// 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) 2022, NVIDIA CORPORATION. All rights reserved. */

/*This code is copied from NVIDIA apex:
 *     https://github.com/NVIDIA/apex
 *     with minor changes. */

#pragma once

#include "ln_bwd_kernels.h"  // NOLINT
#include "ln_fwd_kernels.h"  // NOLINT
#include "ln_utils.h"        // NOLINT

namespace phi {
namespace layer_norm {
template <uint32_t HIDDEN_SIZE_,
          typename weight_t_,
          typename input_t_,
          typename output_t_,
          typename compute_t_,
          typename index_t_,
          uint32_t THREADS_PER_CTA_>
struct KernelTraitsBase {
  using weight_t = weight_t_;
  using input_t = input_t_;
  using output_t = output_t_;
  using compute_t = compute_t_;
  using index_t = index_t_;

  enum { HIDDEN_SIZE = HIDDEN_SIZE_ };
  enum { THREADS_PER_CTA = THREADS_PER_CTA_ };
  enum { THREADS_PER_WARP = 32 };
};

template <uint32_t HIDDEN_SIZE_,
          typename weight_t_,
          typename input_t_,
          typename output_t_,
          typename compute_t_,
          typename index_t_,
          uint32_t THREADS_PER_CTA_,
          uint32_t BYTES_PER_LDG_,
          typename Base = KernelTraitsBase<HIDDEN_SIZE_,
                                           weight_t_,
                                           input_t_,
                                           output_t_,
                                           compute_t_,
                                           index_t_,
                                           THREADS_PER_CTA_>>
struct KernelTraitsFinalize : public Base {
  enum { ROWS_PER_CTA = Base::THREADS_PER_CTA / Base::THREADS_PER_WARP };
  static_assert((int)ROWS_PER_CTA <= (int)Base::THREADS_PER_WARP);  // NOLINT
  // Bytes per global load from the input.
  enum { BYTES_PER_LDG = BYTES_PER_LDG_ };
  // Number of elements fetched by a global load.
  enum { ELTS_PER_LDG = BYTES_PER_LDG / sizeof(compute_t_) };
  // Bytes per global store of the weights.
  enum { BYTES_PER_STG = ELTS_PER_LDG * sizeof(weight_t_) };
  static_assert(
      sizeof(BYTES_PER_LDG) == 4,
      "Conflict-free smem transpose only implemented for 4B compute type!");
  static_assert(Base::THREADS_PER_CTA == ROWS_PER_CTA * Base::THREADS_PER_WARP,
                "We assume one warp per row!");
  // The total number of BYTES_PER_LDG-wide words in a hidden vector.
  enum { COLS = HIDDEN_SIZE_ * sizeof(compute_t_) / BYTES_PER_LDG };
  static_assert(COLS * BYTES_PER_LDG == HIDDEN_SIZE_ * sizeof(compute_t_));

  // Shared memory size to transpose the CTA result.
  enum { SMEM_BYTES_TRANSPOSE = Base::THREADS_PER_CTA * BYTES_PER_LDG };
  // Shared memory size to coalesce the CTA result.
  enum { SMEM_BYTES_OUTPUT = Base::THREADS_PER_WARP * BYTES_PER_LDG };
  // Shared memory requirement per CTA.
  enum {
    SMEM_BYTES_PER_CTA = 2 * SMEM_BYTES_TRANSPOSE + 2 * SMEM_BYTES_OUTPUT
  };

  // The type of the reducer.
  using Reducer = layer_norm::Reducer<compute_t_, 1, 1, 1>;

  // Condition for the whole CTA to participate in syncthreads.
  static_assert(COLS % Base::THREADS_PER_WARP == 0);
  enum { CTAS = COLS / Base::THREADS_PER_WARP };
};

