/*
 * Copyright (c) 2022-2025, NVIDIA CORPORATION.  All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include "flashinfer/trtllm/fused_moe/RoutingKernel.cuh"

namespace moe::dev::routing {
namespace routingLlama4 {

////////////////////////////////////////////////////////////////////////////////////////////////////

static constexpr int NumThreads = 1024;
static constexpr int NumWarps = NumThreads / WarpSize;
static constexpr int MaxNumTopExperts = 1;
static constexpr int MaxNumExperts = 128;
static constexpr int MaxNumTokensSingleCluster = NumBlocksPerCluster * NumThreads;
static constexpr int MaxNumTokensSingleClusterScores = NumBlocksPerCluster * NumWarps;
static constexpr int WarpKernelSmemStride = 33;
// with further optimization to `routingIndicesWarpKernel`, this limit may
// increase. For now, it is a good cut-off point for when the block-wise
// operations are more efficient end-to-end.
static constexpr int WarpKernelMaxNumTokens = 4;

////////////////////////////////////////////////////////////////////////////////////////////////////

template <typename DataType, int VecSize>
__forceinline__ __device__ void routingTopKExperts(cg::thread_block_tile<WarpSize> const& warp,
                                                   DataType (&warpMaxScore)[MaxNumTopExperts],
                                                   int32_t (&warpMaxExpertIdx)[MaxNumTopExperts],
                                                   int32_t const laneIdx, int32_t const numExperts,
                                                   DataType const* ptrScores) {
  DataType minScore = DataType{-INFINITY};
  DataType maxScore = minScore;
  int32_t maxExpertIdx{0};
  using DataTypeVec = std::conditional_t<sizeof(DataType) == 2, float2, float4>;

  // Non-vectorized loading: directly access ptrScores with expertIdx
  for (int i = 0; i < VecSize; ++i) {
    auto expertIdx = i * WarpSize + laneIdx;
    auto newScore = expertIdx < numExperts ? ptrScores[expertIdx] : minScore;
    // note: use `>=` s.t. highest index always wins, just like in `reduceTopK`
    if (newScore > maxScore) {
      maxScore = newScore;
      maxExpertIdx = expertIdx;
    }
  }

  topk::reduceTopK(warp, warpMaxScore, warpMaxExpertIdx, maxScore, maxExpertIdx, minScore);
}

////////////////////////////////////////////////////////////////////////////////////////////////////

template <typename KernelParams>
__global__ void __launch_bounds__(WarpSize) routingIndicesWarpKernel(KernelParams params) {
  // types used in this kernel
  using OutputT = typename KernelParams::OutputT;
  using InputT = typename KernelParams::InputT;
  using TypePacked = PackedScoreIdx<OutputT>;
  // use the default cub warp-scan, with shfl
  using Scan = cub::WarpScan<int32_t>;
  __shared__ typename Scan::TempStorage tempStorage;

  // each thread encodes 4 experts in one `int32_t`. The assumption is that
  // we don't have more than 127 tokens, but `WarpKernelMaxNumTokens` must be
  // smaller than that because other approaches will be more efficient for
  // 127 tokens.
  static constexpr int ExpertsPerThread = sizeof(int32_t);
  static_assert(WarpKernelMaxNumTokens <= 127);
  // this is a full table of which token is routed to which expert.
  // the assumption here is that there are no more than 128 experts.
  // we use a stride of 33 instead of 32 to avoid shared memory bank conflicts.
  __shared__ int32_t __attribute((
      aligned(128))) smemExpertTokenCountFull[WarpKernelMaxNumTokens][WarpKernelSmemStride];
  static_assert(WarpKernelSmemStride == WarpSize + 1);
  static_assert(MaxNumExperts / sizeof(int32_t) <= WarpSize);

  // values needed for the top-1 reduction, if required
  InputT minScore = InputT{-INFINITY};
  auto block = cg::this_thread_block();
  auto warp = cg::tiled_partition<WarpSize>(block);

