// 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.

/*! \file
  \brief Epilogue for threadblock scoped GEMMs using Tensor Ops.

  The epilogue rearranges the result of a matrix product through shared memory
  to match canonical tensor layouts in global memory. Epilogues support
  conversion and reduction operations.

  This is a copy of cutlass/epilogue/threadblock/epilogue.h that can
  handle "row_id" as a first argument, as uses it to get the corresponding
  `m_prime` / `s_prime` to rescale the output.
*/

#pragma once

#if defined(__CUDACC_RTC__)
#include <cuda/std/cassert>
#else
#include <assert.h>
#endif

#include "cutlass/aligned_buffer.h"
#include "cutlass/array.h"
#include "cutlass/cutlass.h"
#include "cutlass/functional.h"
#include "cutlass/layout/tensor.h"
#include "cutlass/layout/vector.h"
#include "cutlass/numeric_types.h"
#include "cutlass/tensor_coord.h"

#include "cutlass/gemm/gemm.h"

#include "cutlass/transform/pitch_linear_thread_map.h"
#include "cutlass/transform/threadblock/regular_tile_iterator.h"

#include "cutlass/epilogue/threadblock/epilogue_base.h"
#include "cutlass/epilogue/threadblock/predicated_tile_iterator.h"

#include "./epilogue_pipelined.h"
#include "cutlass/epilogue/thread/scale_type.h"
#include "cutlass/numeric_conversion.h"

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

namespace cutlass {
namespace epilogue {
namespace thread {

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

/// Applies a linear combination operator to an array of elements.
// output <- alpha * accumulator + beta * source
//   with:
//     alpha = 1 / s_prime (to normalize when isLast=True, 1 otherwise)
//     beta = alpha / m_prime (renormalize the output when the max changes)
//     source is the current output
template <typename ElementOutput_,  /// < Data type used to store tensors
          typename ElementSource_,  // < Data type for source (usually matches
                                    // `ElementOutput`)
          int Count,  ///< Number of elements computed per operation.
                      ///< Usually it is 128/sizeof_bits<ElementOutput_>,
                      ///< but we use 64 or 32 sometimes when there are not
                      ///< enough data to store
          typename ElementAccumulator_,  ///< Accumulator data type
          typename ElementCompute_,      ///< Data type used to compute linear
                                         ///< combination
          bool isFirst,
          bool isLast,
          typename FragmentAlphaBeta_,
          FloatRoundStyle Round = FloatRoundStyle::round_to_nearest>
class MemoryEfficientAttentionNormalize {
 public:
  using ElementOutput = ElementOutput_;
  using ElementSource = ElementSource_;
  using ElementAccumulator = ElementAccumulator_;
  using ElementCompute = ElementCompute_;

  static int const kCount = Count;

  using FragmentOutput = Array<ElementOutput, kCount>;
  using FragmentSource = Array<ElementSource, kCount>;
  using FragmentAccumulator = Array<ElementAccumulator, kCount>;
  using ComputeFragment = Array<ElementCompute, kCount>;
  using FragmentAlphaBeta = FragmentAlphaBeta_;

  static FloatRoundStyle const kRound = Round;

 private:
  //
  // Data members
  //

  FragmentAlphaBeta const& s_prime_;
  FragmentAlphaBeta const& m_prime_;

 public:
  /// Constructs the function object, possibly loading from pointers in host
  /// memory
  CUTLASS_HOST_DEVICE
  MemoryEfficientAttentionNormalize(FragmentAlphaBeta const& s_prime,
                                    FragmentAlphaBeta const& m_prime)
      : s_prime_(s_prime), m_prime_(m_prime) {}

  /// Returns true if source is needed
  CUTLASS_HOST_DEVICE
  bool is_source_needed() const { return !isFirst; }

  /// Functionally required for serial reduction in the epilogue
  CUTLASS_HOST_DEVICE
  void set_k_partition(int k_partition, int k_partition_count) {}

  /// Computes linear scaling: D = alpha * accumulator + beta * source
  CUTLASS_HOST_DEVICE
  FragmentOutput operator()(int row,
                            FragmentAccumulator const& accumulator,
                            FragmentSource const& source) const {
    assert(!isFirst);

    // Convert source to internal compute numeric type
    NumericArrayConverter<ElementCompute, ElementSource, kCount, Round>
        source_converter;
    NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
        accumulator_converter;

    // Convert to destination numeric type
    NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
        destination_converter;

    ComputeFragment converted_source = source_converter(source);
    ComputeFragment converted_accumulator = accumulator_converter(accumulator);

    // Perform binary operations
    ComputeFragment intermediate;

    multiplies<ComputeFragment> mul_add_source;
    multiply_add<ComputeFragment> mul_add_accumulator;

    ElementCompute alpha = isLast ? (1 / s_prime_[row]) : 1;
    ElementCompute beta = alpha * m_prime_[row];

    intermediate = mul_add_source(beta, converted_source);  // X =  beta * C

    intermediate = mul_add_accumulator(
        alpha, converted_accumulator, intermediate);  // D = alpha * Accum + X

    return destination_converter(intermediate);
  }

  /// Computes linear scaling: D = alpha * accumulator
  CUTLASS_HOST_DEVICE
  FragmentOutput operator()(int row,
                            FragmentAccumulator const& accumulator) const {
    assert(isFirst);

    // Convert source to internal compute numeric type
    NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
        accumulator_converter;

    // Convert to destination numeric type
    NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
        destination_converter;

    ComputeFragment converted_accumulator = accumulator_converter(accumulator);

    ComputeFragment intermediate;
    multiplies<ComputeFragment> mul_accumulator;

    ElementCompute alpha = isLast ? (1 / s_prime_[row]) : 1;

    intermediate = mul_accumulator(
        alpha, converted_accumulator);  // X =  alpha * C + uniform

    return destination_converter(intermediate);
  }
};

}  // namespace thread

namespace threadblock {
template <typename EO,
          typename ES,
          int Count,
          typename EA,
          typename EC,
          bool F,
          bool L,
          typename FAB,
          FloatRoundStyle R>
struct ApplyEpilogueOp<thread::MemoryEfficientAttentionNormalize<EO,
                                                                 ES,
                                                                 Count,
                                                                 EA,
                                                                 EC,
                                                                 F,
                                                                 L,
                                                                 FAB,
                                                                 R>> {
  using Op = thread::
      MemoryEfficientAttentionNormalize<EO, ES, Count, EA, EC, F, L, FAB, R>;
  static CUTLASS_DEVICE typename Op::FragmentOutput apply(
      Op const& output_op,
      int row_id,
      typename Op::FragmentAccumulator const& accum,
      typename Op::FragmentSource const& source) {
    return output_op(row_id, accum, source);
  }
  static CUTLASS_DEVICE typename Op::FragmentOutput apply(
      Op const& output_op,
      int row_id,
      typename Op::FragmentAccumulator const& accum) {
    return output_op(row_id, accum);
  }
};

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

}  // namespace threadblock
}  // namespace epilogue
}  // namespace cutlass

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