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

template <typename weight_t_,
          typename input_t_,
          typename output_t_,
          typename compute_t_,
          typename index_t_,
          uint32_t HIDDEN_SIZE_,
          uint32_t CTAS_PER_ROW_,
          uint32_t WARPS_M_,
          uint32_t WARPS_N_,
          uint32_t BYTES_PER_LDG_ = 16,
          typename Base =
              KernelTraitsBase<HIDDEN_SIZE_,
                               weight_t_,
                               input_t_,
                               output_t_,
                               compute_t_,
                               index_t_,
                               WARPS_M_ * WARPS_N_ * THREADS_PER_WARP>>
struct KernelTraits : public Base {
  using input_t = typename Base::input_t;
  using weight_t = typename Base::weight_t;
  using compute_t = typename Base::compute_t;
  using output_t = typename Base::output_t;
  using index_t = typename Base::index_t;

  enum { CTAS_PER_ROW = CTAS_PER_ROW_ };
  enum { WARPS_M = WARPS_M_ };
  enum { WARPS_N = WARPS_N_ };
  enum { COLS = HIDDEN_SIZE_ };
  enum { HIDDEN_SIZE = HIDDEN_SIZE_ };
  enum { BYTES_PER_LDG = BYTES_PER_LDG_ };
  enum { NUM_ELTS = BYTES_PER_LDG / sizeof(input_t) };

  enum { THREADS_PER_ROW = WARPS_N * THREADS_PER_WARP };
  enum { THREADS_PER_CTA = WARPS_M * THREADS_PER_ROW };
  enum { ROWS_PER_CTA = WARPS_M };

  enum { BYTES_PER_ROW = COLS * sizeof(input_t) };
  enum { BYTES_PER_ROW_PER_CTA = THREADS_PER_ROW * BYTES_PER_LDG };
  // Multi-row per CTA not supported for multi-CTA => no smem for WGRAD needed
  enum {
    SMEM_BYTES_WGRAD =
        CTAS_PER_ROW > 1 ? 0 : ROWS_PER_CTA* COLS * sizeof(compute_t)
  };
  static_assert(WARPS_M == 1 || CTAS_PER_ROW == 1);

  using reduce_t = typename layer_norm::TypeToVec2<compute_t>::Type;
  using Reducer = layer_norm::Reducer<reduce_t, CTAS_PER_ROW, WARPS_M, WARPS_N>;

  enum { SMEM_BYTES_DGRAD = Reducer::SMEM_BYTES };
  enum { SMEM_BYTES = SMEM_BYTES_DGRAD + SMEM_BYTES_WGRAD };

  using Ivec = layer_norm::Vec<input_t, NUM_ELTS>;
  using Ovec = layer_norm::Vec<output_t, NUM_ELTS>;
  using Wvec = layer_norm::Vec<weight_t, NUM_ELTS>;
  using Cvec = layer_norm::Vec<compute_t, NUM_ELTS>;
  enum { ELTS_PER_LDG = BYTES_PER_LDG / sizeof(input_t) };

  // Assume that each thread can handle the same number of elements in the
  // output and weights as in the input.
  static_assert(sizeof(input_t) >= sizeof(output_t));
  static_assert(sizeof(input_t) >= sizeof(weight_t));
  // The number of columns fetched per load from input: one per thread.
  enum { VEC_COLS_PER_LDG = CTAS_PER_ROW * THREADS_PER_ROW };
  // The total number of vectorized loads/stores per hidden vector.
  enum { VEC_COLS = COLS / ELTS_PER_LDG };
  // The number of loads per thread for the input.
  enum { LDGS = VEC_COLS / VEC_COLS_PER_LDG };
  static_assert(LDGS * VEC_COLS_PER_LDG == VEC_COLS);
  // static_assert(LDGS * BYTES_PER_ROW_PER_CTA * CTAS_PER_ROW == BYTES_PER_ROW,
  // "");

  using Stats = layer_norm::Stats<compute_t, CTAS_PER_ROW, WARPS_M, WARPS_N>;
  enum { SMEM_BYTES_FWD = Stats::SMEM_BYTES };
};

}  // namespace layer_norm
}  // namespace phi