/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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 <tvm/ffi/extra/cuda/base.h>
#include <tvm/runtime/logging.h>

#include <fstream>
#include <iostream>
#include <sstream>
#include <variant>
#include <vector>

// clang-format off
#include "cutlass/cutlass.h"

#include "cute/tensor.hpp"
#include "cutlass/tensor_ref.h"
#include "cutlass/epilogue/collective/default_epilogue.hpp"
#include "cutlass/epilogue/thread/linear_combination.h"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/gemm/group_array_problem_shape.hpp"
#include "cutlass/gemm/collective/collective_builder.hpp"
#include "cutlass/epilogue/collective/collective_builder.hpp"
#include "cutlass/gemm/device/gemm_universal_adapter.h"
#include "cutlass/gemm/kernel/gemm_universal.hpp"
// clang-format on

#define CUTLASS_CHECK(status)                                      \
  {                                                                \
    cutlass::Status error = status;                                \
    TVM_FFI_ICHECK(error == cutlass::Status::kSuccess)             \
        << "Got cutlass error: " << cutlassGetStatusString(error); \
  }

using namespace cute;
using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int, int, int>>;  // <M,N,K> per group

inline size_t aligned(size_t value, size_t alignment = 16) {
  return (value + alignment - 1) / alignment * alignment;
}

template <typename T>
struct KernelTraits;

template <>
struct KernelTraits<cutlass::half_t> {
  using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedCooperative;
  using TileShape = Shape<_128, _256, _64>;  // Threadblock-level tile size
  using ClusterShape = Shape<_2, _2, _1>;    // Shape of the threadblocks in a cluster
};

template <>
struct KernelTraits<cutlass::bfloat16_t> {
  using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedCooperative;
  using TileShape = Shape<_128, _256, _64>;  // Threadblock-level tile size
  using ClusterShape = Shape<_2, _2, _1>;    // Shape of the threadblocks in a cluster
};

template <typename ElementA, typename ElementB, typename ElementC,
          typename LayoutA = cutlass::layout::RowMajor,
          typename LayoutB = cutlass::layout::ColumnMajor,
          typename LayoutC = cutlass::layout::RowMajor>
struct CutlassGroupGemmRunner {
  static constexpr int AlignmentA =
      128 / cutlass::sizeof_bits<ElementA>::value;  // Alignment of A matrix in units of elements
                                                    // (up to 16 bytes)

  static constexpr int AlignmentB =
      128 / cutlass::sizeof_bits<ElementB>::value;  // Alignment of B matrix in units of elements
                                                    // (up to 16 bytes)

  static constexpr int AlignmentC =
      128 / cutlass::sizeof_bits<ElementC>::value;  // Alignment of C matrix in units of elements
                                                    // (up to 16 bytes)

  // Core kernel configurations
  using ElementAccumulator = float;  // Element type for internal accumulation
  using ScaleType = std::variant<ElementAccumulator, const ElementAccumulator*>;
  using ArchTag =
      cutlass::arch::Sm90;  // Tag indicating the minimum SM that supports the intended feature
  using OperatorClass = cutlass::arch::OpClassTensorOp;  // Operator class tag
  using TileShape = typename KernelTraits<ElementA>::TileShape;
  using ClusterShape = typename KernelTraits<ElementA>::ClusterShape;
  using StageCountType =
      cutlass::gemm::collective::StageCountAuto;  // Stage count maximized based on the tile size
  using KernelSchedule = typename KernelTraits<ElementA>::KernelSchedule;     // Kernel to launch
  using EpilogueSchedule = cutlass::epilogue::PtrArrayNoSmemWarpSpecialized;  // Epilogue to launch

  using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
      cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape, ClusterShape,
      cutlass::epilogue::collective::EpilogueTileAuto, ElementAccumulator, ElementAccumulator,
      ElementC, LayoutC*, AlignmentC, ElementC, LayoutC*, AlignmentC,
      EpilogueSchedule>::CollectiveOp;

  using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
      ArchTag, OperatorClass, ElementA, LayoutA*, AlignmentA, ElementB, LayoutB*, AlignmentB,
      ElementAccumulator, TileShape, ClusterShape,
      cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
          sizeof(typename CollectiveEpilogue::SharedStorage))>,
      KernelSchedule>::CollectiveOp;

  using GemmKernel =
      cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop, CollectiveEpilogue>;

  using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;

  using StrideA = typename Gemm::GemmKernel::InternalStrideA;
  using StrideB = typename Gemm::GemmKernel::InternalStrideB;
  using StrideC = typename Gemm::GemmKernel::InternalStrideC;
  using StrideD = typename Gemm::GemmKernel::InternalStrideD;

  void run_group_gemm(const ElementA** ptr_A, const ElementB** ptr_B, const ElementC** ptr_C,
                      ElementC** ptr_D,
                      typename ProblemShape::UnderlyingProblemShape* problem_sizes,
                      typename ProblemShape::UnderlyingProblemShape* problem_sizes_host,
                      StrideA* stride_A, StrideB* stride_B, StrideC* stride_C, StrideD* stride_D,
                      uint8_t* workspace, int64_t workspace_size, int num_groups, ScaleType alpha,
                      ScaleType beta, cudaStream_t stream) {
    typename Gemm::EpilogueOutputOp::Params epilogue_params = [&]() {
      TVM_FFI_ICHECK(alpha.index() == beta.index()) << "alpha and beta must have the same type";
      if (std::holds_alternative<ElementAccumulator>(alpha)) {
        return typename Gemm::EpilogueOutputOp::Params{std::get<ElementAccumulator>(alpha),
                                                       std::get<ElementAccumulator>(beta)};
      } else if (std::holds_alternative<const ElementAccumulator*>(alpha)) {
        return typename Gemm::EpilogueOutputOp::Params{std::get<const ElementAccumulator*>(alpha),
                                                       std::get<const ElementAccumulator*>(beta)};
      } else {
        LOG(FATAL) << "Unsupported alpha and beta type";
        throw;
      }
    }();

