model, AutodiffMode::Forward, execution_mode)
            .expect("forward autodiff supports every compiled expression node")
    }

    /// Prepares a model with an explicit automatic-differentiation mode.
    ///
    /// # Errors
    ///
    /// Returns [`laddu_autodiff::AutodiffError`] when model lowering or
    /// differentiation fails.
    pub fn prepare_with_autodiff_mode(
        &self,
        model: &CompiledModel,
        mode: AutodiffMode,
    ) -> AutodiffResult<CpuPlan> {
        self.prepare_with_modes(model, mode, CpuExecutionMode::Auto)
    }

    /// Prepares a model with explicit autodiff and scalar execution modes.
    ///
    /// # Errors
    ///
    /// Returns [`laddu_autodiff::AutodiffError`] when model lowering or
    /// differentiation fails.
    pub fn prepare_with_modes(
        &self,
        model: &CompiledModel,
        autodiff_mode: AutodiffMode,
        execution_mode: CpuExecutionMode,
    ) -> AutodiffResult<CpuPlan> {
        self.prepare_with_modes_precision(model, autodiff_mode, execution_mode, Precision::F64)
    }

    fn prepare_with_modes_precision(
        &self,
        model: &CompiledModel,
        autodiff_mode: AutodiffMode,
        execution_mode: CpuExecutionMode,
        precision: Precision,
    ) -> AutodiffResult<CpuPlan> {
        let executable = ExecutablePlan::from_model(model)
            .map_err(|error| laddu_autodiff::AutodiffError::InvalidKernel(error.to_string()))?;
        let scalar_kernel = executable.scalar_kernel().cloned();
        let scalar_executor = scalar_kernel
            .as_ref()
            .and_then(|kernel| ScalarExecutor::prepare(kernel, execution_mode, precision));
        let f32_gradient_fallback_real = if precision == Precision::F32 {
            scalar_kernel
                .as_ref()
                .map(|kernel| {
                    gradient_ir(kernel, model.params().free_params(), OutputComponent::Real)
                })
                .transpose()?
        } else {
            None
        };
        let f32_gradient_fallback_imag = if precision == Precision::F32
            && scalar_kernel.as_ref().is_some_and(|kernel| {
                kernel.values()[kernel.root().index()].kind == KernelValueKind::Complex
            }) {
            scalar_kernel
                .as_ref()
                .map(|kernel| {
                    gradient_ir(kernel, model.params().free_params(), OutputComponent::Imag)
                })
                .transpose()?
        } else {
            None
        };
        let gradient_executor = GradientExecutor::prepare(
            scalar_kernel.as_ref(),
            model.params(),
            execution_mode,
            precision,
            f32_gradient_fallback_real
                .as_ref()
                .map(|real| (real, f32_gradient_fallback_imag.as_ref())),
        )?;
        let constant_factors = executable
            .constant_factor_matrices()
            .iter()
            .map(|_| Arc::new(OnceLock::new()))
            .collect();
        Ok(CpuPlan {
            precision,
            graph: executable.graph().clone(),
            params: executable.params().clone(),
            parameter_slots: executable.parameter_slots().to_vec(),
            autodiff: AutodiffPlan::from_model(model, autodiff_mode)?,
            cache_plan: executable.cache_plan().clone(),
            cache_slots: executable.cache_slots().to_vec(),
            cached_evaluation_nodes: executable.evaluation_nodes().to_vec(),
            cached_value_slots: executable.value_slots().to_vec(),
            scalar_kernel,
            scalar_executor,
            gradient_executor,
            f32_gradient_fallback_real,
            f32_gradient_fallback_imag,
            cache_materialization_nodes: executable.cache_materialization_nodes().to_vec(),
            solve_components: executable.solve_components().to_vec(),
            solve_rhs_elements: executable.solve_rhs_elements().to_vec(),
            solve_row_matrices: executable.solve_row_matrices().to_vec(),
            solve_row_keys: executable.solve_row_keys().to_vec(),
            factor_matrix_slots: executable.factor_matrix_slots().to_vec(),
            factor_matrices: executable.factor_matrices().to_vec(),
            constant_factor_slots: executable.constant_factor_slots().to_vec(),
            constant_factors,
        })
    }
}

/// A complex model value and its derivatives with respect to free parameters.
#[derive(Clone, Debug, PartialEq)]
pub struct ValueGradient {
    value: Complex64,
    gradient: Vec<Complex64>,
}

/// The scalar value and free-parameter gradient produced by a reduction.
#[derive(Clone, Debug, PartialEq)]
pub struct ReductionEvaluation {
    value: f64,
    gradient: Vec<f64>,
}

impl ReductionEvaluation {
    #[cfg(feature = "wgpu")]
    pub(crate) fn new(value: f64, gradient: Vec<f64>) -> Self {
        Self { value, gradient }
    }

    /// Returns the reduced scalar value.
    pub fn value(&self) -> f64 {
        self.value
    }

    /// Returns derivatives in free-parameter order.
    pub fn gradient(&self) -> &[f64] {
        &self.gradient
    }

    /// Consumes the evaluation and returns its value and gradient.
    pub fn into_parts(self) -> (f64, Vec<f64>) {
        (self.value, self.gradient)
    }
}

impl ValueGradient {
    /// Returns the complex model value.
    pub fn value(&self) -> Complex64 {
        self.value
    }

    /// Returns complex derivatives in free-parameter order.
    pub fn gradient(&self) -> &[Complex64] {
        &self.gradient
    }

    /// Consumes the evaluation and returns its value and gradient.
    pub fn into_parts(self) -> (Complex64, Vec<Complex64>) {
        (self.value, self.gradient)
    }
}

struct RealGradientAccumulator {
    value: AccurateF64,
    gradient: Vec<AccurateF64>,
}

impl RealGradientAccumulator {
    fn zero(parameter_count: usize) -> Self {
        Self {
            value: AccurateF64::zero(),
            gradient: (0..parameter_count).map(|_| AccurateF64::zero()).collect(),
        }
    }

    fn push(&mut self, weight: f64, value: f64, derivative: f64, model_gradient: &[Complex64]) {
        self.value.push(weight * value);
        for (sum, model_derivative) in self.gradient.iter_mut().zip(model_gradient) {
            sum.push(weight * derivative * model_derivative.re);
        }
    }

    fn push_f32(&mut self, weight: f64, value: f64, derivative: f64, model_gradient: &[f32]) {
        self.value.push(weight * value);
        for (sum, model_derivative) in self.gradient.iter_mut().zip(model_gradient) {
            sum.push(weight * derivative * f64::from(*model_derivative));
        }
    }

    fn merge(&mut self, other: Self) {
        self.value.merge(other.value);
        for (target, source) in self.gradient.iter_mut().zip(other.gradient) {
            target.merge(source);
        }
    }

    fn finish(self) -> (f64, Vec<f64>) {
        (
            self.value.finish(),
            self.gradient.into_iter().map(AccurateF64::finish).collect(),
        )
    }
}

#[derive(Copy, Clone, Debug)]
struct DatasetScanStats {
    events: usize,
    batches: usize,
    sum_weights: f64,
}

fn scan_dataset_stats(
    dataset: &Dataset,
    read_plan: laddu_data::io::ReadPlan,
) -> RuntimeResult<DatasetScanStats> {
    let mut events = 0;
    let mut batches = 0;
    let mut sum_weights = AccurateF64::zero();
    for batch in dataset
        .stream_with_plan(read_plan)
        .map_err(|error| RuntimeError::Data(error.to_string()))?
    {
        let batch = batch.map_err(|error| RuntimeError::Data(error.to_string()))?;
        events += batch.len();
        batches += 1;
        for row in 0..batch.len() {
            sum_weights.push(batch.weights_at(row));
        }
    }
    Ok(DatasetScanStats {
        events,
        batches,
        sum_weights: sum_weights.finish(),
    })
}

impl CpuPlan {
    fn supports_f32_scalar_execution(&self) -> bool {
        self.scalar_kernel.as_ref().is_some_and(|kernel| {
            kernel.values().iter().all(|value| {
                matches!(value.kind, KernelValueKind::Real | KernelValueKind::Complex)
                    || !matches!(
                        value.instruction,
                        KernelInstruction::SolveRowAdjointElement { .. }
                    )
            })
        })
    }

    fn scalar_interpreter_plan(&self) -> Option<&ScalarEvaluationPlan> {
        match (&self.scalar_kernel, &self.scalar_executor) {
            (Some(_), Some(ScalarExecutor::Interpreter(plan))) => Some(plan),
            #[cfg(feature = "jit")]
            (Some(_), Some(ScalarExecutor::Jit(_))) => None,
            (Some(_), None) | (None, None) => None,
            (None, Some(_)) => unreachable!("executor requires kernel IR"),
        }
    }

    #[cfg(feature = "jit")]
    fn scalar_jit_kernel(&self) -> Option<&JitScalarKernel> {
        match (&self.scalar_kernel, &self.scalar_executor) {
            (Some(_), Some(ScalarExecutor::Jit(kernel))) => Some(kernel),
            (Some(_), Some(ScalarExecutor::Interpreter(_))) | (Some(_), None) | (None, None) => {
                None
            }
            (None, Some(_)) => unreachable!("executor requires kernel IR"),
        }
    }

    #[cfg(feature = "jit")]
    fn gradient_jit_kernel(&self) -> Option<&JitGradientKernel> {
        match &self.gradient_executor {
            GradientExecutor::Jit(kernel) => Some(kernel),
            GradientExecutor::Interpreter(_) => None,
        }
    }

    fn gradient_interpreter(&self) -> Option<&GradientInterpreter> {
        match &self.gradient_executor {
            GradientExecutor::Interpreter(interpreter) => interpreter.as_ref(),
            #[cfg(feature = "jit")]
            GradientExecutor::Jit(_) => None,
        }
    }

    fn parameter_value(&self, params: &ParamValues, node: usize) -> RuntimeResult<f64> {
        let id = self.parameter_slots[node].ok_or_else(|| RuntimeError::InvalidShape {
            index: node,
            message: "node is not a parameter".into(),
        })?;
        params
            .get(id)
            .map_err(|err| RuntimeError::Parameter(err.to_string()))
    }

    /// Returns the number of parameters, including fixed parameters.
    pub fn parameter_count(&self) -> usize {
        self.params.len()
    }

    /// Returns the number of free parameters.
    pub fn free_parameter_count(&self) -> usize {
        self.params.n_free()
    }

    /// Returns the event-cache layout required by this plan.
    pub fn cache_plan(&self) -> &CachePlan {
        &self.cache_plan
    }

    /// Evaluates a model that has no event-dependent inputs.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when parameters are incompatible, the model
    /// requires event data, evaluation fails, or a matrix is singular.
    pub fn evaluate(&self, params: &ParamValues) -> RuntimeResult<Complex64> {
        self.evaluate_inner(params, None)
    }

    /// Evaluates an event-independent model and its free-parameter gradient.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when parameters are incompatible, the model
    /// requires event data, differentiation or evaluation fails, or a solve is
    /// singular.
    pub fn evaluate_with_gradient(&self, params: &ParamValues) -> RuntimeResult<ValueGradient> {
        #[cfg(feature = "jit")]
        if let (Some(value_kernel), Some(gradient_kernel)) =
            (self.scalar_jit_kernel(), self.gradient_jit_kernel())
        {
            let value = value_kernel.evaluate_invariant(params)?;
            let mut real = Vec::new();
            let mut imag = Vec::new();
            gradient_kernel.evaluate_invariant_component(params, 0, &mut real)?;
            gradient_kernel.evaluate_invariant_component(params, 1, &mut imag)?;
            let gradient = real
                .into_iter()
                .zip(imag)
                .map(|(re, im)| Complex64::new(re, im))
                .collect();
            return Ok(ValueGradient { value, gradient });
        }
        if self.precision == Precision::F32 {
            return self.evaluate_f32_gradient(params, F32KernelInput::Cache(None));
        }
        self.require_f64_gradient()?;
        if let Some(interpreter) = self.gradient_interpreter() {
            let (value, gradient) = interpreter.evaluate(params, None)?;
            return Ok(ValueGradient { value, gradient });
        }
        let values = self.evaluate_values(params, None)?;
        self.value_gradient(values, None)
    }

    /// Evaluates the model using values supplied by an event lookup.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when a required event value is missing,
    /// parameters are incompatible, evaluation fails, or a solve is singular.
    pub fn evaluate_with_event(
        &self,
        params: &ParamValues,
        event: &impl EventLookup,
    ) -> RuntimeResult<Complex64> {
        self.evaluate_inner(params, Some(event))
    }

    /// Evaluates the model and gradient using values supplied by an event lookup.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when a required event value is missing,
    /// parameters are incompatible, or differentiation or evaluation fails.
    pub fn evaluate_with_event_and_gradient(
        &self,
        params: &ParamValues,
        event: &impl EventLookup,
    ) -> RuntimeResult<ValueGradient> {
        if self.precision == Precision::F32 {
            return self.evaluate_f32_gradient(params, F32KernelInput::Event(event));
        }
        self.require_f64_gradient()?;
        let values = self.evaluate_values(params, Some(event))?;
        self.value_gradient(values, None)
    }

    /// Materializes the event-dependent cache for a batch.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when required columns are missing, expression
    /// shapes are invalid, cache construction fails, or a matrix is singular.
    ///
    /// # Panics
    ///
    /// Panics if a node selected by the validated cache plan was not evaluated.
    pub fn cache_event_batch(&self, batch: &EventBatch) -> RuntimeResult<CpuBatchCache> {
        let event_columns = self.event_columns(batch.schema())?;
        let mut cache = CpuBatchCache::new(
            &self.cache_plan,
            &self.factor_matrices,
            &self.solve_row_keys,
            batch.len(),
        );
        for row in 0..batch.len() {
            let values = self.evaluate_cache_values_for_row(batch, row, &event_columns)?;
            for (slot, entry) in self.cache_plan.entries().iter().enumerate() {
                let value = values[entry.node().index()]
                    .as_ref()
                    .expect("cacheable node should have been evaluated")
                    .clone();
                cache.push(slot, value)?;
            }
            for plan in &self.solve_row_matrices {
                let (rows, cols, values) = matrix_at_optional(&values, plan.matrix().index())?;
                if rows != plan.dimension() || cols != plan.dimension() {
                    return Err(RuntimeError::InvalidShape {
                        index: plan.matrix().index(),
                        message: format!(
                            "specialized solve expected a {}x{} matrix, got {rows}x{cols}",
                            plan.dimension(),
                            plan.dimension()
                        ),
                    });
                }
                let transpose_factor = DMatrix::from_row_slice(rows, cols, values).transpose().lu();
                for (slot, index) in plan.rows() {
                    let mut basis = DVector::zeros(plan.dimension());
                    basis[*index] = Complex64::ONE;
                    let inverse_row = transpose_factor
                        .solve(&basis)
                        .ok_or(RuntimeError::SingularMatrix(plan.matrix().index()))?;
                    cache.push_solve_row(*slot, inverse_row.iter().copied())?;
                }
            }
            for (slot, (matrix, _)) in self.factor_matrices.iter().enumerate() {
                let (rows, cols, values) = matrix_at_optional(&values, matrix.index())?;
                cache.push_factor(slot, DMatrix::from_row_slice(rows, cols, values).lu())?;
            }
        }
        cache.set_weights((0..batch.len()).map(|row| batch.weights_at(row)).collect());
        Ok(cache)
    }

    /// Evaluates every row in a materialized batch cache.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when parameters or cache layout are
    /// incompatible, evaluation fails, or a matrix is singular.
    pub fn evaluate_cache(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
    ) -> RuntimeResult<Vec<Complex64>> {
        self.check_batch_cache(cache)?;
        #[cfg(feature = "jit")]
        if let Some(kernel) = self.scalar_jit_kernel() {
            let mut output = Vec::with_capacity(cache.len());
            kernel.evaluate(params, cache, 0, cache.len(), &mut output)?;
            return Ok(output);
        }
        let invariant = self.scalar_invariant_values(params)?;
        let mut out = Vec::with_capacity(cache.len());
        let mut workspace = ScalarEventWorkspace::default();
        for row in 0..cache.len() {
            out.push(self.evaluate_cache_row_prepared(
                params,
                cache,
                row,
                invariant.as_ref(),
                &mut workspace,
            )?);
        }
        Ok(out)
    }

    /// Evaluates one row in a materialized batch cache.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when `row` is out of range, parameters or cache
    /// layout are incompatible, evaluation fails, or a matrix is singular.
    pub fn evaluate_cache_row(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
        row: usize,
    ) -> RuntimeResult<Complex64> {
        self.check_batch_cache(cache)?;
        self.evaluate_cache_row_unchecked(params, cache, row)
    }

    fn evaluate_cache_row_unchecked(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
        row: usize,
    ) -> RuntimeResult<Complex64> {
        let invariant = self.scalar_invariant_values(params)?;
        self.evaluate_cache_row_prepared(
            params,
            cache,
            row,
            invariant.as_ref(),
            &mut ScalarEventWorkspace::default(),
        )
    }

    fn evaluate_cache_row_prepared(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
        row: usize,
        invariant: Option<&ScalarInvariantValues>,
        workspace: &mut ScalarEventWorkspace,
    ) -> RuntimeResult<Complex64> {
        #[cfg(feature = "jit")]
        if let Some(kernel) = self.scalar_jit_kernel() {
            let mut output = Vec::with_capacity(1);
            kernel.evaluate(params, cache, row, row + 1, &mut output)?;
            return Ok(output[0]);
        }
        if self.precision == Precision::F32 {
            return self.evaluate_f32_scalar(params, F32KernelInput::Cache(Some((cache, row))));
        }
        if let (Some(plan), Some(invariant)) = (self.scalar_interpreter_plan(), invariant) {
            return self.evaluate_scalar_cache_row(cache, row, plan, invariant, workspace);
        }
        let values = self.evaluate_values_from_cache(params, cache, row)?;
        self.cached_scalar_at(&values, self.graph.root())
    }

    fn scalar_invariant_values(
        &self,
        params: &ParamValues,
    ) -> RuntimeResult<Option<ScalarInvariantValues>> {
        if self.precision == Precision::F32 {
            return Ok(None);
        }
        let Some(plan) = self.scalar_interpreter_plan() else {
            return Ok(None);
        };
        let mut values = ScalarInvariantValues {
            real: vec![0.0; plan.invariant_real_slot_count],
            complex: vec![Complex64::ZERO; plan.invariant_complex_slot_count],
        };
        let event = ScalarEventWorkspace::default();
        for instruction in &plan.invariant_instructions {
            match instruction.output_slot {
                ScalarSlot::Real(slot) => {
                    values.real[slot] = instruction.instruction.evaluate_real(
                        Some(params),
                        None,
                        &values,
                        &event,
                    )?;
                }
                ScalarSlot::Complex(slot) => {
                    values.complex[slot] = instruction.instruction.evaluate_complex(
                        Some(params),
                        None,
                        &values,
                        &event,
                    )?;
                }
            }
        }
        Ok(Some(values))
    }

    fn evaluate_scalar_cache_row(
        &self,
        cache: &CpuBatchCache,
        row: usize,
        plan: &ScalarEvaluationPlan,
        invariant: &ScalarInvariantValues,
        values: &mut ScalarEventWorkspace,
    ) -> RuntimeResult<Complex64> {
        values.real.clear();
        values
            .real
            .resize(plan.event_real_slot_count, [0.0; SCALAR_BLOCK_SIZE]);
        values.complex.clear();
        values.complex.resize(
            plan.event_complex_slot_count,
            [Complex64::ZERO; SCALAR_BLOCK_SIZE],
        );
        for event_instruction in &plan.event_instructions {
            match event_instruction.output_slot {
                ScalarSlot::Real(slot) => {
                    values.real[slot][0] = event_instruction.instruction.evaluate_real(
                        None,
                        Some((cache, row)),
                        invariant,
                        values,
                    )?;
                }
                ScalarSlot::Complex(slot) => {
                    values.complex[slot][0] = event_instruction.instruction.evaluate_complex(
                        None,
                        Some((cache, row)),
                        invariant,
                        values,
                    )?;
                }
            }
        }
        Ok(plan.root().complex_value(invariant, values))
    }

    #[allow(clippy::too_many_arguments)]
    fn evaluate_cache_block_prepared(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
        start: usize,
        end: usize,
        invariant: Option<&ScalarInvariantValues>,
        workspace: &mut ScalarEventWorkspace,
        output: &mut Vec<Complex64>,
        #[cfg(feature = "jit")] jit_cache: Option<&JitCacheView>,
    ) -> RuntimeResult<()> {
        #[cfg(feature = "jit")]
        if let Some(kernel) = self.scalar_jit_kernel() {
            let owned;
            let jit_cache = if let Some(jit_cache) = jit_cache {
                jit_cache
            } else {
                owned = JitScalarKernel::prepare_cache(cache);
                &owned
            };
            return kernel.evaluate_prepared(params, jit_cache, start, end, output);
        }
        if self.precision == Precision::F32 {
            output.clear();
            output.reserve(end - start);
            for row in start..end {
                output.push(
                    self.evaluate_f32_scalar(params, F32KernelInput::Cache(Some((cache, row))))?,
                );
            }
            return Ok(());
        }
        if let (Some(plan), Some(invariant)) = (self.scalar_interpreter_plan(), invariant) {
            return evaluate_scalar_cache_block(
                cache, start, end, plan, invariant, workspace, output,
            );
        }
        output.clear();
        for row in start..end {
            output
                .push(self.evaluate_cache_row_prepared(params, cache, row, invariant, workspace)?);
        }
        Ok(())
    }
}

fn evaluate_scalar_cache_block(
    cache: &CpuBatchCache,
    start: usize,
    end: usize,
    plan: &ScalarEvaluationPlan,
    invariant: &ScalarInvariantValues,
    workspace: &mut ScalarEventWorkspace,
    output: &mut Vec<Complex64>,
) -> RuntimeResult<()> {
    let block_len = end - start;
    workspace
        .real
        .resize(plan.event_real_slot_count, [0.0; SCALAR_BLOCK_SIZE]);
    workspace.complex.resize(
        plan.event_complex_slot_count,
        [Complex64::ZERO; SCALAR_BLOCK_SIZE],
    );

    for event_instruction in &plan.event_instructions {
        match event_instruction.output_slot {
            ScalarSlot::Real(slot) => {
                let output_slot = slot;
                match &event_instruction.instruction {
                    ScalarInstruction::Cached(slot) => {
                        workspace.real[output_slot][..block_len]
                            .copy_from_slice(cache.real_range(*slot, start, end)?);
                    }
                    ScalarInstruction::Unary { op, input } => {
                        for lane in 0..block_len {
                            workspace.real[output_slot][lane] = match op {
                                UnaryOp::Neg => -input.block_real_value(invariant, workspace, lane),
                                UnaryOp::Real | UnaryOp::Conj => {
                                    input.block_complex_value(invariant, workspace, lane).re
                                }
                                UnaryOp::Imag => {
                                    input.block_complex_value(invariant, workspace, lane).im
                                }
                                UnaryOp::NormSqr => input
                                    .block_complex_value(invariant, workspace, lane)
                                    .norm_sqr(),
                                UnaryOp::Sqrt => {
                                    input.block_real_value(invariant, workspace, lane).sqrt()
                                }
                                UnaryOp::Exp => {
                                    input.block_real_value(invariant, workspace, lane).exp()
                                }
                                UnaryOp::Sin => {
                                    input.block_real_value(invariant, workspace, lane).sin()
                                }
                                UnaryOp::Cos => {
                                    input.block_real_value(invariant, workspace, lane).cos()
                                }
                                UnaryOp::Log => {
                                    input.block_real_value(invariant, workspace, lane).ln()
                                }
                                UnaryOp::PowI(power) => input
                                    .block_real_value(invariant, workspace, lane)
                                    .powi(*power),
                            };
                        }
                    }
                    ScalarInstruction::Binary { op, lhs, rhs } => {
                        for lane in 0..block_len {
                            let lhs = lhs.block_real_value(invariant, workspace, lane);
                            let rhs = rhs.block_real_value(invariant, workspace, lane);
                            workspace.real[output_slot][lane] = match op {
                                BinaryOp::Add => lhs + rhs,
                                BinaryOp::Sub => lhs - rhs,
                                BinaryOp::Mul => lhs * rhs,
                                BinaryOp::Div => lhs / rhs,
                                BinaryOp::Atan2 => lhs.atan2(rhs),
                            };
                        }
                    }
                    ScalarInstruction::Add(runs) => {
                        workspace.real[output_slot][..block_len].fill(0.0);
                        for run in runs {
                            match run {
                                OperandRun::InvariantReal(slots) => {
                                    for slot in slots {
                                        let operand = invariant.real[*slot];
                                        for lane in 0..block_len {
                                            workspace.real[output_slot][lane] += operand;
                                        }
                                    }
                                }
                                OperandRun::EventReal(slots) => {
                                    for slot in slots {
                                        for lane in 0..block_len {
                                            workspace.real[output_slot][lane] +=
                                                workspace.real[*slot][lane];
                                        }
                                    }
                                }
                                OperandRun::InvariantComplex(_) | OperandRun::EventComplex(_) => {
                                    unreachable!("complex operand appeared in real add")
                                }
                            }
                        }
                    }
                    ScalarInstruction::Mul(runs) => {
                        workspace.real[output_slot][..block_len].fill(1.0);
                        for run in runs {
                            match run {
                                OperandRun::InvariantReal(slots) => {
                                    for slot in slots {
                                        let operand = invariant.real[*slot];
                                        for lane in 0..block_len {
                                            workspace.real[output_slot][lane] *= operand;
                                        }
                                    }
                                }
                                OperandRun::EventReal(slots) => {
                                    for slot in slots {
                                        for lane in 0..block_len {
                                            workspace.real[output_slot][lane] *=
                                                workspace.real[*slot][lane];
                                        }
                                    }
                                }
                                OperandRun::InvariantComplex(_) | OperandRun::EventComplex(_) => {
                                    unreachable!("complex operand appeared in real multiply")
                                }
                            }
                        }
                    }
                    ScalarInstruction::Constant(_)
                    | ScalarInstruction::Parameter(_)
                    | ScalarInstruction::Complex { .. }
                    | ScalarInstruction::SolveRow { .. }
                    | ScalarInstruction::SolveRowAdjointElement { .. } => {
                        unreachable!("non-real event instruction appeared in a real slot")
                    }
                }
            }
            ScalarSlot::Complex(slot) => {
                let output_slot = slot;
                match &event_instruction.instruction {
                    ScalarInstruction::Cached(slot) => {
                        workspace.complex[output_slot][..block_len]
                            .copy_from_slice(cache.complex_range(*slot, start, end)?);
                    }
                    ScalarInstruction::Unary { op, input } => {
                        for lane in 0..block_len {
                            let input = input.block_complex_value(invariant, workspace, lane);
                            workspace.complex[output_slot][lane] = eval_unary(*op, input);
                        }
                    }
                    ScalarInstruction::Binary { op, lhs, rhs } => {
                        for lane in 0..block_len {
                            let lhs = lhs.block_complex_value(invariant, workspace, lane);
                            let rhs = rhs.block_complex_value(invariant, workspace, lane);
                            workspace.complex[output_slot][lane] = eval_binary(*op, lhs, rhs);
                        }
                    }
                    ScalarInstruction::Add(runs) => {
                        workspace.complex[output_slot][..block_len].fill(Complex64::ZERO);
                        for run in runs {
                            match run {
                                OperandRun::InvariantReal(slots) => {
                                    for slot in slots {
                                        let operand = invariant.real[*slot];
                                        for lane in 0..block_len {
                                            workspace.complex[output_slot][lane] += operand;
                                        }
                                    }
                                }
                                OperandRun::InvariantComplex(slots) => {
                                    for slot in slots {
                                        let operand = invariant.complex[*slot];
                                        for lane in 0..block_len {
                                            workspace.complex[output_slot][lane] += operand;
                                        }
                                    }
                                }
                                OperandRun::EventReal(slots) => {
                                    for slot in slots {
                                        for lane in 0..block_len {
                                            workspace.complex[output_slot][lane] +=
                                                workspace.real[*slot][lane];
                                        }
                                    }
                                }
                                OperandRun::EventComplex(slots) => {
                                    for slot in slots {
                                        for lane in 0..block_len {
                                            let operand = workspace.complex[*slot][lane];
                                            workspace.complex[output_slot][lane] += operand;
                                        }
                                    }
                                }
                            }
                        }
                    }
                    ScalarInstruction::Mul(runs) => {
                        workspace.complex[output_slot][..block_len].fill(Complex64::ONE);
                        for run in runs {
                            match run {
                                OperandRun::InvariantReal(slots) => {
                                    for slot in slots {
                                        let operand = invariant.real[*slot];
                                        for lane in 0..block_len {
                                            workspace.complex[output_slot][lane] *= operand;
                                        }
                                    }
                                }
                                OperandRun::InvariantComplex(slots) => {
                                    for slot in slots {
                                        let operand = invariant.complex[*slot];
                                        for lane in 0..block_len {
                                            workspace.complex[output_slot][lane] *= operand;
                                        }
                                    }
                                }
                                OperandRun::EventReal(slots) => {
                                    for slot in slots {
                                        for lane in 0..block_len {
                                            workspace.complex[output_slot][lane] *=
                                                workspace.real[*slot][lane];
                                        }
                                    }
                                }
                                OperandRun::EventComplex(slots) => {
                                    for slot in slots {
                                        for lane in 0..block_len {
                                            let operand = workspace.complex[*slot][lane];
                                            workspace.complex[output_slot][lane] *= operand;
                                        }
                                    }
                                }
                            }
                        }
                    }
                    ScalarInstruction::Complex { re, im } => {
                        for lane in 0..block_len {
                            workspace.complex[output_slot][lane] = Complex64::new(
                                re.block_real_value(invariant, workspace, lane),
                                im.block_real_value(invariant, workspace, lane),
                            );
                        }
                    }
                    ScalarInstruction::SolveRow { row_slot, rhs } => {
                        for lane in 0..block_len {
                            let inverse_row = cache.solve_row(*row_slot, start + lane)?;
                            if inverse_row.len() != rhs.len() {
                                return Err(RuntimeError::InvalidShape {
                                    index: start + lane,
                                    message: format!(
                                        "specialized solve row has len {}, expected {}",
                                        inverse_row.len(),
                                        rhs.len()
                                    ),
                                });
                            }
                            workspace.complex[output_slot][lane] = inverse_row
                                .iter()
                                .zip(rhs)
                                .map(|(lhs, operand)| {
                                    lhs * operand.block_complex_value(invariant, workspace, lane)
                                })
                                .sum();
                        }
                    }
                    ScalarInstruction::SolveRowAdjointElement {
                        row_slot,
                        index,
                        len,
                        adjoint,
                    } => {
                        for lane in 0..block_len {
                            let inverse_row = cache.solve_row(*row_slot, start + lane)?;
                            if inverse_row.len() != *len {
                                return Err(RuntimeError::InvalidShape {
                                    index: start + lane,
                                    message: format!(
                                        "specialized solve row has len {}, expected {len}",
                                        inverse_row.len()
                                    ),
                                });
                            }
                            workspace.complex[output_slot][lane] = adjoint
                                .block_complex_value(invariant, workspace, lane)
                                * inverse_row[*index].conj();
                        }
                    }
                    ScalarInstruction::Constant(_) | ScalarInstruction::Parameter(_) => {
                        unreachable!("invariant instruction appeared in the event tape")
                    }
                }
            }
        }
    }

    output.clear();
    output.reserve(block_len * plan.outputs.len());
    for lane in 0..block_len {
        for output_operand in &plan.outputs {
            output.push(output_operand.block_complex_value(invariant, workspace, lane));
        }
    }
    Ok(())
}

impl CpuPlan {
    /// Evaluates one cached row and its free-parameter gradient.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when `row` is out of range, parameters or cache
    /// layout are incompatible, or differentiation or evaluation fails.
    pub fn evaluate_cache_row_with_gradient(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
        row: usize,
    ) -> RuntimeResult<ValueGradient> {
        self.check_batch_cache(cache)?;
        self.evaluate_cache_row_with_gradient_unchecked(params, cache, row)
    }

    fn evaluate_cache_row_with_gradient_unchecked(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
        row: usize,
    ) -> RuntimeResult<ValueGradient> {
        #[cfg(feature = "jit")]
        if self.gradient_jit_kernel().is_some() {
            return self
                .evaluate_cache_gradient_jit(params, cache, row, row + 1)?
                .pop()
                .ok_or_else(|| RuntimeError::InvalidShape {
                    index: row,
                    message: "single-row JIT gradient produced no value".into(),
                });
        }
        if self.precision == Precision::F32 {
            return self.evaluate_f32_gradient(params, F32KernelInput::Cache(Some((cache, row))));
        }
        if self.autodiff.mode() == AutodiffMode::Reverse {
            self.require_f64_gradient()?;
            let values = self.evaluate_values_from_cache(params, cache, row)?;
            return self.value_gradient(values, Some((cache, row)));
        }
        if let Some(interpreter) = self.gradient_interpreter() {
            let (value, gradient) = interpreter.evaluate(params, Some((cache, row)))?;
            return Ok(ValueGradient { value, gradient });
        }
        let values = self.evaluate_values_from_cache(params, cache, row)?;
        self.value_gradient(values, Some((cache, row)))
    }

