#include "hip/hip_runtime.h"
/**
 * Copyright (c) Facebook, Inc. and its affiliates.
 *
 * This source code is licensed under the MIT license found in the
 * LICENSE file in the root directory of this source tree.
 */

#include <faiss/impl/FaissAssert.h>
#include <faiss/gpu/impl/L2Select.cuh>

#include <faiss/gpu/utils/DeviceUtils.h>
#include <faiss/gpu/utils/StaticUtils.h>
#include <faiss/gpu/utils/DeviceDefs.cuh>
#include <faiss/gpu/utils/MathOperators.cuh>
#include <faiss/gpu/utils/Pair.cuh>
#include <faiss/gpu/utils/Reductions.cuh>
#include <faiss/gpu/utils/Select.cuh>
#include <faiss/gpu/utils/Tensor.cuh>

namespace faiss {
namespace gpu {

// L2 + select kernel for k == 1, implements re-use of ||c||^2
template <typename T, int kRowsPerBlock, int kBlockSize>
__global__ void l2SelectMin1(
        Tensor<T, 2, true> productDistances,
        Tensor<T, 1, true> centroidDistances,
        Tensor<T, 2, true> outDistances,
        Tensor<int, 2, true> outIndices) {
    // Each block handles kRowsPerBlock rows of the distances (results)
    Pair<T, int> threadMin[kRowsPerBlock];
    __shared__ Pair<T, int> blockMin[kRowsPerBlock * (kBlockSize / kWarpSize)];

    T distance[kRowsPerBlock];

#pragma unroll
    for (int i = 0; i < kRowsPerBlock; ++i) {
        threadMin[i].k = Limits<T>::getMax();
        threadMin[i].v = -1;
    }

    // blockIdx.x: which chunk of rows we are responsible for updating
    int rowStart = blockIdx.x * kRowsPerBlock;

    // FIXME: if we have exact multiples, don't need this
    bool endRow = (blockIdx.x == gridDim.x - 1);

    if (endRow) {
        if (productDistances.getSize(0) % kRowsPerBlock == 0) {
            endRow = false;
        }
    }

    if (endRow) {
        for (int row = rowStart; row < productDistances.getSize(0); ++row) {
            for (int col = threadIdx.x; col < productDistances.getSize(1);
                 col += blockDim.x) {
                distance[0] = Math<T>::add(
                        centroidDistances[col], productDistances[row][col]);

                if (Math<T>::lt(distance[0], threadMin[0].k)) {
                    threadMin[0].k = distance[0];
                    threadMin[0].v = col;
                }
            }

            // Reduce within the block
            threadMin[0] = blockReduceAll<
                    Pair<T, int>,
                    Min<Pair<T, int>>,
                    false,
                    false>(threadMin[0], Min<Pair<T, int>>(), blockMin);

            if (threadIdx.x == 0) {
                outDistances[row][0] = threadMin[0].k;
                outIndices[row][0] = threadMin[0].v;
            }

            // so we can use the shared memory again
            __syncthreads();

            threadMin[0].k = Limits<T>::getMax();
            threadMin[0].v = -1;
        }
    } else {
        for (int col = threadIdx.x; col < productDistances.getSize(1);
             col += blockDim.x) {
            T centroidDistance = centroidDistances[col];

#pragma unroll
            for (int row = 0; row < kRowsPerBlock; ++row) {
                distance[row] = productDistances[rowStart + row][col];
            }

#pragma unroll
            for (int row = 0; row < kRowsPerBlock; ++row) {
                distance[row] = Math<T>::add(distance[row], centroidDistance);
            }

#pragma unroll
            for (int row = 0; row < kRowsPerBlock; ++row) {
                if (Math<T>::lt(distance[row], threadMin[row].k)) {
                    threadMin[row].k = distance[row];
                    threadMin[row].v = col;
                }
            }
        }

        // Reduce within the block
        blockReduceAll<
                kRowsPerBlock,
                Pair<T, int>,
                Min<Pair<T, int>>,
                false,
                false>(threadMin, Min<Pair<T, int>>(), blockMin);

        if (threadIdx.x == 0) {
#pragma unroll
            for (int row = 0; row < kRowsPerBlock; ++row) {
                outDistances[rowStart + row][0] = threadMin[row].k;
                outIndices[rowStart + row][0] = threadMin[row].v;
            }
        }
    }
}

