// Fused Q4_K Matmul Kernel - W4A16 (F16 activations, F16 accum, F16 output)
//
// Batched prefill variant for the Gemma 4 31B experimental all-f16 lane.
// Unlike fused_matmul_q4_batched_f16a.wgsl, this kernel does not widen the
// reduction to f32. It keeps the same Q4_K block format and f16 output ABI, but
// distributes each output dot product across THREADS_PER_COL lanes at Q4_K
// subblock granularity so no single invocation serially accumulates more than
// 32 products into one f16 partial on Gemma 4 31B prefill shapes.

enable f16;
enable subgroups;

const QK_K: u32 = 256u;
const SUBBLOCK_SIZE: u32 = 32u;

struct Uniforms {
    M: u32,
    N: u32,
    K: u32,
    alpha: f32,
    num_blocks_per_row: u32,
    _pad0: u32,
    _pad1: u32,
    _pad2: u32,
}

struct Q4KBlock {
    d_dmin: u32,
    scales: array<u32, 3>,
    qs: array<u32, 32>,
}

@group(0) @binding(0) var<uniform> u: Uniforms;
@group(0) @binding(1) var<storage, read> A: array<f16>;
@group(0) @binding(2) var<storage, read> B_q4k: array<Q4KBlock>;
@group(0) @binding(4) var<storage, read_write> C_f16: array<f16>;

fn unpack_f16_lo(packed: u32) -> f16 {
    return f16(unpack2x16float(packed).x);
}

fn unpack_f16_hi(packed: u32) -> f16 {
    return f16(unpack2x16float(packed).y);
}

fn get_scale_byte(scales: array<u32, 3>, byte_idx: u32) -> u32 {
    let word_idx = byte_idx / 4u;
    let byte_in_word = byte_idx % 4u;
    return (scales[word_idx] >> (byte_in_word * 8u)) & 0xFFu;
}

fn get_scale_min_k4(scales: array<u32, 3>, j: u32) -> vec2<u32> {
    var sc: u32;
    var mn: u32;

    if (j < 4u) {
        sc = get_scale_byte(scales, j) & 63u;
        mn = get_scale_byte(scales, j + 4u) & 63u;
    } else {
        let q_j = get_scale_byte(scales, j + 4u);
        let q_lo = get_scale_byte(scales, j - 4u);
        let q_hi = get_scale_byte(scales, j);
        sc = (q_j & 0xFu) | ((q_lo >> 6u) << 4u);
        mn = (q_j >> 4u) | ((q_hi >> 6u) << 4u);
    }
    return vec2<u32>(sc, mn);
}

fn get_q4(qs: array<u32, 32>, idx: u32) -> u32 {
    let chunk = idx / 64u;
    let pos_in_chunk = idx % 64u;
    let use_upper = pos_in_chunk >= 32u;
    let byte_in_range = select(pos_in_chunk, pos_in_chunk - 32u, use_upper);
    let byte_idx = chunk * 32u + byte_in_range;

    let word_idx = byte_idx / 4u;
    let byte_in_word = byte_idx % 4u;
    let byte_val = (qs[word_idx] >> (byte_in_word * 8u)) & 0xFFu;

    if (use_upper) {
        return (byte_val >> 4u) & 0xFu;
    }
    return byte_val & 0xFu;
}

const MAX_TILE_M: u32 = 4u;
const MAX_THREADS_PER_COL: u32 = 256u;
const MAX_SUBGROUPS_PER_ROW: u32 = 64u;

override TILE_M: u32 = 4u;
override THREADS_PER_COL: u32 = 256u;

var<workgroup> batched_wg_sums: array<f16, MAX_TILE_M * MAX_SUBGROUPS_PER_ROW>;

@compute @workgroup_size(THREADS_PER_COL, TILE_M, 1)
fn main_batched_f16acc_f16a(
    @builtin(local_invocation_id) lid: vec3<u32>,
    @builtin(workgroup_id) wg_id: vec3<u32>,
    @builtin(subgroup_invocation_id) sg_id: u32,
    @builtin(subgroup_size) sg_size: u32
) {
    if (TILE_M > MAX_TILE_M || THREADS_PER_COL > MAX_THREADS_PER_COL) {
        return;
    }

    let local_id = lid.x;
    let row = wg_id.y * TILE_M + lid.y;
    let col = wg_id.x;
    let is_valid = row < u.M && col < u.N;

    var partial_sum: f16 = f16(0.0);

    if (is_valid) {
        let num_blocks = u.num_blocks_per_row;
        let total_subblocks = num_blocks * 8u;
        for (var partial: u32 = local_id; partial < total_subblocks; partial = partial + THREADS_PER_COL) {
            let b = partial / 8u;
            let sb = partial % 8u;
            let block = B_q4k[col * num_blocks + b];
            let d = unpack_f16_lo(block.d_dmin);
            let dmin = unpack_f16_hi(block.d_dmin);
            let k_base = b * QK_K;

            let sm = get_scale_min_k4(block.scales, sb);
            let scale = d * f16(sm.x);
            let min_val = dmin * f16(sm.y);

            for (var i: u32 = 0u; i < SUBBLOCK_SIZE; i = i + 1u) {
                let elem = sb * SUBBLOCK_SIZE + i;
                let k = k_base + elem;
                if (k < u.K) {
                    let a_val = A[row * u.K + k];
                    let q = get_q4(block.qs, elem);
                    let w = scale * f16(q) - min_val;
                    partial_sum = partial_sum + a_val * w;
                }
            }
        }
    }

    let sg_sum = subgroupAdd(partial_sum);
    let num_subgroups = (THREADS_PER_COL + sg_size - 1u) / sg_size;

    if (sg_id == 0u && local_id < THREADS_PER_COL) {
        let sg_idx = local_id / sg_size;
        batched_wg_sums[lid.y * MAX_SUBGROUPS_PER_ROW + sg_idx] = sg_sum;
    }

    workgroupBarrier();

    if (local_id == 0u && is_valid) {
        var final_sum: f16 = f16(0.0);
        for (var i: u32 = 0u; i < num_subgroups; i = i + 1u) {
            final_sum = final_sum + batched_wg_sums[lid.y * MAX_SUBGROUPS_PER_ROW + i];
        }
        C_f16[row * u.N + col] = final_sum * f16(u.alpha);
    }
}