    /// Evaluates every cached row and its free-parameter gradient.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when parameters or cache layout are
    /// incompatible, or differentiation or evaluation fails.
    pub fn evaluate_cache_with_gradient(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
    ) -> RuntimeResult<Vec<ValueGradient>> {
        self.check_batch_cache(cache)?;
        #[cfg(feature = "jit")]
        if self.gradient_jit_kernel().is_some() {
            return self.evaluate_cache_gradient_jit(params, cache, 0, cache.len());
        }
        if self.precision == Precision::F32 {
            return (0..cache.len())
                .map(|row| {
                    self.evaluate_f32_gradient(params, F32KernelInput::Cache(Some((cache, row))))
                })
                .collect();
        }
        self.require_f64_gradient()?;
        (0..cache.len())
            .map(|row| self.evaluate_cache_row_with_gradient_unchecked(params, cache, row))
            .collect()
    }

    #[cfg(feature = "jit")]
    fn evaluate_cache_gradient_jit(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
        start: usize,
        end: usize,
    ) -> RuntimeResult<Vec<ValueGradient>> {
        let (Some(value_kernel), Some(gradient_kernel)) =
            (self.scalar_jit_kernel(), self.gradient_jit_kernel())
        else {
            return Err(RuntimeError::InvalidShape {
                index: self.graph.root().index(),
                message: "JIT gradient evaluation requires both scalar and gradient kernels".into(),
            });
        };
        let view = JitScalarKernel::prepare_cache(cache);
        let mut values = Vec::new();
        let mut real = Vec::new();
        let mut imag = Vec::new();
        value_kernel.evaluate_prepared(params, &view, start, end, &mut values)?;
        gradient_kernel.evaluate_prepared(params, &view, start, end, 0, &mut real)?;
        gradient_kernel.evaluate_prepared(params, &view, start, end, 1, &mut imag)?;
        let parameter_count = self.free_parameter_count();
        Ok(values
            .into_iter()
            .enumerate()
            .map(|(row, value)| ValueGradient {
                value,
                gradient: (0..parameter_count)
                    .map(|parameter| {
                        let index = row * parameter_count + parameter;
                        Complex64::new(real[index], imag[index])
                    })
                    .collect(),
            })
            .collect())
    }

    /// Evaluates the model for every event in a batch.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when cache materialization or evaluation fails.
    pub fn evaluate_batch(
        &self,
        params: &ParamValues,
        batch: &EventBatch,
    ) -> RuntimeResult<Vec<Complex64>> {
        let cache = self.cache_event_batch(batch)?;
        self.evaluate_cache(params, &cache)
    }

    /// Evaluates the model and gradient for every event in a batch.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when cache materialization, differentiation, or
    /// evaluation fails.
    pub fn evaluate_batch_with_gradient(
        &self,
        params: &ParamValues,
        batch: &EventBatch,
    ) -> RuntimeResult<Vec<ValueGradient>> {
        let cache = self.cache_event_batch(batch)?;
        self.evaluate_cache_with_gradient(params, &cache)
    }

    /// Materializes all event-dependent caches for a dataset.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when the dataset cannot be read, a batch schema
    /// is incompatible, cache construction fails, or a matrix is singular.
    pub fn cache_dataset(&self, dataset: &Dataset) -> RuntimeResult<CpuCachedDataset> {
        self.cache_dataset_with_plan(dataset, dataset.read_plan())
    }

    /// Estimates retained compiled-cache bytes for `events`.
    pub fn cache_memory_estimate(&self, events: usize) -> usize {
        self.cache_memory_footprint()
            .map(|footprint| usize::try_from(footprint.peak_bytes(events)).unwrap_or(usize::MAX))
            .unwrap_or(usize::MAX)
    }

    fn cache_memory_footprint(&self) -> Result<MemoryFootprint, FootprintOverflow> {
        let mut fixed = MemoryFootprint::fixed(0);
        for (count, bytes) in [
            (self.cache_plan.entries().len(), size_of::<CachedSlot>()),
            (self.factor_matrices.len(), size_of::<CachedFactorSlot>()),
            (self.solve_row_keys.len(), size_of::<CachedSolveRowSlot>()),
            (self.cache_plan.entries().len(), size_of::<ExprId>()),
            (self.factor_matrices.len(), size_of::<ExprId>()),
            (
                self.solve_row_keys.len(),
                size_of::<(ExprId, usize, usize)>(),
            ),
        ] {
            fixed = fixed.checked_add(
                MemoryFootprint::from_usize_checked(bytes, 0)?.checked_scale_usize(count)?,
            )?;
        }

        let mut per_event = MemoryFootprint::per_event(size_of::<f64>() as u64);
        for entry in self.cache_plan.entries() {
            let bytes = match entry.value_kind() {
                ValueKind::Real => MemoryFootprint::per_event(size_of::<f64>() as u64),
                ValueKind::Complex => MemoryFootprint::per_event(size_of::<Complex64>() as u64),
                ValueKind::Vector { len } => {
                    MemoryFootprint::per_event(size_of::<Complex64>() as u64)
                        .checked_scale_usize(len)?
                }
                ValueKind::Matrix { rows, cols } => {
                    MemoryFootprint::per_event(size_of::<Complex64>() as u64)
                        .checked_scale_usize(rows)?
                        .checked_scale_usize(cols)?
                }
            };
            per_event = per_event.checked_add(bytes)?;
        }
        for (_, dimension) in &self.factor_matrices {
            let bytes = MemoryFootprint::per_event(size_of::<DynamicLu>() as u64)
                .checked_add(
                    MemoryFootprint::per_event(size_of::<Complex64>() as u64)
                        .checked_scale_usize(*dimension)?
                        .checked_scale_usize(*dimension)?,
                )?
                .checked_add(
                    MemoryFootprint::per_event(size_of::<usize>() as u64)
                        .checked_scale_usize(*dimension)?,
                )?;
            per_event = per_event.checked_add(bytes)?;
        }
        for (_, _, dimension) in &self.solve_row_keys {
            per_event = per_event.checked_add(
                MemoryFootprint::per_event(size_of::<Complex64>() as u64)
                    .checked_scale_usize(*dimension)?,
            )?;
        }
        fixed.checked_add(per_event)
    }

    fn cache_dataset_with_plan(
        &self,
        dataset: &Dataset,
        read_plan: laddu_data::io::ReadPlan,
    ) -> RuntimeResult<CpuCachedDataset> {
        let mut batches = Vec::new();
        let mut sum_weights = 0.0;
        for batch in dataset
            .stream_with_plan(read_plan)
            .map_err(|err| RuntimeError::Data(err.to_string()))?
        {
            let batch = batch.map_err(|err| RuntimeError::Data(err.to_string()))?;
            let cached = CpuCachedBatch {
                cache: self.cache_event_batch(&batch)?,
            };
            sum_weights += cached.sum_weights();
            batches.push(cached);
        }
        Ok(CpuCachedDataset {
            batches,
            sum_weights,
        })
    }

    /// Prepares a dataset according to its cache-storage policy.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when dataset reading or cache construction
    /// fails, or another distributed worker reports failure.
    pub fn prepare_dataset(
        &self,
        execution: &Execution,
        dataset: &Dataset,
    ) -> RuntimeResult<CpuPreparedDataset> {
        let mut read_plan = execution.read_plan(dataset.read_plan());
        let schema = dataset
            .schema()
            .map_err(|error| RuntimeError::Data(error.to_string()))?;
        let source_footprint = BatchLayout::from_schema(&schema)
            .schema_footprint(DataPrecision::F64)
            .map_err(|error| RuntimeError::Data(format!("source working-set overflow: {error}")))?;
        let cache_footprint = self
            .cache_memory_footprint()
            .map_err(|error| RuntimeError::Data(format!("cache working-set overflow: {error}")))?;
        let source_bytes_per_event =
            usize::try_from(source_footprint.bytes_per_event).unwrap_or(usize::MAX);
        let cache_zero = usize::try_from(cache_footprint.fixed_bytes).unwrap_or(usize::MAX);
        let cache_bytes_per_event =
            usize::try_from(cache_footprint.bytes_per_event).unwrap_or(usize::MAX);
        let known_local_events = dataset
            .num_events()
            .map_err(|error| RuntimeError::Data(error.to_string()))?
            .and_then(|events| usize::try_from(events).ok());
        let discovered =
            if known_local_events.is_none() && dataset.memory_policy() != MemoryPolicy::Streaming {
                let local = scan_dataset_stats(dataset, read_plan);
                if !execution.all_succeeded(local.is_ok()) {
                    return local.and(Err(RuntimeError::DistributedPeerFailure));
                }
                Some(local?)
            } else {
                None
            };
        let local_event_limit = known_local_events
            .or_else(|| discovered.map(|stats| stats.events))
            .unwrap_or(usize::MAX);
        let host_remaining = execution.host_memory().remaining();
        let resident_plan = resident_cache_plan(
            cache_zero,
            cache_bytes_per_event,
            source_bytes_per_event.saturating_mul(2),
            local_event_limit,
            usize::try_from(host_remaining).unwrap_or(usize::MAX),
        );
        let requested_storage = match dataset.memory_policy() {
            MemoryPolicy::Streaming => CacheStorage::Streaming,
            MemoryPolicy::Resident => {
                if resident_plan.is_none() {
                    let source_staging = source_footprint
                        .checked_scale(2)
                        .and_then(|footprint| {
                            MemoryFootprint::fixed(footprint.bytes_per_event).checked_add(
                                MemoryFootprint::per_event(cache_footprint.bytes_per_event),
                            )
                        })
                        .map_err(|error| {
                            RuntimeError::Data(format!("source working-set overflow: {error}"))
                        })?;
                    let minimum = MemoryFootprint::fixed(cache_footprint.fixed_bytes)
                        .checked_add(source_staging)
                        .map_err(|error| {
                            RuntimeError::Data(format!("cache working-set overflow: {error}"))
                        })?;
                    return Err(laddu_memory::MemoryError::BudgetExceeded {
                        resource: "host".into(),
                        requested: minimum.peak_bytes(local_event_limit),
                        remaining: host_remaining,
                    }
                    .into());
                }
                CacheStorage::Resident
            }
            MemoryPolicy::Fastest if resident_plan.is_some() => CacheStorage::Resident,
            MemoryPolicy::Fastest => CacheStorage::Streaming,
        };
        let persistent_lease = if requested_storage == CacheStorage::Resident {
            let (resident_bytes, _) = resident_plan
                .ok_or_else(|| RuntimeError::Data("resident cache plan was not resolved".into()))?;
            Some(
                execution
                    .host_memory()
                    .reserve(u64::try_from(resident_bytes).unwrap_or(u64::MAX))?,
            )
        } else {
            None
        };
        let available_for_batch = execution.host_memory().remaining();
        // Sources may hold the current decoded batch plus one bounded
        // prefetched batch. A resident cache is already covered by its
        // persistent lease; streaming additionally needs one transient cache.
        let transient_footprint = if requested_storage == CacheStorage::Streaming {
            cache_footprint
                .checked_add(source_footprint.checked_scale(2).map_err(|error| {
                    RuntimeError::Data(format!("source working-set overflow: {error}"))
                })?)
                .map_err(|error| {
                    RuntimeError::Data(format!("cache working-set overflow: {error}"))
                })?
        } else {
            source_footprint.checked_scale(2).map_err(|error| {
                RuntimeError::Data(format!("source working-set overflow: {error}"))
            })?
        };
        let decision = MemoryFitRequest {
            label: "CPU prepared dataset".into(),
            footprint: transient_footprint,
            available_bytes: available_for_batch,
            event_limit: local_event_limit,
            strategy: if requested_storage == CacheStorage::Resident {
                "resident"
            } else {
                "streaming"
            }
            .into(),
        }
        .evaluate()?;
        read_plan.chunk_size = Some(
            read_plan
                .chunk_size
                .map_or(decision.chunk_events, |manual| {
                    manual.min(decision.chunk_events)
                })
                .max(1),
        );
        execution.record_memory_decision(decision.clone());
        match requested_storage {
            CacheStorage::Resident => {
                let local = self.cache_dataset_with_plan(dataset, read_plan);
                if !execution.all_succeeded(local.is_ok()) {
                    return local.and(Err(RuntimeError::DistributedPeerFailure));
                }
                let dataset = local?;
                let stats = PreparedDatasetStats {
                    local_events: dataset.len(),
                    global_events: execution.sum_usize(dataset.len()),
                    local_batches: dataset.batches().len(),
                    sum_weights: execution.sum_f64(dataset.sum_weights()),
                    resident_bytes: dataset.resident_bytes(),
                    storage: CacheStorage::Resident,
                };
                let memory_lease = persistent_lease.ok_or_else(|| {
                    RuntimeError::Data(
                        "resident dataset preparation did not reserve host memory".into(),
                    )
                })?;
                Ok(CpuPreparedDataset::Resident {
                    dataset: Arc::new(dataset),
                    stats,
                    memory_lease,
                })
            }
            CacheStorage::Streaming => {
                let local = scan_dataset_stats(dataset, read_plan);
                if !execution.all_succeeded(local.is_ok()) {
                    return local.and(Err(RuntimeError::DistributedPeerFailure));
                }
                let local = local?;
                Ok(CpuPreparedDataset::Streaming {
                    dataset: dataset.clone(),
                    stats: PreparedDatasetStats {
                        local_events: local.events,
                        global_events: execution.sum_usize(local.events),
                        local_batches: local.batches,
                        sum_weights: execution.sum_f64(local.sum_weights),
                        resident_bytes: 0,
                        storage: CacheStorage::Streaming,
                    },
                    read_plan,
                    transient_bytes: decision.estimated_peak_bytes,
                })
            }
        }
    }

    /// Execute a weighted reduction over a prepared dataset.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when streaming, cache validation, evaluation,
    /// or reduction fails, or another distributed worker reports failure.
    pub fn reduce(
        &self,
        execution: &Execution,
        params: &ParamValues,
        dataset: &CpuPreparedDataset,
        reduction: ReductionPlan,
    ) -> RuntimeResult<f64> {
        let local = match dataset {
            CpuPreparedDataset::Resident { dataset, .. } => {
                self.reduce_cached(execution, params, dataset, reduction)
            }
            CpuPreparedDataset::Streaming {
                dataset,
                read_plan,
                transient_bytes,
                ..
            } => (|| {
                let _memory = execution
                    .host_memory()
                    .reserve(*transient_bytes)
                    .map_err(RuntimeError::from)?;
                let mut total = AccurateF64::zero();
                for batch in dataset
                    .stream_with_plan(*read_plan)
                    .map_err(|error| RuntimeError::Data(error.to_string()))?
                {
                    let batch = batch.map_err(|error| RuntimeError::Data(error.to_string()))?;
                    let cached = CpuCachedDataset {
                        sum_weights: (0..batch.len()).map(|row| batch.weights_at(row)).sum(),
                        batches: vec![CpuCachedBatch {
                            cache: self.cache_event_batch(&batch)?,
                        }],
                    };
                    total.push(self.reduce_cached(execution, params, &cached, reduction)?);
                }
                Ok(total.finish())
            })(),
        };
        if !execution.all_succeeded(local.is_ok()) {
            return local.and(Err(RuntimeError::DistributedPeerFailure));
        }
        Ok(execution.sum_f64(local?))
    }

    /// Execute a weighted reduction and its free-parameter gradient.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when streaming, cache validation,
    /// differentiation, evaluation, or reduction fails, or another distributed
    /// worker reports failure.
    pub fn reduce_with_gradient(
        &self,
        execution: &Execution,
        params: &ParamValues,
        dataset: &CpuPreparedDataset,
        reduction: ReductionPlan,
    ) -> RuntimeResult<ReductionEvaluation> {
        let (value, gradient) =
            self.try_reduce_weighted_with_gradient(execution, params, dataset, |value| {
                reduction
                    .apply(value)
                    .map(|output| output.into_parts())
                    .map_err(RuntimeError::from)
            })?;
        Ok(ReductionEvaluation { value, gradient })
    }

    fn try_reduce_weighted_with_gradient<E, F>(
        &self,
        execution: &Execution,
        params: &ParamValues,
        dataset: &CpuPreparedDataset,
        transform: F,
    ) -> Result<(f64, Vec<f64>), E>
    where
        E: From<RuntimeError> + Send,
        F: Fn(Complex64) -> Result<(f64, f64), E> + Send + Sync,
    {
        let local = match dataset {
            CpuPreparedDataset::Resident { dataset, .. } => {
                self.try_reduce_weighted_with_gradient_cached(execution, params, dataset, transform)
            }
            CpuPreparedDataset::Streaming {
                dataset,
                read_plan,
                transient_bytes,
                ..
            } => (|| {
                let _memory = execution
                    .host_memory()
                    .reserve(*transient_bytes)
                    .map_err(RuntimeError::from)
                    .map_err(E::from)?;
                let mut value = AccurateF64::zero();
                let mut gradient = (0..self.free_parameter_count())
                    .map(|_| AccurateF64::zero())
                    .collect::<Vec<_>>();
                for batch in dataset
                    .stream_with_plan(*read_plan)
                    .map_err(|error| E::from(RuntimeError::Data(error.to_string())))?
                {
                    let batch =
                        batch.map_err(|error| E::from(RuntimeError::Data(error.to_string())))?;
                    let cached = CpuCachedDataset {
                        sum_weights: (0..batch.len()).map(|row| batch.weights_at(row)).sum(),
                        batches: vec![CpuCachedBatch {
                            cache: self.cache_event_batch(&batch)?,
                        }],
                    };
                    let (partial_value, partial_gradient) = self
                        .try_reduce_weighted_with_gradient_cached(
                            execution, params, &cached, &transform,
                        )?;
                    value.push(partial_value);
                    for (sum, partial) in gradient.iter_mut().zip(partial_gradient) {
                        sum.push(partial);
                    }
                }
                Ok::<_, E>((
                    value.finish(),
                    gradient.into_iter().map(AccurateF64::finish).collect(),
                ))
            })(),
        };
        if !execution.all_succeeded(local.is_ok()) {
            return local.and(Err(E::from(RuntimeError::DistributedPeerFailure)));
        }
        let (local_value, local_gradient) = local?;
        Ok((
            execution.sum_f64(local_value),
            execution.sum_slice(&local_gradient),
        ))
    }

    /// Evaluates every event in a fully cached dataset.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when parameters or a cache layout are
    /// incompatible, evaluation fails, or a matrix is singular.
    pub fn evaluate_cached_dataset(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
    ) -> RuntimeResult<Vec<Complex64>> {
        let total_len = dataset.batches.iter().map(CpuCachedBatch::len).sum();
        let mut out = Vec::with_capacity(total_len);
        let invariant = self.scalar_invariant_values(params)?;
        let mut workspace = ScalarEventWorkspace::default();
        for batch in &dataset.batches {
            self.check_batch_cache(batch.cache())?;
            for row in 0..batch.len() {
                out.push(self.evaluate_cache_row_prepared(
                    params,
                    batch.cache(),
                    row,
                    invariant.as_ref(),
                    &mut workspace,
                )?);
            }
        }
        Ok(out)
    }

    /// Evaluates every event and gradient in a fully cached dataset.
    ///
    /// # Errors
    ///
    /// Returns [`RuntimeError`] when parameters or a cache layout are
    /// incompatible, or differentiation or evaluation fails.
    pub fn evaluate_cached_dataset_with_gradient(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
    ) -> RuntimeResult<Vec<ValueGradient>> {
        let total_len = dataset.batches.iter().map(CpuCachedBatch::len).sum();
        let mut out = Vec::with_capacity(total_len);
        for batch in &dataset.batches {
            out.extend(self.evaluate_cache_with_gradient(params, batch.cache())?);
        }
        Ok(out)
    }

    fn try_weighted_sum_cached<E, F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        mut f: F,
    ) -> Result<f64, E>
    where
        E: From<RuntimeError>,
        F: FnMut(Complex64) -> Result<f64, E>,
    {
        let mut sum = 0.0;
        let invariant = self.scalar_invariant_values(params)?;
        let mut workspace = ScalarEventWorkspace::default();
        for batch in dataset.batches() {
            self.check_batch_cache(batch.cache())?;
            for row in 0..batch.len() {
                let value = self.evaluate_cache_row_prepared(
                    params,
                    batch.cache(),
                    row,
                    invariant.as_ref(),
                    &mut workspace,
                )?;
                sum += batch.weights()[row] * f(value)?;
            }
        }
        Ok(sum)
    }

    #[cfg(test)]
    fn weighted_sum_cached<F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        mut f: F,
    ) -> RuntimeResult<f64>
    where
        F: FnMut(Complex64) -> f64,
    {
        self.try_weighted_sum_cached(params, dataset, |value| Ok(f(value)))
    }

    fn try_weighted_real_sum_with_gradient_cached<E, F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        mut transform: F,
    ) -> Result<(f64, Vec<f64>), E>
    where
        E: From<RuntimeError>,
        F: FnMut(Complex64) -> Result<(f64, f64), E>,
    {
        #[cfg(feature = "jit")]
        if let (Some(value_kernel), Some(gradient_kernel)) =
            (self.scalar_jit_kernel(), self.gradient_jit_kernel())
        {
            return self.try_weighted_real_sum_with_jit_gradient_cached(
                params,
                dataset,
                transform,
                value_kernel,
                gradient_kernel,
            );
        }
        if self.precision != Precision::F32
            && let Some(interpreter) = self.gradient_interpreter()
            && let Some(mut state) = interpreter.prepare_real_blocks(params)?
        {
            let mut total = RealGradientAccumulator::zero(self.free_parameter_count());
            let output_count = state.output_count();
            for batch in dataset.batches() {
                self.check_batch_cache(batch.cache())?;
                for block in 0..batch.len().div_ceil(SCALAR_BLOCK_SIZE) {
                    let start = block * SCALAR_BLOCK_SIZE;
                    let end = (start + SCALAR_BLOCK_SIZE).min(batch.len());
                    let outputs = state.evaluate(batch.cache(), start, end)?;
                    for (lane, row) in outputs.chunks_exact(output_count).enumerate() {
                        let (value, derivative) = transform(row[0])?;
                        total.push(batch.weights()[start + lane], value, derivative, &row[1..]);
                    }
                }
            }
            return Ok(total.finish());
        }
        if self.precision == Precision::F32
            && let Some(ir) = self.f32_gradient_fallback_real.as_ref()
        {
            let mut total = RealGradientAccumulator::zero(self.free_parameter_count());
            let mut gradient = Vec::new();
            for batch in dataset.batches() {
                self.check_batch_cache(batch.cache())?;
                for row in 0..batch.len() {
                    let (value, model_gradient) = self.evaluate_f32_gradient_component_prepared(
                        ir,
                        params,
                        F32KernelInput::Cache(Some((batch.cache(), row))),
                        &mut gradient,
                    )?;
                    let (value, derivative) = transform(value)?;
                    total.push_f32(batch.weights()[row], value, derivative, model_gradient);
                }
            }
            return Ok(total.finish());
        }
        let mut total = RealGradientAccumulator::zero(self.free_parameter_count());
        for batch in dataset.batches() {
            self.check_batch_cache(batch.cache())?;
            for row in 0..batch.len() {
                let evaluation =
                    self.evaluate_cache_row_with_gradient_unchecked(params, batch.cache(), row)?;
                let (value, derivative) = transform(evaluation.value())?;
                total.push(
                    batch.weights()[row],
                    value,
                    derivative,
                    evaluation.gradient(),
                );
            }
        }
        Ok(total.finish())
    }

    #[cfg(feature = "jit")]
    fn try_weighted_real_sum_with_jit_gradient_cached<E, F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        mut transform: F,
        value_kernel: &JitScalarKernel,
        gradient_kernel: &JitGradientKernel,
    ) -> Result<(f64, Vec<f64>), E>
    where
        E: From<RuntimeError>,
        F: FnMut(Complex64) -> Result<(f64, f64), E>,
    {
        let mut total = RealGradientAccumulator::zero(self.free_parameter_count());
        let mut values = Vec::new();
        let mut tangents = Vec::new();
        let mut derivatives = Vec::new();
        for batch in dataset.batches() {
            self.check_batch_cache(batch.cache())?;
            let cache = JitScalarKernel::prepare_cache(batch.cache());
            for block in 0..batch.len().div_ceil(SCALAR_BLOCK_SIZE) {
                let start = block * SCALAR_BLOCK_SIZE;
                let end = (start + SCALAR_BLOCK_SIZE).min(batch.len());
                value_kernel.evaluate_prepared(params, &cache, start, end, &mut values)?;
                derivatives.clear();
                derivatives.reserve(values.len());
                for (lane, value) in values.iter().copied().enumerate() {
                    let (value, derivative) = transform(value)?;
                    let weight = batch.weights()[start + lane];
                    total.value.push(weight * value);
                    derivatives.push(weight * derivative);
                }
                gradient_kernel.evaluate_prepared(params, &cache, start, end, 0, &mut tangents)?;
                for (lane, factor) in derivatives.iter().enumerate() {
                    for free_index in 0..self.free_parameter_count() {
                        total.gradient[free_index].push(
                            factor * tangents[lane * self.free_parameter_count() + free_index],
                        );
                    }
                }
            }
        }
        Ok(total.finish())
    }

    #[cfg(test)]
    fn try_weighted_complex_sum_cached<E, F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        mut f: F,
    ) -> Result<Complex64, E>
    where
        E: From<RuntimeError>,
        F: FnMut(Complex64) -> Result<Complex64, E>,
    {
        let mut sum = Complex64::default();
        let invariant = self.scalar_invariant_values(params)?;
        let mut workspace = ScalarEventWorkspace::default();
        for batch in dataset.batches() {
            self.check_batch_cache(batch.cache())?;
            for row in 0..batch.len() {
                let value = self.evaluate_cache_row_prepared(
                    params,
                    batch.cache(),
                    row,
                    invariant.as_ref(),
                    &mut workspace,
                )?;
                sum += f(value)? * batch.weights()[row];
            }
        }
        Ok(sum)
    }

    #[cfg(test)]
    fn weighted_complex_sum_cached<F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        mut f: F,
    ) -> RuntimeResult<Complex64>
    where
        F: FnMut(Complex64) -> Complex64,
    {
        self.try_weighted_complex_sum_cached(params, dataset, |value| Ok(f(value)))
    }

    fn reduce_cached(
        &self,
        execution: &Execution,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        reduction: ReductionPlan,
    ) -> RuntimeResult<f64> {
        if execution.is_parallel() && dataset.len().div_ceil(SCALAR_BLOCK_SIZE) >= 2 {
            execution.install(|| {
                self.par_try_weighted_sum_cached(params, dataset, |value| {
                    self.apply_reduction(reduction, value)
                })
            })
        } else {
            self.try_weighted_sum_cached(params, dataset, |value| {
                self.apply_reduction(reduction, value)
            })
        }
    }

    fn apply_reduction(&self, reduction: ReductionPlan, value: Complex64) -> RuntimeResult<f64> {
        if self.precision != Precision::F32 {
            return reduction
                .apply(value)
                .map(|output| output.value())
                .map_err(RuntimeError::from);
        }
        let real = value.re as f32;
        match reduction.transform() {
            ReductionTransform::Real => Ok(real as f64),
            ReductionTransform::PositiveReal if real > 0.0 => Ok(real as f64),
            ReductionTransform::LogPositiveReal if real > 0.0 => Ok(real.ln() as f64),
            ReductionTransform::PositiveReal | ReductionTransform::LogPositiveReal => reduction
                .apply(Complex64::from(real as f64))
                .map(|output| output.value())
                .map_err(RuntimeError::from),
        }
    }

    fn try_reduce_weighted_with_gradient_cached<E, F>(
        &self,
        execution: &Execution,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        transform: F,
    ) -> Result<(f64, Vec<f64>), E>
    where
        E: From<RuntimeError> + Send,
        F: Fn(Complex64) -> Result<(f64, f64), E> + Send + Sync,
    {
        if execution.is_parallel() && dataset.len().div_ceil(SCALAR_BLOCK_SIZE) >= 2 {
            execution.install(|| {
                self.par_try_weighted_real_sum_with_gradient_cached(params, dataset, transform)
            })
        } else {
            self.try_weighted_real_sum_with_gradient_cached(params, dataset, transform)
        }
    }

    pub(crate) fn par_try_weighted_sum_cached<E, F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        f: F,
    ) -> Result<f64, E>
    where
        E: From<RuntimeError> + Send,
        F: Fn(Complex64) -> Result<f64, E> + Send + Sync,
    {
        let mut total = AccurateF64::zero();
        let invariant = self.scalar_invariant_values(params)?;
        for batch in dataset.batches() {
            self.check_batch_cache(batch.cache())?;
            #[cfg(feature = "jit")]
            let jit_cache = self
                .scalar_jit_kernel()
                .map(|_| JitScalarKernel::prepare_cache(batch.cache()));
            let n_blocks = batch.len().div_ceil(SCALAR_BLOCK_SIZE);
            let partial = (0..n_blocks)
                .into_par_iter()
                .try_fold(
                    || {
                        (
                            AccurateF64::zero(),
                            ScalarEventWorkspace::default(),
                            Vec::new(),
                        )
                    },
                    |(mut acc, mut workspace, mut output), block| {
                        let start = block * SCALAR_BLOCK_SIZE;
                        let end = (start + SCALAR_BLOCK_SIZE).min(batch.len());
                        self.evaluate_cache_block_prepared(
                            params,
                            batch.cache(),
                            start,
                            end,
                            invariant.as_ref(),
                            &mut workspace,
                            &mut output,
                            #[cfg(feature = "jit")]
                            jit_cache.as_ref(),
                        )?;
                        for (lane, value) in output.iter().copied().enumerate() {
                            acc.push(batch.weights()[start + lane] * f(value)?);
                        }
                        Ok::<_, E>((acc, workspace, output))
                    },
                )
                .try_reduce(
                    || {
                        (
                            AccurateF64::zero(),
                            ScalarEventWorkspace::default(),
                            Vec::new(),
                        )
                    },
                    |(mut lhs, workspace, output), (rhs, _, _)| {
                        lhs.merge(rhs);
                        Ok::<_, E>((lhs, workspace, output))
                    },
                )?;
            total.merge(partial.0);
        }
        Ok(total.finish())
    }

