#pragma once
#include <stddef.h>
#include <torch/all.h>

#include <ATen/cuda/CUDAContext.h>

// clang-format will break include orders
// clang-format off
#include "cute/tensor.hpp"
#include "cute/atom/mma_atom.hpp"
#include "cutlass/numeric_types.h"

#include "cutlass/cutlass.h"
#include "cutlass/gemm_coord.h"
#include "cutlass/arch/mma_sm75.h"
#include "cutlass/arch/arch.h"
#include "cutlass/arch/mma.h"
#include "cutlass/gemm/device/gemm.h"
#include "cutlass/gemm/device/gemm_universal_adapter.h"

#include "cutlass/epilogue/threadblock/fusion/visitors.hpp"
#include "cutlass/gemm/kernel/default_gemm_universal_with_visitor.h"

#include "broadcast_load_epilogue_c2x.hpp"
#include "common.hpp"
// clang-format on

using namespace cute;

/*
   Epilogue functions can be defined to post-process the output before it is
   written to GPU memory.
   Epilogues must contain a public type named EVTCompute of type Sm80EVT,
   as well as a static prepare_args function that constructs an
   EVTCompute::Arguments struct.
*/

namespace vllm {

// Wrappers for the GEMM kernel that is used to guard against compilation on
// architectures that will never use the kernel. The purpose of this is to
// reduce the size of the compiled binary.
// __CUDA_ARCH__ is not defined in host code, so this lets us smuggle the ifdef
// into code that will be executed on the device where it is defined.
template <typename Kernel>
struct enable_sm75_to_sm80 : Kernel {
  template <typename... Args>
  CUTLASS_DEVICE static void invoke(Args&&... args) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 750 && __CUDA_ARCH__ < 800
    Kernel::invoke(std::forward<Args>(args)...);
#endif
  }
};

template <typename Kernel>
struct enable_sm80_to_sm89 : Kernel {
  template <typename... Args>
  CUTLASS_DEVICE static void invoke(Args&&... args) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 800 && __CUDA_ARCH__ < 890
    Kernel::invoke(std::forward<Args>(args)...);
#endif
  }
};

template <typename Kernel>
struct enable_sm89_to_sm90 : Kernel {
  template <typename... Args>
  CUTLASS_DEVICE static void invoke(Args&&... args) {
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 890 && __CUDA_ARCH__ < 900
    Kernel::invoke(std::forward<Args>(args)...);
#endif
  }
};

/*
 * This class provides the common load descriptors for the
 * ScaledEpilogue[...] classes
 */
template <typename ElementD, typename OutputTileThreadMap>
struct ScaledEpilogueBase {
 protected:
  using Accum = cutlass::epilogue::threadblock::VisitorAccFetch;

  template <typename T>
  using ColOrScalarLoad =
      cutlass::epilogue::threadblock::VisitorColOrScalarBroadcast<
          OutputTileThreadMap, T, Stride<Int<1>, Int<0>, Int<0>>>;

  template <typename T>
  using RowOrScalarLoad =
      cutlass::epilogue::threadblock::VisitorRowOrScalarBroadcast<
          OutputTileThreadMap, T, Stride<Int<0>, Int<1>, Int<0>>>;

  template <typename T>
  using ColLoad = cutlass::epilogue::threadblock::VisitorColBroadcast<
      OutputTileThreadMap, T, Stride<Int<1>, Int<0>, Int<0>>>;

  template <typename T>
  using RowLoad = cutlass::epilogue::threadblock::VisitorRowBroadcast<
      OutputTileThreadMap, T, Stride<Int<0>, Int<1>, Int<0>>>;

  template <typename T>
  using RowOrZeroLoad =
      cutlass::epilogue::threadblock::VisitorRowOrZeroBroadcast<
          OutputTileThreadMap, T, Stride<Int<0>, Int<1>, Int<0>>>;

  // This utility function constructs the arguments for the load descriptors
  // from a tensor. It can handle both row and column, as well as row/column or
  // scalar cases.
  template <typename Descriptor, typename T>
  static auto args_from_tensor(torch::Tensor const& tensor) {
    using Arguments = typename Descriptor::Arguments;
    auto* data_ptr = static_cast<T*>(tensor.data_ptr());
    if constexpr (std::is_same_v<Descriptor, ColOrScalarLoad<T>> ||
                  std::is_same_v<Descriptor, RowOrScalarLoad<T>>) {
      return Arguments{data_ptr, tensor.numel() != 1};
    } else {
      // it would technically work but no use case as data_ptr is never nullptr
      static_assert(!std::is_same_v<Descriptor, RowOrZeroLoad<T>>);
      return Arguments{data_ptr};
    }
  }