#pragma unroll
  for (int tokenIdx = 0; tokenIdx < WarpKernelMaxNumTokens; ++tokenIdx) {
    // reset full shared memory field to 0
    smemExpertTokenCountFull[tokenIdx][threadIdx.x] = 0;
  }
  __syncwarp();

#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
  // then wait on primary grid
  if constexpr (KernelParams::UsePdl) {
    cudaGridDependencySynchronize();
  }
#endif

  if (params.mPtrScores != nullptr) {
    // if we use `mPtrScores` as input, we need to perform the top-1 reduction
    // for each token, we load the scores then use `reduceTopK` for this.
    // each thread works on 4 experts, so a local reduction is done before
    for (int tokenIdx = 0; tokenIdx < params.mNumTokens; ++tokenIdx) {
      auto scoreOffset = tokenIdx * params.mNumExperts;
      int32_t warpMaxExpertIdx[MaxNumTopExperts];
      InputT warpMaxScore[MaxNumTopExperts];

      // Use routingTopKExperts function instead of inline logic
      routingTopKExperts<InputT, ExpertsPerThread>(warp, warpMaxScore, warpMaxExpertIdx,
                                                   threadIdx.x, params.mNumExperts,
                                                   params.mPtrScores + scoreOffset);

      if (cute::elect_one_sync()) {
        // one thread updates the count linking token to chosen expert
        auto expertTokenCount = 0;
        setBits</* IsZero= */ true>(expertTokenCount, 1, warpMaxExpertIdx[0] % ExpertsPerThread);
        smemExpertTokenCountFull[tokenIdx][warpMaxExpertIdx[0] / ExpertsPerThread] =
            expertTokenCount;
        // we also compute the final score here and write it out if required
        auto finalScore = OutputT{sigmoid_accurate(float{warpMaxScore[0]})};
        if (params.mPtrExpertWeights != nullptr) {
          params.mPtrExpertWeights[tokenIdx] = finalScore;
        }
      }
    }
  } else {
    // if we do not have `mPtrScores` as input, we expect that `mPtrExpertWeights`
    // contains the top-1 packed score and index already.
    // Each thread represents a token here, and we extract the relevant score
    // The assumption is that the #tokens is limited by warp-size
    static_assert(WarpKernelMaxNumTokens <= WarpSize);
    TypePacked scoreIdx =
        threadIdx.x < params.mNumTokens ? params.mPtrExpertIdx[threadIdx.x] : TypePacked{};
    int32_t expertTokenCount = 0;
    setBits</* IsZero= */ true>(expertTokenCount, 1, scoreIdx.idx % ExpertsPerThread);
    if (threadIdx.x < params.mNumTokens) {
      smemExpertTokenCountFull[threadIdx.x][scoreIdx.idx / ExpertsPerThread] = expertTokenCount;
    }
    // we also compute the final score here and write it out if required
    auto finalScore = OutputT{sigmoid_accurate(float{scoreIdx.score})};
    if (params.mPtrExpertWeights != nullptr && threadIdx.x < params.mNumTokens) {
      params.mPtrExpertWeights[threadIdx.x] = finalScore;
    }
  }

  // make the full table available to all threads
  __syncwarp();

  // at this point, each thread keeps a count of its 4 assigned experts in
  // `expertCount`, as well as the offsets for all tokens w.r.t. these 4 experts
  // in `expertOffset`.
  int32_t expertCount = 0;
  int32_t expertOffset[WarpKernelMaxNumTokens + 1];
#pragma unroll
  for (int tokenIdx = 0; tokenIdx < WarpKernelMaxNumTokens + 1; ++tokenIdx) {
    if (tokenIdx > params.mNumTokens) break;
    // simple reduction for `expertCount`, and scan for `expertOffset`
    auto expertTokenCount =
        tokenIdx < params.mNumTokens ? smemExpertTokenCountFull[tokenIdx][threadIdx.x] : 0;
    expertOffset[tokenIdx] = expertCount;
    expertCount += expertTokenCount;
  }

