trueno 0.18.0

//! Fused single-query attention for autoregressive decode.
//!
//! Computes: output = softmax(Q @ K^T / sqrt(head_dim)) @ V
//! in a single pass over the KV cache without materializing the
//! (1, seq_len) attention scores to memory.
//!
//! Uses online softmax (Milakov & Gimelshein, FlashAttention [64] Algorithm 1):
//! For each block of K/V rows:
//!   1. Compute partial scores = Q · K_block^T / sqrt(D)
//!   2. Update running max and running sum
//!   3. Rescale previous output accumulator
//!   4. Accumulate exp(scores - max) @ V_block into output
//!
//! Contract: contracts/cgp/cgp-flash-attn-cpu-v1.yaml
//! FALSIFY: FALSIFY-FLASH-ATTN-001 through 004

/// Fused decode attention: output = softmax(Q @ K^T / sqrt(D)) @ V.
///
/// No heap allocation. Scores stay in a stack buffer (block_size elements).
/// AVX2 GEMV for dot products, scalar exp for transcendentals.
///
/// # Arguments
/// - `q`: query vector, length `head_dim`
/// - `k_cache`: key cache, row-major (seq_len × head_dim)
/// - `v_cache`: value cache, row-major (seq_len × head_dim)
/// - `head_dim`: dimension D
/// - `seq_len`: number of cached K/V rows
/// - `output`: result buffer, length `head_dim` (will be overwritten)
pub fn fused_attention_decode(
    q: &[f32],
    k_cache: &[f32],
    v_cache: &[f32],
    head_dim: usize,
    seq_len: usize,
    output: &mut [f32],
) {
    contract_pre_attention!();
    assert_eq!(q.len(), head_dim);
    assert_eq!(k_cache.len(), seq_len * head_dim);
    assert_eq!(v_cache.len(), seq_len * head_dim);
    assert_eq!(output.len(), head_dim);

    if seq_len == 0 {
        output.fill(0.0);
        contract_post_attention!(output);
        return;
    }

    #[cfg(target_arch = "x86_64")]
    if std::arch::is_x86_feature_detected!("avx2") && std::arch::is_x86_feature_detected!("fma") {
        // SAFETY: AVX2+FMA verified. Slice lengths checked by asserts above.
        unsafe {
            fused_attention_decode_avx2(q, k_cache, v_cache, head_dim, seq_len, output);
        }
        contract_post_attention!(output);
        return;
    }

    fused_attention_decode_scalar(q, k_cache, v_cache, head_dim, seq_len, output);
    contract_post_attention!(output);
}

/// Scalar fallback for non-x86 or non-AVX2 platforms.
fn fused_attention_decode_scalar(
    q: &[f32],
    k_cache: &[f32],
    v_cache: &[f32],
    head_dim: usize,
    seq_len: usize,
    output: &mut [f32],
) {
    let scale = 1.0 / (head_dim as f32).sqrt();
    let mut running_max = f32::NEG_INFINITY;
    let mut running_sum = 0.0f32;
    output.fill(0.0);

    for s in 0..seq_len {
        let k_row = &k_cache[s * head_dim..(s + 1) * head_dim];
        let mut dot = 0.0f32;
        for d in 0..head_dim {
            dot += q[d] * k_row[d];
        }
        let score = dot * scale;

        let new_max = running_max.max(score);
        if running_max != f32::NEG_INFINITY {
            let correction = (running_max - new_max).exp();
            running_sum *= correction;
            for val in output.iter_mut() {
                *val *= correction;
            }
        }

        let w = (score - new_max).exp();
        running_sum += w;

        let v_row = &v_cache[s * head_dim..(s + 1) * head_dim];
        for d in 0..head_dim {
            output[d] += w * v_row[d];
        }
        running_max = new_max;
    }

    if running_sum > 0.0 {
        let inv_sum = 1.0 / running_sum;
        for val in output.iter_mut() {
            *val *= inv_sum;
        }
    }
}

