// Copyright (c) 2023 PaddlePaddle Authors. 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.

//  Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
//
//  This source code is licensed under the BSD license found in the
//  LICENSE file in the root directory of this source tree.

#pragma once

#include "cutlass/functional.h"
#include "cutlass/gemm/warp/mma_simt_tile_iterator.h"
#include "cutlass/gemm/warp/mma_tensor_op_tile_iterator_sm70.h"
#include "cutlass/gemm/warp/mma_tensor_op_tile_iterator_sm80.h"
#include "cutlass/matrix_shape.h"

/*
TensorCores have different accumulator layouts.
This file provides a class to easily map the accumulator
i-th element with the corresponding matrix row/col.
*/

template <typename T, typename accum_t, int kWarpSize>
struct AccumLambdaIteratorSm80 {
  static_assert(cutlass::platform::is_same<typename T::Layout,
                                           cutlass::layout::RowMajor>::value,
                "only RowMajor is supported");

  using Policy = typename T::Policy;
  using InstructionShape = typename T::InstructionShape;
  using OpDelta = typename T::OpDelta;
  using Shape = typename T::Shape;
  static int const kElementsPerAccess = InstructionShape::kN / 4;
  static int const kRowsPerTile = 8;
  static int const kAccumulatorRows = InstructionShape::kM / kRowsPerTile;

  static cutlass::MatrixCoord CUTLASS_DEVICE
  get_lane_offset(int8_t lane_id,
                  int8_t warp_id,
                  typename T::TensorCoord const& tile_offset) {
    int quad = (lane_id >> 2);
    int lane_in_quad = (lane_id & 3);
    return cutlass::MatrixCoord(quad + tile_offset.row() * Shape::kRow,
                                lane_in_quad * kElementsPerAccess +
                                    tile_offset.column() * Shape::kColumn);
  }

  template <typename FA, typename FB, typename FC>
  CUTLASS_DEVICE static void iterateRows(
      cutlass::MatrixCoord& lane_offset,  // NOLINT
      FA beginRow,
      FB op,
      FC endRow) {
    // See cutlass/gemm/warp/mma_tensor_op_tile_iterator.h
    CUTLASS_PRAGMA_UNROLL
    for (int mma_m = 0; mma_m < Policy::MmaIterations::kRow; ++mma_m) {
      CUTLASS_PRAGMA_UNROLL
      for (int row = 0; row < kAccumulatorRows; ++row) {
        int accum_m = mma_m * InstructionShape::kM * OpDelta::kRow +
                      row * kRowsPerTile + lane_offset.row();
        beginRow(accum_m);

        CUTLASS_PRAGMA_UNROLL
        for (int mma_n = 0; mma_n < Policy::MmaIterations::kColumn; ++mma_n) {
          int mma_accum_start = kAccumulatorRows * kElementsPerAccess *
                                (mma_n * Policy::MmaIterations::kRow + mma_m);
          CUTLASS_PRAGMA_UNROLL
          for (int col = 0; col < kElementsPerAccess; ++col) {
            int accum_n = mma_n * InstructionShape::kN * OpDelta::kColumn +
                          col + lane_offset.column();
            int idx = mma_accum_start + row * kElementsPerAccess + col;
            op(accum_m, accum_n, idx);
          }
        }

        endRow(accum_m);
      }
    }
  }

  template <typename DT, typename F>
  CUTLASS_DEVICE static bool reduceSameRow(int lane_id,
                                           DT& myValue,  // NOLINT
                                           F fn) {       // NOLINT
    // In each warp, 4 threads will work on the same row
    // - the ones with the same `quad`
    auto otherV = __shfl_xor_sync(0xffffffff, myValue, 1);
    myValue = fn(myValue, otherV);
    otherV = __shfl_xor_sync(0xffffffff, myValue, 2);
    myValue = fn(myValue, otherV);
    int lane_in_quad = (lane_id & 3);
    return lane_in_quad == 0;
  }
};

template <typename T, typename accum_t, int kWarpSize>
struct AccumLambdaIteratorSm70 {
  static_assert(cutlass::platform::is_same<typename T::Layout,
                                           cutlass::layout::RowMajor>::value,
                "only RowMajor is supported");

  using Policy = typename T::Policy;
  using InstructionShape = typename T::InstructionShape;
  using OpDelta = typename T::OpDelta;
  using Shape = typename T::Shape;
  using Element = accum_t;

  static int const kElementsPerPartial = 4;
  using EleShapePerPatial = typename cutlass::platform::conditional<
      cutlass::platform::is_same<Element, float>::value,
      cutlass::MatrixShape<2, 2>,
      cutlass::MatrixShape<1, 4>>::type;
  static int const kElementsPerMma = 8;
  static int const kAccumulatorPatials = 2;
  using QuadShapePerPatialMma = cutlass::MatrixShape<4, 4>;

