needle-core 0.3.1

//! Needle 3 probe heads.
//!
//! The published `needle3.cact` exports one: confidence. Its `heads.manifest`
//! lists a single code, `2`.
//!
//! That is a property of the released weights, not of the architecture.
//! Upstream defines three heads — embedding (code 1), confidence (2) and
//! router (3) — and `export.py` writes whichever are present in the
//! checkpoint (`present = [h for h in HEADS if h.key in params]`). The
//! released Needle 3 checkpoint carries no `embedding_head`, so none is in the
//! container and `retrieve_tools` has no v3 equivalent *from these weights* —
//! the same situation as the published v1 checkpoint.
//!
//! A container that did carry code 1 would load here unchanged: the embedding
//! head is a `ProbeHead` like the others and exports the same six tensors
//! (only the router adds a seventh), so the canon walk already handles it.
//! Producing the embedding would then be this head's `forward` followed by an
//! L2 normalisation, with the output width read off the projection.
//!
//! A probe head reads the per-layer cells rather than the final hidden state,
//! and pools them twice: once over positions, per (layer, probe), and once
//! over (layer, probe), per query. Both poolings are softmaxes, so the head
//! sees the whole sequence rather than just the last token.

extern crate alloc;
use alloc::vec;
use alloc::vec::Vec;

use crate::kernels::rms_unit;
use crate::math::{exp, sqrt};

/// Head codes in `heads.manifest`, in upstream's canonical order.
pub const HEAD_EMBEDDING: u8 = 1;
pub const HEAD_CONFIDENCE: u8 = 2;
pub const HEAD_ROUTER: u8 = 3;

/// One probe head's weights, already dequantised.
///
/// Small enough that packing buys nothing: on the shipped model this is
/// 84x768 + 4x768 + a few vectors.
pub struct ProbeHead {
    /// `(cells * probes, d_model)`.
    pub probes: Vec<f32>,
    /// `(cells, probes)`.
    pub gain: Vec<f32>,
    /// `(queries, d_model)`.
    pub query: Vec<f32>,
    /// `(queries, cells, probes)`.
    pub row_bias: Vec<f32>,
    /// `(out_dim, queries * d_model)`.
    pub proj: Vec<f32>,
    /// `(out_dim)`.
    pub bias: Vec<f32>,
    pub cells: usize,
    pub probes_per_cell: usize,
    pub queries: usize,
    pub out_dim: usize,
}

impl ProbeHead {
    /// Run the head over `cells`, shaped `(seq, self.cells, d_model)`.
    ///
    /// Returns `out_dim` logits. Mirrors upstream `probe_pool` followed by
    /// `HeadProjection`.
    pub fn forward(&self, cells: &[f32], seq: usize, d: usize) -> Vec<f32> {
        debug_assert_eq!(cells.len(), seq * self.cells * d);
        let (l1, k, q) = (self.cells, self.probes_per_cell, self.queries);
        let scale = 1.0 / sqrt(d as f32);

        // r[l][k] — each (layer, probe) pair's softmax-weighted summary of the
        // sequence.
        let mut r = vec![0.0f32; l1 * k * d];
        let mut scores = vec![0.0f32; seq];
        for l in 0..l1 {
            for kk in 0..k {
                let pr = &self.probes[(l * k + kk) * d..(l * k + kk + 1) * d];
                let mut max = f32::NEG_INFINITY;
                for (t, sc) in scores.iter_mut().enumerate() {
                    let cell = &cells[(t * l1 + l) * d..(t * l1 + l + 1) * d];
                    let mut acc = 0.0f32;
                    for (a, b) in cell.iter().zip(pr) {
                        acc += a * b;
                    }
                    *sc = acc * scale;
                    if *sc > max {
                        max = *sc;
                    }
                }
                let mut sum = 0.0f32;
                for sc in scores.iter_mut() {
                    *sc = exp(*sc - max);
                    sum += *sc;
                }
                let inv = 1.0 / sum;