    #[cfg(test)]
    pub(crate) fn par_weighted_sum_cached<F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        f: F,
    ) -> RuntimeResult<f64>
    where
        F: Fn(Complex64) -> f64 + Send + Sync,
    {
        self.par_try_weighted_sum_cached(params, dataset, |value| Ok(f(value)))
    }

    pub(crate) fn par_try_weighted_real_sum_with_gradient_cached<E, F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        transform: F,
    ) -> Result<(f64, Vec<f64>), E>
    where
        E: From<RuntimeError> + Send,
        F: Fn(Complex64) -> Result<(f64, f64), E> + Send + Sync,
    {
        #[cfg(feature = "jit")]
        if let (Some(value_kernel), Some(gradient_kernel)) =
            (self.scalar_jit_kernel(), self.gradient_jit_kernel())
        {
            return self.par_try_weighted_real_sum_with_jit_gradient_cached(
                params,
                dataset,
                transform,
                value_kernel,
                gradient_kernel,
            );
        }
        if self.precision != Precision::F32
            && let Some(interpreter) = self.gradient_interpreter()
            && let Some(state) = interpreter.prepare_real_blocks(params)?
        {
            let mut total = RealGradientAccumulator::zero(self.free_parameter_count());
            let output_count = state.output_count();
            for batch in dataset.batches() {
                self.check_batch_cache(batch.cache())?;
                let partial = (0..batch.len().div_ceil(SCALAR_BLOCK_SIZE))
                    .into_par_iter()
                    .try_fold(
                        || {
                            (
                                RealGradientAccumulator::zero(self.free_parameter_count()),
                                state.clone(),
                            )
                        },
                        |(mut accumulator, mut state), block| {
                            let start = block * SCALAR_BLOCK_SIZE;
                            let end = (start + SCALAR_BLOCK_SIZE).min(batch.len());
                            let outputs = state.evaluate(batch.cache(), start, end)?;
                            for (lane, row) in outputs.chunks_exact(output_count).enumerate() {
                                let (value, derivative) = transform(row[0])?;
                                accumulator.push(
                                    batch.weights()[start + lane],
                                    value,
                                    derivative,
                                    &row[1..],
                                );
                            }
                            Ok::<_, E>((accumulator, state))
                        },
                    )
                    .try_reduce(
                        || {
                            (
                                RealGradientAccumulator::zero(self.free_parameter_count()),
                                state.clone(),
                            )
                        },
                        |(mut lhs, state), (rhs, _)| {
                            lhs.merge(rhs);
                            Ok::<_, E>((lhs, state))
                        },
                    )?;
                total.merge(partial.0);
            }
            return Ok(total.finish());
        }
        if self.precision == Precision::F32
            && let Some(ir) = self.f32_gradient_fallback_real.as_ref()
        {
            let mut total = RealGradientAccumulator::zero(self.free_parameter_count());
            for batch in dataset.batches() {
                self.check_batch_cache(batch.cache())?;
                let partial = (0..batch.len())
                    .into_par_iter()
                    .try_fold(
                        || {
                            (
                                RealGradientAccumulator::zero(self.free_parameter_count()),
                                Vec::new(),
                            )
                        },
                        |(mut accumulator, mut gradient), row| {
                            let (value, model_gradient) = self
                                .evaluate_f32_gradient_component_prepared(
                                    ir,
                                    params,
                                    F32KernelInput::Cache(Some((batch.cache(), row))),
                                    &mut gradient,
                                )?;
                            let (value, derivative) = transform(value)?;
                            accumulator.push_f32(
                                batch.weights()[row],
                                value,
                                derivative,
                                model_gradient,
                            );
                            Ok::<_, E>((accumulator, gradient))
                        },
                    )
                    .try_reduce(
                        || {
                            (
                                RealGradientAccumulator::zero(self.free_parameter_count()),
                                Vec::new(),
                            )
                        },
                        |(mut lhs, gradient), (rhs, _)| {
                            lhs.merge(rhs);
                            Ok::<_, E>((lhs, gradient))
                        },
                    )?;
                total.merge(partial.0);
            }
            return Ok(total.finish());
        }
        let mut total = RealGradientAccumulator::zero(self.free_parameter_count());
        for batch in dataset.batches() {
            self.check_batch_cache(batch.cache())?;
            let partial = (0..batch.len())
                .into_par_iter()
                .try_fold(
                    || RealGradientAccumulator::zero(self.free_parameter_count()),
                    |mut accumulator, row| {
                        let evaluation = self.evaluate_cache_row_with_gradient_unchecked(
                            params,
                            batch.cache(),
                            row,
                        )?;
                        let (value, derivative) = transform(evaluation.value())?;
                        accumulator.push(
                            batch.weights()[row],
                            value,
                            derivative,
                            evaluation.gradient(),
                        );
                        Ok::<_, E>(accumulator)
                    },
                )
                .try_reduce(
                    || RealGradientAccumulator::zero(self.free_parameter_count()),
                    |mut lhs, rhs| {
                        lhs.merge(rhs);
                        Ok::<_, E>(lhs)
                    },
                )?;
            total.merge(partial);
        }
        Ok(total.finish())
    }

    #[cfg(feature = "jit")]
    fn par_try_weighted_real_sum_with_jit_gradient_cached<E, F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        transform: F,
        value_kernel: &JitScalarKernel,
        gradient_kernel: &JitGradientKernel,
    ) -> Result<(f64, Vec<f64>), E>
    where
        E: From<RuntimeError> + Send,
        F: Fn(Complex64) -> Result<(f64, f64), E> + Send + Sync,
    {
        let mut total = RealGradientAccumulator::zero(self.free_parameter_count());
        for batch in dataset.batches() {
            self.check_batch_cache(batch.cache())?;
            let cache = JitScalarKernel::prepare_cache(batch.cache());
            let n_blocks = batch.len().div_ceil(SCALAR_BLOCK_SIZE);
            let partial = (0..n_blocks)
                .into_par_iter()
                .try_fold(
                    || {
                        (
                            RealGradientAccumulator::zero(self.free_parameter_count()),
                            Vec::new(),
                            Vec::new(),
                            Vec::new(),
                        )
                    },
                    |(mut accumulator, mut values, mut tangents, mut derivatives), block| {
                        let start = block * SCALAR_BLOCK_SIZE;
                        let end = (start + SCALAR_BLOCK_SIZE).min(batch.len());
                        value_kernel.evaluate_prepared(params, &cache, start, end, &mut values)?;
                        derivatives.clear();
                        derivatives.reserve(values.len());
                        for (lane, value) in values.iter().copied().enumerate() {
                            let (value, derivative) = transform(value)?;
                            let weight = batch.weights()[start + lane];
                            accumulator.value.push(weight * value);
                            derivatives.push(weight * derivative);
                        }
                        gradient_kernel.evaluate_prepared(
                            params,
                            &cache,
                            start,
                            end,
                            0,
                            &mut tangents,
                        )?;
                        for (lane, factor) in derivatives.iter().enumerate() {
                            for free_index in 0..self.free_parameter_count() {
                                accumulator.gradient[free_index].push(
                                    factor
                                        * tangents[lane * self.free_parameter_count() + free_index],
                                );
                            }
                        }
                        Ok::<_, E>((accumulator, values, tangents, derivatives))
                    },
                )
                .try_reduce(
                    || {
                        (
                            RealGradientAccumulator::zero(self.free_parameter_count()),
                            Vec::new(),
                            Vec::new(),
                            Vec::new(),
                        )
                    },
                    |(mut lhs, values, tangents, derivatives), (rhs, _, _, _)| {
                        lhs.merge(rhs);
                        Ok::<_, E>((lhs, values, tangents, derivatives))
                    },
                )?;
            total.merge(partial.0);
        }
        Ok(total.finish())
    }

    #[cfg(test)]
    pub(crate) fn par_try_weighted_complex_sum_cached<E, F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        f: F,
    ) -> Result<Complex64, E>
    where
        E: From<RuntimeError> + Send,
        F: Fn(Complex64) -> Result<Complex64, E> + Send + Sync,
    {
        let mut total = AccurateComplex64::zero();
        let invariant = self.scalar_invariant_values(params)?;
        for batch in dataset.batches() {
            self.check_batch_cache(batch.cache())?;
            #[cfg(feature = "jit")]
            let jit_cache = self
                .scalar_jit_kernel()
                .map(|_| JitScalarKernel::prepare_cache(batch.cache()));
            let n_blocks = batch.len().div_ceil(SCALAR_BLOCK_SIZE);
            let partial = (0..n_blocks)
                .into_par_iter()
                .try_fold(
                    || {
                        (
                            AccurateComplex64::zero(),
                            ScalarEventWorkspace::default(),
                            Vec::new(),
                        )
                    },
                    |(mut acc, mut workspace, mut output), block| {
                        let start = block * SCALAR_BLOCK_SIZE;
                        let end = (start + SCALAR_BLOCK_SIZE).min(batch.len());
                        self.evaluate_cache_block_prepared(
                            params,
                            batch.cache(),
                            start,
                            end,
                            invariant.as_ref(),
                            &mut workspace,
                            &mut output,
                            #[cfg(feature = "jit")]
                            jit_cache.as_ref(),
                        )?;
                        for (lane, value) in output.iter().copied().enumerate() {
                            acc.push(f(value)? * batch.weights()[start + lane]);
                        }
                        Ok::<_, E>((acc, workspace, output))
                    },
                )
                .try_reduce(
                    || {
                        (
                            AccurateComplex64::zero(),
                            ScalarEventWorkspace::default(),
                            Vec::new(),
                        )
                    },
                    |(mut lhs, workspace, output), (rhs, _, _)| {
                        lhs.merge(rhs);
                        Ok::<_, E>((lhs, workspace, output))
                    },
                )?;
            total.merge(partial.0);
        }
        Ok(total.finish())
    }

    #[cfg(test)]
    pub(crate) fn par_weighted_complex_sum_cached<F>(
        &self,
        params: &ParamValues,
        dataset: &CpuCachedDataset,
        f: F,
    ) -> RuntimeResult<Complex64>
    where
        F: Fn(Complex64) -> Complex64 + Send + Sync,
    {
        self.par_try_weighted_complex_sum_cached(params, dataset, |value| Ok(f(value)))
    }

    fn evaluate_inner(
        &self,
        params: &ParamValues,
        event: Option<&dyn EventLookup>,
    ) -> RuntimeResult<Complex64> {
        #[cfg(feature = "jit")]
        if event.is_none()
            && let Some(kernel) = self.scalar_jit_kernel()
        {
            if params.as_slice().len() != self.params.len() {
                return Err(RuntimeError::Parameter(format!(
                    "expected {} parameter values, got {}",
                    self.params.len(),
                    params.as_slice().len()
                )));
            }
            return kernel.evaluate_invariant(params);
        }
        if self.precision == Precision::F32 {
            let input = match event {
                Some(event) => F32KernelInput::Event(event),
                None => F32KernelInput::Cache(None),
            };
            return self.evaluate_f32_scalar(params, input);
        }
        let values = self.evaluate_values(params, event)?;
        scalar_at(&values, self.graph.root().index())
    }

    fn require_f64_gradient(&self) -> RuntimeResult<()> {
        if self.precision == Precision::F32 {
            return Err(crate::ExecutionError::UnsupportedCpuF32Gradient.into());
        }
        Ok(())
    }

    fn evaluate_f32_scalar(
        &self,
        params: &ParamValues,
        input: F32KernelInput<'_>,
    ) -> RuntimeResult<Complex64> {
        let kernel = self
            .scalar_kernel
            .as_ref()
            .ok_or(crate::ExecutionError::UnsupportedCpuF32Model)?;
        let values = self.evaluate_f32_kernel_values(kernel.values(), params, input)?;
        let value = f32_scalar_at(&values, kernel.root())?;
        Ok(Complex64::new(value.re as f64, value.im as f64))
    }

    fn evaluate_f32_kernel_values(
        &self,
        kernel_values: &[KernelValue],
        params: &ParamValues,
        input: F32KernelInput<'_>,
    ) -> RuntimeResult<Vec<F32Value>> {
        let mut values = Vec::with_capacity(kernel_values.len());
        for (index, value) in kernel_values.iter().enumerate() {
            let result = match &value.instruction {
                KernelInstruction::Cached(slot) => self.evaluate_f32_cached_value(
                    *slot,
                    params,
                    input,
                    crate::ExecutionError::UnsupportedCpuF32Model,
                )?,
                KernelInstruction::RealConstant(value) => {
                    F32Value::Scalar(Complex32::from(*value as f32))
                }
                KernelInstruction::ComplexConstant(value) => {
                    F32Value::Scalar(Complex32::new(value.re as f32, value.im as f32))
                }
                KernelInstruction::Parameter(id) => F32Value::Scalar(Complex32::from(
                    params
                        .get(*id)
                        .map_err(|error| RuntimeError::Parameter(error.to_string()))?
                        as f32,
                )),
                KernelInstruction::Unary { op, input } => {
                    F32Value::Scalar(eval_unary(*op, f32_scalar_at(&values, *input)?))
                }
                KernelInstruction::Binary { op, lhs, rhs } => F32Value::Scalar(eval_binary(
                    *op,
                    f32_scalar_at(&values, *lhs)?,
                    f32_scalar_at(&values, *rhs)?,
                )),
                KernelInstruction::Add(terms) => F32Value::Scalar(
                    terms
                        .iter()
                        .map(|id| f32_scalar_at(&values, *id))
                        .sum::<RuntimeResult<Complex32>>()?,
                ),
                KernelInstruction::Mul(factors) => {
                    F32Value::Scalar(factors.iter().try_fold(Complex32::ONE, |product, id| {
                        Ok::<_, RuntimeError>(product * f32_scalar_at(&values, *id)?)
                    })?)
                }
                KernelInstruction::Complex { re, im } => F32Value::Scalar(Complex32::new(
                    f32_scalar_at(&values, *re)?.re,
                    f32_scalar_at(&values, *im)?.re,
                )),
                KernelInstruction::Vector(elements) => F32Value::Vector(
                    elements
                        .iter()
                        .map(|id| f32_scalar_at(&values, *id))
                        .collect::<RuntimeResult<_>>()?,
                ),
                KernelInstruction::Matrix {
                    rows,
                    cols,
                    elements,
                } => F32Value::Matrix {
                    rows: *rows,
                    cols: *cols,
                    values: elements
                        .iter()
                        .map(|id| f32_scalar_at(&values, *id))
                        .collect::<RuntimeResult<_>>()?,
                },
                KernelInstruction::Component {
                    input,
                    index: element,
                } => {
                    let vector = f32_vector_at(&values, *input)?;
                    F32Value::Scalar(*vector.get(*element).ok_or_else(|| {
                        RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "component index {element} out of bounds for len {}",
                                vector.len()
                            ),
                        }
                    })?)
                }
                KernelInstruction::MatrixElement { input, row, col } => {
                    let (rows, cols, matrix) = f32_matrix_at(&values, *input)?;
                    if *row >= rows || *col >= cols {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "matrix element ({row}, {col}) out of bounds for shape {rows}x{cols}"
                            ),
                        });
                    }
                    F32Value::Scalar(matrix[row * cols + col])
                }
                KernelInstruction::Dot { lhs, rhs } => {
                    let lhs = f32_vector_at(&values, *lhs)?;
                    let rhs = f32_vector_at(&values, *rhs)?;
                    if lhs.len() != rhs.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot dot len {} vector with len {} vector",
                                lhs.len(),
                                rhs.len()
                            ),
                        });
                    }
                    F32Value::Scalar(lhs.iter().zip(rhs).map(|(lhs, rhs)| lhs * rhs).sum())
                }
                KernelInstruction::MatVec { matrix, vector } => {
                    let (rows, cols, matrix) = f32_matrix_at(&values, *matrix)?;
                    let vector = f32_vector_at(&values, *vector)?;
                    if cols != vector.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot multiply {rows}x{cols} matrix by len {} vector",
                                vector.len()
                            ),
                        });
                    }
                    let output = DMatrix::from_row_slice(rows, cols, matrix)
                        * DVector::from_row_slice(vector);
                    F32Value::Vector(output.iter().copied().collect())
                }
                KernelInstruction::MatMul { lhs, rhs } => {
                    let (lhs_rows, lhs_cols, lhs) = f32_matrix_at(&values, *lhs)?;
                    let (rhs_rows, rhs_cols, rhs) = f32_matrix_at(&values, *rhs)?;
                    if lhs_cols != rhs_rows {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot multiply {lhs_rows}x{lhs_cols} by {rhs_rows}x{rhs_cols}"
                            ),
                        });
                    }
                    let output = DMatrix::from_row_slice(lhs_rows, lhs_cols, lhs)
                        * DMatrix::from_row_slice(rhs_rows, rhs_cols, rhs);
                    F32Value::Matrix {
                        rows: output.nrows(),
                        cols: output.ncols(),
                        values: matrix_values_row_major_f32(&output),
                    }
                }
                KernelInstruction::Solve { matrix, rhs } => {
                    let (rows, cols, matrix) = f32_matrix_at(&values, *matrix)?;
                    let rhs = f32_vector_at(&values, *rhs)?;
                    if rows != cols || rows != rhs.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot solve {rows}x{cols} matrix against len {} vector",
                                rhs.len()
                            ),
                        });
                    }
                    let solution = DMatrix::from_row_slice(rows, cols, matrix)
                        .lu()
                        .solve(&DVector::from_row_slice(rhs))
                        .ok_or(RuntimeError::SingularMatrix(index))?;
                    F32Value::Vector(solution.iter().copied().collect())
                }
                KernelInstruction::SolveRow { row_slot, rhs } => {
                    let (cache, row) = input
                        .cache()
                        .ok_or(crate::ExecutionError::UnsupportedCpuF32Model)?;
                    let inverse = cache.solve_row(*row_slot, row)?;
                    if inverse.len() != rhs.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "specialized solve row has len {}, expected {}",
                                inverse.len(),
                                rhs.len()
                            ),
                        });
                    }
                    F32Value::Scalar(
                        inverse
                            .iter()
                            .zip(rhs)
                            .map(|(coefficient, rhs)| {
                                Ok::<_, RuntimeError>(
                                    Complex32::new(coefficient.re as f32, coefficient.im as f32)
                                        * f32_scalar_at(&values, *rhs)?,
                                )
                            })
                            .sum::<RuntimeResult<Complex32>>()?,
                    )
                }
                KernelInstruction::SolveRowAdjointElement {
                    row_slot,
                    index: element,
                    len,
                    adjoint,
                } => {
                    let (cache, row) = input
                        .cache()
                        .ok_or(crate::ExecutionError::UnsupportedCpuF32Model)?;
                    let inverse = cache.solve_row(*row_slot, row)?;
                    if inverse.len() != *len {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "specialized solve row has len {}, expected {len}",
                                inverse.len()
                            ),
                        });
                    }
                    let coefficient = inverse[*element];
                    F32Value::Scalar(
                        f32_scalar_at(&values, *adjoint)?
                            * Complex32::new(coefficient.re as f32, coefficient.im as f32).conj(),
                    )
                }
            };
            values.push(result);
        }
        Ok(values)
    }

    fn evaluate_f32_cached_value(
        &self,
        slot: usize,
        params: &ParamValues,
        input: F32KernelInput<'_>,
        missing_input: crate::ExecutionError,
    ) -> RuntimeResult<F32Value> {
        match input {
            F32KernelInput::Cache(Some((cache, row))) => {
                Ok(F32Value::from_value(cache.value(slot, row)?))
            }
            F32KernelInput::Cache(None) => Err(missing_input.into()),
            F32KernelInput::Event(event) => {
                let entry =
                    self.cache_plan
                        .entries()
                        .get(slot)
                        .ok_or(RuntimeError::InvalidShape {
                            index: self.graph.root().index(),
                            message: format!("cache slot {slot} is out of bounds"),
                        })?;
                let values = self.evaluate_values(params, Some(event))?;
                let value = values
                    .get(entry.node().index())
                    .ok_or(RuntimeError::InvalidShape {
                        index: entry.node().index(),
                        message: "cached node is out of bounds".into(),
                    })?
                    .clone();
                Ok(F32Value::from_value(value))
            }
        }
    }

    fn evaluate_f32_gradient(
        &self,
        params: &ParamValues,
        input: F32KernelInput<'_>,
    ) -> RuntimeResult<ValueGradient> {
        let real_ir = self
            .f32_gradient_fallback_real
            .as_ref()
            .ok_or(crate::ExecutionError::UnsupportedCpuF32Model)?;
        let mut real = Vec::new();
        let (value, _) =
            self.evaluate_f32_gradient_component_prepared(real_ir, params, input, &mut real)?;
        let imag = if let Some(imag_ir) = self.f32_gradient_fallback_imag.as_ref() {
            let mut imag = Vec::new();
            self.evaluate_f32_gradient_component_prepared(imag_ir, params, input, &mut imag)?;
            imag
        } else {
            vec![0.0; real.len()]
        };
        Ok(ValueGradient {
            value,
            gradient: real
                .into_iter()
                .zip(imag)
                .map(|(re, im)| Complex64::new(re as f64, im as f64))
                .collect(),
        })
    }

    fn evaluate_f32_gradient_component_prepared<'a>(
        &self,
        ir: &GradientKernelIr,
        params: &ParamValues,
        input: F32KernelInput<'_>,
        gradient: &'a mut Vec<f32>,
    ) -> RuntimeResult<(Complex64, &'a [f32])> {
        let values = self.evaluate_f32_kernel_values(ir.values(), params, input)?;
        let value = f32_scalar_at(&values, ir.primal_root())?;
        gradient.clear();
        gradient.reserve(ir.outputs().len());
        for output in ir.outputs() {
            gradient.push(f32_scalar_at(&values, *output)?.re);
        }
        Ok((Complex64::new(value.re as f64, value.im as f64), gradient))
    }

    fn value_gradient(
        &self,
        values: Vec<Value>,
        cached_factors: Option<(&CpuBatchCache, usize)>,
    ) -> RuntimeResult<ValueGradient> {
        let value = if cached_factors.is_some() {
            self.cached_scalar_at(&values, self.graph.root())?
        } else {
            scalar_at(&values, self.graph.root().index())?
        };
        let gradient = match self.autodiff.mode() {
            AutodiffMode::Auto => unreachable!("autodiff mode is resolved during preparation"),
            AutodiffMode::Forward => {
                DerivativeWorkspace::new(self, &values, cached_factors).gradient()?
            }
            AutodiffMode::Reverse => {
                ReverseDerivativeWorkspace::new(self, &values, cached_factors).gradient()?
            }
        };
        Ok(ValueGradient { value, gradient })
    }

    fn solve_primal(
        &self,
        matrix_id: ExprId,
        dimension: usize,
        matrix: &[Complex64],
        rhs: &DVector<Complex64>,
        node_index: usize,
        cached: Option<(&CpuBatchCache, usize)>,
    ) -> RuntimeResult<DVector<Complex64>> {
        let solution = if let (Some(slot), Some((cache, row))) =
            (self.factor_matrix_slots[matrix_id.index()], cached)
        {
            cache.factor(slot, row)?.solve(rhs)
        } else if let Some(slot) = self.constant_factor_slots[matrix_id.index()] {
            self.constant_factors[slot]
                .get_or_init(|| DMatrix::from_row_slice(dimension, dimension, matrix).lu())
                .solve(rhs)
        } else {
            DMatrix::from_row_slice(dimension, dimension, matrix)
                .lu()
                .solve(rhs)
        };
        solution.ok_or(RuntimeError::SingularMatrix(node_index))
    }

    fn event_columns(&self, schema: &Schema) -> RuntimeResult<Vec<Option<EventColumn>>> {
        self.graph
            .nodes()
            .iter()
            .map(|node| {
                if let ExprNode::EventScalar(name) = node {
                    Ok(Some(EventColumn::Scalar(
                        schema
                            .scalar_index(name)
                            .ok_or_else(|| RuntimeError::MissingEventColumn(name.to_string()))?,
                    )))
                } else if let ExprNode::EventP4Component { name, component } = node {
                    Ok(Some(EventColumn::P4Component {
                        col: schema
                            .p4_index(name)
                            .ok_or_else(|| RuntimeError::MissingEventColumn(name.to_string()))?,
                        component: *component,
                    }))
                } else {
                    Ok(None)
                }
            })
            .collect()
    }

    fn evaluate_cache_values_for_row(
        &self,
        batch: &EventBatch,
        row: usize,
        event_columns: &[Option<EventColumn>],
    ) -> RuntimeResult<Vec<Option<Value>>> {
        let mut values = vec![None; self.graph.nodes().len()];

        for id in &self.cache_materialization_nodes {
            let index = id.index();
            let node = &self.graph.nodes()[index];
            let value = match node {
                ExprNode::RealConst(value) => Value::Scalar(Complex64::from(*value)),
                ExprNode::ComplexConst(value) => Value::Scalar(*value),
                ExprNode::EventScalar(name) => {
                    let col = event_columns[index]
                        .ok_or_else(|| RuntimeError::MissingEventColumn(name.to_string()))?;
                    let EventColumn::Scalar(col) = col else {
                        return Err(RuntimeError::MissingEventColumn(name.to_string()));
                    };
                    Value::Scalar(Complex64::from(batch.scalar_at(col, row)))
                }
                ExprNode::EventP4Component { name, component } => {
                    let col = event_columns[index]
                        .ok_or_else(|| RuntimeError::MissingEventColumn(name.to_string()))?;
                    let EventColumn::P4Component {
                        col,
                        component: actual,
                    } = col
                    else {
                        return Err(RuntimeError::MissingEventColumn(name.to_string()));
                    };
                    debug_assert_eq!(actual, *component);
                    let p4 = batch.p4_at(col, row);
                    let value = match component {
                        P4Component::Px => p4.px,
                        P4Component::Py => p4.py,
                        P4Component::Pz => p4.pz,
                        P4Component::E => p4.e,
                    };
                    Value::Scalar(Complex64::from(value))
                }
                ExprNode::Unary { op, input } => {
                    let input = scalar_at_optional(&values, input.index())?;
                    Value::Scalar(eval_unary(*op, input))
                }
                ExprNode::Binary { op, lhs, rhs } => {
                    let lhs = scalar_at_optional(&values, lhs.index())?;
                    let rhs = scalar_at_optional(&values, rhs.index())?;
                    Value::Scalar(eval_binary(*op, lhs, rhs))
                }
                ExprNode::NaryAdd { terms } => {
                    let mut sum = Complex64::ZERO;
                    for term in terms {
                        sum += scalar_at_optional(&values, term.index())?;
                    }
                    Value::Scalar(sum)
                }
                ExprNode::NaryMul { factors } => {
                    let mut product = Complex64::ONE;
                    for factor in factors {
                        product *= scalar_at_optional(&values, factor.index())?;
                    }
                    Value::Scalar(product)
                }
                ExprNode::Complex { re, im } => {
                    let re = scalar_at_optional(&values, re.index())?;
                    let im = scalar_at_optional(&values, im.index())?;
                    Value::Scalar(Complex64::new(re.re, im.re))
                }
                ExprNode::Vector { elements } => Value::Vector(
                    elements
                        .iter()
                        .map(|id| scalar_at_optional(&values, id.index()))
                        .collect::<RuntimeResult<_>>()?,
                ),
                ExprNode::Matrix {
                    rows,
                    cols,
                    elements,
                } => {
                    if elements.len() != rows * cols {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "matrix has {} elements for shape {rows}x{cols}",
                                elements.len()
                            ),
                        });
                    }
                    Value::Matrix {
                        rows: *rows,
                        cols: *cols,
                        values: elements
                            .iter()
                            .map(|id| scalar_at_optional(&values, id.index()))
                            .collect::<RuntimeResult<_>>()?,
                    }
                }
                ExprNode::Component { input, index: i } => {
                    let vector = vector_at_optional(&values, input.index())?;
                    Value::Scalar(*vector.get(*i).ok_or_else(|| RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "component index {i} out of bounds for len {}",
                            vector.len()
                        ),
                    })?)
                }
                ExprNode::MatrixElement { input, row, col } => {
                    let (rows, cols, matrix) = matrix_at_optional(&values, input.index())?;
                    if *row >= rows || *col >= cols {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "matrix element ({row}, {col}) out of bounds for shape {rows}x{cols}"
                            ),
                        });
                    }
                    Value::Scalar(matrix[row * cols + col])
                }
                ExprNode::MatMul { lhs, rhs } => {
                    let (lhs_rows, lhs_cols, lhs) = matrix_at_optional(&values, lhs.index())?;
                    let (rhs_rows, rhs_cols, rhs) = matrix_at_optional(&values, rhs.index())?;
                    if lhs_cols != rhs_rows {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot multiply {lhs_rows}x{lhs_cols} by {rhs_rows}x{rhs_cols}"
                            ),
                        });
                    }
                    let lhs = DMatrix::from_row_slice(lhs_rows, lhs_cols, lhs);
                    let rhs = DMatrix::from_row_slice(rhs_rows, rhs_cols, rhs);
                    let out = lhs * rhs;
                    Value::Matrix {
                        rows: out.nrows(),
                        cols: out.ncols(),
                        values: matrix_values_row_major(&out),
                    }
                }
                ExprNode::MatVec { matrix, vector } => {
                    let (rows, cols, matrix) = matrix_at_optional(&values, matrix.index())?;
                    let vector = vector_at_optional(&values, vector.index())?;
                    if cols != vector.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot multiply {rows}x{cols} matrix by len {} vector",
                                vector.len()
                            ),
                        });
                    }
                    let matrix = DMatrix::from_row_slice(rows, cols, matrix);
                    let vector = DVector::from_row_slice(vector);
                    Value::Vector((matrix * vector).iter().copied().collect())
                }
                ExprNode::Dot { lhs, rhs } => {
                    let lhs = vector_at_optional(&values, lhs.index())?;
                    let rhs = vector_at_optional(&values, rhs.index())?;
                    if lhs.len() != rhs.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot dot len {} vector with len {} vector",
                                lhs.len(),
                                rhs.len()
                            ),
                        });
                    }
                    Value::Scalar(lhs.iter().zip(rhs).map(|(lhs, rhs)| lhs * rhs).sum())
                }
                ExprNode::Solve { matrix, rhs } => {
                    let matrix_id = *matrix;
                    let (rows, cols, matrix) = matrix_at_optional(&values, matrix_id.index())?;
                    let rhs = vector_at_optional(&values, rhs.index())?;
                    if rows != cols || rows != rhs.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot solve {rows}x{cols} matrix against len {} vector",
                                rhs.len()
                            ),
                        });
                    }
                    let rhs = DVector::from_row_slice(rhs);
                    let solution = self.solve_primal(matrix_id, rows, matrix, &rhs, index, None)?;
                    Value::Vector(solution.iter().copied().collect())
                }
                ExprNode::ScalarParam(_) => {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: "parameter-dependent node cannot be part of an event cache".into(),
                    });
                }
            };
            values[index] = Some(value);
        }