  // This overload handles the case where there might not be a tensor, in which
  // case a nullptr is passed and a constant (0) is used.
  template <typename Descriptor, typename T>
  static auto args_from_tensor(c10::optional<torch::Tensor> const& tensor) {
    static_assert(std::is_same_v<Descriptor, RowOrZeroLoad<T>>);
    using Arguments = typename Descriptor::Arguments;
    auto* data_ptr = tensor ? static_cast<T*>(tensor->data_ptr()) : nullptr;
    return Arguments{data_ptr};
  }
};

/*
 This epilogue function defines a quantized GEMM operation similar to
 torch._scaled_mm.

 A and B may be both either int8 or fp8_e4m3. A can be quantized per-tensor or
 per-row. B can be quantized per-tensor or per-column.
 Any combination of per-tensor and per-row or column is supported.
 A and B must have symmetric quantization (zero point == 0).

 So the GEMM operation is D = (a_scales * A) (b_scales * B), where the
 scales are applied elementwise with numpy-style broadcasting.

 ScaleA and ScaleB define the epilogue functions that apply the scales for
 the A and B operands respectively. These scales may be either per-tensor or
 per row or column.
*/
template <typename ElementD, typename OutputTileThreadMap>
struct ScaledEpilogue
    : private ScaledEpilogueBase<ElementD, OutputTileThreadMap> {
 private:
  using SUPER = ScaledEpilogueBase<ElementD, OutputTileThreadMap>;
  using Accum = typename SUPER::Accum;
  using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
  using ScaleB = typename SUPER::template RowOrScalarLoad<float>;

  using Compute0 = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::multiplies, float, float,
      cutlass::FloatRoundStyle::round_to_nearest>;

  using EVTCompute0 =
      cutlass::epilogue::threadblock::Sm80EVT<Compute0, ScaleB, Accum>;

  using Compute1 = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::multiplies, ElementD, float,
      cutlass::FloatRoundStyle::round_to_nearest>;

 public:
  using EVTCompute =
      cutlass::epilogue::threadblock::Sm80EVT<Compute1, ScaleA, EVTCompute0>;
  using ArgumentType = typename EVTCompute::Arguments;

  static ArgumentType prepare_args(torch::Tensor const& a_scales,
                                   torch::Tensor const& b_scales) {
    auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
    auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);

    typename EVTCompute0::Arguments evt0_args{b_args};
    return ArgumentType{a_args, evt0_args};
  }
};

/*
 * This epilogue performs the same operation as ScaledEpilogue, but adds a bias.
 * This bias can also be used in the per-tensor azp case, where the activation
 * zero point (azp) is used to compute an azp correction term,
 * which is folded into the bias.
 *
 * The bias tensor must be per-output channel.
 * ScaleA and ScaleB can be per-tensor or per-token/per-channel.
 */
template <typename ElementD, typename OutputTileThreadMap>
struct ScaledEpilogueBias
    : protected ScaledEpilogueBase<ElementD, OutputTileThreadMap> {
 protected:
  using SUPER = ScaledEpilogueBase<ElementD, OutputTileThreadMap>;
  using Accum = typename SUPER::Accum;
  using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
  using ScaleB = typename SUPER::template RowOrScalarLoad<float>;
  using Bias = typename SUPER::template RowLoad<ElementD>;
  using Compute0 = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::multiplies, float, float,
      cutlass::FloatRoundStyle::round_to_nearest>;

  using EVTCompute0 =
      cutlass::epilogue::threadblock::Sm80EVT<Compute0, ScaleB, Accum>;

  using Compute1 = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::multiply_add, ElementD, float,
      cutlass::FloatRoundStyle::round_to_nearest>;

 public:
  using EVTCompute = cutlass::epilogue::threadblock::Sm80EVT<Compute1, ScaleA,
                                                             EVTCompute0, Bias>;
  using ArgumentType = typename EVTCompute::Arguments;
  static ArgumentType prepare_args(torch::Tensor const& a_scales,
                                   torch::Tensor const& b_scales,
                                   torch::Tensor const& bias) {
    auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
    auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);
    auto bias_args = SUPER::template args_from_tensor<Bias, ElementD>(bias);

    typename EVTCompute0::Arguments evt0_args{b_args};
    return ArgumentType{a_args, evt0_args, bias_args};
  }
};