  // at this point, we are ready for the scan across all experts to get the
  // thread-wise offsets across experts
  // first, we need to reduce across our 4 experts into `numCta`
  int32_t numCta = 0;
#pragma unroll
  for (int ii = 0; ii < ExpertsPerThread; ++ii) {
    auto count = getBits(expertCount, ii);
    numCta += divUpLog2<int32_t>(count, params.mPaddingLog2);
  }
  // second, we perform the exclusive sum across the warp
  int32_t ctaOffset;
  int32_t numNonExitingCtas;
  Scan(tempStorage).ExclusiveSum(numCta, ctaOffset, numNonExitingCtas);

  // finally, we perform a scan across our local experts, starting with the
  // warp-wide scan result (`ctaOffset`)
  auto ctaOffsetExp = ctaOffset;
#pragma unroll
  for (int ii = 0; ii < ExpertsPerThread; ++ii) {
    auto count = getBits(expertCount, ii);
    auto finalNumCta = divUpLog2<int32_t>(count, params.mPaddingLog2);
    auto expertIdx = threadIdx.x * ExpertsPerThread + ii;
    // during the scan for expert offsets, we can already write out
    // both `mPtrCtaIdxXyToBatchIdx` and `mPtrCtaIdxXyToMnLimit`
    for (int cta = 0; cta < finalNumCta; ++cta) {
      params.mPtrCtaIdxXyToBatchIdx[ctaOffsetExp + cta] = expertIdx;
      params.mPtrCtaIdxXyToMnLimit[ctaOffsetExp + cta] =
          min(mulLog2<int32_t>(ctaOffsetExp + cta + 1, params.mPaddingLog2),
              mulLog2<int32_t>(ctaOffsetExp, params.mPaddingLog2) + count);
    }
    ctaOffsetExp += finalNumCta;
  }

  // at this point, we can write out padded count from the warp-aggregate
  if (cute::elect_one_sync()) {
    const int32_t permutedIdxSize = mulLog2<int32_t>(numNonExitingCtas, params.mPaddingLog2);
    params.mPtrPermutedIdxSize[0] = permutedIdxSize;
    params.mPtrNumNonExitingCtas[0] = numNonExitingCtas;
  }

#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
#if !defined(PDL_PROFILE) || PDL_PROFILE == 0
  // we can trigger the next kernel at this point
  if constexpr (KernelParams::UsePdl) {
    cudaTriggerProgrammaticLaunchCompletion();
  }
#endif
#endif

  // at this point, all values for offsets are ready, except the final offsets
  // within the padded index (`permutedIdx`)
  // for this, we perform a scan similar to the one directly after the warp-scan:
  // here, we keep the local offset for each of the thread's experts in a field
  // of registers
  auto localExpertExtent = params.mNumLocalExperts << params.mLocalExpertsStrideLog2;
  int32_t finalExpertOffset[ExpertsPerThread];
  finalExpertOffset[0] = mulLog2<int32_t>(ctaOffset, params.mPaddingLog2);
#pragma unroll
  for (int ii = 1; ii < ExpertsPerThread; ++ii) {
    finalExpertOffset[ii] =
        finalExpertOffset[ii - 1] +
        divUpMulLog2<int32_t>(getBits(expertCount, ii - 1), params.mPaddingLog2);
  }

#pragma unroll
  for (int tokenIdx = 0; tokenIdx < WarpKernelMaxNumTokens; ++tokenIdx) {
    // at this point, we can calculate the final index:
    // we simply loop over all tokens, and all experts assigned to this thread.
    // For each pair, we determine whether that token was routed to that expert
    // based on whether the offset for that token changed.
    // we can then easily compute the final `expertIdx` and `permutedIdx` relative
    // to this token and expert, and write them out.
    if (tokenIdx >= params.mNumTokens) break;