        Ok(values)
    }

    fn evaluate_values(
        &self,
        params: &ParamValues,
        event: Option<&dyn EventLookup>,
    ) -> RuntimeResult<Vec<Value>> {
        let mut values = Vec::with_capacity(self.graph.nodes().len());

        for (index, node) in self.graph.nodes().iter().enumerate() {
            let value = match node {
                ExprNode::RealConst(value) => Value::Scalar(Complex64::from(*value)),
                ExprNode::ComplexConst(value) => Value::Scalar(*value),
                ExprNode::ScalarParam(_) => {
                    Value::Scalar(Complex64::from(self.parameter_value(params, index)?))
                }
                ExprNode::EventScalar(name) => {
                    let Some(event) = event else {
                        return Err(RuntimeError::MissingEventScalar(name.to_string()));
                    };
                    Value::Scalar(Complex64::from(
                        event
                            .scalar(name)
                            .ok_or_else(|| RuntimeError::MissingEventScalar(name.to_string()))?,
                    ))
                }
                ExprNode::EventP4Component { name, component } => {
                    let Some(event) = event else {
                        return Err(RuntimeError::MissingEventScalar(format!(
                            "{name}.{}",
                            component.label()
                        )));
                    };
                    Value::Scalar(Complex64::from(
                        event.p4_component(name, *component).ok_or_else(|| {
                            RuntimeError::MissingEventScalar(format!(
                                "{name}.{}",
                                component.label()
                            ))
                        })?,
                    ))
                }
                ExprNode::Unary { op, input } => {
                    let input = scalar_at(&values, input.index())?;
                    Value::Scalar(eval_unary(*op, input))
                }
                ExprNode::Binary { op, lhs, rhs } => {
                    let lhs = scalar_at(&values, lhs.index())?;
                    let rhs = scalar_at(&values, rhs.index())?;
                    Value::Scalar(eval_binary(*op, lhs, rhs))
                }
                ExprNode::NaryAdd { terms } => {
                    let mut sum = Complex64::ZERO;
                    for term in terms {
                        sum += scalar_at(&values, term.index())?;
                    }
                    Value::Scalar(sum)
                }
                ExprNode::NaryMul { factors } => {
                    let mut product = Complex64::ONE;
                    for factor in factors {
                        product *= scalar_at(&values, factor.index())?;
                    }
                    Value::Scalar(product)
                }
                ExprNode::Complex { re, im } => {
                    let re = scalar_at(&values, re.index())?;
                    let im = scalar_at(&values, im.index())?;
                    Value::Scalar(Complex64::new(re.re, im.re))
                }
                ExprNode::Vector { elements } => Value::Vector(
                    elements
                        .iter()
                        .map(|id| scalar_at(&values, id.index()))
                        .collect::<RuntimeResult<_>>()?,
                ),
                ExprNode::Matrix {
                    rows,
                    cols,
                    elements,
                } => {
                    if elements.len() != rows * cols {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "matrix has {} elements for shape {rows}x{cols}",
                                elements.len()
                            ),
                        });
                    }
                    Value::Matrix {
                        rows: *rows,
                        cols: *cols,
                        values: elements
                            .iter()
                            .map(|id| scalar_at(&values, id.index()))
                            .collect::<RuntimeResult<_>>()?,
                    }
                }
                ExprNode::Component { input, index: i } => {
                    let vector = vector_at(&values, input.index())?;
                    Value::Scalar(*vector.get(*i).ok_or_else(|| RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "component index {i} out of bounds for len {}",
                            vector.len()
                        ),
                    })?)
                }
                ExprNode::MatrixElement { input, row, col } => {
                    let (rows, cols, matrix) = matrix_at(&values, input.index())?;
                    if *row >= rows || *col >= cols {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "matrix element ({row}, {col}) out of bounds for shape {rows}x{cols}"
                            ),
                        });
                    }
                    Value::Scalar(matrix[row * cols + col])
                }
                ExprNode::MatMul { lhs, rhs } => {
                    let (lhs_rows, lhs_cols, lhs) = matrix_at(&values, lhs.index())?;
                    let (rhs_rows, rhs_cols, rhs) = matrix_at(&values, rhs.index())?;
                    if lhs_cols != rhs_rows {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot multiply {lhs_rows}x{lhs_cols} by {rhs_rows}x{rhs_cols}"
                            ),
                        });
                    }
                    let lhs = DMatrix::from_row_slice(lhs_rows, lhs_cols, lhs);
                    let rhs = DMatrix::from_row_slice(rhs_rows, rhs_cols, rhs);
                    let out = lhs * rhs;
                    Value::Matrix {
                        rows: out.nrows(),
                        cols: out.ncols(),
                        values: matrix_values_row_major(&out),
                    }
                }
                ExprNode::MatVec { matrix, vector } => {
                    let (rows, cols, matrix) = matrix_at(&values, matrix.index())?;
                    let vector = vector_at(&values, vector.index())?;
                    if cols != vector.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot multiply {rows}x{cols} matrix by len {} vector",
                                vector.len()
                            ),
                        });
                    }
                    let matrix = DMatrix::from_row_slice(rows, cols, matrix);
                    let vector = DVector::from_row_slice(vector);
                    Value::Vector((matrix * vector).iter().copied().collect())
                }
                ExprNode::Dot { lhs, rhs } => {
                    let lhs = vector_at(&values, lhs.index())?;
                    let rhs = vector_at(&values, rhs.index())?;
                    if lhs.len() != rhs.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot dot len {} vector with len {} vector",
                                lhs.len(),
                                rhs.len()
                            ),
                        });
                    }
                    Value::Scalar(lhs.iter().zip(rhs).map(|(lhs, rhs)| lhs * rhs).sum())
                }
                ExprNode::Solve { matrix, rhs } => {
                    let matrix_id = *matrix;
                    let (rows, cols, matrix) = matrix_at(&values, matrix_id.index())?;
                    let rhs = vector_at(&values, rhs.index())?;
                    if rows != cols || rows != rhs.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot solve {rows}x{cols} matrix against len {} vector",
                                rhs.len()
                            ),
                        });
                    }
                    let rhs = DVector::from_row_slice(rhs);
                    let solution = self.solve_primal(matrix_id, rows, matrix, &rhs, index, None)?;
                    Value::Vector(solution.iter().copied().collect())
                }
            };
            values.push(value);
        }

        Ok(values)
    }

    fn evaluate_values_from_cache(
        &self,
        params: &ParamValues,
        cache: &CpuBatchCache,
        row: usize,
    ) -> RuntimeResult<Vec<Value>> {
        let mut values = Vec::with_capacity(self.cached_evaluation_nodes.len());

        for id in &self.cached_evaluation_nodes {
            let index = id.index();
            let node = &self.graph.nodes()[index];
            if let Some(slot) = self.cache_slots[index] {
                values.push(cache.value(slot, row)?);
                continue;
            }
            let value = match node {
                ExprNode::RealConst(value) => Value::Scalar(Complex64::from(*value)),
                ExprNode::ComplexConst(value) => Value::Scalar(*value),
                ExprNode::ScalarParam(_) => {
                    Value::Scalar(Complex64::from(self.parameter_value(params, index)?))
                }
                ExprNode::EventScalar(name) => {
                    return Err(RuntimeError::MissingEventScalar(name.to_string()));
                }
                ExprNode::EventP4Component { name, component } => {
                    return Err(RuntimeError::MissingEventScalar(format!(
                        "{name}.{}",
                        component.label()
                    )));
                }
                ExprNode::Unary { op, input } => {
                    let input = self.cached_scalar_at(&values, *input)?;
                    Value::Scalar(eval_unary(*op, input))
                }
                ExprNode::Binary { op, lhs, rhs } => {
                    let lhs = self.cached_scalar_at(&values, *lhs)?;
                    let rhs = self.cached_scalar_at(&values, *rhs)?;
                    Value::Scalar(eval_binary(*op, lhs, rhs))
                }
                ExprNode::NaryAdd { terms } => {
                    let mut sum = Complex64::ZERO;
                    for term in terms {
                        sum += self.cached_scalar_at(&values, *term)?;
                    }
                    Value::Scalar(sum)
                }
                ExprNode::NaryMul { factors } => {
                    let mut product = Complex64::ONE;
                    for factor in factors {
                        product *= self.cached_scalar_at(&values, *factor)?;
                    }
                    Value::Scalar(product)
                }
                ExprNode::Complex { re, im } => {
                    let re = self.cached_scalar_at(&values, *re)?;
                    let im = self.cached_scalar_at(&values, *im)?;
                    Value::Scalar(Complex64::new(re.re, im.re))
                }
                ExprNode::Vector { elements } => Value::Vector(
                    elements
                        .iter()
                        .map(|id| self.cached_scalar_at(&values, *id))
                        .collect::<RuntimeResult<_>>()?,
                ),
                ExprNode::Matrix {
                    rows,
                    cols,
                    elements,
                } => {
                    if elements.len() != rows * cols {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "matrix has {} elements for shape {rows}x{cols}",
                                elements.len()
                            ),
                        });
                    }
                    Value::Matrix {
                        rows: *rows,
                        cols: *cols,
                        values: elements
                            .iter()
                            .map(|id| self.cached_scalar_at(&values, *id))
                            .collect::<RuntimeResult<_>>()?,
                    }
                }
                ExprNode::Component { input, index: i } => {
                    if let Some(plan) = self.solve_components[index] {
                        let inverse_row = cache.solve_row(plan.row_slot(), row)?;
                        if inverse_row.len() != plan.dimension() {
                            return Err(RuntimeError::InvalidShape {
                                index,
                                message: format!(
                                    "specialized solve expected row len {}, got {}",
                                    plan.dimension(),
                                    inverse_row.len()
                                ),
                            });
                        }
                        if let Some(elements) = &self.solve_rhs_elements[plan.rhs().index()] {
                            if elements.len() != plan.dimension() {
                                return Err(RuntimeError::InvalidShape {
                                    index,
                                    message: format!(
                                        "specialized solve expected {} RHS elements, got {}",
                                        plan.dimension(),
                                        elements.len()
                                    ),
                                });
                            }
                            Value::Scalar(
                                inverse_row
                                    .iter()
                                    .zip(elements)
                                    .map(|(lhs, rhs)| {
                                        Ok(lhs * self.cached_scalar_at(&values, *rhs)?)
                                    })
                                    .sum::<RuntimeResult<Complex64>>()?,
                            )
                        } else {
                            let rhs = self.cached_vector_at(&values, plan.rhs())?;
                            if rhs.len() != plan.dimension() {
                                return Err(RuntimeError::InvalidShape {
                                    index,
                                    message: format!(
                                        "specialized solve expected RHS len {}, got {}",
                                        plan.dimension(),
                                        rhs.len()
                                    ),
                                });
                            }
                            Value::Scalar(
                                inverse_row
                                    .iter()
                                    .zip(rhs)
                                    .map(|(lhs, rhs)| lhs * rhs)
                                    .sum(),
                            )
                        }
                    } else {
                        let vector = self.cached_vector_at(&values, *input)?;
                        Value::Scalar(*vector.get(*i).ok_or_else(|| {
                            RuntimeError::InvalidShape {
                                index,
                                message: format!(
                                    "component index {i} out of bounds for len {}",
                                    vector.len()
                                ),
                            }
                        })?)
                    }
                }
                ExprNode::MatrixElement { input, row, col } => {
                    let (rows, cols, matrix) = self.cached_matrix_at(&values, *input)?;
                    if *row >= rows || *col >= cols {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "matrix element ({row}, {col}) out of bounds for shape {rows}x{cols}"
                            ),
                        });
                    }
                    Value::Scalar(matrix[row * cols + col])
                }
                ExprNode::MatMul { lhs, rhs } => {
                    let (lhs_rows, lhs_cols, lhs) = self.cached_matrix_at(&values, *lhs)?;
                    let (rhs_rows, rhs_cols, rhs) = self.cached_matrix_at(&values, *rhs)?;
                    if lhs_cols != rhs_rows {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot multiply {lhs_rows}x{lhs_cols} by {rhs_rows}x{rhs_cols}"
                            ),
                        });
                    }
                    let lhs = DMatrix::from_row_slice(lhs_rows, lhs_cols, lhs);
                    let rhs = DMatrix::from_row_slice(rhs_rows, rhs_cols, rhs);
                    let out = lhs * rhs;
                    Value::Matrix {
                        rows: out.nrows(),
                        cols: out.ncols(),
                        values: matrix_values_row_major(&out),
                    }
                }
                ExprNode::MatVec { matrix, vector } => {
                    let (rows, cols, matrix) = self.cached_matrix_at(&values, *matrix)?;
                    let vector = self.cached_vector_at(&values, *vector)?;
                    if cols != vector.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot multiply {rows}x{cols} matrix by len {} vector",
                                vector.len()
                            ),
                        });
                    }
                    let matrix = DMatrix::from_row_slice(rows, cols, matrix);
                    let vector = DVector::from_row_slice(vector);
                    Value::Vector((matrix * vector).iter().copied().collect())
                }
                ExprNode::Dot { lhs, rhs } => {
                    let lhs = self.cached_vector_at(&values, *lhs)?;
                    let rhs = self.cached_vector_at(&values, *rhs)?;
                    if lhs.len() != rhs.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot dot len {} vector with len {} vector",
                                lhs.len(),
                                rhs.len()
                            ),
                        });
                    }
                    Value::Scalar(lhs.iter().zip(rhs).map(|(lhs, rhs)| lhs * rhs).sum())
                }
                ExprNode::Solve { matrix, rhs } => {
                    let matrix_id = *matrix;
                    let (rows, cols, matrix) = self.cached_matrix_at(&values, matrix_id)?;
                    let rhs = self.cached_vector_at(&values, *rhs)?;
                    if rows != cols || rows != rhs.len() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "cannot solve {rows}x{cols} matrix against len {} vector",
                                rhs.len()
                            ),
                        });
                    }
                    let rhs = DVector::from_row_slice(rhs);
                    let solution = self.solve_primal(
                        matrix_id,
                        rows,
                        matrix,
                        &rhs,
                        index,
                        Some((cache, row)),
                    )?;
                    Value::Vector(solution.iter().copied().collect())
                }
            };
            values.push(value);
        }

        Ok(values)
    }

    fn cached_value_slot(&self, id: ExprId) -> RuntimeResult<usize> {
        self.cached_value_slots[id.index()].ok_or_else(|| RuntimeError::InvalidShape {
            index: id.index(),
            message: "node is not part of the cached evaluation schedule".into(),
        })
    }

    fn cached_scalar_at(&self, values: &[Value], id: ExprId) -> RuntimeResult<Complex64> {
        scalar_at(values, self.cached_value_slot(id)?)
    }

    fn cached_vector_at<'a>(
        &self,
        values: &'a [Value],
        id: ExprId,
    ) -> RuntimeResult<&'a [Complex64]> {
        vector_at(values, self.cached_value_slot(id)?)
    }

    fn cached_matrix_at<'a>(
        &self,
        values: &'a [Value],
        id: ExprId,
    ) -> RuntimeResult<(usize, usize, &'a [Complex64])> {
        matrix_at(values, self.cached_value_slot(id)?)
    }

    fn check_batch_cache(&self, cache: &CpuBatchCache) -> RuntimeResult<()> {
        if cache.nodes
            == self
                .cache_plan
                .entries()
                .iter()
                .map(|entry| entry.node())
                .collect::<Vec<_>>()
            && cache.factor_nodes
                == self
                    .factor_matrices
                    .iter()
                    .map(|(node, _)| *node)
                    .collect::<Vec<_>>()
            && cache.solve_row_keys == self.solve_row_keys
        {
            Ok(())
        } else {
            Err(RuntimeError::InvalidCacheLayout)
        }
    }
}

fn resident_cache_plan(
    fixed_per_batch: usize,
    cache_bytes_per_event: usize,
    source_bytes_per_event: usize,
    events: usize,
    available: usize,
) -> Option<(usize, usize)> {
    if events == 0 {
        return Some((fixed_per_batch, 1));
    }
    let event_cache = cache_bytes_per_event.checked_mul(events)?;
    let minimum = event_cache
        .checked_add(fixed_per_batch)?
        .checked_add(source_bytes_per_event)?;
    if minimum > available {
        return None;
    }
    let mut chunk = events;
    for _ in 0..16 {
        let batches = events.saturating_add(chunk - 1) / chunk;
        let resident = event_cache.checked_add(fixed_per_batch.checked_mul(batches)?)?;
        let next = available
            .saturating_sub(resident)
            .checked_div(source_bytes_per_event.max(1))?
            .min(events);
        if next == 0 {
            return None;
        }
        if next == chunk {
            return Some((resident, chunk));
        }
        chunk = next;
    }
    let batches = events.saturating_add(chunk - 1) / chunk;
    let resident = event_cache.checked_add(fixed_per_batch.checked_mul(batches)?)?;
    (resident.checked_add(source_bytes_per_event.checked_mul(chunk)?)? <= available)
        .then_some((resident, chunk))
}

#[derive(Clone, Debug, PartialEq)]
enum Value {
    Scalar(Complex64),
    Vector(Vec<Complex64>),
    Matrix {
        rows: usize,
        cols: usize,
        values: Vec<Complex64>,
    },
}

#[derive(Clone, Copy)]
enum F32KernelInput<'a> {
    Cache(Option<(&'a CpuBatchCache, usize)>),
    Event(&'a dyn EventLookup),
}

impl<'a> F32KernelInput<'a> {
    fn cache(self) -> Option<(&'a CpuBatchCache, usize)> {
        match self {
            Self::Cache(cache) => cache,
            Self::Event(_) => None,
        }
    }
}

#[derive(Clone, Debug, PartialEq)]
enum F32Value {
    Scalar(Complex32),
    Vector(Vec<Complex32>),
    Matrix {
        rows: usize,
        cols: usize,
        values: Vec<Complex32>,
    },
}

impl F32Value {
    fn from_value(value: Value) -> Self {
        match value {
            Value::Scalar(value) => Self::Scalar(Complex32::new(value.re as f32, value.im as f32)),
            Value::Vector(values) => Self::Vector(
                values
                    .into_iter()
                    .map(|value| Complex32::new(value.re as f32, value.im as f32))
                    .collect(),
            ),
            Value::Matrix { rows, cols, values } => Self::Matrix {
                rows,
                cols,
                values: values
                    .into_iter()
                    .map(|value| Complex32::new(value.re as f32, value.im as f32))
                    .collect(),
            },
        }
    }

    fn kind(&self) -> &'static str {
        match self {
            Self::Scalar(_) => "scalar",
            Self::Vector(_) => "vector",
            Self::Matrix { .. } => "matrix",
        }
    }
}

type DynamicLu = LU<Complex64, Dyn, Dyn>;

struct DerivativeWorkspace<'a> {
    plan: &'a CpuPlan,
    primals: &'a [Value],
    tangents: Vec<Option<Value>>,
    factors: HashMap<usize, DynamicLu>,
    cached_factors: Option<(&'a CpuBatchCache, usize)>,
}

impl<'a> DerivativeWorkspace<'a> {
    fn new(
        plan: &'a CpuPlan,
        primals: &'a [Value],
        cached_factors: Option<(&'a CpuBatchCache, usize)>,
    ) -> Self {
        Self {
            plan,
            primals,
            tangents: vec![None; plan.graph.nodes().len()],
            factors: HashMap::new(),
            cached_factors,
        }
    }

    fn gradient(&mut self) -> RuntimeResult<Vec<Complex64>> {
        let mut gradient = Vec::with_capacity(self.plan.autodiff.parameter_count());
        for parameter in 0..self.plan.autodiff.parameter_count() {
            let active = self
                .plan
                .autodiff
                .active_nodes(parameter)
                .expect("free parameter index is valid");
            for id in active {
                self.differentiate_node(*id)?;
            }
            gradient.push(self.scalar_tangent(self.plan.graph.root())?);
            for id in active {
                self.tangents[id.index()] = None;
            }
        }
        Ok(gradient)
    }

    fn differentiate_node(&mut self, id: ExprId) -> RuntimeResult<()> {
        let index = id.index();
        let node = self.plan.graph.nodes()[index].clone();
        let tangent = match node {
            ExprNode::ScalarParam(_) => Value::Scalar(Complex64::ONE),
            ExprNode::Unary { op, input } => {
                let input_value = self.primal_scalar(input)?;
                let output_value = self.primal_scalar(id)?;
                let input_tangent = self.scalar_tangent(input)?;
                let value = match op {
                    UnaryOp::Neg => -input_tangent,
                    UnaryOp::Real => Complex64::from(input_tangent.re),
                    UnaryOp::Imag => Complex64::from(input_tangent.im),
                    UnaryOp::Conj => input_tangent.conj(),
                    UnaryOp::NormSqr => {
                        Complex64::from(2.0 * (input_value.conj() * input_tangent).re)
                    }
                    UnaryOp::Sqrt => input_tangent / (2.0 * output_value),
                    UnaryOp::Exp => output_value * input_tangent,
                    UnaryOp::Sin => input_value.cos() * input_tangent,
                    UnaryOp::Cos => -input_value.sin() * input_tangent,
                    UnaryOp::Log => input_tangent / input_value,
                    UnaryOp::PowI(power) => {
                        if power == 0 {
                            Complex64::ZERO
                        } else if power == i32::MIN {
                            power as f64 * output_value * input_tangent / input_value
                        } else {
                            power as f64 * input_value.powi(power - 1) * input_tangent
                        }
                    }
                };
                Value::Scalar(value)
            }
            ExprNode::Binary { op, lhs, rhs } => {
                let lhs_value = self.primal_scalar(lhs)?;
                let rhs_value = self.primal_scalar(rhs)?;
                let lhs_tangent = self.scalar_tangent(lhs)?;
                let rhs_tangent = self.scalar_tangent(rhs)?;
                let value = match op {
                    BinaryOp::Add => lhs_tangent + rhs_tangent,
                    BinaryOp::Sub => lhs_tangent - rhs_tangent,
                    BinaryOp::Mul => lhs_tangent * rhs_value + lhs_value * rhs_tangent,
                    BinaryOp::Div => {
                        (lhs_tangent * rhs_value - lhs_value * rhs_tangent) / rhs_value.powi(2)
                    }
                    BinaryOp::Atan2 => {
                        let denominator = lhs_value.re.powi(2) + rhs_value.re.powi(2);
                        Complex64::from(
                            (rhs_value.re * lhs_tangent.re - lhs_value.re * rhs_tangent.re)
                                / denominator,
                        )
                    }
                };
                Value::Scalar(value)
            }
            ExprNode::NaryAdd { terms } => {
                Value::Scalar(terms.into_iter().try_fold(Complex64::ZERO, |sum, term| {
                    Ok::<_, RuntimeError>(sum + self.scalar_tangent(term)?)
                })?)
            }
            ExprNode::NaryMul { factors } => {
                let mut product = Complex64::ONE;
                let mut derivative = Complex64::ZERO;
                for factor in factors {
                    let value = self.primal_scalar(factor)?;
                    derivative = derivative * value + product * self.scalar_tangent(factor)?;
                    product *= value;
                }
                Value::Scalar(derivative)
            }
            ExprNode::Complex { re, im } => Value::Scalar(Complex64::new(
                self.scalar_tangent(re)?.re,
                self.scalar_tangent(im)?.re,
            )),
            ExprNode::Vector { .. }
                if self.cached_factors.is_some()
                    && self.plan.cached_value_slots[index].is_none()
                    && self.plan.solve_rhs_elements[index].is_some() =>
            {
                Value::Vector(Vec::new())
            }
            ExprNode::Vector { elements } => Value::Vector(
                elements
                    .into_iter()
                    .map(|element| self.scalar_tangent(element))
                    .collect::<RuntimeResult<_>>()?,
            ),
            ExprNode::Matrix {
                rows,
                cols,
                elements,
            } => {
                if elements.len() != rows * cols {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "matrix has {} elements for shape {rows}x{cols}",
                            elements.len()
                        ),
                    });
                }
                Value::Matrix {
                    rows,
                    cols,
                    values: elements
                        .into_iter()
                        .map(|element| self.scalar_tangent(element))
                        .collect::<RuntimeResult<_>>()?,
                }
            }
            ExprNode::Component { input, index: i } => {
                if let (Some(plan), Some((cache, row))) =
                    (self.plan.solve_components[index], self.cached_factors)
                {
                    let inverse_row = cache.solve_row(plan.row_slot(), row)?;
                    if let Some(elements) = &self.plan.solve_rhs_elements[plan.rhs().index()] {
                        Value::Scalar(
                            inverse_row
                                .iter()
                                .zip(elements)
                                .map(|(lhs, rhs)| Ok(lhs * self.scalar_tangent(*rhs)?))
                                .sum::<RuntimeResult<Complex64>>()?,
                        )
                    } else {
                        let rhs_tangent =
                            self.vector_tangent_value(plan.rhs(), plan.dimension())?;
                        Value::Scalar(
                            inverse_row
                                .iter()
                                .zip(rhs_tangent)
                                .map(|(lhs, rhs)| lhs * rhs)
                                .sum(),
                        )
                    }
                } else {
                    let vector = self.vector_tangent(input)?;
                    Value::Scalar(*vector.get(i).ok_or_else(|| RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "component index {i} out of bounds for len {}",
                            vector.len()
                        ),
                    })?)
                }
            }
            ExprNode::MatrixElement { input, row, col } => {
                let (rows, cols, matrix) = self.matrix_tangent(input)?;
                if row >= rows || col >= cols {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "matrix element ({row}, {col}) out of bounds for shape {rows}x{cols}"
                        ),
                    });
                }
                Value::Scalar(matrix[row * cols + col])
            }
            ExprNode::MatMul { lhs, rhs } => {
                let (lhs_rows, lhs_cols, lhs_value) = self.primal_matrix(lhs)?;
                let (rhs_rows, rhs_cols, rhs_value) = self.primal_matrix(rhs)?;
                if lhs_cols != rhs_rows {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "cannot multiply {lhs_rows}x{lhs_cols} by {rhs_rows}x{rhs_cols}"
                        ),
                    });
                }
                let lhs_value = DMatrix::from_row_slice(lhs_rows, lhs_cols, lhs_value);
                let rhs_value = DMatrix::from_row_slice(rhs_rows, rhs_cols, rhs_value);
                let lhs_tangent = self.matrix_tangent_value(lhs, lhs_rows, lhs_cols)?;
                let rhs_tangent = self.matrix_tangent_value(rhs, rhs_rows, rhs_cols)?;
                let output = lhs_tangent * &rhs_value + lhs_value * rhs_tangent;
                Value::Matrix {
                    rows: output.nrows(),
                    cols: output.ncols(),
                    values: matrix_values_row_major(&output),
                }
            }
            ExprNode::MatVec { matrix, vector } => {
                let (rows, cols, matrix_value) = self.primal_matrix(matrix)?;
                let vector_value = self.primal_vector(vector)?;
                if cols != vector_value.len() {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "cannot multiply {rows}x{cols} matrix by len {} vector",
                            vector_value.len()
                        ),
                    });
                }
                let matrix_value = DMatrix::from_row_slice(rows, cols, matrix_value);
                let vector_value = DVector::from_row_slice(vector_value);
                let matrix_tangent = self.matrix_tangent_value(matrix, rows, cols)?;
                let vector_tangent = DVector::from_vec(self.vector_tangent_value(vector, cols)?);
                Value::Vector(
                    (matrix_tangent * vector_value + matrix_value * vector_tangent)
                        .iter()
                        .copied()
                        .collect(),
                )
            }
            ExprNode::Dot { lhs, rhs } => {
                let lhs_value = self.primal_vector(lhs)?;
                let rhs_value = self.primal_vector(rhs)?;
                if lhs_value.len() != rhs_value.len() {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "cannot dot len {} vector with len {} vector",
                            lhs_value.len(),
                            rhs_value.len()
                        ),
                    });
                }
                let lhs_tangent = self.vector_tangent_value(lhs, lhs_value.len())?;
                let rhs_tangent = self.vector_tangent_value(rhs, rhs_value.len())?;
                Value::Scalar(
                    lhs_tangent
                        .iter()
                        .zip(rhs_value)
                        .map(|(lhs, rhs)| lhs * rhs)
                        .sum::<Complex64>()
                        + lhs_value
                            .iter()
                            .zip(rhs_tangent)
                            .map(|(lhs, rhs)| lhs * rhs)
                            .sum::<Complex64>(),
                )
            }
            ExprNode::Solve { matrix, rhs } => {
                if self.cached_factors.is_some() && self.plan.cached_value_slots[index].is_none() {
                    // Specialized components differentiate the RHS directly and never read this.
                    self.tangents[index] = Some(Value::Vector(Vec::new()));
                    return Ok(());
                }
                let (rows, cols, matrix_value) = self.primal_matrix(matrix)?;
                let solution = self.primal_vector(id)?;
                let rhs_value = self.primal_vector(rhs)?;
                if rows != cols || rows != rhs_value.len() {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "cannot solve {rows}x{cols} matrix against len {} vector",
                            rhs_value.len()
                        ),
                    });
                }
                let matrix_tangent = self.matrix_tangent_value(matrix, rows, cols)?;
                let rhs_tangent = DVector::from_vec(self.vector_tangent_value(rhs, rows)?);
                let solution = DVector::from_row_slice(solution);
                let tangent_rhs = rhs_tangent - matrix_tangent * solution;
                let tangent = if let (Some(slot), Some((cache, row))) = (
                    self.plan.factor_matrix_slots[matrix.index()],
                    self.cached_factors,
                ) {
                    cache
                        .factor(slot, row)?
                        .solve(&tangent_rhs)
                        .ok_or(RuntimeError::SingularMatrix(index))?
                } else if let Some(slot) = self.plan.constant_factor_slots[matrix.index()] {
                    self.plan.constant_factors[slot]
                        .get_or_init(|| DMatrix::from_row_slice(rows, cols, matrix_value).lu())
                        .solve(&tangent_rhs)
                        .ok_or(RuntimeError::SingularMatrix(index))?
                } else {
                    let matrix_value = DMatrix::from_row_slice(rows, cols, matrix_value);
                    self.factors
                        .entry(matrix.index())
                        .or_insert_with(|| matrix_value.lu())
                        .solve(&tangent_rhs)
                        .ok_or(RuntimeError::SingularMatrix(index))?
                };
                Value::Vector(tangent.iter().copied().collect())
            }
            ExprNode::RealConst(_)
            | ExprNode::ComplexConst(_)
            | ExprNode::EventScalar(_)
            | ExprNode::EventP4Component { .. } => {
                return Err(RuntimeError::InvalidShape {
                    index,
                    message: "parameter-independent node appeared in a derivative lane".into(),
                });
            }
        };
        self.tangents[index] = Some(tangent);
        Ok(())
    }

    fn primal_scalar(&self, id: ExprId) -> RuntimeResult<Complex64> {
        if self.cached_factors.is_some() {
            self.plan.cached_scalar_at(self.primals, id)
        } else {
            scalar_at(self.primals, id.index())
        }
    }

    fn primal_vector(&self, id: ExprId) -> RuntimeResult<&[Complex64]> {
        if self.cached_factors.is_some() {
            self.plan.cached_vector_at(self.primals, id)
        } else {
            vector_at(self.primals, id.index())
        }
    }

    fn primal_matrix(&self, id: ExprId) -> RuntimeResult<(usize, usize, &[Complex64])> {
        if self.cached_factors.is_some() {
            self.plan.cached_matrix_at(self.primals, id)
        } else {
            matrix_at(self.primals, id.index())
        }
    }

    fn scalar_tangent(&self, id: ExprId) -> RuntimeResult<Complex64> {
        match &self.tangents[id.index()] {
            Some(Value::Scalar(value)) => Ok(*value),
            Some(value) => Err(RuntimeError::TypeMismatch {
                index: id.index(),
                expected: "scalar tangent",
                actual: value.kind(),
            }),
            None => Ok(Complex64::ZERO),
        }
    }

    fn vector_tangent(&self, id: ExprId) -> RuntimeResult<&[Complex64]> {
        match &self.tangents[id.index()] {
            Some(Value::Vector(values)) => Ok(values),
            Some(value) => Err(RuntimeError::TypeMismatch {
                index: id.index(),
                expected: "vector tangent",
                actual: value.kind(),
            }),
            None => Err(RuntimeError::InvalidShape {
                index: id.index(),
                message: "inactive vector tangent requested without a target length".into(),
            }),
        }
    }

    fn vector_tangent_value(&self, id: ExprId, len: usize) -> RuntimeResult<Vec<Complex64>> {
        match &self.tangents[id.index()] {
            Some(Value::Vector(values)) if values.len() == len => Ok(values.clone()),
            Some(Value::Vector(values)) => Err(RuntimeError::InvalidShape {
                index: id.index(),
                message: format!("vector tangent has len {}, expected {len}", values.len()),
            }),
            Some(value) => Err(RuntimeError::TypeMismatch {
                index: id.index(),
                expected: "vector tangent",
                actual: value.kind(),
            }),
            None => Ok(vec![Complex64::ZERO; len]),
        }
    }

    fn matrix_tangent(&self, id: ExprId) -> RuntimeResult<(usize, usize, &[Complex64])> {
        match &self.tangents[id.index()] {
            Some(Value::Matrix { rows, cols, values }) => Ok((*rows, *cols, values)),
            Some(value) => Err(RuntimeError::TypeMismatch {
                index: id.index(),
                expected: "matrix tangent",
                actual: value.kind(),
            }),
            None => Err(RuntimeError::InvalidShape {
                index: id.index(),
                message: "inactive matrix tangent requested without a target shape".into(),
            }),
        }
    }

    fn matrix_tangent_value(
        &self,
        id: ExprId,
        rows: usize,
        cols: usize,
    ) -> RuntimeResult<DMatrix<Complex64>> {
        match &self.tangents[id.index()] {
            Some(Value::Matrix {
                rows: actual_rows,
                cols: actual_cols,
                values,
            }) if *actual_rows == rows && *actual_cols == cols => {
                Ok(DMatrix::from_row_slice(rows, cols, values))
            }
            Some(Value::Matrix {
                rows: actual_rows,
                cols: actual_cols,
                ..
            }) => Err(RuntimeError::InvalidShape {
                index: id.index(),
                message: format!(
                    "matrix tangent has shape {actual_rows}x{actual_cols}, expected {rows}x{cols}"
                ),
            }),
            Some(value) => Err(RuntimeError::TypeMismatch {
                index: id.index(),
                expected: "matrix tangent",
                actual: value.kind(),
            }),
            None => Ok(DMatrix::zeros(rows, cols)),
        }
    }
}