// L2 + select kernel for k > 1, no re-use of ||c||^2
template <typename T, int NumWarpQ, int NumThreadQ, int ThreadsPerBlock>
__global__ void l2SelectMinK(
        Tensor<T, 2, true> productDistances,
        Tensor<T, 1, true> centroidDistances,
        Tensor<T, 2, true> outDistances,
        Tensor<int, 2, true> outIndices,
        int k,
        T initK) {
    // Each block handles a single row of the distances (results)
    constexpr int kNumWarps = ThreadsPerBlock / kWarpSize;

    __shared__ T smemK[kNumWarps * NumWarpQ];
    __shared__ int smemV[kNumWarps * NumWarpQ];

    BlockSelect<
            T,
            int,
            false,
            Comparator<T>,
            NumWarpQ,
            NumThreadQ,
            ThreadsPerBlock>
            heap(initK, -1, smemK, smemV, k);

    int row = blockIdx.x;

    // Whole warps must participate in the selection
    int limit = utils::roundDown(productDistances.getSize(1), kWarpSize);
    int i = threadIdx.x;

    for (; i < limit; i += blockDim.x) {
        T v = Math<T>::add(centroidDistances[i], productDistances[row][i]);
        heap.add(v, i);
    }

    if (i < productDistances.getSize(1)) {
        T v = Math<T>::add(centroidDistances[i], productDistances[row][i]);
        heap.addThreadQ(v, i);
    }

    heap.reduce();
    for (int i = threadIdx.x; i < k; i += blockDim.x) {
        outDistances[row][i] = smemK[i];
        outIndices[row][i] = smemV[i];
    }
}

template <typename T>
void runL2SelectMin(
        Tensor<T, 2, true>& productDistances,
        Tensor<T, 1, true>& centroidDistances,
        Tensor<T, 2, true>& outDistances,
        Tensor<int, 2, true>& outIndices,
        int k,
        hipStream_t stream) {
    FAISS_ASSERT(productDistances.getSize(0) == outDistances.getSize(0));
    FAISS_ASSERT(productDistances.getSize(0) == outIndices.getSize(0));
    FAISS_ASSERT(centroidDistances.getSize(0) == productDistances.getSize(1));
    FAISS_ASSERT(outDistances.getSize(1) == k);
    FAISS_ASSERT(outIndices.getSize(1) == k);
    FAISS_ASSERT(k <= GPU_MAX_SELECTION_K);

    if (k == 1) {
        constexpr int kThreadsPerBlock = 256;
        constexpr int kRowsPerBlock = 8;

        auto block = dim3(kThreadsPerBlock);
        auto grid = dim3(utils::divUp(outDistances.getSize(0), kRowsPerBlock));

        hipLaunchKernelGGL(HIP_KERNEL_NAME(l2SelectMin1<T, kRowsPerBlock, kThreadsPerBlock>), dim3(grid), dim3(block), 0, stream, 
                        productDistances,
                        centroidDistances,
                        outDistances,
                        outIndices);
    } else {
        auto grid = dim3(outDistances.getSize(0));

#define RUN_L2_SELECT(BLOCK, NUM_WARP_Q, NUM_THREAD_Q)   \
    do {                                                 \
        l2SelectMinK<T, NUM_WARP_Q, NUM_THREAD_Q, BLOCK> \
                <<<grid, BLOCK, 0, stream>>>(            \
                        productDistances,                \
                        centroidDistances,               \
                        outDistances,                    \
                        outIndices,                      \
                        k,                               \
                        Limits<T>::getMax());            \
    } while (0)

        // block size 128 for everything <= 1024
        if (k <= 32) {
            RUN_L2_SELECT(128, 32, 2);
        } else if (k <= 64) {
            RUN_L2_SELECT(128, 64, 3);
        } else if (k <= 128) {
            RUN_L2_SELECT(128, 128, 3);
        } else if (k <= 256) {
            RUN_L2_SELECT(128, 256, 4);
        } else if (k <= 512) {
            RUN_L2_SELECT(128, 512, 8);
        } else if (k <= 1024) {
            RUN_L2_SELECT(128, 1024, 8);

#if GPU_MAX_SELECTION_K >= 2048
        } else if (k <= 2048) {
            // smaller block for less shared memory
            RUN_L2_SELECT(64, 2048, 8);
#endif

        } else {
            FAISS_ASSERT(false);
        }
    }

    CUDA_TEST_ERROR();
}

void runL2SelectMin(
        Tensor<float, 2, true>& productDistances,
        Tensor<float, 1, true>& centroidDistances,
        Tensor<float, 2, true>& outDistances,
        Tensor<int, 2, true>& outIndices,
        int k,
        hipStream_t stream) {
    runL2SelectMin<float>(
            productDistances,
            centroidDistances,
            outDistances,
            outIndices,
            k,
            stream);
}

} // namespace gpu
} // namespace faiss