                let dst = &mut r[(l * k + kk) * d..(l * k + kk + 1) * d];
                dst.fill(0.0);
                for (t, &sc) in scores.iter().enumerate() {
                    let w = sc * inv;
                    let cell = &cells[(t * l1 + l) * d..(t * l1 + l + 1) * d];
                    for (o, &c) in dst.iter_mut().zip(cell) {
                        *o += w * c;
                    }
                }
                // Normalise, then scale by this pair's learned gain.
                rms_unit(dst);
                let g = self.gain[l * k + kk];
                for o in dst.iter_mut() {
                    *o *= g;
                }
            }
        }

        self.pool_and_project(&r, l1, k, q, d)
    }

    /// The second softmax — over `(layer, probe)` pairs, per query — and the
    /// final projection. Shared by the two-pass and streaming paths so they
    /// cannot drift apart.
    fn pool_and_project(&self, r: &[f32], l1: usize, k: usize, q: usize, d: usize) -> Vec<f32> {
        let scale = 1.0 / sqrt(d as f32);
        // For each query, a softmax over all (layer, probe) pairs.
        let m = l1 * k;
        let mut pooled = vec![0.0f32; q * d];
        let mut u = vec![0.0f32; m];
        for qi in 0..q {
            let qv = &self.query[qi * d..(qi + 1) * d];
            let mut max = f32::NEG_INFINITY;
            for (idx, uu) in u.iter_mut().enumerate() {
                let rv = &r[idx * d..(idx + 1) * d];
                let mut acc = 0.0f32;
                for (a, b) in rv.iter().zip(qv) {
                    acc += a * b;
                }
                *uu = acc * scale + self.row_bias[qi * m + idx];
                if *uu > max {
                    max = *uu;
                }
            }
            let mut sum = 0.0f32;
            for uu in u.iter_mut() {
                *uu = exp(*uu - max);
                sum += *uu;
            }
            let inv = 1.0 / sum;

            let dst = &mut pooled[qi * d..(qi + 1) * d];
            dst.fill(0.0);
            for (idx, &uu) in u.iter().enumerate() {
                let w = uu * inv;
                let rv = &r[idx * d..(idx + 1) * d];
                for (o, &v) in dst.iter_mut().zip(rv) {
                    *o += w * v;
                }
            }
        }

        // Final projection over the flattened (queries * d_model) vector.
        let mut out = vec![0.0f32; self.out_dim];
        for (o, dst) in out.iter_mut().enumerate() {
            let row = &self.proj[o * q * d..(o + 1) * q * d];
            let mut acc = 0.0f32;
            for (a, b) in row.iter().zip(&pooled) {
                acc += a * b;
            }
            *dst = acc + self.bias.get(o).copied().unwrap_or(0.0);
        }
        out
    }
}

/// Pools a probe head's first softmax **as positions arrive**, so the cells
/// are never all held at once.
///
/// Materialising them costs `seq * cells * d_model` floats — 504 MB at full
/// context on the shipped model, which is not survivable in a browser tab.
/// This holds `cells * probes * d_model` regardless of sequence length: 252 KB.
///
/// The trick is the online softmax. `r = Σ_t softmax(s)_t · cell_t` is computed
/// by carrying a running maximum, denominator and numerator, and rescaling both
/// whenever a larger score appears — algebraically identical to the two-pass
/// form, which [`ProbeHead::forward`] still does and the tests compare against.
pub struct ProbePool<'a> {
    head: &'a ProbeHead,
    d: usize,
    /// Running max per (cell, probe).
    max: Vec<f32>,
    /// Running denominator per (cell, probe).
    den: Vec<f32>,
    /// Running numerator, `(cells * probes, d_model)`.
    num: Vec<f32>,
}

impl<'a> ProbePool<'a> {
    pub fn new(head: &'a ProbeHead, d_model: usize) -> Self {
        let m = head.cells * head.probes_per_cell;
        Self {
            head,
            d: d_model,
            max: vec![f32::NEG_INFINITY; m],
            den: vec![0.0; m],
            num: vec![0.0; m * d_model],
        }
    }

