// Batched scaled-dot-product attention helpers (Phase T10).
//
// The QKᵀ and PV matmuls run as cublasSgemmStridedBatched in Rust; the only
// kernel attention needs of its own is a CAUSAL row-wise softmax over the score
// rows. Scores are [B*nh, S, S] flattened to rows of length S; for a flat row r
// the query position within its sequence is `r % S`, so columns j > r%S are
// future positions and get probability 0 (no additive -1e9 mask tensor needed).
//
// The forward also folds in the 1/sqrt(head_dim) scale (applied to logits before
// the max/exp) so we don't need a separate scale pass. Backward is the ordinary
// softmax Jacobian (csrc/ops/nn.cu launch_softmax_dx_f32): masked entries have
// y=0, so their contribution vanishes — no causal-specific backward needed.
//
// All F32, row-major, contiguous. Reduction helpers mirror nn.cu (inlined so the
// file is self-contained, matching the csrc/ layout).

#include <math.h>

extern "C" {

__device__ __forceinline__ float att_warp_sum(float v) {
    #pragma unroll
    for (int off = 16; off > 0; off >>= 1)
        v += __shfl_down_sync(0xffffffff, v, off);
    return v;
}
__device__ __forceinline__ float att_warp_max(float v) {
    #pragma unroll
    for (int off = 16; off > 0; off >>= 1)
        v = fmaxf(v, __shfl_down_sync(0xffffffff, v, off));
    return v;
}
__device__ __forceinline__ float att_block_sum(float v) {
    __shared__ float sh[32];
    int lane = threadIdx.x & 31, warp = threadIdx.x >> 5;
    int nwarps = (blockDim.x + 31) >> 5;
    v = att_warp_sum(v);
    if (lane == 0) sh[warp] = v;
    __syncthreads();
    v = (threadIdx.x < nwarps) ? sh[threadIdx.x] : 0.0f;
    if (warp == 0) v = att_warp_sum(v);
    __shared__ float bc;
    if (threadIdx.x == 0) bc = v;
    __syncthreads();
    return bc;
}
__device__ __forceinline__ float att_block_max(float v) {
    __shared__ float sh[32];
    int lane = threadIdx.x & 31, warp = threadIdx.x >> 5;
    int nwarps = (blockDim.x + 31) >> 5;
    v = att_warp_max(v);
    if (lane == 0) sh[warp] = v;
    __syncthreads();
    v = (threadIdx.x < nwarps) ? sh[threadIdx.x] : -INFINITY;
    if (warp == 0) v = att_warp_max(v);
    __shared__ float bc;
    if (threadIdx.x == 0) bc = v;
    __syncthreads();
    return bc;
}

// One block per score row. rows = B*nh*S total; the query position within its
// sequence is (blockIdx.x % seq). Logits are scaled by `scale` (= 1/sqrt(hd))
// before softmax; columns j > qpos are masked to probability 0.
__global__ void softmax_causal_k(const float* x, float* y, int seq, float scale) {
    int r = blockIdx.x;
    int qpos = r % seq;
    const float* xr = x + (size_t)r * seq;
    float* yr = y + (size_t)r * seq;
    int valid = qpos + 1; // attend to columns [0, qpos]
    float m = -INFINITY;
    for (int c = threadIdx.x; c < valid; c += blockDim.x)
        m = fmaxf(m, xr[c] * scale);
    m = att_block_max(m);
    float sum = 0.0f;
    for (int c = threadIdx.x; c < valid; c += blockDim.x) {
        float e = expf(xr[c] * scale - m);
        yr[c] = e;
        sum += e;
    }
    sum = att_block_sum(sum);
    float inv = 1.0f / sum;
    for (int c = threadIdx.x; c < seq; c += blockDim.x)
        yr[c] = (c < valid) ? yr[c] * inv : 0.0f;
}
void launch_softmax_causal_f32(const float* x, float* y, int rows, int seq,
                               float scale, void* s) {
    int blk = seq < 1024 ? seq : 1024;
    if (blk < 32) blk = 32;
    softmax_causal_k<<<rows, blk, 0, (cudaStream_t)s>>>(x, y, seq, scale);
}

} // extern "C"