#[derive(Clone, Copy, Debug, Default, PartialEq)]
struct ScalarAdjoint {
    dz: Complex64,
    dz_conj: Complex64,
}

impl ScalarAdjoint {
    fn seed() -> Self {
        Self {
            dz: Complex64::ONE,
            dz_conj: Complex64::ZERO,
        }
    }

    fn gradient(self) -> Complex64 {
        self.dz + self.dz_conj
    }
}

#[derive(Clone, Debug, PartialEq)]
enum ReverseAdjoint {
    Scalar(ScalarAdjoint),
    Vector(Vec<ScalarAdjoint>),
    Matrix {
        rows: usize,
        cols: usize,
        values: Vec<ScalarAdjoint>,
    },
}

impl ReverseAdjoint {
    fn kind(&self) -> &'static str {
        match self {
            Self::Scalar(_) => "scalar adjoint",
            Self::Vector(_) => "vector adjoint",
            Self::Matrix { .. } => "matrix adjoint",
        }
    }
}

struct ReverseDerivativeWorkspace<'a> {
    plan: &'a CpuPlan,
    primals: &'a [Value],
    adjoints: Vec<Option<ReverseAdjoint>>,
    cached_factors: Option<(&'a CpuBatchCache, usize)>,
}

impl<'a> ReverseDerivativeWorkspace<'a> {
    fn new(
        plan: &'a CpuPlan,
        primals: &'a [Value],
        cached_factors: Option<(&'a CpuBatchCache, usize)>,
    ) -> Self {
        Self {
            plan,
            primals,
            adjoints: vec![None; plan.graph.nodes().len()],
            cached_factors,
        }
    }

    fn gradient(&mut self) -> RuntimeResult<Vec<Complex64>> {
        self.accumulate_scalar(self.plan.graph.root(), ScalarAdjoint::seed())?;
        if self.cached_factors.is_some() {
            for id in self.plan.cached_evaluation_nodes.iter().rev().copied() {
                self.propagate_node(id)?;
            }
        } else {
            for index in (0..self.plan.graph.nodes().len()).rev() {
                let id = ExprId::from_index(index);
                self.propagate_node(id)?;
            }
        }

        let mut gradient = vec![Complex64::ZERO; self.plan.autodiff.parameter_count()];
        for (index, parameter) in self.plan.parameter_slots.iter().enumerate() {
            let Some(parameter) = parameter else {
                continue;
            };
            let Ok(Some(free_id)) = self.plan.params.free_id(*parameter) else {
                continue;
            };
            if let Some(adjoint) = self.scalar_adjoint_at(index)? {
                gradient[free_id.index()] += adjoint.gradient();
            }
        }
        Ok(gradient)
    }

    fn propagate_node(&mut self, id: ExprId) -> RuntimeResult<()> {
        let index = id.index();
        if matches!(self.plan.graph.nodes()[index], ExprNode::ScalarParam(_)) {
            return Ok(());
        }
        let Some(adjoint) = self.adjoints[index].take() else {
            return Ok(());
        };
        if self.cached_factors.is_some() && self.plan.cache_slots[index].is_some() {
            return Ok(());
        }
        let node = self.plan.graph.nodes()[index].clone();
        match node {
            ExprNode::Unary { op, input } => {
                let adjoint = Self::expect_scalar_adjoint(index, adjoint)?;
                let input_value = self.primal_scalar(input)?;
                let output_value = self.primal_scalar(id)?;
                self.propagate_unary(op, input, input_value, output_value, adjoint)?;
            }
            ExprNode::Binary { op, lhs, rhs } => {
                let adjoint = Self::expect_scalar_adjoint(index, adjoint)?;
                let lhs_value = self.primal_scalar(lhs)?;
                let rhs_value = self.primal_scalar(rhs)?;
                self.propagate_binary(op, lhs, rhs, lhs_value, rhs_value, adjoint)?;
            }
            ExprNode::NaryAdd { terms } => {
                let adjoint = Self::expect_scalar_adjoint(index, adjoint)?;
                for term in terms {
                    self.accumulate_scalar(term, adjoint)?;
                }
            }
            ExprNode::NaryMul { factors } => {
                let adjoint = Self::expect_scalar_adjoint(index, adjoint)?;
                let values = factors
                    .iter()
                    .map(|factor| self.primal_scalar(*factor))
                    .collect::<RuntimeResult<Vec<_>>>()?;
                for (target, _) in factors.iter().enumerate() {
                    let mut derivative = Complex64::ONE;
                    for (source, value) in values.iter().copied().enumerate() {
                        if source != target {
                            derivative *= value;
                        }
                    }
                    self.accumulate_analytic_scalar(factors[target], adjoint, derivative)?;
                }
            }
            ExprNode::Complex { re, im } => {
                let adjoint = Self::expect_scalar_adjoint(index, adjoint)?;
                let re_part = (adjoint.dz + adjoint.dz_conj) * 0.5;
                let im_part = Complex64::I * (adjoint.dz - adjoint.dz_conj) * 0.5;
                self.accumulate_scalar(
                    re,
                    ScalarAdjoint {
                        dz: re_part,
                        dz_conj: re_part,
                    },
                )?;
                self.accumulate_scalar(
                    im,
                    ScalarAdjoint {
                        dz: im_part,
                        dz_conj: im_part,
                    },
                )?;
            }
            ExprNode::Vector { elements } => {
                let adjoint = Self::expect_vector_adjoint(index, adjoint)?;
                if elements.len() != adjoint.len() {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "vector adjoint has len {}, expected {}",
                            adjoint.len(),
                            elements.len()
                        ),
                    });
                }
                for (element, contribution) in elements.into_iter().zip(adjoint) {
                    self.accumulate_scalar(element, contribution)?;
                }
            }
            ExprNode::Matrix {
                rows,
                cols,
                elements,
            } => {
                let adjoint = Self::expect_matrix_adjoint(index, adjoint)?;
                if adjoint.0 != rows || adjoint.1 != cols || elements.len() != adjoint.2.len() {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "matrix adjoint has shape {}x{}, expected {rows}x{cols}",
                            adjoint.0, adjoint.1
                        ),
                    });
                }
                for (element, contribution) in elements.into_iter().zip(adjoint.2) {
                    self.accumulate_scalar(element, contribution)?;
                }
            }
            ExprNode::Component { input, index: i } => {
                let adjoint = Self::expect_scalar_adjoint(index, adjoint)?;
                if let (Some(plan), Some((cache, row))) =
                    (self.plan.solve_components[index], self.cached_factors)
                {
                    let inverse_row = cache.solve_row(plan.row_slot(), row)?;
                    if inverse_row.len() != plan.dimension() {
                        return Err(RuntimeError::InvalidShape {
                            index,
                            message: format!(
                                "specialized solve expected row len {}, got {}",
                                plan.dimension(),
                                inverse_row.len()
                            ),
                        });
                    }
                    let rhs_contributions = inverse_row
                        .iter()
                        .map(|value| ScalarAdjoint {
                            dz: adjoint.dz * value,
                            dz_conj: adjoint.dz_conj * value.conj(),
                        })
                        .collect::<Vec<_>>();
                    self.accumulate_solve_rhs_adjoint(
                        plan.rhs(),
                        plan.dimension(),
                        rhs_contributions,
                    )?;
                } else {
                    self.accumulate_vector_element(input, i, adjoint)?;
                }
            }
            ExprNode::MatrixElement { input, row, col } => {
                let adjoint = Self::expect_scalar_adjoint(index, adjoint)?;
                let (rows, cols, _) = self.primal_matrix(input)?;
                if row >= rows || col >= cols {
                    return Err(RuntimeError::InvalidShape {
                        index,
                        message: format!(
                            "matrix element ({row}, {col}) out of bounds for shape {rows}x{cols}"
                        ),
                    });
                }
                self.accumulate_matrix_element(input, rows, cols, row, col, adjoint)?;
            }
            ExprNode::MatMul { lhs, rhs } => {
                let (out_rows, out_cols, adjoint) = Self::expect_matrix_adjoint(index, adjoint)?;
                self.propagate_matmul(index, lhs, rhs, out_rows, out_cols, &adjoint)?;
            }
            ExprNode::MatVec { matrix, vector } => {
                let adjoint = Self::expect_vector_adjoint(index, adjoint)?;
                self.propagate_matvec(index, matrix, vector, &adjoint)?;
            }
            ExprNode::Dot { lhs, rhs } => {
                let adjoint = Self::expect_scalar_adjoint(index, adjoint)?;
                self.propagate_dot(index, lhs, rhs, adjoint)?;
            }
            ExprNode::Solve { matrix, rhs } => {
                let adjoint = Self::expect_vector_adjoint(index, adjoint)?;
                self.propagate_solve(index, matrix, rhs, &adjoint)?;
            }
            ExprNode::RealConst(_)
            | ExprNode::ComplexConst(_)
            | ExprNode::ScalarParam(_)
            | ExprNode::EventScalar(_)
            | ExprNode::EventP4Component { .. } => {}
        }
        Ok(())
    }

    fn propagate_unary(
        &mut self,
        op: UnaryOp,
        input: ExprId,
        input_value: Complex64,
        output_value: Complex64,
        adjoint: ScalarAdjoint,
    ) -> RuntimeResult<()> {
        match op {
            UnaryOp::Neg => self.accumulate_scalar(
                input,
                ScalarAdjoint {
                    dz: -adjoint.dz,
                    dz_conj: -adjoint.dz_conj,
                },
            ),
            UnaryOp::Real => {
                let contribution = (adjoint.dz + adjoint.dz_conj) * 0.5;
                self.accumulate_scalar(
                    input,
                    ScalarAdjoint {
                        dz: contribution,
                        dz_conj: contribution,
                    },
                )
            }
            UnaryOp::Imag => {
                let contribution = -Complex64::I * (adjoint.dz + adjoint.dz_conj) * 0.5;
                self.accumulate_scalar(
                    input,
                    ScalarAdjoint {
                        dz: contribution,
                        dz_conj: -contribution,
                    },
                )
            }
            UnaryOp::Conj => self.accumulate_scalar(
                input,
                ScalarAdjoint {
                    dz: adjoint.dz_conj,
                    dz_conj: adjoint.dz,
                },
            ),
            UnaryOp::NormSqr => {
                let sum = adjoint.dz + adjoint.dz_conj;
                self.accumulate_scalar(
                    input,
                    ScalarAdjoint {
                        dz: sum * input_value.conj(),
                        dz_conj: sum * input_value,
                    },
                )
            }
            UnaryOp::Sqrt => {
                self.accumulate_analytic_scalar(input, adjoint, 1.0 / (2.0 * output_value))
            }
            UnaryOp::Exp => self.accumulate_analytic_scalar(input, adjoint, output_value),
            UnaryOp::Sin => self.accumulate_analytic_scalar(input, adjoint, input_value.cos()),
            UnaryOp::Cos => self.accumulate_analytic_scalar(input, adjoint, -input_value.sin()),
            UnaryOp::Log => self.accumulate_analytic_scalar(input, adjoint, 1.0 / input_value),
            UnaryOp::PowI(power) => {
                let derivative = if power == 0 {
                    Complex64::ZERO
                } else if power == i32::MIN {
                    power as f64 * output_value / input_value
                } else {
                    power as f64 * input_value.powi(power - 1)
                };
                self.accumulate_analytic_scalar(input, adjoint, derivative)
            }
        }
    }

    fn propagate_binary(
        &mut self,
        op: BinaryOp,
        lhs: ExprId,
        rhs: ExprId,
        lhs_value: Complex64,
        rhs_value: Complex64,
        adjoint: ScalarAdjoint,
    ) -> RuntimeResult<()> {
        match op {
            BinaryOp::Add => {
                self.accumulate_scalar(lhs, adjoint)?;
                self.accumulate_scalar(rhs, adjoint)
            }
            BinaryOp::Sub => {
                self.accumulate_scalar(lhs, adjoint)?;
                self.accumulate_scalar(
                    rhs,
                    ScalarAdjoint {
                        dz: -adjoint.dz,
                        dz_conj: -adjoint.dz_conj,
                    },
                )
            }
            BinaryOp::Mul => {
                self.accumulate_analytic_scalar(lhs, adjoint, rhs_value)?;
                self.accumulate_analytic_scalar(rhs, adjoint, lhs_value)
            }
            BinaryOp::Div => {
                self.accumulate_analytic_scalar(lhs, adjoint, 1.0 / rhs_value)?;
                self.accumulate_analytic_scalar(rhs, adjoint, -lhs_value / rhs_value.powi(2))
            }
            BinaryOp::Atan2 => {
                let denominator = lhs_value.re.powi(2) + rhs_value.re.powi(2);
                let sum = adjoint.dz + adjoint.dz_conj;
                self.accumulate_real_linear_scalar(lhs, sum * rhs_value.re / denominator)?;
                self.accumulate_real_linear_scalar(rhs, -sum * lhs_value.re / denominator)
            }
        }
    }

    fn propagate_matmul(
        &mut self,
        index: usize,
        lhs: ExprId,
        rhs: ExprId,
        out_rows: usize,
        out_cols: usize,
        adjoint: &[ScalarAdjoint],
    ) -> RuntimeResult<()> {
        let (lhs_rows, lhs_cols, lhs_value) = self.primal_matrix(lhs)?;
        let (rhs_rows, rhs_cols, rhs_value) = self.primal_matrix(rhs)?;
        if lhs_cols != rhs_rows || lhs_rows != out_rows || rhs_cols != out_cols {
            return Err(RuntimeError::InvalidShape {
                index,
                message: format!(
                    "matmul adjoint has shape {out_rows}x{out_cols} for {lhs_rows}x{lhs_cols} by {rhs_rows}x{rhs_cols}"
                ),
            });
        }
        let mut lhs_adjoint = vec![ScalarAdjoint::default(); lhs_rows * lhs_cols];
        let mut rhs_adjoint = vec![ScalarAdjoint::default(); rhs_rows * rhs_cols];
        for row in 0..lhs_rows {
            for col in 0..rhs_cols {
                let output_adjoint = adjoint[row * rhs_cols + col];
                for mid in 0..lhs_cols {
                    let rhs_entry = rhs_value[mid * rhs_cols + col];
                    let lhs_entry = lhs_value[row * lhs_cols + mid];
                    let lhs_target = &mut lhs_adjoint[row * lhs_cols + mid];
                    lhs_target.dz += output_adjoint.dz * rhs_entry;
                    lhs_target.dz_conj += output_adjoint.dz_conj * rhs_entry.conj();
                    let rhs_target = &mut rhs_adjoint[mid * rhs_cols + col];
                    rhs_target.dz += output_adjoint.dz * lhs_entry;
                    rhs_target.dz_conj += output_adjoint.dz_conj * lhs_entry.conj();
                }
            }
        }
        self.accumulate_matrix(lhs, lhs_rows, lhs_cols, lhs_adjoint)?;
        self.accumulate_matrix(rhs, rhs_rows, rhs_cols, rhs_adjoint)
    }

    fn propagate_matvec(
        &mut self,
        index: usize,
        matrix: ExprId,
        vector: ExprId,
        adjoint: &[ScalarAdjoint],
    ) -> RuntimeResult<()> {
        let (rows, cols, matrix_value) = self.primal_matrix(matrix)?;
        let vector_value = self.primal_vector(vector)?;
        if cols != vector_value.len() || rows != adjoint.len() {
            return Err(RuntimeError::InvalidShape {
                index,
                message: format!(
                    "matvec adjoint has len {}, expected {rows} for matrix {rows}x{cols} and vector len {}",
                    adjoint.len(),
                    vector_value.len()
                ),
            });
        }
        let mut matrix_adjoint = vec![ScalarAdjoint::default(); rows * cols];
        let mut vector_adjoint = vec![ScalarAdjoint::default(); cols];
        for row in 0..rows {
            let output_adjoint = adjoint[row];
            for col in 0..cols {
                let vector_entry = vector_value[col];
                let matrix_entry = matrix_value[row * cols + col];
                let matrix_target = &mut matrix_adjoint[row * cols + col];
                matrix_target.dz += output_adjoint.dz * vector_entry;
                matrix_target.dz_conj += output_adjoint.dz_conj * vector_entry.conj();
                vector_adjoint[col].dz += output_adjoint.dz * matrix_entry;
                vector_adjoint[col].dz_conj += output_adjoint.dz_conj * matrix_entry.conj();
            }
        }
        self.accumulate_matrix(matrix, rows, cols, matrix_adjoint)?;
        self.accumulate_vector(vector, vector_adjoint)
    }

    fn propagate_dot(
        &mut self,
        index: usize,
        lhs: ExprId,
        rhs: ExprId,
        adjoint: ScalarAdjoint,
    ) -> RuntimeResult<()> {
        let lhs_value = self.primal_vector(lhs)?;
        let rhs_value = self.primal_vector(rhs)?;
        if lhs_value.len() != rhs_value.len() {
            return Err(RuntimeError::InvalidShape {
                index,
                message: format!(
                    "cannot dot len {} vector with len {} vector",
                    lhs_value.len(),
                    rhs_value.len()
                ),
            });
        }
        let lhs_adjoint = rhs_value
            .iter()
            .map(|value| ScalarAdjoint {
                dz: adjoint.dz * value,
                dz_conj: adjoint.dz_conj * value.conj(),
            })
            .collect();
        let rhs_adjoint = lhs_value
            .iter()
            .map(|value| ScalarAdjoint {
                dz: adjoint.dz * value,
                dz_conj: adjoint.dz_conj * value.conj(),
            })
            .collect();
        self.accumulate_vector(lhs, lhs_adjoint)?;
        self.accumulate_vector(rhs, rhs_adjoint)
    }

    fn propagate_solve(
        &mut self,
        index: usize,
        matrix: ExprId,
        rhs: ExprId,
        adjoint: &[ScalarAdjoint],
    ) -> RuntimeResult<()> {
        let (rows, cols, matrix_value) = self.primal_matrix(matrix)?;
        let rhs_value = self.primal_vector(rhs)?;
        let solution = self.primal_vector(ExprId::from_index(index))?;
        if rows != cols || rows != rhs_value.len() || rows != adjoint.len() {
            return Err(RuntimeError::InvalidShape {
                index,
                message: format!(
                    "solve adjoint has len {}, expected {rows} for {rows}x{cols} solve",
                    adjoint.len()
                ),
            });
        }
        let matrix_value = DMatrix::from_row_slice(rows, cols, matrix_value);
        let transposed = matrix_value.transpose();
        let conjugate_transposed = matrix_value.map(|value| value.conj()).transpose();
        let alpha = DVector::from_iterator(rows, adjoint.iter().map(|adjoint| adjoint.dz));
        let beta = DVector::from_iterator(rows, adjoint.iter().map(|adjoint| adjoint.dz_conj));
        let lambda = transposed
            .lu()
            .solve(&alpha)
            .ok_or(RuntimeError::SingularMatrix(index))?;
        let lambda_conj = conjugate_transposed
            .lu()
            .solve(&beta)
            .ok_or(RuntimeError::SingularMatrix(index))?;
        let solution = DVector::from_row_slice(solution);
        let mut matrix_adjoint = vec![ScalarAdjoint::default(); rows * cols];
        for row in 0..rows {
            for col in 0..cols {
                matrix_adjoint[row * cols + col].dz -= lambda[row] * solution[col];
                matrix_adjoint[row * cols + col].dz_conj -= lambda_conj[row] * solution[col].conj();
            }
        }
        let rhs_adjoint = (0..rows)
            .map(|row| ScalarAdjoint {
                dz: lambda[row],
                dz_conj: lambda_conj[row],
            })
            .collect();
        self.accumulate_matrix(matrix, rows, cols, matrix_adjoint)?;
        self.accumulate_vector(rhs, rhs_adjoint)
    }

    fn accumulate_analytic_scalar(
        &mut self,
        id: ExprId,
        adjoint: ScalarAdjoint,
        derivative: Complex64,
    ) -> RuntimeResult<()> {
        self.accumulate_scalar(
            id,
            ScalarAdjoint {
                dz: adjoint.dz * derivative,
                dz_conj: adjoint.dz_conj * derivative.conj(),
            },
        )
    }

    fn accumulate_real_linear_scalar(
        &mut self,
        id: ExprId,
        contribution: Complex64,
    ) -> RuntimeResult<()> {
        self.accumulate_scalar(
            id,
            ScalarAdjoint {
                dz: contribution * 0.5,
                dz_conj: contribution * 0.5,
            },
        )
    }

    fn accumulate_solve_rhs_adjoint(
        &mut self,
        rhs: ExprId,
        len: usize,
        contributions: Vec<ScalarAdjoint>,
    ) -> RuntimeResult<()> {
        if let Some(elements) = &self.plan.solve_rhs_elements[rhs.index()] {
            if elements.len() != len || elements.len() != contributions.len() {
                return Err(RuntimeError::InvalidShape {
                    index: rhs.index(),
                    message: format!(
                        "specialized solve expected {len} RHS elements, got {}",
                        elements.len()
                    ),
                });
            }
            for (element, contribution) in elements.iter().copied().zip(contributions) {
                self.accumulate_scalar(element, contribution)?;
            }
            Ok(())
        } else {
            self.accumulate_vector(rhs, contributions)
        }
    }

    fn accumulate_scalar(&mut self, id: ExprId, contribution: ScalarAdjoint) -> RuntimeResult<()> {
        match &mut self.adjoints[id.index()] {
            Some(ReverseAdjoint::Scalar(adjoint)) => {
                adjoint.dz += contribution.dz;
                adjoint.dz_conj += contribution.dz_conj;
            }
            Some(value) => {
                return Err(RuntimeError::TypeMismatch {
                    index: id.index(),
                    expected: "scalar adjoint",
                    actual: value.kind(),
                });
            }
            None => {
                self.adjoints[id.index()] = Some(ReverseAdjoint::Scalar(contribution));
            }
        }
        Ok(())
    }

    fn accumulate_vector(
        &mut self,
        id: ExprId,
        contributions: Vec<ScalarAdjoint>,
    ) -> RuntimeResult<()> {
        match &mut self.adjoints[id.index()] {
            Some(ReverseAdjoint::Vector(adjoint)) if adjoint.len() == contributions.len() => {
                for (target, source) in adjoint.iter_mut().zip(contributions) {
                    target.dz += source.dz;
                    target.dz_conj += source.dz_conj;
                }
            }
            Some(ReverseAdjoint::Vector(adjoint)) => {
                return Err(RuntimeError::InvalidShape {
                    index: id.index(),
                    message: format!(
                        "vector adjoint has len {}, expected {}",
                        adjoint.len(),
                        contributions.len()
                    ),
                });
            }
            Some(value) => {
                return Err(RuntimeError::TypeMismatch {
                    index: id.index(),
                    expected: "vector adjoint",
                    actual: value.kind(),
                });
            }
            None => {
                self.adjoints[id.index()] = Some(ReverseAdjoint::Vector(contributions));
            }
        }
        Ok(())
    }

    fn accumulate_vector_element(
        &mut self,
        id: ExprId,
        element: usize,
        contribution: ScalarAdjoint,
    ) -> RuntimeResult<()> {
        let len = self.primal_vector(id)?.len();
        if element >= len {
            return Err(RuntimeError::InvalidShape {
                index: id.index(),
                message: format!("component index {element} out of bounds for len {len}"),
            });
        }
        let mut contributions = vec![ScalarAdjoint::default(); len];
        contributions[element] = contribution;
        self.accumulate_vector(id, contributions)
    }

    fn accumulate_matrix(
        &mut self,
        id: ExprId,
        rows: usize,
        cols: usize,
        contributions: Vec<ScalarAdjoint>,
    ) -> RuntimeResult<()> {
        match &mut self.adjoints[id.index()] {
            Some(ReverseAdjoint::Matrix {
                rows: actual_rows,
                cols: actual_cols,
                values,
            }) if *actual_rows == rows
                && *actual_cols == cols
                && values.len() == contributions.len() =>
            {
                for (target, source) in values.iter_mut().zip(contributions) {
                    target.dz += source.dz;
                    target.dz_conj += source.dz_conj;
                }
            }
            Some(ReverseAdjoint::Matrix {
                rows: actual_rows,
                cols: actual_cols,
                ..
            }) => {
                return Err(RuntimeError::InvalidShape {
                    index: id.index(),
                    message: format!(
                        "matrix adjoint has shape {actual_rows}x{actual_cols}, expected {rows}x{cols}"
                    ),
                });
            }
            Some(value) => {
                return Err(RuntimeError::TypeMismatch {
                    index: id.index(),
                    expected: "matrix adjoint",
                    actual: value.kind(),
                });
            }
            None => {
                self.adjoints[id.index()] = Some(ReverseAdjoint::Matrix {
                    rows,
                    cols,
                    values: contributions,
                });
            }
        }
        Ok(())
    }

    fn accumulate_matrix_element(
        &mut self,
        id: ExprId,
        rows: usize,
        cols: usize,
        row: usize,
        col: usize,
        contribution: ScalarAdjoint,
    ) -> RuntimeResult<()> {
        let mut contributions = vec![ScalarAdjoint::default(); rows * cols];
        contributions[row * cols + col] = contribution;
        self.accumulate_matrix(id, rows, cols, contributions)
    }

    fn scalar_adjoint_at(&self, index: usize) -> RuntimeResult<Option<ScalarAdjoint>> {
        match &self.adjoints[index] {
            Some(ReverseAdjoint::Scalar(adjoint)) => Ok(Some(*adjoint)),
            Some(value) => Err(RuntimeError::TypeMismatch {
                index,
                expected: "scalar adjoint",
                actual: value.kind(),
            }),
            None => Ok(None),
        }
    }

    fn expect_scalar_adjoint(
        index: usize,
        adjoint: ReverseAdjoint,
    ) -> RuntimeResult<ScalarAdjoint> {
        match adjoint {
            ReverseAdjoint::Scalar(adjoint) => Ok(adjoint),
            value => Err(RuntimeError::TypeMismatch {
                index,
                expected: "scalar adjoint",
                actual: value.kind(),
            }),
        }
    }

    fn expect_vector_adjoint(
        index: usize,
        adjoint: ReverseAdjoint,
    ) -> RuntimeResult<Vec<ScalarAdjoint>> {
        match adjoint {
            ReverseAdjoint::Vector(adjoint) => Ok(adjoint),
            value => Err(RuntimeError::TypeMismatch {
                index,
                expected: "vector adjoint",
                actual: value.kind(),
            }),
        }
    }

    fn expect_matrix_adjoint(
        index: usize,
        adjoint: ReverseAdjoint,
    ) -> RuntimeResult<(usize, usize, Vec<ScalarAdjoint>)> {
        match adjoint {
            ReverseAdjoint::Matrix { rows, cols, values } => Ok((rows, cols, values)),
            value => Err(RuntimeError::TypeMismatch {
                index,
                expected: "matrix adjoint",
                actual: value.kind(),
            }),
        }
    }

    fn primal_scalar(&self, id: ExprId) -> RuntimeResult<Complex64> {
        if self.cached_factors.is_some() {
            self.plan.cached_scalar_at(self.primals, id)
        } else {
            scalar_at(self.primals, id.index())
        }
    }

    fn primal_vector(&self, id: ExprId) -> RuntimeResult<&[Complex64]> {
        if self.cached_factors.is_some() {
            self.plan.cached_vector_at(self.primals, id)
        } else {
            vector_at(self.primals, id.index())
        }
    }

    fn primal_matrix(&self, id: ExprId) -> RuntimeResult<(usize, usize, &[Complex64])> {
        if self.cached_factors.is_some() {
            self.plan.cached_matrix_at(self.primals, id)
        } else {
            matrix_at(self.primals, id.index())
        }
    }
}

