/***************************************************************************************************
 * Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
 * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
 * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 **************************************************************************************************/
/*! \file
    \brief This file contains definitions and utility functions for describing convolution problem sizes.

  Conv3dProblem desciption:
    activation (NDHWC), 
    filter (KTRSC), 
    output (NZPQK), 
    pading (pad_d, pad_h, pad_w), 
    stride (stride_d, stride_h, stride_w), 
    dilation (dilation_d, dilation_h, dilation_w).
  
  Free functions to map:
    Map tensor extents (Conv3d -> ImplicitGemm)      : implicit_gemm_tensor_[a|b|c]_extent(ConvolutionOperator)
    Map tensor sizes (Conv3d -> ImplicitGemm)        : implicit_gemm_tensor_[a|b|c]_size(ConvolutionOperator)
    Map tensor problem sizes (Conv3d -> ImplicitGemm): implicit_gemm_problem_size(ConvolutionOperator)  
*/

#pragma once

#include "cutlass/conv/convolution.h"
#include "cutlass/conv/conv2d_problem_size.h"

namespace cutlass {
namespace conv {

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

/// Problem size structure
struct Conv3dProblemSize : public Conv2dProblemSize {
  //
  // Type definitions
  //

  // 3D coordinate for padding, stride, and dilation in (d, h, w) dimensions
  using Coord3D = Coord<3>;

  //
  // Data members
  //

  // Conv3d strictly problem size parameters
  int D, T, Z;    // input depth, filter depth, output depth
  int pad_d;      // padding in depth dimension
  int stride_d;   // stride in depth dimension
  int dilation_d; // dilation in depth dimension

  //
  // Methods
  //
public:
  CUTLASS_HOST_DEVICE
  Conv3dProblemSize(): 
    D(0), T(0), Z(0), 
    pad_d(0), 
    stride_d(1), 
    dilation_d(1),
    Conv2dProblemSize() { }
 
  /// Constructor for default padding, stride, dilation, and split-K
  CUTLASS_HOST_DEVICE
  Conv3dProblemSize(
    int N,
    int D,
    int H,
    int W,
    int C,
    int Z,
    int P,
    int Q,
    int K,
    int T,
    int R,
    int S,
    Mode mode
  ): 
    D(D), T(T), Z(Z), 
    pad_d(T / 2), stride_d(1), dilation_d(1),
    Conv2dProblemSize(N, H, W, C, P, Q, K, R, S, mode) { }

  /// Constructor
  CUTLASS_HOST_DEVICE
  Conv3dProblemSize(
    int N,
    int D,
    int H,
    int W,
    int C,
    int K,
    int T,
    int R,
    int S,
    int Z,
    int P,
    int Q,
    int pad_d,
    int pad_h,
    int pad_w,
    int stride_d,
    int stride_h,
    int stride_w,
    int dilation_d,
    int dilation_h,
    int dilation_w,
    Mode mode,
    int split_k_slices = 1,
    int groups = 1
  ): 
    D(D), T(T), Z(Z), 
    pad_d(pad_d), stride_d(stride_d), dilation_d(dilation_d),
    Conv2dProblemSize(
      N, H, W, C, K, R, S, P, Q, 
      pad_h, pad_w, 
      stride_h, stride_w, 
      dilation_h, dilation_w,
      mode, split_k_slices, groups) { }