/// AVX2 fused attention: SIMD dot product, SIMD V accumulation, SIMD rescale.
///
/// Three SIMD-accelerated hot paths:
/// 1. Q·K dot product: 4 ymm accumulators × 8 f32 = 32-wide, hadd reduction
/// 2. Output rescale (correction *= exp(...)): broadcast + vfmadd
/// 3. w * V accumulation: broadcast weight, vfmadd per 8 elements
///
/// Uses AVX2 (not AVX-512) because attention is bandwidth-bound [60][61]:
/// Zen 4 throttles clock during 512-bit ops, and GEMV-class workloads
/// cannot compensate with wider SIMD.
#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2", enable = "fma")]
unsafe fn fused_attention_decode_avx2(
    q: &[f32],
    k_cache: &[f32],
    v_cache: &[f32],
    head_dim: usize,
    seq_len: usize,
    output: &mut [f32],
) {
    unsafe {
        use std::arch::x86_64::*;

        let scale = 1.0 / (head_dim as f32).sqrt();
        let d8 = head_dim / 8 * 8;

        let mut running_max = f32::NEG_INFINITY;
        let mut running_sum = 0.0f32;
        output.fill(0.0);

        // Process one K/V row per iteration (online softmax, no blocking needed
        // since we SIMD the inner dim, not the seq_len dim).
        for s in 0..seq_len {
            let k_ptr = k_cache.as_ptr().add(s * head_dim);
            let q_ptr = q.as_ptr();

            // SIMD dot product: Q · K[s] with 4 ymm accumulators
            let mut dot0 = _mm256_setzero_ps();
            let mut dot1 = _mm256_setzero_ps();
            let mut dot2 = _mm256_setzero_ps();
            let mut dot3 = _mm256_setzero_ps();

            let mut j = 0;
            let d32 = head_dim / 32 * 32;
            while j < d32 {
                dot0 = _mm256_fmadd_ps(
                    _mm256_loadu_ps(q_ptr.add(j)),
                    _mm256_loadu_ps(k_ptr.add(j)),
                    dot0,
                );
                dot1 = _mm256_fmadd_ps(
                    _mm256_loadu_ps(q_ptr.add(j + 8)),
                    _mm256_loadu_ps(k_ptr.add(j + 8)),
                    dot1,
                );
                dot2 = _mm256_fmadd_ps(
                    _mm256_loadu_ps(q_ptr.add(j + 16)),
                    _mm256_loadu_ps(k_ptr.add(j + 16)),
                    dot2,
                );
                dot3 = _mm256_fmadd_ps(
                    _mm256_loadu_ps(q_ptr.add(j + 24)),
                    _mm256_loadu_ps(k_ptr.add(j + 24)),
                    dot3,
                );
                j += 32;
            }
            while j < d8 {
                dot0 = _mm256_fmadd_ps(
                    _mm256_loadu_ps(q_ptr.add(j)),
                    _mm256_loadu_ps(k_ptr.add(j)),
                    dot0,
                );
                j += 8;
            }

            // Horizontal sum: dot0+dot1+dot2+dot3 → scalar
            dot0 = _mm256_add_ps(_mm256_add_ps(dot0, dot1), _mm256_add_ps(dot2, dot3));
            // 256-bit → 128-bit: add high and low halves
            let hi = _mm256_extractf128_ps(dot0, 1);
            let lo = _mm256_castps256_ps128(dot0);
            let sum128 = _mm_add_ps(lo, hi);
            // 128-bit → scalar: hadd twice
            let sum64 = _mm_hadd_ps(sum128, sum128);
            let sum32 = _mm_hadd_ps(sum64, sum64);
            let mut dot_scalar = _mm_cvtss_f32(sum32);

            // Scalar remainder
            while j < head_dim {
                dot_scalar += *q.get_unchecked(j) * *k_cache.get_unchecked(s * head_dim + j);
                j += 1;
            }

            let score = dot_scalar * scale;