  static cutlass::MatrixCoord CUTLASS_DEVICE
  get_lane_offset(int8_t lane_id,
                  int8_t warp_id,
                  typename T::TensorCoord const& tile_offset) {
    int quad = (lane_id >> 2);
    int lane_in_quad = (lane_id & 3);
    int accum_m, accum_n;

    if (cutlass::platform::is_same<Element, float>::value) {
      // (quad[2],quad[0])+lane_in_quad[0]
      accum_m = (((quad & 0x4) >> 1) + (quad & 0x1)) * 8 + (lane_in_quad & 1);
      // (quad[1])+lane_in_quad[1]
      accum_n =
          ((quad >> 1) & 0x1) * kElementsPerPartial * kAccumulatorPatials +
          (lane_in_quad & 2);
    } else {
      accum_m = (((quad & 0x4) >> 1) + (quad & 0x1)) * 8 +
                lane_in_quad;  // (quad[2],quad[0])
      accum_n = ((quad >> 1) & 0x1) * kElementsPerPartial * kAccumulatorPatials;
    }
    return cutlass::MatrixCoord(
        accum_m + tile_offset.row() * Shape::kRow,
        accum_n + tile_offset.column() * Shape::kColumn);
  }

  template <typename DT, typename F>
  CUTLASS_DEVICE static bool reduceSameRow(int lane_id,
                                           DT& myValue,  // NOLINT
                                           F fn) {       // NOLINT
    static_assert(cutlass::platform::is_same<Element, float>::value,
                  "update to support non-float accum");
    // https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-fragment-mma-884-f16
    // T0 & T2 share same line within a quad
    auto otherV = __shfl_xor_sync(0xffffffff, myValue, 1 << 1);
    myValue = fn(myValue, otherV);
    // quad 0 and quad 2 are on the same lines
    otherV = __shfl_xor_sync(0xffffffff, myValue, 1 << 3);
    myValue = fn(myValue, otherV);
    return (lane_id & ((1 << 1) | (1 << 3))) == 0;
  }

  template <typename FA, typename FB, typename FC>
  CUTLASS_DEVICE static void iterateRows(
      cutlass::MatrixCoord& lane_offset,  // NOLINT
      FA beginRow,
      FB op,
      FC endRow) {
    CUTLASS_PRAGMA_UNROLL
    for (int tile_m = 0; tile_m < Policy::TileIterations::kRow; ++tile_m) {
      CUTLASS_PRAGMA_UNROLL
      for (int mma_m = 0; mma_m < Policy::MmaIterations::kRow; ++mma_m) {
        CUTLASS_PRAGMA_UNROLL
        for (int m = 0; m < EleShapePerPatial::kRow; ++m) {
          int accum_m = tile_m * Policy::InterleavedTile::kRow +
                        mma_m * QuadShapePerPatialMma::kRow + m * 2 +
                        lane_offset.row();
          beginRow(accum_m);

          CUTLASS_PRAGMA_UNROLL
          for (int tile_n = 0; tile_n < Policy::TileIterations::kColumn;
               ++tile_n) {
            CUTLASS_PRAGMA_UNROLL
            for (int mma_n = 0; mma_n < Policy::MmaIterations::kColumn;
                 ++mma_n) {
              CUTLASS_PRAGMA_UNROLL
              for (int p = 0; p < kAccumulatorPatials; ++p) {
                CUTLASS_PRAGMA_UNROLL
                for (int n = 0; n < EleShapePerPatial::kColumn; ++n) {
                  int mma_accum_start =
                      (((tile_n * Policy::TileIterations::kRow + tile_m) *
                            Policy::MmaIterations::kColumn +
                        mma_n) *
                           Policy::MmaIterations::kRow +
                       mma_m) *
                      kElementsPerMma;
                  int accum_n = tile_n * Policy::InterleavedTile::kColumn +
                                mma_n * QuadShapePerPatialMma::kColumn +
                                p * Policy::InterleavedTile::kColumn / 2 + n +
                                lane_offset.column();
                  int idx = mma_accum_start + p * kElementsPerPartial +
                            m * EleShapePerPatial::kColumn + n;
                  op(accum_m, accum_n, idx);
                }
              }
            }
          }
          endRow(accum_m);
        }
      }
    }
  }
};

template <typename T, typename accum_t, int kWarpSize>
struct AccumLambdaIteratorSimt {
  using Policy = typename T::Policy;
  using Iterations = typename T::Iterations;
  using Element = typename T::Element;
  using Delta = typename T::Delta;
  using Shape = typename T::Shape;
  static_assert(cutlass::platform::is_same<typename T::Layout,
                                           cutlass::layout::RowMajor>::value,
                "only RowMajor is supported");