#[derive(Copy, Clone, Debug, PartialEq, Eq)]
enum EventColumn {
    Scalar(usize),
    P4Component { col: usize, component: P4Component },
}

/// Materialized event-dependent values for one batch.
#[derive(Clone, Debug)]
pub struct CpuBatchCache {
    len: usize,
    weights: Vec<f64>,
    sum_weights: f64,
    nodes: Vec<ExprId>,
    pub(crate) slots: Vec<CachedSlot>,
    factor_nodes: Vec<ExprId>,
    factor_slots: Vec<CachedFactorSlot>,
    solve_row_keys: Vec<(ExprId, usize, usize)>,
    pub(crate) solve_row_slots: Vec<CachedSolveRowSlot>,
}

impl CpuBatchCache {
    fn new(
        cache_plan: &CachePlan,
        factor_matrices: &[(ExprId, usize)],
        solve_row_keys: &[(ExprId, usize, usize)],
        len: usize,
    ) -> Self {
        Self {
            len,
            weights: vec![1.0; len],
            sum_weights: len as f64,
            nodes: cache_plan
                .entries()
                .iter()
                .map(|entry| entry.node())
                .collect(),
            slots: cache_plan
                .entries()
                .iter()
                .map(|entry| CachedSlot::new(entry.value_kind(), len))
                .collect(),
            factor_nodes: factor_matrices.iter().map(|(node, _)| *node).collect(),
            factor_slots: factor_matrices
                .iter()
                .map(|(_, dimension)| CachedFactorSlot::new(*dimension))
                .collect(),
            solve_row_keys: solve_row_keys.to_vec(),
            solve_row_slots: solve_row_keys
                .iter()
                .map(|(_, _, dimension)| CachedSolveRowSlot::new(*dimension))
                .collect(),
        }
    }

    /// Returns the number of cached events.
    pub fn len(&self) -> usize {
        self.len
    }

    /// Returns whether the cache contains no events.
    pub fn is_empty(&self) -> bool {
        self.len == 0
    }

    /// Returns per-event weights.
    pub fn weights(&self) -> &[f64] {
        &self.weights
    }

    /// Returns the sum of event weights.
    pub fn sum_weights(&self) -> f64 {
        self.sum_weights
    }

    /// Estimates heap memory retained by this cache, in bytes.
    pub fn resident_bytes(&self) -> usize {
        self.weights.capacity() * size_of::<f64>()
            + self.nodes.capacity() * size_of::<ExprId>()
            + self
                .slots
                .iter()
                .map(CachedSlot::resident_bytes)
                .sum::<usize>()
            + self.factor_nodes.capacity() * size_of::<ExprId>()
            + self
                .factor_slots
                .iter()
                .map(CachedFactorSlot::resident_bytes)
                .sum::<usize>()
            + self.solve_row_keys.capacity() * size_of::<(ExprId, usize, usize)>()
            + self
                .solve_row_slots
                .iter()
                .map(CachedSolveRowSlot::resident_bytes)
                .sum::<usize>()
    }

    fn set_weights(&mut self, weights: Vec<f64>) {
        self.sum_weights = weights.iter().sum();
        self.weights = weights;
    }

    fn push(&mut self, slot: usize, value: Value) -> RuntimeResult<()> {
        let len = self.slots.len();
        self.slots
            .get_mut(slot)
            .ok_or(RuntimeError::InvalidCache {
                expected: len,
                actual: slot + 1,
            })?
            .push(value)
    }

    fn value(&self, slot: usize, row: usize) -> RuntimeResult<Value> {
        if row >= self.len {
            return Err(RuntimeError::InvalidShape {
                index: row,
                message: format!("cache row {row} out of bounds for len {}", self.len),
            });
        }
        self.slots
            .get(slot)
            .ok_or(RuntimeError::InvalidCache {
                expected: self.slots.len(),
                actual: slot + 1,
            })?
            .value(row)
    }

    fn scalar(&self, slot: usize, row: usize) -> RuntimeResult<Complex64> {
        if row >= self.len {
            return Err(RuntimeError::InvalidShape {
                index: row,
                message: format!("cache row {row} out of bounds for len {}", self.len),
            });
        }
        self.slots
            .get(slot)
            .ok_or(RuntimeError::InvalidCache {
                expected: self.slots.len(),
                actual: slot + 1,
            })?
            .scalar(row)
    }

    fn real_range(&self, slot: usize, start: usize, end: usize) -> RuntimeResult<&[f64]> {
        if start > end || end > self.len {
            return Err(RuntimeError::InvalidShape {
                index: start,
                message: format!(
                    "cache range {start}..{end} out of bounds for len {}",
                    self.len
                ),
            });
        }
        self.slots
            .get(slot)
            .ok_or(RuntimeError::InvalidCache {
                expected: self.slots.len(),
                actual: slot + 1,
            })?
            .real_range(start, end)
    }

    fn complex_range(&self, slot: usize, start: usize, end: usize) -> RuntimeResult<&[Complex64]> {
        if start > end || end > self.len {
            return Err(RuntimeError::InvalidShape {
                index: start,
                message: format!(
                    "cache range {start}..{end} out of bounds for len {}",
                    self.len
                ),
            });
        }
        self.slots
            .get(slot)
            .ok_or(RuntimeError::InvalidCache {
                expected: self.slots.len(),
                actual: slot + 1,
            })?
            .complex_range(start, end)
    }

    fn push_factor(&mut self, slot: usize, factor: DynamicLu) -> RuntimeResult<()> {
        let len = self.factor_slots.len();
        self.factor_slots
            .get_mut(slot)
            .ok_or(RuntimeError::InvalidCache {
                expected: len,
                actual: slot + 1,
            })?
            .push(factor)
    }

    fn factor(&self, slot: usize, row: usize) -> RuntimeResult<&DynamicLu> {
        self.factor_slots
            .get(slot)
            .ok_or(RuntimeError::InvalidCache {
                expected: self.factor_slots.len(),
                actual: slot + 1,
            })?
            .factor(row)
    }

    fn push_solve_row(
        &mut self,
        slot: usize,
        values: impl IntoIterator<Item = Complex64>,
    ) -> RuntimeResult<()> {
        let len = self.solve_row_slots.len();
        self.solve_row_slots
            .get_mut(slot)
            .ok_or(RuntimeError::InvalidCache {
                expected: len,
                actual: slot + 1,
            })?
            .push(values)
    }

    fn solve_row(&self, slot: usize, row: usize) -> RuntimeResult<&[Complex64]> {
        self.solve_row_slots
            .get(slot)
            .ok_or(RuntimeError::InvalidCache {
                expected: self.solve_row_slots.len(),
                actual: slot + 1,
            })?
            .row(row)
    }
}

/// A cached event batch and its associated weights.
#[derive(Clone, Debug)]
pub struct CpuCachedBatch {
    cache: CpuBatchCache,
}

impl CpuCachedBatch {
    /// Returns the underlying materialized cache.
    pub fn cache(&self) -> &CpuBatchCache {
        &self.cache
    }

    /// Returns the number of events.
    pub fn len(&self) -> usize {
        self.cache.len()
    }

    /// Returns whether the batch contains no events.
    pub fn is_empty(&self) -> bool {
        self.cache.is_empty()
    }

    /// Returns per-event weights.
    pub fn weights(&self) -> &[f64] {
        self.cache.weights()
    }

    /// Returns the sum of event weights.
    pub fn sum_weights(&self) -> f64 {
        self.cache.sum_weights()
    }

    /// Estimates retained heap memory, in bytes.
    pub fn resident_bytes(&self) -> usize {
        self.cache.resident_bytes()
    }
}

/// A dataset whose event-dependent model values are fully cached in memory.
#[derive(Clone, Debug, Default)]
pub struct CpuCachedDataset {
    batches: Vec<CpuCachedBatch>,
    sum_weights: f64,
}

#[derive(Copy, Clone, Debug, PartialEq)]
/// Fixed statistics collected when a dataset is prepared for repeated evaluation.
pub struct PreparedDatasetStats {
    local_events: usize,
    global_events: usize,
    local_batches: usize,
    sum_weights: f64,
    resident_bytes: usize,
    storage: CacheStorage,
}

impl PreparedDatasetStats {
    pub(crate) fn new(
        local_events: usize,
        global_events: usize,
        local_batches: usize,
        sum_weights: f64,
        resident_bytes: usize,
        storage: CacheStorage,
    ) -> Self {
        Self {
            local_events,
            global_events,
            local_batches,
            sum_weights,
            resident_bytes,
            storage,
        }
    }

    /// Returns the number of events assigned to this rank.
    pub fn local_events(&self) -> usize {
        self.local_events
    }

    /// Returns the total number of events across all ranks.
    pub fn global_events(&self) -> usize {
        self.global_events
    }

    /// Returns the number of batches assigned to this rank.
    pub fn local_batches(&self) -> usize {
        self.local_batches
    }

    /// Returns the total event-weight sum across all ranks.
    pub fn sum_weights(&self) -> f64 {
        self.sum_weights
    }

    /// Returns the number of bytes retained for prepared data on this rank.
    pub fn resident_bytes(&self) -> usize {
        self.resident_bytes
    }

    /// Returns the dataset's cache-storage policy.
    pub fn storage(&self) -> CacheStorage {
        self.storage
    }
}

#[derive(Clone)]
/// A dataset prepared according to its [`CacheStorage`] policy.
///
/// Resident datasets own all event-dependent cache values. Streaming datasets retain the source
/// and read plan and rebuild transient batch caches on every reduction.
pub enum CpuPreparedDataset {
    /// A dataset whose event caches are resident in memory.
    Resident {
        /// Fully cached dataset.
        dataset: Arc<CpuCachedDataset>,
        /// Preparation statistics.
        stats: PreparedDatasetStats,
        /// Persistent host-memory reservation shared by clones.
        memory_lease: MemoryLease,
    },
    /// A dataset whose event caches are rebuilt while streaming.
    Streaming {
        /// Source dataset.
        dataset: Dataset,
        /// Read plan used for each pass.
        read_plan: laddu_data::io::ReadPlan,
        /// Preparation statistics.
        stats: PreparedDatasetStats,
        /// Peak transient bytes reserved during each reduction.
        transient_bytes: u64,
    },
}

impl std::fmt::Debug for CpuPreparedDataset {
    fn fmt(&self, formatter: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        formatter
            .debug_struct("CpuPreparedDataset")
            .field("stats", self.stats())
            .finish_non_exhaustive()
    }
}

impl CpuPreparedDataset {
    /// Returns statistics collected while preparing the dataset.
    pub fn stats(&self) -> &PreparedDatasetStats {
        match self {
            Self::Resident { stats, .. } | Self::Streaming { stats, .. } => stats,
        }
    }
}

impl CpuCachedDataset {
    /// Returns the cached batches.
    pub fn batches(&self) -> &[CpuCachedBatch] {
        &self.batches
    }

    /// Returns the total number of cached events.
    pub fn len(&self) -> usize {
        self.batches.iter().map(CpuCachedBatch::len).sum()
    }

    /// Returns whether the dataset contains no events.
    pub fn is_empty(&self) -> bool {
        self.batches.iter().all(CpuCachedBatch::is_empty)
    }

    /// Returns the sum of all event weights.
    pub fn sum_weights(&self) -> f64 {
        self.sum_weights
    }

    /// Estimates retained heap memory, in bytes.
    pub fn resident_bytes(&self) -> usize {
        self.batches
            .iter()
            .map(CpuCachedBatch::resident_bytes)
            .sum()
    }
}

#[derive(Clone, Debug)]
struct CachedFactorSlot {
    dimension: usize,
    factors: Vec<DynamicLu>,
}

#[derive(Clone, Debug)]
pub(crate) struct CachedSolveRowSlot {
    pub(crate) dimension: usize,
    pub(crate) values: Vec<Complex64>,
}

impl CachedSolveRowSlot {
    fn new(dimension: usize) -> Self {
        Self {
            dimension,
            values: Vec::new(),
        }
    }

    fn push(&mut self, values: impl IntoIterator<Item = Complex64>) -> RuntimeResult<()> {
        let start = self.values.len();
        self.values.extend(values);
        let actual = self.values.len() - start;
        if actual != self.dimension {
            return Err(RuntimeError::InvalidShape {
                index: start / self.dimension,
                message: format!(
                    "cached solve row has len {actual}, expected {}",
                    self.dimension
                ),
            });
        }
        Ok(())
    }

    fn row(&self, row: usize) -> RuntimeResult<&[Complex64]> {
        let start = row
            .checked_mul(self.dimension)
            .ok_or_else(|| RuntimeError::InvalidShape {
                index: row,
                message: "cached solve row offset overflowed".into(),
            })?;
        self.values
            .get(start..start + self.dimension)
            .ok_or_else(|| RuntimeError::InvalidShape {
                index: row,
                message: format!(
                    "cached solve row {row} out of bounds for len {}",
                    self.values.len() / self.dimension
                ),
            })
    }

    fn resident_bytes(&self) -> usize {
        self.values.capacity() * size_of::<Complex64>()
    }
}

impl CachedFactorSlot {
    fn new(dimension: usize) -> Self {
        Self {
            dimension,
            factors: Vec::new(),
        }
    }

    fn push(&mut self, factor: DynamicLu) -> RuntimeResult<()> {
        self.factors.push(factor);
        Ok(())
    }

    fn factor(&self, row: usize) -> RuntimeResult<&DynamicLu> {
        self.factors
            .get(row)
            .ok_or_else(|| RuntimeError::InvalidShape {
                index: row,
                message: format!(
                    "factor row {row} out of bounds for len {}",
                    self.factors.len()
                ),
            })
    }

    fn resident_bytes(&self) -> usize {
        self.factors.capacity()
            * (self.dimension * self.dimension * size_of::<Complex64>()
                + self.dimension * size_of::<usize>())
    }
}

#[derive(Clone, Debug, PartialEq)]
pub(crate) enum CachedSlot {
    Real(Vec<f64>),
    Complex(Vec<Complex64>),
    Vector {
        len: usize,
        values: Vec<Complex64>,
    },
    Matrix {
        rows: usize,
        cols: usize,
        values: Vec<Complex64>,
    },
}

impl CachedSlot {
    #[cfg(feature = "jit")]
    pub(crate) fn values_ptr(&self) -> *const u8 {
        match self {
            Self::Real(values) => values.as_ptr().cast(),
            Self::Complex(values) | Self::Vector { values, .. } | Self::Matrix { values, .. } => {
                values.as_ptr().cast()
            }
        }
    }

    #[cfg(feature = "jit")]
    pub(crate) fn width(&self) -> usize {
        match self {
            Self::Real(_) | Self::Complex(_) => 1,
            Self::Vector { len, .. } => *len,
            Self::Matrix { rows, cols, .. } => rows * cols,
        }
    }

    fn new(kind: ValueKind, events: usize) -> Self {
        match kind {
            ValueKind::Real => Self::Real(Vec::with_capacity(events)),
            ValueKind::Complex => Self::Complex(Vec::with_capacity(events)),
            ValueKind::Vector { len } => Self::Vector {
                len,
                values: Vec::with_capacity(events.saturating_mul(len)),
            },
            ValueKind::Matrix { rows, cols } => Self::Matrix {
                rows,
                cols,
                values: Vec::with_capacity(events.saturating_mul(rows).saturating_mul(cols)),
            },
        }
    }

    fn resident_bytes(&self) -> usize {
        match self {
            Self::Real(values) => values.capacity() * size_of::<f64>(),
            Self::Complex(values) => values.capacity() * size_of::<Complex64>(),
            Self::Vector { values, .. } | Self::Matrix { values, .. } => {
                values.capacity() * size_of::<Complex64>()
            }
        }
    }

    fn push(&mut self, value: Value) -> RuntimeResult<()> {
        match (self, value) {
            (Self::Real(values), Value::Scalar(value)) => {
                values.push(value.re);
                Ok(())
            }
            (Self::Complex(values), Value::Scalar(value)) => {
                values.push(value);
                Ok(())
            }
            (Self::Vector { len, values }, Value::Vector(value)) if *len == value.len() => {
                values.extend(value);
                Ok(())
            }
            (
                Self::Matrix { rows, cols, values },
                Value::Matrix {
                    rows: value_rows,
                    cols: value_cols,
                    values: value,
                },
            ) if *rows == value_rows && *cols == value_cols => {
                values.extend(value);
                Ok(())
            }
            (_, value) => Err(RuntimeError::InvalidShape {
                index: 0,
                message: format!("cached value kind did not match slot: {}", value.kind()),
            }),
        }
    }

    fn value(&self, row: usize) -> RuntimeResult<Value> {
        match self {
            Self::Real(values) => values
                .get(row)
                .copied()
                .map(Complex64::from)
                .map(Value::Scalar)
                .ok_or_else(|| RuntimeError::InvalidShape {
                    index: row,
                    message: format!("cache row {row} out of bounds"),
                }),
            Self::Complex(values) => values.get(row).copied().map(Value::Scalar).ok_or_else(|| {
                RuntimeError::InvalidShape {
                    index: row,
                    message: format!("cache row {row} out of bounds"),
                }
            }),
            Self::Vector { len, values } => {
                let start = row
                    .checked_mul(*len)
                    .ok_or_else(|| RuntimeError::InvalidShape {
                        index: row,
                        message: "cache vector row offset overflowed".into(),
                    })?;
                let end = start + *len;
                values
                    .get(start..end)
                    .map(|value| Value::Vector(value.to_vec()))
                    .ok_or_else(|| RuntimeError::InvalidShape {
                        index: row,
                        message: format!("cache row {row} out of bounds"),
                    })
            }
            Self::Matrix { rows, cols, values } => {
                let len = rows * cols;
                let start = row
                    .checked_mul(len)
                    .ok_or_else(|| RuntimeError::InvalidShape {
                        index: row,
                        message: "cache matrix row offset overflowed".into(),
                    })?;
                let end = start + len;
                values
                    .get(start..end)
                    .map(|value| Value::Matrix {
                        rows: *rows,
                        cols: *cols,
                        values: value.to_vec(),
                    })
                    .ok_or_else(|| RuntimeError::InvalidShape {
                        index: row,
                        message: format!("cache row {row} out of bounds"),
                    })
            }
        }
    }

    fn scalar(&self, row: usize) -> RuntimeResult<Complex64> {
        match self {
            Self::Real(values) => values
                .get(row)
                .copied()
                .map(Complex64::from)
                .ok_or_else(|| RuntimeError::InvalidShape {
                    index: row,
                    message: format!("cache row {row} out of bounds"),
                }),
            Self::Complex(values) => {
                values
                    .get(row)
                    .copied()
                    .ok_or_else(|| RuntimeError::InvalidShape {
                        index: row,
                        message: format!("cache row {row} out of bounds"),
                    })
            }
            Self::Vector { .. } | Self::Matrix { .. } => Err(RuntimeError::TypeMismatch {
                index: row,
                expected: "scalar",
                actual: match self {
                    Self::Vector { .. } => "vector",
                    Self::Matrix { .. } => "matrix",
                    Self::Real(_) | Self::Complex(_) => unreachable!(),
                },
            }),
        }
    }

    fn real_range(&self, start: usize, end: usize) -> RuntimeResult<&[f64]> {
        match self {
            Self::Real(values) => {
                values
                    .get(start..end)
                    .ok_or_else(|| RuntimeError::InvalidShape {
                        index: start,
                        message: format!("cache range {start}..{end} out of bounds"),
                    })
            }
            Self::Complex(_) | Self::Vector { .. } | Self::Matrix { .. } => {
                Err(RuntimeError::TypeMismatch {
                    index: start,
                    expected: "real scalar",
                    actual: match self {
                        Self::Complex(_) => "complex scalar",
                        Self::Vector { .. } => "vector",
                        Self::Matrix { .. } => "matrix",
                        Self::Real(_) => unreachable!(),
                    },
                })
            }
        }
    }

    fn complex_range(&self, start: usize, end: usize) -> RuntimeResult<&[Complex64]> {
        match self {
            Self::Complex(values) => {
                values
                    .get(start..end)
                    .ok_or_else(|| RuntimeError::InvalidShape {
                        index: start,
                        message: format!("cache range {start}..{end} out of bounds"),
                    })
            }
            Self::Real(_) | Self::Vector { .. } | Self::Matrix { .. } => {
                Err(RuntimeError::TypeMismatch {
                    index: start,
                    expected: "complex scalar",
                    actual: match self {
                        Self::Real(_) => "real scalar",
                        Self::Vector { .. } => "vector",
                        Self::Matrix { .. } => "matrix",
                        Self::Complex(_) => unreachable!(),
                    },
                })
            }
        }
    }
}

impl Value {
    fn kind(&self) -> &'static str {
        match self {
            Self::Scalar(_) => "scalar",
            Self::Vector(_) => "vector",
            Self::Matrix { .. } => "matrix",
        }
    }
}

fn f32_scalar_at(values: &[F32Value], id: KernelValueId) -> RuntimeResult<Complex32> {
    match &values[id.index()] {
        F32Value::Scalar(value) => Ok(*value),
        value => Err(RuntimeError::TypeMismatch {
            index: id.index(),
            expected: "scalar",
            actual: value.kind(),
        }),
    }
}

fn f32_vector_at(values: &[F32Value], id: KernelValueId) -> RuntimeResult<&[Complex32]> {
    match &values[id.index()] {
        F32Value::Vector(values) => Ok(values),
        value => Err(RuntimeError::TypeMismatch {
            index: id.index(),
            expected: "vector",
            actual: value.kind(),
        }),
    }
}

fn f32_matrix_at(
    values: &[F32Value],
    id: KernelValueId,
) -> RuntimeResult<(usize, usize, &[Complex32])> {
    match &values[id.index()] {
        F32Value::Matrix { rows, cols, values } => Ok((*rows, *cols, values)),
        value => Err(RuntimeError::TypeMismatch {
            index: id.index(),
            expected: "matrix",
            actual: value.kind(),
        }),
    }
}

fn scalar_at(values: &[Value], index: usize) -> RuntimeResult<Complex64> {
    match &values[index] {
        Value::Scalar(value) => Ok(*value),
        value => Err(RuntimeError::TypeMismatch {
            index,
            expected: "scalar",
            actual: value.kind(),
        }),
    }
}

fn vector_at(values: &[Value], index: usize) -> RuntimeResult<&[Complex64]> {
    match &values[index] {
        Value::Vector(value) => Ok(value),
        value => Err(RuntimeError::TypeMismatch {
            index,
            expected: "vector",
            actual: value.kind(),
        }),
    }
}

fn matrix_at(values: &[Value], index: usize) -> RuntimeResult<(usize, usize, &[Complex64])> {
    match &values[index] {
        Value::Matrix { rows, cols, values } => Ok((*rows, *cols, values)),
        value => Err(RuntimeError::TypeMismatch {
            index,
            expected: "matrix",
            actual: value.kind(),
        }),
    }
}

fn scalar_at_optional(values: &[Option<Value>], index: usize) -> RuntimeResult<Complex64> {
    match values.get(index).and_then(Option::as_ref) {
        Some(Value::Scalar(value)) => Ok(*value),
        Some(value) => Err(RuntimeError::TypeMismatch {
            index,
            expected: "scalar",
            actual: value.kind(),
        }),
        None => Err(RuntimeError::InvalidShape {
            index,
            message: "required cache prerequisite was not evaluated".into(),
        }),
    }
}

fn vector_at_optional(values: &[Option<Value>], index: usize) -> RuntimeResult<&[Complex64]> {
    match values.get(index).and_then(Option::as_ref) {
        Some(Value::Vector(value)) => Ok(value),
        Some(value) => Err(RuntimeError::TypeMismatch {
            index,
            expected: "vector",
            actual: value.kind(),
        }),
        None => Err(RuntimeError::InvalidShape {
            index,
            message: "required cache prerequisite was not evaluated".into(),
        }),
    }
}

fn matrix_at_optional(
    values: &[Option<Value>],
    index: usize,
) -> RuntimeResult<(usize, usize, &[Complex64])> {
    match values.get(index).and_then(Option::as_ref) {
        Some(Value::Matrix { rows, cols, values }) => Ok((*rows, *cols, values)),
        Some(value) => Err(RuntimeError::TypeMismatch {
            index,
            expected: "matrix",
            actual: value.kind(),
        }),
        None => Err(RuntimeError::InvalidShape {
            index,
            message: "required cache prerequisite was not evaluated".into(),
        }),
    }
}

fn matrix_values_row_major(matrix: &DMatrix<Complex64>) -> Vec<Complex64> {
    let mut values = Vec::with_capacity(matrix.nrows() * matrix.ncols());
    for row in 0..matrix.nrows() {
        for col in 0..matrix.ncols() {
            values.push(matrix[(row, col)]);
        }
    }
    values
}

fn matrix_values_row_major_f32(matrix: &DMatrix<Complex32>) -> Vec<Complex32> {
    let mut values = Vec::with_capacity(matrix.nrows() * matrix.ncols());
    for row in 0..matrix.nrows() {
        for col in 0..matrix.ncols() {
            values.push(matrix[(row, col)]);
        }
    }
    values
}

fn eval_unary<T: Float>(op: UnaryOp, input: Complex<T>) -> Complex<T> {
    match op {
        UnaryOp::Neg => -input,
        UnaryOp::Real => Complex::from(input.re),
        UnaryOp::Imag => Complex::from(input.im),
        UnaryOp::Conj => input.conj(),
        UnaryOp::NormSqr => Complex::from(input.norm_sqr()),
        UnaryOp::Sqrt => input.sqrt(),
        UnaryOp::Exp => input.exp(),
        UnaryOp::Sin => input.sin(),
        UnaryOp::Cos => input.cos(),
        UnaryOp::Log => input.ln(),
        UnaryOp::PowI(power) => input.powi(power),
    }
}

fn eval_binary<T: Float>(op: BinaryOp, lhs: Complex<T>, rhs: Complex<T>) -> Complex<T> {
    match op {
        BinaryOp::Add => lhs + rhs,
        BinaryOp::Sub => lhs - rhs,
        BinaryOp::Mul => lhs * rhs,
        BinaryOp::Div => lhs / rhs,
        BinaryOp::Atan2 => Complex::from(lhs.re.atan2(rhs.re)),
    }
}

#[cfg(test)]
mod tests {
    use std::sync::Arc;

    use laddu_compile::{CompileOptions, CompiledModel};
    use laddu_data::{
        RealVec4,
        data::{Dataset, EventBatch, OwnedEvent},
        schema::Schema,
    };
    use laddu_expr::{
        P4Component, atan2, complex, dot, event_p4_component, event_scalar, matmul, matrix, matvec,
        parameter, polar_complex, solve, vector,
    };

    use super::*;

    #[test]
    fn resident_cache_plan_accounts_for_batch_overhead_and_source_prefetch() {
        // 100 events require 1,000 cache bytes. With 100 bytes of fixed
        // overhead per cached batch and 20 source bytes per event, a
        // 2,000-byte budget first suggests 45 events, then accounts for three
        // fixed-overhead batches and converges to a 35-event chunk.
        let (resident, chunk) = resident_cache_plan(100, 10, 20, 100, 2_000).unwrap();
        let batches = 100_usize.div_ceil(chunk);
        assert_eq!(resident, 1_000 + 100 * batches);
        assert!(resident + 20 * chunk <= 2_000);
        assert!(resident_cache_plan(100, 10, 20, 100, 1_119).is_none());
    }

    fn evaluate(expr: &laddu_expr::Expr) -> Complex64 {
        let model = CompiledModel::from_expr(expr).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        CpuBackend.prepare(&model).evaluate(&params).unwrap()
    }

    fn finite_difference(plan: &CpuPlan, params: &ParamValues, parameter: usize) -> Complex64 {
        let h = 1.0e-6;
        let mut plus = params.clone();
        let mut minus = params.clone();
        let id = params.layout().free_params()[parameter];
        let free_id = params.layout().free_id(id).unwrap().unwrap();
        let value = params.get(id).unwrap();
        plus.set_free(free_id, value + h).unwrap();
        minus.set_free(free_id, value - h).unwrap();
        (plan.evaluate(&plus).unwrap() - plan.evaluate(&minus).unwrap()) / (2.0 * h)
    }

    fn assert_gradient_close(actual: &[Complex64], expected: &[Complex64], tolerance: f64) {
        assert_eq!(actual.len(), expected.len());
        for (actual, expected) in actual.iter().zip(expected) {
            assert!(
                (actual - expected).norm() < tolerance,
                "{actual} != {expected}"
            );
        }
    }

    #[cfg(feature = "jit")]
    fn f32_execution(jit: JitPolicy) -> Execution {
        Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions {
                jit,
                ..crate::CpuOptions::default()
            }),
            precision: Precision::F32,
            ..crate::ExecutionOptions::default()
        })
        .unwrap()
    }

    #[cfg(feature = "jit")]
    fn f32_jit_and_interpreter(model: &CompiledModel) -> (CpuPlan, CpuPlan) {
        let automatic = CpuBackend
            .prepare_for_execution(model, &f32_execution(JitPolicy::Auto))
            .unwrap();
        let interpreted = CpuBackend
            .prepare_for_execution(model, &f32_execution(JitPolicy::Disabled))
            .unwrap();

        let Some(ScalarExecutor::Jit(kernel)) = &automatic.scalar_executor else {
            panic!("f32 auto execution should select scalar JIT");
        };
        assert_eq!(kernel.precision(), JitPrecision::F32);
        let GradientExecutor::Jit(kernel) = &automatic.gradient_executor else {
            panic!("f32 auto execution should select gradient JIT");
        };
        assert_eq!(kernel.precision(), JitPrecision::F32);
        if interpreted.scalar_executor.is_some() {
            assert!(matches!(
                interpreted.scalar_executor,
                Some(ScalarExecutor::Interpreter(_))
            ));
        }
        (automatic, interpreted)
    }

    #[cfg(feature = "jit")]
    fn f64_jit_and_interpreter(model: &CompiledModel) -> (CpuPlan, CpuPlan) {
        let automatic = CpuBackend.prepare(model);
        let interpreted =
            CpuBackend.prepare_with_execution_mode(model, CpuExecutionMode::Interpreter);

        if automatic.scalar_executor.is_some() {
            assert!(matches!(
                automatic.scalar_executor,
                Some(ScalarExecutor::Jit(_))
            ));
        }
        if interpreted.scalar_executor.is_some() {
            assert!(matches!(
                interpreted.scalar_executor,
                Some(ScalarExecutor::Interpreter(_))
            ));
        }
        (automatic, interpreted)
    }

    #[cfg(feature = "jit")]
    fn assert_complex_close(actual: Complex64, expected: Complex64) {
        assert!(
            (actual - expected).norm() < 1.0e-6,
            "{actual} != {expected}"
        );
    }

    #[cfg(feature = "jit")]
    fn assert_complex_slices_close(actual: &[Complex64], expected: &[Complex64]) {
        assert_eq!(actual.len(), expected.len());
        for (actual, expected) in actual.iter().zip(expected) {
            assert_complex_close(*actual, *expected);
        }
    }