    cutlass::KernelHardwareInfo hw_info;
    hw_info.device_id = 0;
    hw_info.sm_count =
        cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
    typename Gemm::Arguments arguments{cutlass::gemm::GemmUniversalMode::kGrouped,
                                       {num_groups, problem_sizes, problem_sizes_host},
                                       {ptr_A, stride_A, ptr_B, stride_B},
                                       {epilogue_params, ptr_C, stride_C, ptr_D, stride_D},
                                       hw_info};
    Gemm gemm_op;
    CUTLASS_CHECK(gemm_op.can_implement(arguments));
    TVM_FFI_ICHECK_GE(workspace_size, gemm_op.get_workspace_size(arguments));
    CUTLASS_CHECK(gemm_op.initialize(arguments, workspace, stream));
    CUTLASS_CHECK(gemm_op.run(stream));
  }
};

template <typename ElementA, typename ElementB, typename ElementC, typename StrideA,
          typename StrideB, typename StrideC>
__global__ void prepare_group_gemm_arguments(
    const ElementA** ptr_A, const ElementB** ptr_B, ElementC** ptr_D,
    typename ProblemShape::UnderlyingProblemShape* problem_sizes, StrideA* stride_A,
    StrideB* stride_B, StrideC* stride_D, const ElementA* x, const ElementB* weight, ElementC* out,
    int64_t* indptr, int64_t n, int64_t k, int64_t num_groups) {
  int group_id = threadIdx.x;
  if (group_id >= num_groups) return;
  int prev_rows = group_id == 0 ? 0 : indptr[group_id - 1];
  ptr_A[group_id] = x + prev_rows * k;
  ptr_B[group_id] = weight + group_id * k * n;
  ptr_D[group_id] = out + prev_rows * n;
  problem_sizes[group_id] = {static_cast<int>(indptr[group_id] - prev_rows), static_cast<int>(n),
                             static_cast<int>(k)};
  stride_A[group_id] = cute::make_stride(k, Int<1>{}, Int<0>{});
  stride_B[group_id] = cute::make_stride(k, Int<1>{}, Int<0>{});
  stride_D[group_id] = cute::make_stride(n, Int<1>{}, Int<0>{});
}

template <typename ElementA, typename ElementB, typename ElementC>
void cutlass_group_gemm_sm90(ElementA* x, ElementB* weight, int64_t* indptr, uint8_t* workspace,
                             int64_t workspace_size, int64_t n, int64_t k, int64_t num_groups,
                             std::variant<float, const float*> alpha,
                             std::variant<float, const float*> beta, ElementC* out,
                             cudaStream_t stream) {
  using Runner = CutlassGroupGemmRunner<ElementA, ElementB, ElementC>;
  using StrideA = typename Runner::StrideA;
  using StrideB = typename Runner::StrideB;
  using StrideC = typename Runner::StrideC;

  Runner runner;
  std::ptrdiff_t offset = 0;
  const ElementA** ptr_A = reinterpret_cast<const ElementA**>(workspace + offset);
  offset += aligned(sizeof(ElementA*) * num_groups);
  const ElementB** ptr_B = reinterpret_cast<const ElementB**>(workspace + offset);
  offset += aligned(sizeof(ElementB*) * num_groups);
  ElementC** ptr_D = reinterpret_cast<ElementC**>(workspace + offset);
  offset += aligned(sizeof(ElementC*) * num_groups);
  typename ProblemShape::UnderlyingProblemShape* problem_sizes =
      reinterpret_cast<typename ProblemShape::UnderlyingProblemShape*>(workspace + offset);
  offset += aligned(sizeof(typename ProblemShape::UnderlyingProblemShape) * num_groups);
  StrideA* stride_A = reinterpret_cast<StrideA*>(workspace + offset);
  offset += aligned(sizeof(StrideA) * num_groups);
  StrideB* stride_B = reinterpret_cast<StrideB*>(workspace + offset);
  offset += aligned(sizeof(StrideB) * num_groups);
  StrideC* stride_D = reinterpret_cast<StrideC*>(workspace + offset);
  offset += aligned(sizeof(StrideC) * num_groups);
  prepare_group_gemm_arguments<<<1, num_groups, 0, stream>>>(ptr_A, ptr_B, ptr_D, problem_sizes,
                                                             stride_A, stride_B, stride_D, x,
                                                             weight, out, indptr, n, k, num_groups);
  offset = aligned(offset, 256);
  runner.run_group_gemm(ptr_A, ptr_B, const_cast<const ElementC**>(ptr_D), ptr_D, problem_sizes,
                        nullptr, stride_A, stride_B, stride_D, stride_D, workspace + offset,
                        workspace_size - offset, num_groups, alpha, beta, stream);
}