#pragma unroll
    for (int ii = 0; ii < ExpertsPerThread; ++ii) {
      // determine whether the offset for this expert and token changes
      auto localOffsetToken = getBits(expertOffset[tokenIdx], ii);
      auto isTokenRouted = getBits(expertOffset[tokenIdx + 1], ii) > localOffsetToken;
      // the expert index of this expert
      auto expertIdx = threadIdx.x * ExpertsPerThread + ii;
      auto localExpertIdx = static_cast<int32_t>(expertIdx) - params.mLocalExpertsStartIdx;
      auto isLocalExpert = localExpertIdx >= 0 && localExpertIdx < localExpertExtent &&
                           (localExpertIdx & params.mLocalExpertsStrideLog2) == 0;
      // the permuted index: we add the local offset relative to this expert and token
      // to the global offset from the scan for this expert
      auto permutedIdx = isLocalExpert ? finalExpertOffset[ii] + localOffsetToken : int32_t{-1};
      // write out `mPtrExpandedIdxToPermutedIdx` if required
      if (params.mPtrExpandedIdxToPermutedIdx != nullptr && isTokenRouted) {
        params.mPtrExpandedIdxToPermutedIdx[tokenIdx] = permutedIdx;
      }
      // write out `mPtrPermutedIdxToTokenIdx` if required
      if (params.mPtrPermutedIdxToTokenIdx != nullptr && isLocalExpert && isTokenRouted) {
        params.mPtrPermutedIdxToTokenIdx[permutedIdx] = tokenIdx;
      }
    }
  }
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename KernelParams>
#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
__global__ void __cluster_dims__(NumBlocksPerCluster, 1, 1) __launch_bounds__(NumThreads)
    routingIndicesClusterKernel(KernelParams params) {
  // number of tokens/expanded idx is bounded by total number of warps
  using OutputT = typename KernelParams::OutputT;
  using InputT = typename KernelParams::InputT;
  using TypePacked = PackedScoreIdx<OutputT>;
  __shared__ TypePacked __attribute((aligned(128))) smemPackedScoreIdx[NumWarps];

  uint32_t const clusterBlockRank = blockIdx.x;
  int32_t const warpIdx = __shfl_sync(0xffffffff, threadIdx.x / WarpSize, 0);
  int32_t const laneIdx = cutlass::arch::LaneId();

  // TODO(mjoux): expand to more tokens (possibly)
  auto warpTokenIdx = clusterBlockRank * NumWarps + warpIdx;
  auto scoreOffset = warpTokenIdx * params.mNumExperts;
  bool validToken = warpTokenIdx < params.mNumTokens;
  InputT minScore = InputT{-INFINITY};

  auto block = cg::this_thread_block();
  auto warp = cg::tiled_partition<WarpSize>(block);

  // then wait on primary grid
  if constexpr (KernelParams::UsePdl) {
    cudaGridDependencySynchronize();
  }

  if (params.mPtrScores != nullptr) {
    // in this case, each warp represents a token
    // we then exchange all token max scores, s.t. afterwards, each thread
    // represents a token
    InputT warpMaxScore[MaxNumTopExperts];
    int32_t warpMaxExpertIdx[MaxNumTopExperts];

    if (validToken) {
      routingTopKExperts<InputT, MaxNumExperts / WarpSize>(warp, warpMaxScore, warpMaxExpertIdx,
                                                           laneIdx, params.mNumExperts,
                                                           params.mPtrScores + scoreOffset);
      if (cute::elect_one_sync()) {
        auto finalScore = OutputT{sigmoid_accurate(float{warpMaxScore[0]})};
        TypePacked packedScore{finalScore, static_cast<int16_t>(warpMaxExpertIdx[0])};
        smemPackedScoreIdx[warpIdx] = packedScore;
      }
    }
    // make packed scores available to all threads in cluster
    __cluster_barrier_arrive();
    __cluster_barrier_wait();
  }

  routingPermutation<KernelParams, OutputT, NumThreads, NumWarps, MaxNumTopExperts,
                     /*LoadExpertIdxFromGlobal=*/false>(params, smemPackedScoreIdx, warpIdx,
                                                        clusterBlockRank);
}
#else
__global__ void routingIndicesClusterKernel(KernelParams params) {
  assert(false && "routingIndicesClusterKernel is only supported on SM90+ architectures");
}
#endif