  /// Constructs convolution problem size from cutlass Tensor5DCoord and Coord3D 
  // set *user-defined* output size and sets Z, P, and Q (include all data members in ctor)
  CUTLASS_HOST_DEVICE
  Conv3dProblemSize(
    cutlass::Tensor5DCoord input_size,    // NDHWC
    cutlass::Tensor5DCoord filter_size,   // KTRSC
    Coord3D padding,                      // pad_d, pad_h, pad_w
    Coord3D stride,                       // stride_d, stride_h, stride_w
    Coord3D dilation,                     // dilation_d, dilation_h, dilation_w
    cutlass::Tensor5DCoord output_size,   // NZPQK
    cutlass::conv::Mode mode = cutlass::conv::Mode::kCrossCorrelation,
    int split_k_slices = 1,
    int groups = 1
  ):
    D(input_size.d()), T(filter_size.d()), Z(output_size.d()),
    pad_d(padding[0]), stride_d(stride[0]), dilation_d(dilation[0]),
    Conv2dProblemSize(
      {input_size.n(), input_size.h(), input_size.w(), input_size.c()},
      {filter_size.n(), filter_size.h(), filter_size.w(), filter_size.c()},
      {padding[1], padding[1], padding[2], padding[2]},
      {stride[1], stride[2]},
      {dilation[1], dilation[2]},
      {output_size.n(), output_size.h(), output_size.w(), output_size.c()},
      mode, split_k_slices, groups
    ) { }

  /// Constructs convolution problem size from cutlass Tensor5DCoord and Coord3D 
  // *computes* output size and sets Z, P and Q (include all data members in ctor)
  CUTLASS_HOST_DEVICE
  Conv3dProblemSize(
    cutlass::Tensor5DCoord input_size,    // NDHWC
    cutlass::Tensor5DCoord filter_size,   // KTRSC
    Coord3D padding,                      // pad_d, pad_h, pad_w
    Coord3D stride,                       // stride_d, stride_h, stride_w
    Coord3D dilation,                     // dilation_d, dilation_h, dilation_w
    cutlass::conv::Mode mode = cutlass::conv::Mode::kCrossCorrelation,
    int split_k_slices = 1,
    int groups = 1
  ):
    D(input_size.d()), T(filter_size.d()),
    pad_d(padding[0]), stride_d(stride[0]), dilation_d(dilation[0]),
    Conv2dProblemSize(
      {input_size.n(), input_size.h(), input_size.w(), input_size.c()},
      {filter_size.n(), filter_size.h(), filter_size.w(), filter_size.c()},
      {padding[1], padding[1], padding[2], padding[2]},
      {stride[1], stride[2]},
      {dilation[1], dilation[2]},
      mode, split_k_slices, groups
    ) { 
      // set output Z
      Z = ((D + pad_d * 2 - T * dilation_d) / stride_d) + 1;      
    }

  /// Equality operator (ignores mode and split_k_slice)
  CUTLASS_HOST_DEVICE
  bool operator==(Conv3dProblemSize const &conv) const {
    return (
      (N == conv.N) && (D == conv.D) && (H == conv.H) && (W == conv.W) && (C == conv.C) &&
      (K == conv.K) && (T == conv.T) && (R == conv.R) && (S == conv.S) &&
      (Z == conv.Z) &&(P == conv.P) && (Q == conv.Q) &&
      (pad_d == conv.pad_d) && (pad_h == conv.pad_h) && (pad_w == conv.pad_w) &&
      (stride_d == conv.stride_d) && (stride_h == conv.stride_h) && (stride_w == conv.stride_h) &&
      (dilation_d == conv.dilation_d) && (dilation_h == conv.dilation_h) && (dilation_h == conv.dilation_h)
    );  
  }

  /// Inequality operator
  CUTLASS_HOST_DEVICE
  bool operator!=(Conv3dProblemSize const &rhs) const {
    return !(*this == rhs);
  }

  // Reset covolution mode in the problem
  CUTLASS_HOST_DEVICE
  Conv3dProblemSize reset_mode(cutlass::conv::Mode mode_) {
    Conv3dProblemSize tmp(*this);
    tmp.mode = mode_; 
    return tmp; 
  }

  // Reset covolution mode in the problem
  CUTLASS_HOST_DEVICE
  Conv3dProblemSize reset_split_k_slices(int split_k_slices_) {
    Conv3dProblemSize tmp(*this);
    tmp.split_k_slices = split_k_slices_; 
    return tmp; 
  }
  