  template <typename DT, typename F>
  CUTLASS_DEVICE static bool reduceSameRow(int lane_id,
                                           DT& myValue,  // NOLINT
                                           F fn) {       // NOLINT
    CUTLASS_PRAGMA_UNROLL
    for (int bit = 1; bit < Policy::WarpShape::kColumn; bit *= 2) {
      auto otherV = __shfl_xor_sync(0xffffffff, myValue, bit);
      myValue = fn(myValue, otherV);
    }
    return (lane_id & (Policy::WarpShape::kColumn - 1)) == 0;
  }

  template <typename FA, typename FB, typename FC>
  CUTLASS_DEVICE static void iterateRows(
      cutlass::MatrixCoord& lane_offset,  // NOLINT
      FA beginRow,
      FB op,
      FC endRow) {
    CUTLASS_PRAGMA_UNROLL
    for (int mma_m = 0; mma_m < Iterations::kRow; ++mma_m) {
      CUTLASS_PRAGMA_UNROLL
      for (int m = 0; m < Policy::LaneMmaShape::kM; ++m) {
        int accum_m = mma_m * Delta::kRow + m + lane_offset.row();
        beginRow(accum_m);

        CUTLASS_PRAGMA_UNROLL
        for (int mma_n = 0; mma_n < Iterations::kColumn; ++mma_n) {
          int accum_n =
              mma_n * Policy::WarpShape::kColumn * Policy::LaneMmaShape::kN +
              lane_offset.column();
          CUTLASS_PRAGMA_UNROLL
          for (int n = 0; n < Policy::LaneMmaShape::kN; ++n) {
            int idx =
                n + Policy::LaneMmaShape::kN *
                        (mma_n + Iterations::kColumn *
                                     (m + mma_m * Policy::LaneMmaShape::kM));
            op(accum_m, accum_n + n, idx);
          }
        }
        endRow(accum_m);
      }
    }
  }

  static cutlass::MatrixCoord CUTLASS_DEVICE
  get_lane_offset(int8_t lane_id,
                  int8_t warp_id,
                  typename T::TensorCoord const& tile_offset) {
    static_assert(cutlass::platform::is_same<
                      typename Policy::LaneLayout,
                      cutlass::layout::RowMajorInterleaved<1>>::value,
                  "");
    typename Policy::LaneLayout lane_layout = Policy::get_lane_layout();

    cutlass::MatrixCoord lane_offset =
        lane_layout.inverse(lane_id) *
        cutlass::MatrixCoord(Policy::LaneMmaShape::kM,
                             Policy::LaneMmaShape::kN);
    return lane_offset +
           tile_offset * cutlass::MatrixCoord(Shape::kRow, Shape::kColumn);
  }
};

template <typename T, typename accum_t, int kWarpSize>
struct DefaultMmaAccumLambdaIterator;

// Simt
template <typename S, typename P, typename accum_t, int kWarpSize>
struct DefaultMmaAccumLambdaIterator<
    cutlass::gemm::warp::MmaSimtTileIterator<S,
                                             cutlass::gemm::Operand::kC,
                                             accum_t,
                                             cutlass::layout::RowMajor,
                                             P,
                                             1,
                                             1>,
    accum_t,
    kWarpSize> {
  using WarpIterator = typename cutlass::gemm::warp::MmaSimtTileIterator<
      S,
      cutlass::gemm::Operand::kC,
      accum_t,
      cutlass::layout::RowMajor,
      P,
      1,
      1>;
  using Iterator = AccumLambdaIteratorSimt<WarpIterator, accum_t, kWarpSize>;
};

// TensorOp - Volta
template <typename S1, typename S2, typename accum_t, int kWarpSize>
struct DefaultMmaAccumLambdaIterator<
    cutlass::gemm::warp::MmaVoltaTensorOpAccumulatorTileIterator<
        S1,
        accum_t,
        cutlass::layout::RowMajor,
        S2,
        cutlass::MatrixShape<1, 1>>,
    accum_t,
    kWarpSize> {
  using WarpIterator =
      typename cutlass::gemm::warp::MmaVoltaTensorOpAccumulatorTileIterator<
          S1,
          accum_t,
          cutlass::layout::RowMajor,
          S2,
          cutlass::MatrixShape<1, 1>>;
  using Iterator = AccumLambdaIteratorSm70<WarpIterator, accum_t, kWarpSize>;
};

// TensorOp - Sm75+
template <typename S1,
          typename S2,
          typename S3,
          typename accum_t,
          int kWarpSize>
struct DefaultMmaAccumLambdaIterator<
    cutlass::gemm::warp::MmaTensorOpAccumulatorTileIterator<
        S1,
        accum_t,
        cutlass::layout::RowMajor,
        S2,
        S3>,
    accum_t,
    kWarpSize> {
  using WarpIterator =
      typename cutlass::gemm::warp::MmaTensorOpAccumulatorTileIterator<
          S1,
          accum_t,
          cutlass::layout::RowMajor,
          S2,
          S3>;
  using Iterator = AccumLambdaIteratorSm80<WarpIterator, accum_t, kWarpSize>;
};