/*
 * This epilogue directly supports per-tensor azp in int32 form.
 * As opposed to the per-token epilogue below, this epilogue only has an azp_adj
 * term, which should already be multiplied with the scalar azp.
 * The azp_adj term is a 1D tensor of shape (1,n), computed as azp * J @ B.
 *
 * This epilogue also supports bias, which remains per-channel.
 */
template <typename ElementD, typename OutputTileThreadMap>
struct ScaledEpilogueBiasAzp
    : protected ScaledEpilogueBase<ElementD, OutputTileThreadMap> {
 private:
  using SUPER = ScaledEpilogueBase<ElementD, OutputTileThreadMap>;
  using Accum = typename SUPER::Accum;
  using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
  using ScaleB = typename SUPER::template RowOrScalarLoad<float>;
  using Bias = typename SUPER::template RowOrZeroLoad<ElementD>;

  // This is the full AZP term, azp * J @ B, shape (1,n)
  using AzpWithAdj = typename SUPER::template RowLoad<int32_t>;

  // Compute float(accum - azp_adj), both operands are int32_t
  using ComputeAzp = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::minus, float, int32_t,
      cutlass::FloatRoundStyle::round_to_nearest>;

  using EVTComputeAzp =
      cutlass::epilogue::threadblock::Sm80EVT<ComputeAzp, Accum, AzpWithAdj>;

  using ComputeScaleB = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::multiplies, float, float,
      cutlass::FloatRoundStyle::round_to_nearest>;

  using EVTComputeScaleB =
      cutlass::epilogue::threadblock::Sm80EVT<ComputeScaleB, ScaleB,
                                              EVTComputeAzp>;

  using ComputeScaleBiasA = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::multiply_add, ElementD, float,
      cutlass::FloatRoundStyle::round_to_nearest>;

 public:
  using EVTCompute =
      cutlass::epilogue::threadblock::Sm80EVT<ComputeScaleBiasA, ScaleA,
                                              EVTComputeScaleB, Bias>;

  using ArgumentType = typename EVTCompute::Arguments;

  static ArgumentType prepare_args(torch::Tensor const& a_scales,
                                   torch::Tensor const& b_scales,
                                   torch::Tensor const& azp_adj,
                                   c10::optional<torch::Tensor> const& bias) {
    auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
    auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);
    auto bias_args = SUPER::template args_from_tensor<Bias, ElementD>(bias);
    auto azp_adj_args =
        SUPER::template args_from_tensor<AzpWithAdj, int32_t>(azp_adj);

    typename EVTComputeAzp::Arguments evt_azp_args{{}, azp_adj_args};
    typename EVTComputeScaleB::Arguments evt_scale_b_args{b_args, evt_azp_args};
    return ArgumentType{a_args, evt_scale_b_args, bias_args};
  }
};

/*
 * This epilogue supports per-token azp by computing and applying
 * the correction term using a rank-1 update. If the term were materialized,
 * it would require O(m*n) space, and this way it only requires O(m+n) space.
 * The azp term is a 1D tensor of shape (m,1), and represents the unscaled zero
 * point for each row of A.
 * The azp_adj term is a 1D tensor of shape (1,n), computed as J @ B.
 *
 * This epilogue also supports bias, which remains per-channel.
 */
template <typename ElementD, typename OutputTileThreadMap>
struct ScaledEpilogueBiasAzpToken
    : protected ScaledEpilogueBase<ElementD, OutputTileThreadMap> {
 private:
  using SUPER = ScaledEpilogueBase<ElementD, OutputTileThreadMap>;
  using Accum = typename SUPER::Accum;
  using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
  using ScaleB = typename SUPER::template RowOrScalarLoad<float>;
  using Bias = typename SUPER::template RowOrZeroLoad<ElementD>;

  // Per-token azp term, shape (m,1)
  using Azp = typename SUPER::template ColLoad<int32_t>;