////////////////////////////////////////////////////////////////////////////////////////////////////

// this kernel is needed in case we have scores as input for the histogram kernel
template <typename KernelParams>
__global__ void __launch_bounds__(NumThreadsHist)
    routingIndicesHistogramScoresKernel(KernelParams params) {
  using OutputT = typename KernelParams::OutputT;
  using InputT = typename KernelParams::InputT;
  using TypePacked = PackedScoreIdx<OutputT>;
  static constexpr int VecSize = MaxNumExperts / WarpSize;
  //  we assume that #experts is a multiple of 4, so VecSize must be 4.
  static_assert(VecSize == 4);

  int32_t const laneIdx = cutlass::arch::LaneId();
  int32_t const warpIdx = threadIdx.x / WarpSize;
  int32_t const globalWarpIdx = blockIdx.x * NumWarpsHist + warpIdx;
  int32_t const globalWarpStride = gridDim.x * NumWarpsHist;
  InputT minScore = InputT{-INFINITY};
  auto block = cg::this_thread_block();
  auto warp = cg::tiled_partition<WarpSize>(block);

  // initialize the mPtrExpertCounts
  int32_t expertCountsNum = 2 * params.mNumExperts;
  int32_t globalThreadIdx = blockIdx.x * NumThreads + threadIdx.x;
  int32_t globalThreadStride = gridDim.x * NumThreads;
  initArr(globalThreadIdx, expertCountsNum, globalThreadStride, params.mPtrExpertCounts, 0);

#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
  // Wait on primary grid and trigger secondary kernel.
  if constexpr (KernelParams::UsePdl) {
    cudaGridDependencySynchronize();
    cudaTriggerProgrammaticLaunchCompletion();
  }
#endif

  // in this case, each warp represents a token, and we use a grid-stride loop
  // over all warps/tokens
  for (int tokenIdx = globalWarpIdx; tokenIdx < params.mNumTokens; tokenIdx += globalWarpStride) {
    auto scoreOffset = tokenIdx * params.mNumExperts;
    int32_t warpMaxExpertIdx[MaxNumTopExperts];
    InputT warpMaxScore[MaxNumTopExperts];

    routingTopKExperts<InputT, MaxNumExperts / WarpSize>(warp, warpMaxScore, warpMaxExpertIdx,
                                                         laneIdx, params.mNumExperts,
                                                         params.mPtrScores + scoreOffset);

    if (cute::elect_one_sync()) {
      auto finalScore = OutputT{sigmoid_accurate(float{warpMaxScore[0]})};
      TypePacked packedScore{finalScore, static_cast<int16_t>(warpMaxExpertIdx[0])};
      params.mPtrExpertIdx[tokenIdx] = packedScore;
    }
  }
}

////////////////////////////////////////////////////////////////////////////////////////////////////

void run(Data const& data, void* stream) {
  TORCH_CHECK(data.mPtrExpertIdx != nullptr || data.mPtrScores != nullptr,
              "Routing kernel requires at least one input parameter");
  TORCH_CHECK(data.mPtrPermutedIdxSize != nullptr && data.mPtrCtaIdxXyToBatchIdx != nullptr &&
                  data.mPtrCtaIdxXyToMnLimit != nullptr && data.mPtrNumNonExitingCtas != nullptr,
              "Llama4 routing kernel expects permuted idx and grouped Gemm launch config buffers");
  TORCH_CHECK(data.mTopK <= MaxNumTopExperts,
              "Routing kernel expects topK experts <= ", MaxNumTopExperts, ", got ", data.mTopK);
  TORCH_CHECK(data.mNumExperts <= MaxNumExperts, "Routing kernel expects #experts ",
              data.mNumExperts, " to be at most max #experts ", MaxNumExperts);
  static_assert(MaxNumExperts <= NumThreads, "#experts must be bounded by #threads");
  static_assert(MaxNumExperts <= NumThreadsHist, "#experts must be bounded by #threads");
  TORCH_CHECK(data.mNumExperts % 4 == 0, "Routing kernel expects #experts ", data.mNumExperts,
              " to be a multiple of 4.");
  TORCH_CHECK(data.mPaddingLog2 < 8, "Routing kernel expects padding log2 < 8, got ",
              data.mPaddingLog2);