  /// Returns activation extent as Tensor5DCoord
  CUTLASS_HOST_DEVICE
  cutlass::Tensor5DCoord activation_extent() const {

    return cutlass::Tensor5DCoord ({N, D, H, W, C});
  }

  /// Returns filter extent as Tensor5DCoord
  CUTLASS_HOST_DEVICE
  cutlass::Tensor5DCoord filter_extent() const {

    return cutlass::Tensor5DCoord ({K, T, R, S, C});
  }

  /// Returns output extent as Tensor5DCoord
  CUTLASS_HOST_DEVICE
  cutlass::Tensor5DCoord output_extent() const {

    return cutlass::Tensor5DCoord ({N, Z, P, Q, K});
  }

  /// Returns activation size in number of elements
  CUTLASS_HOST_DEVICE
  int64_t activation_size() const {

    return (N * D * H * W * C);
  }

  /// Returns filter size in number of elements
  CUTLASS_HOST_DEVICE
  int64_t filter_size() const {

    return (K * T * R * S * C);
  }

  /// Returns output size in number of elements
  CUTLASS_HOST_DEVICE
  int64_t output_size() const {

    return (N * Z * P * Q * K);
  }

  /// Returns output extent as Tensor5DCoord
  CUTLASS_HOST_DEVICE
  Coord3D padding() const {

    return Coord3D ({pad_d, pad_h, pad_w});
  }

  /// Returns stride as MatrixCoord
  CUTLASS_HOST_DEVICE
  Coord3D stride() const {

    return Coord3D ({stride_d, stride_h, stride_w});
  }

  /// Returns dilation as MatrixCoord
  CUTLASS_HOST_DEVICE
  Coord3D dilation() const {

    return Coord3D ({dilation_d, dilation_h, dilation_w});
  }

};


////////////////////////////////////////////////////////////////////////////////////////////////////
//                                  ImplicitGemm helper functions                                 //
////////////////////////////////////////////////////////////////////////////////////////////////////

/// Determine the problem size of the implicit GEMM operation
CUTLASS_HOST_DEVICE
cutlass::gemm::GemmCoord implicit_gemm_problem_size(
  Operator conv_operator, 
  Conv3dProblemSize const &problem_size) {
  // Compute problem size
  switch (conv_operator) {
  case Operator::kFprop:
    return gemm::GemmCoord(
      problem_size.N * problem_size.Z * problem_size.P * problem_size.Q,
      problem_size.K,
      problem_size.T * problem_size.R * problem_size.S * problem_size.C
    );
  case Operator::kDgrad:
    return gemm::GemmCoord(
      problem_size.N * problem_size.D * problem_size.H * problem_size.W,
      problem_size.C,
      problem_size.T * problem_size.R * problem_size.S * problem_size.K
    );
  case Operator::kWgrad:
    return gemm::GemmCoord(
      problem_size.K,
      problem_size.T * problem_size.R * problem_size.S * problem_size.C,
      problem_size.N * problem_size.Z * problem_size.P * problem_size.Q
    );
  default:
    break;
  }
  return gemm::GemmCoord();
}

// Determine the number of gemm_k iterations for conv2d problem using implicit gemm algorithm
CUTLASS_HOST_DEVICE
int implicit_gemm_k_iterations(
  Operator conv_operator, 
  int threadblock_K, 
  Conv3dProblemSize const &problem_size,
  IteratorAlgorithm algorithm = IteratorAlgorithm::kAnalytic) {

  int iterations = 0;
  int elements_per_split_k_slice = 0;

  switch (conv_operator) {
    case Operator::kFprop:
      elements_per_split_k_slice = (problem_size.C + problem_size.split_k_slices - 1) / problem_size.split_k_slices;
      iterations = problem_size.T * problem_size.R * problem_size.S * ((elements_per_split_k_slice + threadblock_K - 1) / threadblock_K);
      break;
  
    case Operator::kDgrad:
      elements_per_split_k_slice =  (problem_size.K + problem_size.split_k_slices - 1) / problem_size.split_k_slices;
      iterations = problem_size.T * problem_size.R * problem_size.S * ((elements_per_split_k_slice + threadblock_K - 1) / threadblock_K);
      break;
  