  // This is the AZP adjustment term, J @ B, shape (1,n)
  using AzpAdj = typename SUPER::template RowLoad<int32_t>;

  // Compute azp * azp_adj
  using ComputeAzp = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::multiplies, int32_t, int32_t,
      cutlass::FloatRoundStyle::round_to_nearest>;

  using EVTComputeAzp =
      cutlass::epilogue::threadblock::Sm80EVT<ComputeAzp, Azp, AzpAdj>;

  // Compute float(accum - azp*azp_adj), all operands are int32_t
  using ComputeAcc = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::minus, float, int32_t,
      cutlass::FloatRoundStyle::round_to_nearest>;

  using EVTComputeAcc =
      cutlass::epilogue::threadblock::Sm80EVT<ComputeAcc, Accum, EVTComputeAzp>;

  using ComputeScaleB = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::multiplies, float, float,
      cutlass::FloatRoundStyle::round_to_nearest>;

  using EVTComputeScaleB =
      cutlass::epilogue::threadblock::Sm80EVT<ComputeScaleB, ScaleB,
                                              EVTComputeAcc>;

  using ComputeScaleBiasA = cutlass::epilogue::threadblock::VisitorCompute<
      cutlass::multiply_add, ElementD, float,
      cutlass::FloatRoundStyle::round_to_nearest>;

 public:
  using EVTCompute =
      cutlass::epilogue::threadblock::Sm80EVT<ComputeScaleBiasA, ScaleA,
                                              EVTComputeScaleB, Bias>;

  using ArgumentType = typename EVTCompute::Arguments;

  static ArgumentType prepare_args(torch::Tensor const& a_scales,
                                   torch::Tensor const& b_scales,
                                   torch::Tensor const& azp_adj,
                                   torch::Tensor const& azp,
                                   c10::optional<torch::Tensor> const& bias) {
    auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
    auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);
    auto bias_args = SUPER::template args_from_tensor<Bias, ElementD>(bias);
    auto azp_args = SUPER::template args_from_tensor<Azp, int32_t>(azp);
    auto azp_adj_args =
        SUPER::template args_from_tensor<AzpAdj, int32_t>(azp_adj);

    typename EVTComputeAzp::Arguments evt_azp_args{azp_args, azp_adj_args};
    typename EVTComputeAcc::Arguments evt_acc_args{{}, evt_azp_args};
    typename EVTComputeScaleB::Arguments evt_scale_b_args{b_args, evt_acc_args};
    return ArgumentType{a_args, evt_scale_b_args, bias_args};
  }
};

template <typename Arch, template <typename> typename ArchGuard,
          typename ElementAB_, typename ElementD_,
          template <typename, typename> typename Epilogue_, typename TileShape,
          typename WarpShape, typename InstructionShape, int32_t MainLoopStages,
          typename FP8MathOperator = cutlass::arch::OpMultiplyAdd>
struct cutlass_2x_gemm {
  using ElementAB = ElementAB_;
  using ElementD = ElementD_;

  using ElementAcc =
      typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
                                float>::type;

  using Operator =
      typename std::conditional<std::is_same_v<ElementAB, int8_t>,
                                cutlass::arch::OpMultiplyAddSaturate,
                                FP8MathOperator>::type;

  using OutputTileThreadMap =
      cutlass::epilogue::threadblock::OutputTileThreadLayout<
          TileShape, WarpShape, float, 4, 1 /* epilogue stages */
          >;

  using Epilogue = Epilogue_<ElementD, OutputTileThreadMap>;
  using EVTCompute = typename Epilogue::EVTCompute;

  using D = cutlass::epilogue::threadblock::VisitorAuxStore<
      OutputTileThreadMap, ElementD, cutlass::FloatRoundStyle::round_to_nearest,
      Stride<int64_t, Int<1>, Int<0>>>;

  using EVTD = cutlass::epilogue::threadblock::Sm80EVT<D, EVTCompute>;