  bool const useSingleWarp =
      (data.mPtrScores == nullptr && data.mNumTokens <= WarpKernelMaxNumTokens) ||
      data.mNumTokens < WarpKernelMaxNumTokens;
  bool const useSingleCluster =
      data.mNumTokens <=
      (data.mPtrScores != nullptr ? MaxNumTokensSingleClusterScores : MaxNumTokensSingleCluster);
  if (!useSingleCluster) {
    TORCH_CHECK(data.mPtrExpertIdx != nullptr,
                "When #tokens is large, `mPtrExpertIdx` is a required input.");
    TORCH_CHECK(data.mPtrExpertCounts != nullptr,
                "When #tokens is large, `mPtrExpertCounts` is a required input.");
  }

  if (useSingleWarp) {
    LAUNCH_ROUTING(data,
                   /*coopLaunch=*/false, routingIndicesWarpKernel, 1, WarpSize,
                   /*smemSize=*/0,  // No dynamic smem
                   stream);
  } else if (useSingleCluster) {
    LAUNCH_ROUTING(data,
                   /*coopLaunch=*/false, routingIndicesClusterKernel, NumBlocksPerCluster,
                   NumThreads,
                   /*smemSize=*/0,  // No dynamic smem
                   stream);
  } else {
    const uint32_t expandedIdxSize = data.mNumTokens * data.mTopK;

    const uint32_t histogramEltsPerBlock = 8 * NumThreadsHist;
    const uint32_t offsetEltsPerBlock = NumEltsPerOffsetTilePerThread * NumThreadsHist;

    // Limit grid size (all kernels use a grid-stride loop).
    const uint32_t maxNumBlocks = 1024;

    int const numBlocksHistogram = std::min(
        (expandedIdxSize + histogramEltsPerBlock - 1) / histogramEltsPerBlock, maxNumBlocks);
    int const numBlocksOffsets =
        std::min((expandedIdxSize + offsetEltsPerBlock - 1) / offsetEltsPerBlock, maxNumBlocks);

    if (data.mPtrScores != nullptr) {
      LAUNCH_ROUTING(data,
                     /*coopLaunch=*/false, routingIndicesHistogramScoresKernel, maxNumBlocks,
                     NumThreadsHist,
                     /*smemSize=*/0,  // No dynamic smem
                     stream);
    } else {
      // Reset the global histograms.
      CHECK_CUDA_ERROR(cudaMemsetAsync(data.mPtrExpertCounts, 0,
                                       static_cast<size_t>(2 * data.mNumExperts) * sizeof(int32_t),
                                       (cudaStream_t)stream));
    }
    LAUNCH_ROUTING(data,
                   /*coopLaunch=*/false, routingIndicesHistogramKernel, numBlocksHistogram,
                   NumThreadsHist,
                   /*smemSize=*/0,  // No dynamic smem
                   stream);
    LAUNCH_ROUTING(data,
                   /*coopLaunch=*/false, routingIndicesOffsetsKernel, numBlocksOffsets,
                   NumThreadsHist,
                   /*smemSize=*/0,  // No dynamic smem
                   stream);
  }
}

////////////////////////////////////////////////////////////////////////////////////////////////////

}  // namespace routingLlama4
}  // namespace moe::dev::routing