    /// Feed one position's cell for one cell index (0 is the input embedding).
    ///
    /// Order does not matter — the pooling is a sum over positions — so a
    /// caller may stream all positions for one cell before moving to the next,
    /// which is how the forward pass is laid out.
    pub fn observe(&mut self, cell_index: usize, cell: &[f32]) {
        debug_assert_eq!(cell.len(), self.d);
        let (k, d) = (self.head.probes_per_cell, self.d);
        let scale = 1.0 / sqrt(d as f32);
        for kk in 0..k {
            let idx = cell_index * k + kk;
            let pr = &self.head.probes[idx * d..(idx + 1) * d];
            let mut dot = 0.0f32;
            for (a, b) in cell.iter().zip(pr) {
                dot += a * b;
            }
            let s = dot * scale;

            let m0 = self.max[idx];
            let m1 = if s > m0 { s } else { m0 };
            // Rescale what is already accumulated onto the new maximum.
            let shift = if m0.is_finite() { exp(m0 - m1) } else { 0.0 };
            let w = exp(s - m1);
            self.den[idx] = self.den[idx] * shift + w;
            let dst = &mut self.num[idx * d..(idx + 1) * d];
            for (o, &c) in dst.iter_mut().zip(cell) {
                *o = *o * shift + w * c;
            }
            self.max[idx] = m1;
        }
    }

    /// Finish the pooling and run the head's second softmax and projection.
    pub fn finish(self) -> Vec<f32> {
        let (l1, k, q, d) = (
            self.head.cells,
            self.head.probes_per_cell,
            self.head.queries,
            self.d,
        );
        let mut r = self.num;
        for idx in 0..l1 * k {
            let inv = 1.0 / self.den[idx];
            let dst = &mut r[idx * d..(idx + 1) * d];
            for o in dst.iter_mut() {
                *o *= inv;
            }
            rms_unit(dst);
            let g = self.head.gain[idx];
            for o in dst.iter_mut() {
                *o *= g;
            }
        }
        self.head.pool_and_project(&r, l1, k, q, d)
    }
}

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

    /// A head with one cell, one probe and one query, so the pooling collapses
    /// to something checkable by hand.
    fn trivial(d: usize) -> ProbeHead {
        ProbeHead {
            probes: vec![0.0; d],
            gain: vec![1.0],
            query: vec![0.0; d],
            row_bias: vec![0.0],
            proj: vec![1.0; d],
            bias: vec![0.5],
            cells: 1,
            probes_per_cell: 1,
            queries: 1,
            out_dim: 1,
        }
    }

    #[test]
    fn a_zero_probe_averages_the_sequence() {
        // With a zero probe every position scores 0, so the softmax is uniform
        // and r is the mean cell — then rms_unit normalises it.
        let d = 4;
        let h = trivial(d);
        let cells = vec![
            1.0f32, 0.0, 0.0, 0.0, // t=0
            3.0, 0.0, 0.0, 0.0, // t=1
        ];
        let out = h.forward(&cells, 2, d);
        // mean = [2,0,0,0]; rms over 4 dims = sqrt(4/4) = 1 → unit = [2,0,0,0]/1
        // proj is all ones, so the logit is the sum plus the bias.
        assert!(
            (out[0] - (2.0 / (4.0f32 / 4.0).sqrt() + 0.5)).abs() < 1e-5,
            "{out:?}"
        );
    }

    #[test]
    fn the_gain_scales_the_summary() {
        let d = 4;
        let mut h = trivial(d);
        let cells = vec![2.0f32, 0.0, 0.0, 0.0];
        let base = h.forward(&cells, 1, d)[0] - 0.5;
        h.gain[0] = 3.0;
        let scaled = h.forward(&cells, 1, d)[0] - 0.5;
        assert!((scaled - 3.0 * base).abs() < 1e-4, "{scaled} vs 3 * {base}");
    }
}