  // clang-format off
  using RowMajor = typename cutlass::layout::RowMajor;
  using ColumnMajor = typename cutlass::layout::ColumnMajor;
  using KernelType =
    ArchGuard<typename cutlass::gemm::kernel::DefaultGemmWithVisitor<
      ElementAB, RowMajor, cutlass::ComplexTransform::kNone, 16,
      ElementAB, ColumnMajor, cutlass::ComplexTransform::kNone, 16,
      float, cutlass::layout::RowMajor, 4,
      ElementAcc, float, cutlass::arch::OpClassTensorOp,
      Arch,
      TileShape, WarpShape, InstructionShape,
      EVTD,
      cutlass::gemm::threadblock::ThreadblockSwizzleStreamK,
      MainLoopStages, Operator,
      1 /* epilogue stages */
      >::GemmKernel>;
  // clang-format on

  using Op = cutlass::gemm::device::GemmUniversalAdapter<KernelType>;
};

template <typename Gemm, typename... EpilogueArgs>
inline void cutlass_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
                                torch::Tensor const& b,
                                EpilogueArgs&&... epilogue_params) {
  using ElementAB = typename Gemm::ElementAB;
  using ElementD = typename Gemm::ElementD;

  int32_t m = a.size(0);
  int32_t n = b.size(1);
  int32_t k = a.size(1);
  cutlass::gemm::GemmCoord problem_size{m, n, k};

  int64_t lda = a.stride(0);
  int64_t ldb = b.stride(1);
  int64_t ldc = out.stride(0);

  using StrideC = Stride<int64_t, Int<1>, Int<0>>;
  StrideC c_stride{ldc, Int<1>{}, Int<0>{}};

  auto a_ptr = static_cast<ElementAB const*>(a.data_ptr());
  auto b_ptr = static_cast<ElementAB const*>(b.data_ptr());
  auto c_ptr = static_cast<ElementD*>(out.data_ptr());

  typename Gemm::D::Arguments d_args{c_ptr, c_stride};

  using Epilogue = typename Gemm::Epilogue;
  auto evt_args =
      Epilogue::prepare_args(std::forward<EpilogueArgs>(epilogue_params)...);

  typename Gemm::EVTD::Arguments epilogue_args{
      evt_args,
      d_args,
  };

  typename Gemm::Op::Arguments args{
      cutlass::gemm::GemmUniversalMode::kGemmSplitKParallel,  // universal mode
      problem_size,                                           // problem size
      1,                                                      // batch count
      epilogue_args,
      a_ptr,
      b_ptr,
      nullptr,
      nullptr,
      0,
      0,
      0,
      0,
      lda,
      ldb,
      ldc,
      ldc};

  // Launch the CUTLASS GEMM kernel.
  typename Gemm::Op gemm_op;
  size_t workspace_size = gemm_op.get_workspace_size(args);
  auto const workspace_options =
      torch::TensorOptions().dtype(torch::kUInt8).device(a.device());
  auto workspace = torch::empty(workspace_size, workspace_options);

  auto stream = at::cuda::getCurrentCUDAStream(a.get_device());

  CUTLASS_CHECK(gemm_op.can_implement(args));
  cutlass::Status status = gemm_op(args, workspace.data_ptr(), stream);
  CUTLASS_CHECK(status);
}

template <typename Gemm, typename FallbackGemm, typename... EpilogueArgs>
inline void fallback_cutlass_gemm_caller(torch::Tensor& out,
                                         torch::Tensor const& a,
                                         torch::Tensor const& b,
                                         EpilogueArgs&&... args) {
  // In some cases, the GPU isn't able to accommodate the
  // shared memory requirements of the Gemm. In such cases, use
  // the FallbackGemm instead.
  static const int max_shared_mem_per_block_opt_in =
      get_cuda_max_shared_memory_per_block_opt_in(0);

  size_t const gemm_shared_mem_size =
      sizeof(typename Gemm::KernelType::SharedStorage);
  size_t const fallback_gemm_shared_mem_size =
      sizeof(typename FallbackGemm::KernelType::SharedStorage);

  if (gemm_shared_mem_size <= max_shared_mem_per_block_opt_in) {
    return cutlass_gemm_caller<Gemm>(out, a, b,
                                     std::forward<EpilogueArgs>(args)...);
  } else {
    TORCH_CHECK(fallback_gemm_shared_mem_size <=
                max_shared_mem_per_block_opt_in);
    return cutlass_gemm_caller<FallbackGemm>(
        out, a, b, std::forward<EpilogueArgs>(args)...);
  }
}

}  // namespace vllm