    #[cfg(feature = "jit")]
    fn assert_complex_close_f64(actual: Complex64, expected: Complex64) {
        assert!(
            (actual - expected).norm() < 1.0e-10,
            "{actual} != {expected}"
        );
    }

    #[cfg(feature = "jit")]
    fn assert_complex_slices_close_f64(actual: &[Complex64], expected: &[Complex64]) {
        assert_eq!(actual.len(), expected.len());
        for (actual, expected) in actual.iter().zip(expected) {
            assert_complex_close_f64(*actual, *expected);
        }
    }

    #[test]
    fn evaluates_scalar_expression_with_parameters() {
        let expr = (2.0 * parameter!("x", initial: 3.0)
            + complex(
                parameter!("re", initial: 1.0),
                parameter!("im", initial: 2.0),
            ))
        .norm_sqr();

        assert_eq!(evaluate(&expr), Complex64::from(53.0));
    }

    #[test]
    fn forward_gradients_match_scalar_complex_finite_differences() {
        let x = laddu_expr::Expr::from(parameter!("x", initial: 0.4));
        let y = laddu_expr::Expr::from(parameter!("y", initial: -0.2));
        let expression = complex(x.clone().sin(), y.clone().exp()).norm_sqr() + (x * y).cos();
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let result = plan.evaluate_with_gradient(&params).unwrap();
        let ir_result = gradient_interpreter::GradientInterpreter::new(
            plan.scalar_kernel.as_ref().unwrap(),
            model.params().free_params(),
        )
        .unwrap()
        .evaluate(&params, None)
        .unwrap()
        .1;

        for (actual, expected) in ir_result.iter().zip(result.gradient()) {
            assert!(
                (actual - expected).norm() < 1.0e-12,
                "{actual} != {expected}"
            );
        }

        for (parameter, derivative) in result.gradient().iter().enumerate() {
            let expected = finite_difference(&plan, &params, parameter);
            assert!((derivative - expected).norm() < 1.0e-8);
        }
    }

    #[test]
    fn reverse_gradients_match_forward_for_scalar_complex_operations() {
        let x = laddu_expr::Expr::from(parameter!("x", initial: 0.8));
        let y = laddu_expr::Expr::from(parameter!("y", initial: -0.3));
        let z = complex(x.clone(), y.clone());
        let expression = x.clone().sqrt()
            + x.clone().log()
            + x.clone().powi(-2)
            + x.clone().sin()
            + x.clone().cos()
            + x.clone().exp()
            + z.clone().conj()
            + z.clone().real()
            + z.clone().imag()
            + z.norm_sqr()
            + atan2(y.clone(), x.clone())
            + x * y;
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let forward = CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let reverse = CpuBackend
            .prepare_with_autodiff_mode(&model, AutodiffMode::Reverse)
            .unwrap();

        let expected = forward.evaluate_with_gradient(&params).unwrap();
        let actual = reverse.evaluate_with_gradient(&params).unwrap();

        assert!((actual.value() - expected.value()).norm() < 1.0e-12);
        assert_gradient_close(actual.gradient(), expected.gradient(), 1.0e-12);
    }

    #[test]
    fn reverse_gradients_match_forward_for_structured_linear_algebra() {
        let a = laddu_expr::Expr::from(parameter!("a", initial: 0.7));
        let b = laddu_expr::Expr::from(parameter!("b", initial: -0.2));
        let c = laddu_expr::Expr::from(parameter!("c", initial: 1.1));
        let d = laddu_expr::Expr::from(parameter!("d", initial: 0.4));
        let x = laddu_expr::Expr::from(parameter!("x", initial: -0.3));
        let y = laddu_expr::Expr::from(parameter!("y", initial: 0.9));
        let left = matrix([
            [a.clone(), complex(b.clone(), 0.2)],
            [1.3.into(), c.clone()],
        ]);
        let right = matrix([[complex(0.5, -0.1), d.clone()], [b.clone(), 0.8.into()]]);
        let product = matmul(left, right);
        let input_vector = vector([x.clone(), complex(y.clone(), -0.4)]);
        let projected = matvec(product.clone(), input_vector);
        let expression = dot(projected.clone(), vector([complex(0.25, 0.3), c.clone()]))
            + product.matrix_element(1, 0)
            + projected.component(1);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let forward = CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let reverse = CpuBackend
            .prepare_with_autodiff_mode(&model, AutodiffMode::Reverse)
            .unwrap();

        let expected = forward.evaluate_with_gradient(&params).unwrap();
        let actual = reverse.evaluate_with_gradient(&params).unwrap();

        assert!((actual.value() - expected.value()).norm() < 1.0e-12);
        assert_gradient_close(actual.gradient(), expected.gradient(), 1.0e-12);
    }

    #[test]
    fn reverse_gradients_match_forward_for_parameter_dependent_solve() {
        let a = laddu_expr::Expr::from(parameter!("a", initial: 2.0));
        let b = laddu_expr::Expr::from(parameter!("b", initial: 0.3));
        let r = laddu_expr::Expr::from(parameter!("r", initial: 1.2));
        let solution = solve(
            matrix([[a, complex(b.clone(), 0.1)], [b, 1.7.into()]]),
            vector([r, complex(0.5, -0.1)]),
        );
        let expression = dot(solution, vector([complex(1.0, 0.2), (-0.4).into()]));
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let forward = CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let reverse = CpuBackend
            .prepare_with_autodiff_mode(&model, AutodiffMode::Reverse)
            .unwrap();

        let expected = forward.evaluate_with_gradient(&params).unwrap();
        let actual = reverse.evaluate_with_gradient(&params).unwrap();

        assert!((actual.value() - expected.value()).norm() < 1.0e-12);
        assert_gradient_close(actual.gradient(), expected.gradient(), 1.0e-12);
    }

    #[test]
    fn reverse_cached_event_gradients_match_forward() {
        let x = event_scalar("x");
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 0.4));
        let phase = laddu_expr::Expr::from(parameter!("phase", initial: -0.2));
        let expression =
            complex((x.clone() * &scale).sin(), (x.clone() + phase).cos()).norm_sqr() + x * scale;
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let forward = CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let reverse = CpuBackend
            .prepare_with_autodiff_mode(&model, AutodiffMode::Reverse)
            .unwrap();
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [
                OwnedEvent::new(vec![], vec![0.25]),
                OwnedEvent::new(vec![], vec![0.75]),
                OwnedEvent::new(vec![], vec![1.25]),
            ],
        )
        .unwrap();

        let expected = forward
            .evaluate_cache_with_gradient(&params, &forward.cache_event_batch(&batch).unwrap())
            .unwrap();
        let actual = reverse
            .evaluate_cache_with_gradient(&params, &reverse.cache_event_batch(&batch).unwrap())
            .unwrap();

        for (actual, expected) in actual.iter().zip(&expected) {
            assert!((actual.value() - expected.value()).norm() < 1.0e-12);
            assert_gradient_close(actual.gradient(), expected.gradient(), 1.0e-12);
        }
    }

    #[test]
    fn reverse_cached_event_materialization_is_a_leaf() {
        let event_sum = event_scalar("x") + event_scalar("y").sin();
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.25));
        let expression = scale * event_sum;
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let forward = CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let reverse = CpuBackend
            .prepare_with_autodiff_mode(&model, AutodiffMode::Reverse)
            .unwrap();
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x", "y"], false).unwrap()),
            [OwnedEvent::new(vec![], vec![0.5, 0.25])],
        )
        .unwrap();
        assert!(
            reverse
                .cache_slots
                .iter()
                .enumerate()
                .any(|(index, slot)| slot.is_some() && reverse.cached_value_slots[index].is_some())
        );

        let expected = forward
            .evaluate_cache_row_with_gradient(
                &params,
                &forward.cache_event_batch(&batch).unwrap(),
                0,
            )
            .unwrap();
        let actual = reverse
            .evaluate_cache_row_with_gradient(
                &params,
                &reverse.cache_event_batch(&batch).unwrap(),
                0,
            )
            .unwrap();

        assert_eq!(actual.value(), expected.value());
        assert_gradient_close(actual.gradient(), expected.gradient(), 1.0e-12);
    }

    #[test]
    fn reverse_f32_gradients_match_forward() {
        let expression = laddu_expr::Expr::from(parameter!("x", initial: 0.4)).sin();
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let reverse = CpuBackend
            .prepare_with_modes_precision(
                &model,
                AutodiffMode::Reverse,
                CpuExecutionMode::Interpreter,
                Precision::F32,
            )
            .unwrap();
        let forward = CpuBackend
            .prepare_with_modes_precision(
                &model,
                AutodiffMode::Forward,
                CpuExecutionMode::Interpreter,
                Precision::F32,
            )
            .unwrap();

        assert_eq!(
            reverse.evaluate_with_gradient(&params).unwrap(),
            forward.evaluate_with_gradient(&params).unwrap()
        );
    }

    #[cfg(feature = "jit")]
    #[test]
    fn forward_and_reverse_gradients_match_interpreter_and_jit_in_both_precisions() {
        let event = event_scalar("x");
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 0.4));
        let phase = laddu_expr::Expr::from(parameter!("phase", initial: -0.2));
        let expression = complex(
            (event.clone() * scale.clone()).sin(),
            (event.clone() + phase).cos(),
        )
        .norm_sqr()
            + event * scale;
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let schema = Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], true).unwrap());
        let batch = EventBatch::from_events(
            schema,
            [
                OwnedEvent::weighted(vec![], vec![0.25], 0.5),
                OwnedEvent::weighted(vec![], vec![0.75], 1.5),
            ],
        )
        .unwrap();

        for (precision, tolerance) in [(Precision::F32, 1.0e-6), (Precision::F64, 1.0e-12)] {
            let baseline = CpuBackend
                .prepare_with_modes_precision(
                    &model,
                    AutodiffMode::Forward,
                    CpuExecutionMode::Interpreter,
                    precision,
                )
                .unwrap();
            let expected = baseline
                .evaluate_cache_with_gradient(&params, &baseline.cache_event_batch(&batch).unwrap())
                .unwrap();

            for (autodiff, execution) in [
                (AutodiffMode::Reverse, CpuExecutionMode::Interpreter),
                (AutodiffMode::Forward, CpuExecutionMode::Auto),
                (AutodiffMode::Reverse, CpuExecutionMode::Auto),
            ] {
                let plan = CpuBackend
                    .prepare_with_modes_precision(&model, autodiff, execution, precision)
                    .unwrap();
                if execution == CpuExecutionMode::Auto {
                    assert!(matches!(plan.gradient_executor, GradientExecutor::Jit(_)));
                }
                let actual = plan
                    .evaluate_cache_with_gradient(&params, &plan.cache_event_batch(&batch).unwrap())
                    .unwrap();
                for (actual, expected) in actual.iter().zip(&expected) {
                    assert!((actual.value() - expected.value()).norm() < tolerance);
                    assert_gradient_close(actual.gradient(), expected.gradient(), tolerance);
                }
            }
        }
    }

    #[cfg(feature = "jit")]
    #[test]
    fn jit_gradient_reduction_matches_interpreter() {
        let x = event_scalar("x").real();
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 0.4));
        let phase = laddu_expr::Expr::from(parameter!("phase", initial: -0.2));
        let intensity =
            complex((x.clone() * &scale).sin(), (x.clone() + phase).cos()).norm_sqr() + 0.5;
        let expression = complex(intensity, x * scale);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let automatic = CpuBackend.prepare(&model);
        let interpreted =
            CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        assert!(matches!(
            automatic.gradient_executor,
            GradientExecutor::Jit(_)
        ));

        let schema = Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], true).unwrap());
        let batch = EventBatch::from_events(
            schema,
            [
                OwnedEvent::weighted(vec![], vec![0.25], 0.5),
                OwnedEvent::weighted(vec![], vec![0.75], 1.5),
                OwnedEvent::weighted(vec![], vec![1.25], 2.0),
            ],
        )
        .unwrap();
        let actual = automatic
            .evaluate_cache_with_gradient(&params, &automatic.cache_event_batch(&batch).unwrap())
            .unwrap();
        let expected = interpreted
            .evaluate_cache_with_gradient(&params, &interpreted.cache_event_batch(&batch).unwrap())
            .unwrap();
        for (actual, expected) in actual.iter().zip(&expected) {
            assert!(
                (actual.value() - expected.value()).norm() < 1.0e-12,
                "{} != {}",
                actual.value(),
                expected.value()
            );
            for (actual, expected) in actual.gradient().iter().zip(expected.gradient()) {
                assert!((actual - expected).norm() < 1.0e-12);
            }
        }
        let dataset = Dataset::from_batch(batch);
        let execution = Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions {
                threads: crate::ThreadPolicy::Serial,
                ..crate::CpuOptions::default()
            }),
            ..crate::ExecutionOptions::default()
        })
        .unwrap();
        let automatic_data = automatic.prepare_dataset(&execution, &dataset).unwrap();
        let interpreted_data = interpreted.prepare_dataset(&execution, &dataset).unwrap();
        let automatic_result = automatic
            .reduce_with_gradient(
                &execution,
                &params,
                &automatic_data,
                ReductionPlan::weighted_log_positive_real(),
            )
            .unwrap();
        let interpreted_result = interpreted
            .reduce_with_gradient(
                &execution,
                &params,
                &interpreted_data,
                ReductionPlan::weighted_log_positive_real(),
            )
            .unwrap();

        assert!((automatic_result.value() - interpreted_result.value()).abs() < 1.0e-12);
        for (actual, expected) in automatic_result
            .gradient()
            .iter()
            .zip(interpreted_result.gradient())
        {
            assert!((actual - expected).abs() < 1.0e-12);
        }
    }

    #[test]
    fn forward_gradients_cover_unary_atan2_and_zero_products() {
        let x = laddu_expr::Expr::from(parameter!("x", initial: 0.8));
        let y = laddu_expr::Expr::from(parameter!("y", initial: 0.0));
        let z = complex(x.clone(), y.clone());
        let expression = x.clone().sqrt()
            + x.clone().log()
            + x.clone().powi(-2)
            + x.clone().sin()
            + x.clone().cos()
            + x.clone().exp()
            + z.clone().conj().real()
            + z.clone().imag()
            + z.norm_sqr()
            + atan2(y.clone(), x.clone())
            + y * x;
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let result = plan.evaluate_with_gradient(&params).unwrap();

        for (parameter, derivative) in result.gradient().iter().enumerate() {
            let expected = finite_difference(&plan, &params, parameter);
            assert!((derivative - expected).norm() < 1.0e-7);
        }
    }

    #[test]
    fn forward_gradients_cover_matrix_vector_and_dot_operations() {
        let a = laddu_expr::Expr::from(parameter!("a", initial: 0.7));
        let b = laddu_expr::Expr::from(parameter!("b", initial: -0.2));
        let c = laddu_expr::Expr::from(parameter!("c", initial: 1.1));
        let d = laddu_expr::Expr::from(parameter!("d", initial: 0.4));
        let x = laddu_expr::Expr::from(parameter!("x", initial: -0.3));
        let y = laddu_expr::Expr::from(parameter!("y", initial: 0.9));
        let left = matrix([
            [a.clone(), complex(b.clone(), 0.2)],
            [1.3.into(), c.clone()],
        ]);
        let right = matrix([[complex(0.5, -0.1), d.clone()], [b.clone(), 0.8.into()]]);
        let product = matmul(left, right);
        let input_vector = vector([x.clone(), complex(y.clone(), -0.4)]);
        let projected = matvec(product.clone(), input_vector);
        let expression = dot(projected.clone(), vector([complex(0.25, 0.3), c.clone()]))
            + product.matrix_element(1, 0)
            + projected.component(1);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let result = plan.evaluate_with_gradient(&params).unwrap();

        for (parameter, derivative) in result.gradient().iter().enumerate() {
            let expected = finite_difference(&plan, &params, parameter);
            assert!(
                (derivative - expected).norm() < 1.0e-7,
                "{derivative} != {expected}"
            );
        }
    }

    #[test]
    fn solve_gradients_match_finite_differences_for_matrix_and_rhs_parameters() {
        let a = laddu_expr::Expr::from(parameter!("a", initial: 2.0));
        let b = laddu_expr::Expr::from(parameter!("b", initial: 0.3));
        let r = laddu_expr::Expr::from(parameter!("r", initial: 1.2));
        let solution = solve(
            matrix([[a, b], [0.2.into(), 1.7.into()]]),
            vector([r, complex(0.5, -0.1)]),
        );
        let expression = dot(solution, vector([complex(1.0, 0.2), (-0.4).into()]));
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let result = plan.evaluate_with_gradient(&params).unwrap();

        for (parameter, derivative) in result.gradient().iter().enumerate() {
            let expected = finite_difference(&plan, &params, parameter);
            assert!((derivative - expected).norm() < 1.0e-8);
        }
    }

    #[test]
    fn evaluates_event_scalars() {
        let expr = laddu_expr::event_scalar("x") * 2.0;
        let model = CompiledModel::from_expr(&expr).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let event = HashMap::from([("x".to_owned(), 3.0)]);

        assert_eq!(
            plan.evaluate_with_event(&params, &event).unwrap(),
            Complex64::from(6.0)
        );
    }

    #[test]
    fn scalar_kernel_ir_preserves_typed_dependency_classes() {
        let coefficient = complex(parameter!("re", initial: 2.0), 1.0);
        let expr = coefficient * event_scalar("x");
        let model = CompiledModel::from_expr(&expr).unwrap();
        let plan = CpuBackend.prepare(&model);
        let kernel = plan.scalar_kernel.as_ref().unwrap();

        assert!(
            kernel
                .values()
                .iter()
                .any(|value| value.class == KernelValueClass::Invariant)
        );
        assert!(
            kernel
                .values()
                .iter()
                .any(|value| value.class == KernelValueClass::Event)
        );
        let root = &kernel.values()[kernel.root().index()];
        assert_eq!(root.kind, KernelValueKind::Complex);
        assert_eq!(root.class, KernelValueClass::Event);
        assert!(matches!(root.instruction, KernelInstruction::Mul(_)));
    }

    #[test]
    fn cpu_execution_mode_selects_retained_interpreter() {
        let expr = laddu_expr::Expr::from(parameter!("x", initial: 2.0)).exp() + 1.0;
        let model = CompiledModel::from_expr(&expr).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let automatic = CpuBackend.prepare(&model);
        let interpreted =
            CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let execution = Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions {
                jit: crate::JitPolicy::Disabled,
                ..crate::CpuOptions::default()
            }),
            ..crate::ExecutionOptions::default()
        })
        .unwrap();
        let configured = CpuBackend
            .prepare_for_execution(&model, &execution)
            .unwrap();

        assert!(matches!(
            interpreted.scalar_executor,
            Some(ScalarExecutor::Interpreter(_))
        ));
        assert!(matches!(
            configured.scalar_executor,
            Some(ScalarExecutor::Interpreter(_))
        ));
        assert!(matches!(
            configured.gradient_executor,
            GradientExecutor::Interpreter(_)
        ));
        assert_eq!(
            automatic.evaluate(&params).unwrap(),
            interpreted.evaluate(&params).unwrap()
        );

        let empty_model = CompiledModel::from_expr(&laddu_expr::Expr::from(1.0)).unwrap();
        let wrong_params = empty_model.params().default_values();
        assert!(matches!(
            automatic.evaluate(&wrong_params),
            Err(RuntimeError::Parameter(_))
        ));
    }

    #[test]
    fn cpu_plan_executes_parameter_only_scalar_kernel_in_f32() {
        let x = laddu_expr::Expr::from(parameter!("x", initial: 16_777_216.0));
        let y = laddu_expr::Expr::from(parameter!("y", initial: 1.0));
        let model = CompiledModel::from_expr(&(x + y)).unwrap();
        let params = model.params().default_values();
        let execution = Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions::default()),
            precision: Precision::F32,
            ..crate::ExecutionOptions::default()
        })
        .unwrap();
        let f32_plan = CpuBackend
            .prepare_for_execution(&model, &execution)
            .unwrap();
        let f64_plan = CpuBackend.prepare(&model);

        assert_eq!(f32_plan.evaluate(&params).unwrap().re, 16_777_216.0);
        assert_eq!(f64_plan.evaluate(&params).unwrap().re, 16_777_217.0);
        let gradient = f32_plan.evaluate_with_gradient(&params).unwrap();
        assert_eq!(gradient.value().re, 16_777_216.0);
        assert_eq!(gradient.gradient(), &[Complex64::ONE, Complex64::ONE]);
    }

    #[cfg(feature = "jit")]
    #[test]
    fn cpu_f32_auto_jit_matches_f32_interpreter_for_parameter_arithmetic() {
        let x = laddu_expr::Expr::from(parameter!("x", initial: 16_777_216.0));
        let y = laddu_expr::Expr::from(parameter!("y", initial: 1.0));
        let model = CompiledModel::from_expr(&(x + y)).unwrap();
        let params = model.params().default_values();
        let (automatic, interpreted) = f32_jit_and_interpreter(&model);
        let GradientExecutor::Jit(kernel) = &automatic.gradient_executor else {
            unreachable!("f32_jit_and_interpreter already requires a gradient JIT")
        };
        assert_eq!(kernel.compiled_component_count(), 1);

        assert_eq!(
            automatic.evaluate(&params).unwrap(),
            interpreted.evaluate(&params).unwrap()
        );
    }

    #[cfg(feature = "jit")]
    #[test]
    fn cpu_f32_auto_jit_matches_f32_interpreter_for_unary_and_binary_ops() {
        let x = laddu_expr::Expr::from(parameter!("x", initial: 0.8));
        let y = laddu_expr::Expr::from(parameter!("y", initial: -0.35));
        let z = complex(x.clone(), y.clone());
        let expression = x.clone().sqrt()
            + x.clone().log()
            + x.clone().powi(-2)
            + x.clone().sin()
            + x.clone().cos()
            + x.clone().exp()
            + z.clone().conj().real()
            + z.clone().imag()
            + z.norm_sqr()
            + atan2(y.clone(), x.clone())
            + complex(x.clone(), y.clone()) / complex(1.25, -0.5);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = model.params().default_values();
        let (automatic, interpreted) = f32_jit_and_interpreter(&model);

        assert_complex_close(
            automatic.evaluate(&params).unwrap(),
            interpreted.evaluate(&params).unwrap(),
        );
    }

    #[test]
    fn cpu_f32_reduces_event_scalar_arithmetic_with_f64_accumulation() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.0));
        let model = CompiledModel::from_expr(&(event_scalar("x") + scale)).unwrap();
        let params = model.params().default_values();
        let execution = Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions {
                threads: crate::ThreadPolicy::Serial,
                ..crate::CpuOptions::default()
            }),
            precision: Precision::F32,
            ..crate::ExecutionOptions::default()
        })
        .unwrap();
        let plan = CpuBackend
            .prepare_for_execution(&model, &execution)
            .unwrap();
        let dataset = Dataset::from_batch(
            EventBatch::from_events(
                Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
                [OwnedEvent::new(vec![], vec![16_777_216.0])],
            )
            .unwrap(),
        );
        let prepared = plan.prepare_dataset(&execution, &dataset).unwrap();

        assert_eq!(
            plan.reduce(
                &execution,
                &params,
                &prepared,
                ReductionPlan::weighted_real(),
            )
            .unwrap(),
            16_777_216.0
        );
    }

    #[test]
    fn cpu_f32_evaluates_complex_linear_algebra() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.25));
        let lhs = matrix([
            [scale.clone() + 2.0, complex(0.25, -0.5)],
            [0.5.into(), scale.clone() + 3.0],
        ]);
        let rhs = vector([1.0.into(), scale]);
        let model = CompiledModel::from_expr(&dot(
            vector([1.0.into(), complex(0.0, 1.0)]),
            solve(lhs, rhs),
        ))
        .unwrap();
        let params = model.params().default_values();
        let execution = Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions::default()),
            precision: Precision::F32,
            ..crate::ExecutionOptions::default()
        })
        .unwrap();
        let f32_plan = CpuBackend
            .prepare_for_execution(&model, &execution)
            .unwrap();
        let f64_plan = CpuBackend.prepare(&model);

        let actual = f32_plan.evaluate(&params).unwrap();
        let expected = f64_plan.evaluate(&params).unwrap();
        assert!((actual.re - expected.re).abs() < 1.0e-6);
        assert!((actual.im - expected.im).abs() < 1.0e-6);
    }

    #[test]
    fn cpu_f32_evaluates_computed_event_cache_entries() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 2.0));
        let model =
            CompiledModel::from_expr(&(event_scalar("x").sin() * scale + 16_777_216.0)).unwrap();
        let execution = Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions::default()),
            precision: Precision::F32,
            ..crate::ExecutionOptions::default()
        })
        .unwrap();
        let plan = CpuBackend
            .prepare_for_execution(&model, &execution)
            .unwrap();
        let params = model.params().default_values();
        let dataset = Dataset::from_batch(
            EventBatch::from_events(
                Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
                [OwnedEvent::new(vec![], vec![1.0])],
            )
            .unwrap(),
        );
        let prepared = plan.prepare_dataset(&execution, &dataset).unwrap();

        let reduction = plan
            .reduce_with_gradient(
                &execution,
                &params,
                &prepared,
                ReductionPlan::weighted_real(),
            )
            .unwrap();
        assert_eq!(reduction.value(), 16_777_218.0);
        assert_eq!(reduction.gradient(), &[(1.0_f32.sin() as f64)]);
    }

    #[test]
    fn cpu_f32_direct_event_gradient_matches_cached_event_gradient() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.5));
        let offset = laddu_expr::Expr::from(parameter!("offset", initial: -0.25));
        let x = event_scalar("x");
        let expression =
            (x.clone().sin() * scale.clone() + offset.clone()).exp() + complex(scale, x).norm_sqr();
        let model = CompiledModel::from_expr(&expression).unwrap();
        let execution = Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions::default()),
            precision: Precision::F32,
            ..crate::ExecutionOptions::default()
        })
        .unwrap();
        let plan = CpuBackend
            .prepare_for_execution(&model, &execution)
            .unwrap();
        let params = model.params().default_values();
        let event = HashMap::from([("x".to_owned(), 0.75)]);
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [OwnedEvent::new(vec![], vec![0.75])],
        )
        .unwrap();
        let cache = plan.cache_event_batch(&batch).unwrap();

        let direct_value = plan.evaluate_with_event(&params, &event).unwrap();
        let direct = plan
            .evaluate_with_event_and_gradient(&params, &event)
            .unwrap();
        let cached_value = plan.evaluate_cache_row(&params, &cache, 0).unwrap();
        let cached = plan
            .evaluate_cache_row_with_gradient(&params, &cache, 0)
            .unwrap();

        assert_eq!(direct_value, cached_value);
        assert_eq!(direct.value(), cached.value());
        assert_eq!(direct.gradient(), cached.gradient());
    }

    #[cfg(feature = "jit")]
    #[test]
    fn cpu_f32_auto_jit_matches_f32_interpreter_for_cached_linear_algebra() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.25));
        let matrix = matrix([
            [event_scalar("x") + 2.0, complex(0.25, -0.5)],
            [0.5.into(), scale.clone() + 3.0],
        ]);
        let rhs = vector([1.0.into(), scale]);
        let model = CompiledModel::from_expr(&dot(
            vector([1.0.into(), complex(0.0, 1.0)]),
            solve(matrix, rhs),
        ))
        .unwrap();
        let params = model.params().default_values();
        let (automatic, interpreted) = f32_jit_and_interpreter(&model);
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [OwnedEvent::new(vec![], vec![0.75])],
        )
        .unwrap();
        let automatic_cache = automatic.cache_event_batch(&batch).unwrap();
        let interpreted_cache = interpreted.cache_event_batch(&batch).unwrap();

        let actual = automatic.evaluate_cache(&params, &automatic_cache).unwrap();
        let expected = interpreted
            .evaluate_cache(&params, &interpreted_cache)
            .unwrap();
        assert_complex_slices_close(&actual, &expected);
    }

    #[cfg(feature = "jit")]
    #[test]
    fn cpu_f32_auto_jit_matches_f32_interpreter_for_event_cache_ops() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.75));
        let x = event_scalar("x");
        let y = event_scalar("y");
        let expression = ((x.clone() + scale.clone()).sin()
            + (y.clone() - 0.25).cos()
            + (x.clone() * y.clone()).exp()
            + atan2(y.clone(), x.clone() + 1.0))
            / complex(scale, -0.5);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = model.params().default_values();
        let (automatic, interpreted) = f32_jit_and_interpreter(&model);
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x", "y"], false).unwrap()),
            [
                OwnedEvent::new(vec![], vec![0.25, -0.5]),
                OwnedEvent::new(vec![], vec![0.75, 0.125]),
                OwnedEvent::new(vec![], vec![1.5, 0.5]),
            ],
        )
        .unwrap();
        let automatic_cache = automatic.cache_event_batch(&batch).unwrap();
        let interpreted_cache = interpreted.cache_event_batch(&batch).unwrap();

        assert_complex_slices_close(
            &automatic.evaluate_cache(&params, &automatic_cache).unwrap(),
            &interpreted
                .evaluate_cache(&params, &interpreted_cache)
                .unwrap(),
        );
    }

    #[cfg(feature = "jit")]
    #[test]
    fn cpu_f32_auto_jit_matches_f32_interpreter_for_reductions_and_gradients() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.25));
        let offset = laddu_expr::Expr::from(parameter!("offset", initial: 0.5));
        let x = event_scalar("x");
        let expression = (x.clone() * scale.clone() + offset.clone()).sin()
            + complex(scale, offset).norm_sqr()
            + 2.0;
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = model.params().default_values();
        let (automatic, interpreted) = f32_jit_and_interpreter(&model);
        let auto_execution = f32_execution(JitPolicy::Auto);
        let interpreter_execution = f32_execution(JitPolicy::Disabled);
        let dataset = Dataset::from_batch(
            EventBatch::from_events(
                Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], true).unwrap()),
                [
                    OwnedEvent::weighted(vec![], vec![0.25], 0.5),
                    OwnedEvent::weighted(vec![], vec![0.75], 1.5),
                    OwnedEvent::weighted(vec![], vec![1.25], 2.0),
                ],
            )
            .unwrap(),
        );
        let automatic_data = automatic
            .prepare_dataset(&auto_execution, &dataset)
            .unwrap();
        let interpreted_data = interpreted
            .prepare_dataset(&interpreter_execution, &dataset)
            .unwrap();

        let actual = automatic
            .reduce_with_gradient(
                &auto_execution,
                &params,
                &automatic_data,
                ReductionPlan::weighted_real(),
            )
            .unwrap();
        let expected = interpreted
            .reduce_with_gradient(
                &interpreter_execution,
                &params,
                &interpreted_data,
                ReductionPlan::weighted_real(),
            )
            .unwrap();
        assert!((actual.value() - expected.value()).abs() < 1.0e-6);
        assert_eq!(actual.gradient().len(), expected.gradient().len());
        for (actual, expected) in actual.gradient().iter().zip(expected.gradient()) {
            assert!((actual - expected).abs() < 1.0e-6);
        }
    }