            // Online softmax update
            let new_max = running_max.max(score);
            if running_max != f32::NEG_INFINITY {
                let correction = (running_max - new_max).exp();
                running_sum *= correction;

                // SIMD rescale output: output[d] *= correction
                let corr_v = _mm256_set1_ps(correction);
                let out_ptr = output.as_mut_ptr();
                let mut d = 0;
                while d < d8 {
                    let ov = _mm256_loadu_ps(out_ptr.add(d));
                    _mm256_storeu_ps(out_ptr.add(d), _mm256_mul_ps(ov, corr_v));
                    d += 8;
                }
                while d < head_dim {
                    *output.get_unchecked_mut(d) *= correction;
                    d += 1;
                }
            }

            let w = (score - new_max).exp();
            running_sum += w;

            // SIMD V accumulation: output[d] += w * V[s][d]
            let w_v = _mm256_set1_ps(w);
            let v_ptr = v_cache.as_ptr().add(s * head_dim);
            let out_ptr = output.as_mut_ptr();
            let mut d = 0;
            while d < d8 {
                let ov = _mm256_loadu_ps(out_ptr.add(d));
                let vv = _mm256_loadu_ps(v_ptr.add(d));
                _mm256_storeu_ps(out_ptr.add(d), _mm256_fmadd_ps(w_v, vv, ov));
                d += 8;
            }
            while d < head_dim {
                *output.get_unchecked_mut(d) += w * *v_cache.get_unchecked(s * head_dim + d);
                d += 1;
            }

            running_max = new_max;
        }

        // Final normalization: output /= running_sum
        if running_sum > 0.0 {
            let inv_v = _mm256_set1_ps(1.0 / running_sum);
            let out_ptr = output.as_mut_ptr();
            let mut d = 0;
            while d < d8 {
                let ov = _mm256_loadu_ps(out_ptr.add(d));
                _mm256_storeu_ps(out_ptr.add(d), _mm256_mul_ps(ov, inv_v));
                d += 8;
            }
            while d < head_dim {
                *output.get_unchecked_mut(d) /= running_sum;
                d += 1;
            }
        }
    } // unsafe
}

/// Unfused reference: separate Q@K^T, softmax, scores@V for validation.
#[cfg(test)]
fn unfused_attention_decode_reference(
    q: &[f32],
    k_cache: &[f32],
    v_cache: &[f32],
    head_dim: usize,
    seq_len: usize,
    output: &mut [f32],
) {
    let scale = 1.0 / (head_dim as f32).sqrt();

    // Q @ K^T → scores
    let mut scores = vec![0.0f32; seq_len];
    for s in 0..seq_len {
        let k_row = &k_cache[s * head_dim..(s + 1) * head_dim];
        let mut dot = 0.0f32;
        for d in 0..head_dim {
            dot += q[d] * k_row[d];
        }
        scores[s] = dot * scale;
    }

    // softmax(scores)
    let max_score = scores.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
    let mut sum = 0.0f32;
    for s in scores.iter_mut() {
        *s = (*s - max_score).exp();
        sum += *s;
    }
    for s in scores.iter_mut() {
        *s /= sum;
    }

    // scores @ V → output
    output.fill(0.0);
    for s in 0..seq_len {
        let v_row = &v_cache[s * head_dim..(s + 1) * head_dim];
        let w = scores[s];
        for d in 0..head_dim {
            output[d] += w * v_row[d];
        }
    }
}

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

    fn gen_data(head_dim: usize, seq_len: usize) -> (Vec<f32>, Vec<f32>, Vec<f32>) {
        let q: Vec<f32> = (0..head_dim).map(|i| ((i * 7 + 3) % 100) as f32 / 100.0 - 0.5).collect();
        let k: Vec<f32> =
            (0..seq_len * head_dim).map(|i| ((i * 13 + 7) % 100) as f32 / 100.0 - 0.5).collect();
        let v: Vec<f32> =
            (0..seq_len * head_dim).map(|i| ((i * 11 + 5) % 100) as f32 / 100.0 - 0.5).collect();
        (q, k, v)
    }