    case Operator::kWgrad:
      elements_per_split_k_slice = (problem_size.N * problem_size.Z * problem_size.P * problem_size.Q + problem_size.split_k_slices - 1) / problem_size.split_k_slices;
      iterations = (elements_per_split_k_slice + threadblock_K - 1) / threadblock_K;
      break;
  
    default:
      break;
  }

  return iterations;
}

////////////////////////////////////////////////////////////////////////////////
//  Mapping function (ImplicitGemm A, B, C -> Conv Activation, Filter, Output)
////////////////////////////////////////////////////////////////////////////////
/// Returns ImplicitGemm tensor A extent as Tensor5DCoord
CUTLASS_HOST_DEVICE
cutlass::Tensor5DCoord implicit_gemm_tensor_a_extent(
  Operator conv_operator,
  Conv3dProblemSize const &problem_size) {
  switch (conv_operator) {
    case cutlass::conv::Operator::kFprop: return problem_size.activation_extent();
    case cutlass::conv::Operator::kDgrad: return problem_size.output_extent();
    case cutlass::conv::Operator::kWgrad: return problem_size.output_extent();
    default : break;
  }
  return cutlass::Tensor5DCoord();
}

/// Returns ImplicitGemm tensor B extent as Tensor5DCoord
CUTLASS_HOST_DEVICE
cutlass::Tensor5DCoord implicit_gemm_tensor_b_extent(
  Operator conv_operator,
  Conv3dProblemSize const &problem_size) {
  switch (conv_operator) {
    case cutlass::conv::Operator::kFprop: return problem_size.filter_extent();
    case cutlass::conv::Operator::kDgrad: return problem_size.filter_extent();
    case cutlass::conv::Operator::kWgrad: return problem_size.activation_extent();
    default : break;
  }
  return cutlass::Tensor5DCoord();
}

/// Returns ImplicitGemm tensor C extent as Tensor5DCoord
CUTLASS_HOST_DEVICE
cutlass::Tensor5DCoord implicit_gemm_tensor_c_extent(
  Operator conv_operator,
  Conv3dProblemSize const &problem_size) {
  switch (conv_operator) {
    case cutlass::conv::Operator::kFprop: return problem_size.output_extent();
    case cutlass::conv::Operator::kDgrad: return problem_size.activation_extent();
    case cutlass::conv::Operator::kWgrad: return problem_size.filter_extent();
    default : break;
  }
  return cutlass::Tensor5DCoord();
}

/// Returns ImplicitGemm tensor A size in number of elements
CUTLASS_HOST_DEVICE
int64_t implicit_gemm_tensor_a_size(
  Operator conv_operator,
  Conv3dProblemSize const &problem_size) {
  switch (conv_operator) {
    case cutlass::conv::Operator::kFprop: return problem_size.activation_size();
    case cutlass::conv::Operator::kDgrad: return problem_size.output_size();
    case cutlass::conv::Operator::kWgrad: return problem_size.output_size();
    default : break;
  }
  return 0;
}

/// Returns ImplicitGemm tensor B size in number of elements
CUTLASS_HOST_DEVICE
int64_t implicit_gemm_tensor_b_size(
  Operator conv_operator,
  Conv3dProblemSize const &problem_size) {
  switch (conv_operator) {
    case cutlass::conv::Operator::kFprop: return problem_size.filter_size();
    case cutlass::conv::Operator::kDgrad: return problem_size.filter_size();
    case cutlass::conv::Operator::kWgrad: return problem_size.activation_size();
    default : break;
  }
  return 0;
}

/// Returns ImplicitGemm tensor C size in number of elements
CUTLASS_HOST_DEVICE
int64_t implicit_gemm_tensor_c_size(
  Operator conv_operator,
  Conv3dProblemSize const &problem_size) {
  switch (conv_operator) {
    case cutlass::conv::Operator::kFprop: return problem_size.output_size();
    case cutlass::conv::Operator::kDgrad: return problem_size.activation_size();
    case cutlass::conv::Operator::kWgrad: return problem_size.filter_size();
    default : break;
  }
  return 0;
}

} // namespace conv
} // namespace cutlass

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