    #[cfg(feature = "jit")]
    #[test]
    fn auto_jit_matches_interpreter_for_supported_real_arithmetic() {
        let x = laddu_expr::Expr::from(parameter!("x", initial: 2.0));
        let y = laddu_expr::Expr::from(parameter!("y", initial: -0.5));
        let expr = (x * 3.0 + y) / 2.0;
        let model = CompiledModel::from_expr(&expr).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let automatic = CpuBackend.prepare(&model);
        let interpreted =
            CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);

        assert!(matches!(
            automatic.scalar_executor,
            Some(ScalarExecutor::Jit(_))
        ));
        assert!(matches!(
            automatic.gradient_executor,
            GradientExecutor::Jit(_)
        ));
        assert_eq!(
            automatic.evaluate(&params).unwrap(),
            interpreted.evaluate(&params).unwrap()
        );
        assert_eq!(
            automatic.evaluate_with_gradient(&params).unwrap(),
            interpreted.evaluate_with_gradient(&params).unwrap()
        );
    }

    #[cfg(feature = "jit")]
    #[test]
    fn auto_jit_supports_complex_transcendentals() {
        let expr = laddu_expr::Expr::from(parameter!("x", initial: 2.0)).exp();
        let model = CompiledModel::from_expr(&expr).unwrap();
        let plan = CpuBackend.prepare(&model);

        assert!(matches!(plan.scalar_executor, Some(ScalarExecutor::Jit(_))));
        let params = Arc::new(model.params().clone()).default_values();
        assert_eq!(plan.evaluate(&params).unwrap(), Complex64::from(2.0).exp());
    }

    #[cfg(feature = "jit")]
    #[test]
    fn cpu_f64_auto_jit_matches_interpreter_for_unary_binary_ops_and_gradients() {
        let x = laddu_expr::Expr::from(parameter!("x", initial: 0.8));
        let y = laddu_expr::Expr::from(parameter!("y", initial: -0.35));
        let z = complex(x.clone(), y.clone());
        let expression = x.clone().sqrt()
            + x.clone().log()
            + x.clone().powi(-2)
            + x.clone().sin()
            + x.clone().cos()
            + x.clone().exp()
            + z.clone().conj().real()
            + z.clone().imag()
            + z.norm_sqr()
            + atan2(y.clone(), x.clone())
            + complex(x.clone(), y.clone()) / complex(1.25, -0.5);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = model.params().default_values();
        let (automatic, interpreted) = f64_jit_and_interpreter(&model);

        assert_complex_close_f64(
            automatic.evaluate(&params).unwrap(),
            interpreted.evaluate(&params).unwrap(),
        );
        let actual_gradient = automatic.evaluate_with_gradient(&params).unwrap();
        let expected_gradient = interpreted.evaluate_with_gradient(&params).unwrap();
        assert_complex_close_f64(actual_gradient.value(), expected_gradient.value());
        assert_complex_slices_close_f64(actual_gradient.gradient(), expected_gradient.gradient());
    }

    #[cfg(feature = "jit")]
    #[test]
    fn cpu_f64_auto_jit_matches_interpreter_for_event_cache_ops_and_gradients() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.75));
        let x = event_scalar("x");
        let y = event_scalar("y");
        let expression = ((x.clone() + scale.clone()).sin()
            + (y.clone() - 0.25).cos()
            + (x.clone() * y.clone()).exp()
            + atan2(y.clone(), x.clone() + 1.0))
            / complex(scale, -0.5);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = model.params().default_values();
        let (automatic, interpreted) = f64_jit_and_interpreter(&model);
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x", "y"], false).unwrap()),
            [
                OwnedEvent::new(vec![], vec![0.25, -0.5]),
                OwnedEvent::new(vec![], vec![0.75, 0.125]),
                OwnedEvent::new(vec![], vec![1.5, 0.5]),
            ],
        )
        .unwrap();
        let automatic_cache = automatic.cache_event_batch(&batch).unwrap();
        let interpreted_cache = interpreted.cache_event_batch(&batch).unwrap();

        assert_complex_slices_close_f64(
            &automatic.evaluate_cache(&params, &automatic_cache).unwrap(),
            &interpreted
                .evaluate_cache(&params, &interpreted_cache)
                .unwrap(),
        );
        for (actual, expected) in automatic
            .evaluate_cache_with_gradient(&params, &automatic_cache)
            .unwrap()
            .iter()
            .zip(
                interpreted
                    .evaluate_cache_with_gradient(&params, &interpreted_cache)
                    .unwrap(),
            )
        {
            assert_complex_close_f64(actual.value(), expected.value());
            assert_complex_slices_close_f64(actual.gradient(), expected.gradient());
        }
    }

    #[cfg(feature = "jit")]
    #[test]
    fn cpu_f64_auto_jit_matches_interpreter_for_reductions_and_gradients() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.25));
        let offset = laddu_expr::Expr::from(parameter!("offset", initial: 0.5));
        let x = event_scalar("x");
        let expression = (x.clone() * scale.clone() + offset.clone()).sin()
            + complex(scale, offset).norm_sqr()
            + 2.0;
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = model.params().default_values();
        let (automatic, interpreted) = f64_jit_and_interpreter(&model);
        assert!(matches!(
            automatic.gradient_executor,
            GradientExecutor::Jit(_)
        ));
        let execution = Execution::default();
        let interpreter_execution = Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions {
                jit: JitPolicy::Disabled,
                ..crate::CpuOptions::default()
            }),
            ..crate::ExecutionOptions::default()
        })
        .unwrap();
        let dataset = Dataset::from_batch(
            EventBatch::from_events(
                Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], true).unwrap()),
                [
                    OwnedEvent::weighted(vec![], vec![0.25], 0.5),
                    OwnedEvent::weighted(vec![], vec![0.75], 1.5),
                    OwnedEvent::weighted(vec![], vec![1.25], 2.0),
                ],
            )
            .unwrap(),
        );
        let automatic_data = automatic.prepare_dataset(&execution, &dataset).unwrap();
        let interpreted_data = interpreted
            .prepare_dataset(&interpreter_execution, &dataset)
            .unwrap();

        let actual = automatic
            .reduce_with_gradient(
                &execution,
                &params,
                &automatic_data,
                ReductionPlan::weighted_real(),
            )
            .unwrap();
        let expected = interpreted
            .reduce_with_gradient(
                &interpreter_execution,
                &params,
                &interpreted_data,
                ReductionPlan::weighted_real(),
            )
            .unwrap();
        assert!((actual.value() - expected.value()).abs() < 1.0e-10);
        assert_eq!(actual.gradient().len(), expected.gradient().len());
        for (actual, expected) in actual.gradient().iter().zip(expected.gradient()) {
            assert!((actual - expected).abs() < 1.0e-10);
        }
    }

    #[test]
    fn evaluates_p4_schema_components_and_atan2() {
        let expr = event_p4_component("ks1", P4Component::E)
            + event_p4_component("ks1", P4Component::Px)
            + atan2(
                event_p4_component("ks1", P4Component::Py),
                event_p4_component("ks1", P4Component::Px),
            );
        let model = CompiledModel::from_expr(&expr).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(["ks1"], std::iter::empty::<&str>(), false).unwrap()),
            [OwnedEvent::new(
                vec![RealVec4::new(10.0, 3.0, 4.0, 5.0)],
                vec![],
            )],
        )
        .unwrap();

        assert_eq!(
            plan.evaluate_batch(&params, &batch).unwrap()[0],
            Complex64::from(13.0 + 4.0_f64.atan2(3.0))
        );
    }

    #[test]
    fn batch_cache_evaluates_without_original_event_batch() {
        let expr = event_scalar("x").real().sin() * parameter!("scale", initial: 2.0);
        let model = CompiledModel::from_expr(&expr).unwrap();
        let layout = Arc::new(model.params().clone());
        let mut params = layout.default_values();
        let plan = CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [
                OwnedEvent::new(vec![], vec![0.5]),
                OwnedEvent::new(vec![], vec![1.0]),
            ],
        )
        .unwrap();
        let cache = plan.cache_event_batch(&batch).unwrap();

        assert_eq!(cache.weights(), &[1.0, 1.0]);
        assert!(matches!(&cache.slots[0], CachedSlot::Real(values) if values.len() == 2));
        assert_eq!(cache.slots[0].resident_bytes(), 2 * size_of::<f64>());
        assert_eq!(
            plan.evaluate_cache(&params, &cache).unwrap(),
            vec![
                Complex64::from(2.0 * 0.5_f64.sin()),
                Complex64::from(2.0 * 1.0_f64.sin())
            ]
        );

        let scale = layout
            .free_id(layout.id("scale").unwrap())
            .unwrap()
            .unwrap();
        params.set_free(scale, 3.0).unwrap();
        assert_eq!(
            plan.evaluate_cache(&params, &cache).unwrap(),
            vec![
                Complex64::from(3.0 * 0.5_f64.sin()),
                Complex64::from(3.0 * 1.0_f64.sin())
            ]
        );
    }

    #[test]
    fn real_cache_slots_use_half_the_scalar_payload_of_complex_slots() {
        let real_model =
            CompiledModel::from_expr(&(parameter!("scale") * event_scalar("x").real().sin()))
                .unwrap();
        let x = event_scalar("x");
        let complex_model =
            CompiledModel::from_expr(&(parameter!("scale") * complex(x.clone().sin(), x.cos())))
                .unwrap();
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [
                OwnedEvent::new(vec![], vec![0.5]),
                OwnedEvent::new(vec![], vec![1.0]),
            ],
        )
        .unwrap();
        let real_cache = CpuBackend
            .prepare(&real_model)
            .cache_event_batch(&batch)
            .unwrap();
        let complex_cache = CpuBackend
            .prepare(&complex_model)
            .cache_event_batch(&batch)
            .unwrap();

        assert!(matches!(&real_cache.slots[0], CachedSlot::Real(_)));
        assert!(matches!(&complex_cache.slots[0], CachedSlot::Complex(_)));
        assert_eq!(
            complex_cache.slots[0].resident_bytes(),
            2 * real_cache.slots[0].resident_bytes()
        );
    }

    #[test]
    fn selected_event_only_solve_components_cache_inverse_rows() {
        let expression = solve(
            matrix([[event_scalar("x") + 2.0]]),
            vector([parameter!("rhs", initial: 3.0)]),
        )
        .component(0);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let scalar_plan = plan.scalar_interpreter_plan().unwrap();
        assert!(!scalar_plan.invariant_instructions.is_empty());
        assert!(!scalar_plan.event_instructions.is_empty());
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [
                OwnedEvent::new(vec![], vec![0.0]),
                OwnedEvent::new(vec![], vec![1.0]),
            ],
        )
        .unwrap();
        let cache = plan.cache_event_batch(&batch).unwrap();

        assert!(cache.factor_slots.is_empty());
        assert_eq!(cache.solve_row_slots.len(), 1);
        assert_eq!(cache.solve_row_slots[0].values.len(), 2);
        assert!(cache.resident_bytes() > 0);
        let first = plan
            .evaluate_cache_row_with_gradient(&params, &cache, 0)
            .unwrap();
        let second = plan
            .evaluate_cache_row_with_gradient(&params, &cache, 1)
            .unwrap();
        assert_eq!(first.value(), Complex64::from(1.5));
        assert_eq!(first.gradient(), &[Complex64::from(0.5)]);
        assert_eq!(second.value(), Complex64::from(1.0));
        assert_eq!(second.gradient(), &[Complex64::from(1.0 / 3.0)]);
    }

    #[test]
    fn cached_solve_component_matches_general_complex_nonsymmetric_solve() {
        let expression = solve(
            matrix([
                [event_scalar("x") + 2.0, Complex64::I.into()],
                [Complex64::new(2.0, -1.0).into(), 3.0.into()],
            ]),
            vector([
                parameter!("p", initial: 1.5),
                parameter!("q", initial: -0.25),
            ]),
        )
        .component(1);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        assert!(plan.solve_components.iter().any(Option::is_some));

        let event = HashMap::from([("x".to_owned(), 0.75)]);
        let direct = plan
            .evaluate_with_event_and_gradient(&params, &event)
            .unwrap();
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [OwnedEvent::new(vec![], vec![0.75])],
        )
        .unwrap();
        let cache = plan.cache_event_batch(&batch).unwrap();
        let cached = plan
            .evaluate_cache_row_with_gradient(&params, &cache, 0)
            .unwrap();
        let ir_gradient = gradient_interpreter::GradientInterpreter::new(
            plan.scalar_kernel.as_ref().unwrap(),
            model.params().free_params(),
        )
        .unwrap()
        .evaluate(&params, Some((&cache, 0)))
        .unwrap()
        .1;

        assert!((cached.value() - direct.value()).norm() < 1.0e-12);
        for (cached, direct) in cached.gradient().iter().zip(direct.gradient()) {
            assert!((cached - direct).norm() < 1.0e-12);
        }
        for (actual, expected) in ir_gradient.iter().zip(cached.gradient()) {
            assert!((actual - expected).norm() < 1.0e-12);
        }
    }

    #[test]
    fn reverse_cached_solve_component_matches_forward_nonsymmetric_solve() {
        let expression = solve(
            matrix([
                [event_scalar("x") + 2.0, Complex64::I.into()],
                [Complex64::new(2.0, -1.0).into(), 3.0.into()],
            ]),
            vector([
                parameter!("p", initial: 1.5),
                parameter!("q", initial: -0.25),
            ]),
        )
        .component(1);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let forward = CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let reverse = CpuBackend
            .prepare_with_autodiff_mode(&model, AutodiffMode::Reverse)
            .unwrap();
        assert!(reverse.solve_components.iter().any(Option::is_some));

        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [OwnedEvent::new(vec![], vec![0.75])],
        )
        .unwrap();
        let expected = forward
            .evaluate_cache_row_with_gradient(
                &params,
                &forward.cache_event_batch(&batch).unwrap(),
                0,
            )
            .unwrap();
        let actual = reverse
            .evaluate_cache_row_with_gradient(
                &params,
                &reverse.cache_event_batch(&batch).unwrap(),
                0,
            )
            .unwrap();

        assert!((actual.value() - expected.value()).norm() < 1.0e-12);
        assert_gradient_close(actual.gradient(), expected.gradient(), 1.0e-12);
    }

    #[cfg(feature = "jit")]
    #[test]
    fn jit_gradient_reduction_handles_cached_solve_rows() {
        let expression = solve(
            matrix([
                [event_scalar("x") + 2.0, Complex64::I.into()],
                [Complex64::new(2.0, -1.0).into(), 3.0.into()],
            ]),
            vector([
                parameter!("p", initial: 1.5),
                parameter!("q", initial: -0.25),
            ]),
        )
        .component(1);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let automatic = CpuBackend.prepare(&model);
        let interpreted =
            CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        assert!(matches!(
            automatic.gradient_executor,
            GradientExecutor::Jit(_)
        ));

        let dataset = Dataset::from_batch(
            EventBatch::from_events(
                Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
                [
                    OwnedEvent::new(vec![], vec![0.25]),
                    OwnedEvent::new(vec![], vec![0.75]),
                    OwnedEvent::new(vec![], vec![1.25]),
                ],
            )
            .unwrap(),
        );
        let execution = Execution::local(crate::ExecutionOptions {
            device: crate::Device::Cpu(crate::CpuOptions {
                threads: crate::ThreadPolicy::Fixed(2),
                ..crate::CpuOptions::default()
            }),
            ..crate::ExecutionOptions::default()
        })
        .unwrap();
        let automatic_data = automatic.prepare_dataset(&execution, &dataset).unwrap();
        let interpreted_data = interpreted.prepare_dataset(&execution, &dataset).unwrap();
        let actual = automatic
            .reduce_with_gradient(
                &execution,
                &params,
                &automatic_data,
                ReductionPlan::weighted_real(),
            )
            .unwrap();
        let expected = interpreted
            .reduce_with_gradient(
                &execution,
                &params,
                &interpreted_data,
                ReductionPlan::weighted_real(),
            )
            .unwrap();

        assert!((actual.value() - expected.value()).abs() < 1.0e-12);
        for (actual, expected) in actual.gradient().iter().zip(expected.gradient()) {
            assert!((actual - expected).abs() < 1.0e-12);
        }
    }

    #[test]
    fn batch_cache_reports_missing_event_columns() {
        let expr = event_scalar("missing");
        let model = CompiledModel::from_expr(&expr).unwrap();
        let plan = CpuBackend.prepare(&model);
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [OwnedEvent::new(vec![], vec![0.5])],
        )
        .unwrap();

        assert!(matches!(
            plan.cache_event_batch(&batch),
            Err(RuntimeError::MissingEventColumn(name)) if name == "missing"
        ));
    }

    #[test]
    fn cached_dataset_preserves_transformed_batches_and_weights() {
        let expr = event_scalar("x") * parameter!("scale", initial: 2.0);
        let model = CompiledModel::from_expr(&expr).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let schema = Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], true).unwrap());
        let batch = EventBatch::from_events(
            schema,
            [
                OwnedEvent::weighted(vec![], vec![0.5], 2.0),
                OwnedEvent::weighted(vec![], vec![1.0], 3.0),
            ],
        )
        .unwrap();
        let dataset = Dataset::from_batch(batch).filter(|event| event.scalar(0) > 0.75);
        let cached = plan.cache_dataset(&dataset).unwrap();

        assert_eq!(cached.len(), 1);
        assert_eq!(cached.batches()[0].weights(), &[3.0]);
        assert_eq!(cached.batches()[0].sum_weights(), 3.0);
        assert_eq!(
            plan.evaluate_cached_dataset(&params, &cached).unwrap(),
            vec![Complex64::from(2.0)]
        );
    }

    #[test]
    fn cached_dataset_weighted_reductions_match_dataset_path() {
        let expr = event_scalar("x") * parameter!("scale", initial: 2.0);
        let model = CompiledModel::from_expr(&expr).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let schema = Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], true).unwrap());
        let first = EventBatch::from_events(
            Arc::clone(&schema),
            [
                OwnedEvent::weighted(vec![], vec![1.0], 2.0),
                OwnedEvent::weighted(vec![], vec![2.0], 3.0),
            ],
        )
        .unwrap();
        let second =
            EventBatch::from_events(schema, [OwnedEvent::weighted(vec![], vec![3.0], 4.0)])
                .unwrap();
        let dataset = Dataset::from_batches(vec![first, second]).unwrap();
        let cached = plan.cache_dataset(&dataset).unwrap();

        let expected = dataset.weighted_sum(|event| 2.0 * event.scalar(0)).unwrap();
        assert_eq!(cached.sum_weights(), dataset.sum_weights().unwrap());
        assert_eq!(
            plan.weighted_sum_cached(&params, &cached, |value| value.re)
                .unwrap(),
            expected
        );
        assert_eq!(
            plan.weighted_complex_sum_cached(&params, &cached, |value| value * Complex64::I)
                .unwrap(),
            Complex64::I * expected
        );
        assert_eq!(
            plan.par_weighted_sum_cached(&params, &cached, |value| value.re)
                .unwrap(),
            expected
        );
        assert_eq!(
            plan.par_weighted_complex_sum_cached(&params, &cached, |value| value * Complex64::I)
                .unwrap(),
            Complex64::I * expected
        );
        let serial_gradient = plan
            .try_weighted_real_sum_with_gradient_cached(&params, &cached, |value| {
                Ok::<_, RuntimeError>((value.re.powi(2), 2.0 * value.re))
            })
            .unwrap();
        let parallel_gradient = plan
            .par_try_weighted_real_sum_with_gradient_cached(&params, &cached, |value| {
                Ok::<_, RuntimeError>((value.re.powi(2), 2.0 * value.re))
            })
            .unwrap();
        assert_eq!(serial_gradient, parallel_gradient);
    }

    #[test]
    fn reduction_plans_match_across_storage_and_thread_policies() {
        let expr = event_scalar("x") * parameter!("scale", initial: 2.0) + 1.0;
        let model = CompiledModel::from_expr(&expr).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let schema = Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], true).unwrap());
        let first = EventBatch::from_events(
            Arc::clone(&schema),
            [
                OwnedEvent::weighted(vec![], vec![1.0], 2.0),
                OwnedEvent::weighted(vec![], vec![2.0], 3.0),
            ],
        )
        .unwrap();
        let second =
            EventBatch::from_events(schema, [OwnedEvent::weighted(vec![], vec![3.0], 4.0)])
                .unwrap();
        let resident = Dataset::from_batches(vec![first, second]).unwrap();
        let datasets = [resident.clone(), resident.streaming()];
        let executions = [
            Execution::local(crate::ExecutionOptions {
                device: crate::Device::Cpu(crate::CpuOptions {
                    threads: crate::ThreadPolicy::Serial,
                    ..crate::CpuOptions::default()
                }),
                ..crate::ExecutionOptions::default()
            })
            .unwrap(),
            Execution::local(crate::ExecutionOptions {
                device: crate::Device::Cpu(crate::CpuOptions {
                    threads: crate::ThreadPolicy::Fixed(2),
                    ..crate::CpuOptions::default()
                }),
                ..crate::ExecutionOptions::default()
            })
            .unwrap(),
        ];
        let expected_real = 49.0;
        let expected_log = 2.0 * 3.0_f64.ln() + 3.0 * 5.0_f64.ln() + 4.0 * 7.0_f64.ln();
        let expected_log_gradient = 2.0 / 3.0 + 6.0 / 5.0 + 12.0 / 7.0;

        for execution in &executions {
            for dataset in &datasets {
                let prepared = plan.prepare_dataset(execution, dataset).unwrap();
                assert_eq!(
                    plan.reduce(
                        execution,
                        &params,
                        &prepared,
                        ReductionPlan::weighted_real(),
                    )
                    .unwrap(),
                    expected_real
                );
                assert_eq!(
                    plan.reduce(
                        execution,
                        &params,
                        &prepared,
                        ReductionPlan::weighted_positive_real(),
                    )
                    .unwrap(),
                    expected_real
                );
                let evaluation = plan
                    .reduce_with_gradient(
                        execution,
                        &params,
                        &prepared,
                        ReductionPlan::weighted_log_positive_real(),
                    )
                    .unwrap();
                assert!((evaluation.value() - expected_log).abs() < 1.0e-12);
                assert!((evaluation.gradient()[0] - expected_log_gradient).abs() < 1.0e-12);
            }
        }
    }

    #[test]
    fn positive_reduction_reports_the_invalid_value() {
        let model = CompiledModel::from_expr(&event_scalar("x")).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare(&model);
        let dataset = Dataset::from_batch(
            EventBatch::from_events(
                Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
                [OwnedEvent::new(vec![], vec![-2.0])],
            )
            .unwrap(),
        );
        let execution = Execution::default();
        let prepared = plan.prepare_dataset(&execution, &dataset).unwrap();

        assert!(matches!(
            plan.reduce(
                &execution,
                &params,
                &prepared,
                ReductionPlan::weighted_log_positive_real(),
            ),
            Err(RuntimeError::Reduction(
                laddu_compile::ReductionError::NonPositiveValue {
                    transform: laddu_compile::ReductionTransform::LogPositiveReal,
                    value: -2.0,
                }
            ))
        ));
    }

    #[test]
    fn evaluates_linear_algebra_nodes() {
        let a = matrix([[2.0, 0.0], [0.0, 4.0]]);
        let b = vector([8.0, 12.0]);
        let x = solve(a, b);
        let expr = dot(&x, vector([1.0, 1.0]));
        let model = CompiledModel::from_expr(&expr).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let plan = CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);

        assert_eq!(plan.evaluate(&params).unwrap(), Complex64::from(7.0));
        assert_eq!(plan.constant_factors.len(), 1);
        assert!(plan.constant_factors[0].get().is_some());
    }

    #[cfg(feature = "jit")]
    #[test]
    fn auto_jit_matches_interpreter_for_complex_linear_algebra() {
        let diagonal = laddu_expr::Expr::from(parameter!("diagonal", initial: 4.0));
        let matrix = matrix([
            [complex(2.0, 0.5), complex(0.25, -0.1)],
            [complex(-0.2, 0.3), diagonal],
        ]);
        let rhs = vector([complex(8.0, 1.0), complex(12.0, -0.5)]);
        let solution = solve(matrix, rhs);
        let expression = dot(solution, vector([complex(1.0, -0.2), complex(0.5, 0.3)])).exp();
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let automatic = CpuBackend.prepare(&model);
        let interpreted =
            CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);

        assert!(matches!(
            automatic.scalar_executor,
            Some(ScalarExecutor::Jit(_))
        ));
        assert!(matches!(
            automatic.gradient_executor,
            GradientExecutor::Jit(_)
        ));
        let actual = automatic.evaluate(&params).unwrap();
        let expected = interpreted.evaluate(&params).unwrap();
        assert!(
            (actual - expected).norm() < 1.0e-12,
            "{actual} != {expected}"
        );
        let actual = automatic.evaluate_with_gradient(&params).unwrap();
        let expected = interpreted.evaluate_with_gradient(&params).unwrap();
        assert!((actual.value() - expected.value()).norm() < 1.0e-12);
        for (actual, expected) in actual.gradient().iter().zip(expected.gradient()) {
            assert!((actual - expected).norm() < 1.0e-12);
        }
    }

    #[cfg(feature = "jit")]
    #[test]
    fn auto_jit_gradients_support_parameter_dependent_solve() {
        let x = event_scalar("x");
        let coupling = laddu_expr::Expr::from(parameter!("coupling", initial: 0.2));
        let drive = laddu_expr::Expr::from(parameter!("drive", initial: -0.4));
        let matrix = matrix([
            [x.clone() + 2.0, complex(coupling.clone(), 0.1)],
            [complex(-0.3, coupling), 3.0.into()],
        ]);
        let expression = solve(
            matrix,
            vector([x.clone().sin() + drive, complex(x.cos(), 0.5)]),
        )
        .component(1)
        .norm_sqr();
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let automatic = CpuBackend.prepare(&model);
        let interpreted =
            CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);
        let batch = EventBatch::from_events(
            Arc::new(Schema::new(std::iter::empty::<&str>(), ["x"], false).unwrap()),
            [
                OwnedEvent::new(vec![], vec![0.25]),
                OwnedEvent::new(vec![], vec![0.75]),
                OwnedEvent::new(vec![], vec![1.25]),
            ],
        )
        .unwrap();
        let automatic_cache = automatic.cache_event_batch(&batch).unwrap();
        let interpreted_cache = interpreted.cache_event_batch(&batch).unwrap();

        assert!(matches!(
            automatic.scalar_executor,
            Some(ScalarExecutor::Jit(_))
        ));
        assert!(matches!(
            automatic.gradient_executor,
            GradientExecutor::Jit(_)
        ));
        let actual = automatic.evaluate_cache(&params, &automatic_cache).unwrap();
        let expected = interpreted
            .evaluate_cache(&params, &interpreted_cache)
            .unwrap();
        for (actual, expected) in actual.iter().zip(expected) {
            assert!(
                (*actual - expected).norm() < 1.0e-12,
                "{actual} != {expected}"
            );
        }
        let actual = automatic
            .evaluate_cache_with_gradient(&params, &automatic_cache)
            .unwrap();
        let expected = interpreted
            .evaluate_cache_with_gradient(&params, &interpreted_cache)
            .unwrap();
        for (actual, expected) in actual.iter().zip(&expected) {
            assert!((actual.value() - expected.value()).norm() < 1.0e-12);
            for (actual, expected) in actual.gradient().iter().zip(expected.gradient()) {
                assert!((actual - expected).norm() < 1.0e-12);
            }
        }
        let ir_interpreter = gradient_interpreter::GradientInterpreter::new(
            automatic.scalar_kernel.as_ref().unwrap(),
            model.params().free_params(),
        )
        .unwrap();
        for (row, expected) in expected.iter().enumerate() {
            let actual = ir_interpreter
                .evaluate(&params, Some((&automatic_cache, row)))
                .unwrap()
                .1;
            for (actual, expected) in actual.iter().zip(expected.gradient()) {
                assert!((actual - expected).norm() < 1.0e-12);
            }
        }
        for (row, actual) in actual.iter().enumerate() {
            for parameter in 0..params.layout().n_free() {
                let h = 1.0e-6;
                let id = params.layout().free_params()[parameter];
                let free_id = params.layout().free_id(id).unwrap().unwrap();
                let value = params.get(id).unwrap();
                let mut plus = params.clone();
                let mut minus = params.clone();
                plus.set_free(free_id, value + h).unwrap();
                minus.set_free(free_id, value - h).unwrap();
                let expected = (automatic.evaluate_cache(&plus, &automatic_cache).unwrap()[row]
                    - automatic.evaluate_cache(&minus, &automatic_cache).unwrap()[row])
                    / (2.0 * h);
                assert!((actual.gradient()[parameter] - expected).norm() < 1.0e-8);
            }
        }
    }

    #[cfg(feature = "jit")]
    #[test]
    fn jit_and_interpreter_reject_singular_parameter_dependent_solve() {
        let scale = laddu_expr::Expr::from(parameter!("scale", initial: 1.0));
        let expression = solve(
            matrix([[scale.clone(), 2.0.into()], [scale * 2.0, 4.0.into()]]),
            vector([1.0, 2.0]),
        )
        .component(0);
        let model = CompiledModel::from_expr(&expression).unwrap();
        let params = Arc::new(model.params().clone()).default_values();
        let automatic = CpuBackend.prepare(&model);
        let interpreted =
            CpuBackend.prepare_with_execution_mode(&model, CpuExecutionMode::Interpreter);

        assert!(matches!(
            automatic.gradient_executor,
            GradientExecutor::Jit(_)
        ));
        assert!(automatic.evaluate_with_gradient(&params).is_err());
        assert!(interpreted.evaluate_with_gradient(&params).is_err());
    }

    #[test]
    fn optimized_and_unoptimized_plans_evaluate_the_same_expression() {
        let solved = solve(matrix([[2.0, 0.0], [0.0, 4.0]]), vector([8.0, 12.0]));
        let complex_offset = complex(
            parameter!("offset_re", initial: 1.5),
            parameter!("offset_im", initial: -0.5),
        );
        let polar_product = polar_complex(
            parameter!("mag1", initial: 2.0),
            parameter!("phase1", initial: 0.25),
        ) * polar_complex(
            parameter!("mag2", initial: 3.0),
            parameter!("phase2", initial: -0.5),
        );
        let expr = ((laddu_expr::event_scalar("mass") + 0.0) * 1.0
            + dot(solved, vector([1.0, 1.0]))
            + complex_offset.conj().real()
            + polar_product.real()
            + parameter!("unused", initial: 3.0) * 0.0)
            .norm_sqr();
        let no_optimization = CompileOptions::without_optimizations();
        let optimized = CompiledModel::from_expr(&expr).unwrap();
        let unoptimized = CompiledModel::from_expr_with_options(&expr, &no_optimization).unwrap();
        let optimized_params = Arc::new(optimized.params().clone()).default_values();
        let unoptimized_params = Arc::new(unoptimized.params().clone()).default_values();
        let event = HashMap::from([("mass".to_owned(), 2.0)]);

        let optimized = CpuBackend
            .prepare(&optimized)
            .evaluate_with_event_and_gradient(&optimized_params, &event)
            .unwrap();
        let unoptimized = CpuBackend
            .prepare(&unoptimized)
            .evaluate_with_event_and_gradient(&unoptimized_params, &event)
            .unwrap();
        assert_eq!(optimized.value(), unoptimized.value());
        for (optimized, unoptimized) in optimized.gradient().iter().zip(unoptimized.gradient()) {
            assert!((optimized - unoptimized).norm() < 1.0e-12);
        }
    }
}