    /// FALSIFY-FLASH-ATTN-001: Fused matches unfused reference.
    #[test]
    fn test_fused_matches_reference() {
        for &(d, s) in &[(128, 64), (128, 512), (128, 1024), (64, 256)] {
            let (q, k, v) = gen_data(d, s);
            let mut out_fused = vec![0.0f32; d];
            let mut out_ref = vec![0.0f32; d];

            fused_attention_decode(&q, &k, &v, d, s, &mut out_fused);
            unfused_attention_decode_reference(&q, &k, &v, d, s, &mut out_ref);

            let max_diff = out_fused
                .iter()
                .zip(out_ref.iter())
                .map(|(a, b)| (a - b).abs())
                .fold(0.0f32, f32::max);

            assert!(max_diff < 1e-4, "FALSIFY-FLASH-ATTN-001: d={d} s={s} max_diff={max_diff}");
        }
    }

    /// FALSIFY-FLASH-ATTN-004: softmax weights sum to 1.0.
    #[test]
    fn test_softmax_sums_to_one() {
        let d = 128;
        let s = 512;
        let (q, k, v) = gen_data(d, s);
        let scale = 1.0 / (d as f32).sqrt();

        // Compute scores via fused path's logic
        let mut running_max = f32::NEG_INFINITY;
        let mut running_sum = 0.0f32;

        for i in 0..s {
            let k_row = &k[i * d..(i + 1) * d];
            let dot: f32 = q.iter().zip(k_row.iter()).map(|(a, b)| a * b).sum();
            let score = dot * scale;
            let new_max = running_max.max(score);
            if running_max != f32::NEG_INFINITY {
                running_sum *= (running_max - new_max).exp();
            }
            running_sum += (score - new_max).exp();
            running_max = new_max;
        }

        // Sum should be positive and normalization should yield ~1.0
        assert!(running_sum > 0.0);

        // Verify via unfused reference
        let mut out = vec![0.0f32; d];
        fused_attention_decode(&q, &k, &v, d, s, &mut out);
        // Output should be bounded (not NaN or Inf)
        assert!(out.iter().all(|x| x.is_finite()), "FALSIFY-FLASH-ATTN-004: NaN/Inf in output");
    }

    /// FALSIFY-FLASH-ATTN-001b: Edge case — seq_len=1.
    #[test]
    fn test_fused_seq_len_one() {
        let d = 128;
        let (q, k, v) = gen_data(d, 1);
        let mut out_fused = vec![0.0f32; d];
        let mut out_ref = vec![0.0f32; d];

        fused_attention_decode(&q, &k, &v, d, 1, &mut out_fused);
        unfused_attention_decode_reference(&q, &k, &v, d, 1, &mut out_ref);

        // With seq_len=1, softmax weight is 1.0, output = V[0]
        let max_diff =
            out_fused.iter().zip(out_ref.iter()).map(|(a, b)| (a - b).abs()).fold(0.0f32, f32::max);
        assert!(max_diff < 1e-6, "seq_len=1: max_diff={max_diff}");
    }

    /// FALSIFY-FLASH-ATTN-001c: Edge case — seq_len=0.
    #[test]
    fn test_fused_seq_len_zero() {
        let d = 128;
        let q = vec![1.0f32; d];
        let mut out = vec![99.0f32; d];
        fused_attention_decode(&q, &[], &[], d, 0, &mut out);
        assert!(out.iter().all(|&x| x == 0.0), "seq_len=0 should zero output");
    }

    /// Benchmark helper: measure fused vs unfused time.
    #[test]
    fn test_fused_perf_smoke() {
        let d = 128;
        let s = 512;
        let (q, k, v) = gen_data(d, s);
        let mut out = vec![0.0f32; d];

        // Just verify it runs without panic at benchmark-representative size
        fused_attention_decode(&q, &k, &v, d, s, &mut out);
        assert!(out.iter().any(|&x| x != 0.0), "Output should be non-zero");
    }
}