#pragma once

namespace cacheflow {

template<int WARPS_PER_BLOCK, int WARP_SIZE = 32>
inline __device__ float block_sum(float* red_smem, float sum)
{

    // Decompose the thread index into warp / lane.
    int warp = threadIdx.x / WARP_SIZE;
    int lane = threadIdx.x % WARP_SIZE;

    // Compute the sum per warp.
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask >= 1; mask /= 2) {
        sum += __shfl_xor_sync(uint32_t(-1), sum, mask);
    }

    // Warp leaders store the data to shared memory.
    if (lane == 0) {
        red_smem[warp] = sum;
    }

    // Make sure the data is in shared memory.
    __syncthreads();

    // The warps compute the final sums.
    if (lane < WARPS_PER_BLOCK) {
        sum = red_smem[lane];
    }

// Parallel reduction inside the warp.
#pragma unroll
    for (int mask = WARPS_PER_BLOCK / 2; mask >= 1; mask /= 2) {
        sum += __shfl_xor_sync(uint32_t(-1), sum, mask);
    }

    // Broadcast to other threads.
    return __shfl_sync(uint32_t(-1), sum, 0);
}

#define FINAL_MASK 0xffffffff

template<typename T>
__inline__ __device__ T warpReduceSum(T val)
{
#pragma unroll
    for (int mask = 16; mask > 0; mask >>= 1)
        val += __shfl_xor_sync(FINAL_MASK, val, mask, 32);
    return val;
}

/* Calculate the sum of all elements in a block */
template<typename T>
__inline__ __device__ T blockReduceSum(T val)
{
    static __shared__ T shared[32];
    int lane = threadIdx.x & 0x1f;
    int wid = threadIdx.x >> 5;

    val = warpReduceSum<T>(val);

    if (lane == 0)
        shared[wid] = val;

    __syncthreads();

    // Modify from blockDim.x << 5 to blockDim.x / 32. to prevent
    // blockDim.x is not divided by 32
    val = (threadIdx.x < (blockDim.x / 32.f)) ? shared[lane] : (T)(0.0f);
    val = warpReduceSum<T>(val);

    return val;
}

} // namespace cacheflow