/*
 *  ******************************************************************************
 *  *
 *  *
 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
 *  *
 *  * See the NOTICE file distributed with this work for additional
 *  * information regarding copyright ownership.
 *  * 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.
 *  *
 *  * SPDX-License-Identifier: Apache-2.0
 *  *****************************************************************************
 */

//
// @author Yurii Shyrma, created on 05.02.2018
//

#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_conv3dnew)

#include <helpers/MmulHelper.h>
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/addBias.h>
#include <ops/declarable/helpers/convolutions.h>

namespace sd {
namespace ops {

CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) {
  auto input = INPUT_VARIABLE(0);    // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
  auto weights = INPUT_VARIABLE(1);  // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
  auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;  // [oC]
  auto output = OUTPUT_VARIABLE(0);  // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)

  REQUIRE_TRUE(input->rankOf() == 5, 0,
               "CUSTOM CONV3D OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
  REQUIRE_TRUE(weights->rankOf() == 5, 0,
               "CUSTOM CONV3D OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());

  LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0));  // filter(kernel) depth
  LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1));  // filter(kernel) height
  LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(weights->sizeAt(2));  // filter(kernel) width
  LongType sD = INT_ARG(3);                                                          // strides depth
  LongType sH = INT_ARG(4);                                                          // strides height
  LongType sW = INT_ARG(5);                                                          // strides width
  LongType pD = INT_ARG(6);                                                          // paddings depth
  LongType pH = INT_ARG(7);                                                          // paddings height
  LongType pW = INT_ARG(8);                                                          // paddings width
  LongType dD = INT_ARG(9);                                                          // dilations depth
  LongType dH = INT_ARG(10);                                                         // dilations height
  LongType dW = INT_ARG(11);                                                         // dilations width
  int paddingMode = INT_ARG(12);                                                // 0-SAME,  1-VALID
  int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;          // INT_ARG(13): 1-NDHWC, 0-NCDHW
  int wFormat = block.getIArguments()->size() > 14
                ? INT_ARG(14)
                : 0;  // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]

  LongType bS, iC, iD, iH, iW, oC, oD, oH,
      oW;  // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
  LongType indIOioC, indIOioD, indWoC, indWiC, indWkD;  // corresponding indexes
  ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW,
                                             indIOioC, indIOioD, indWiC, indWoC, indWkD);

  REQUIRE_TRUE(paddingMode < 2, 0,
               "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
  REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
               "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
  if (bias)
  REQUIRE_TRUE(
      bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
      "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !",
      oC, bias->rankOf(), bias->lengthOf());

  ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);

  sd_debug("MKL-DNN is not used for conv3dnew!\n", 0);

  std::vector<LongType> permutForOutput;

  std::vector<LongType> permuteDims = {0,4,1,2,3};
  if (isNCDHW)
    permutForOutput = {0, 2, 3, 4, 1};  // [bS, oC, oD, oH, oW] -> [bS, oD, oH, oW, oC]
  else
    input = input->permute(permuteDims, false, false);

  std::vector<LongType> wAxes;
  if (0 == wFormat)
    wAxes = {3, 0, 1, 2};
  else if (1 == wFormat)
    wAxes = {1, 2, 3, 4};
  else
    wAxes = {4, 1, 2, 3};
  std::vector<sd::LongType> colShape = {bS, iC, kD, kH, kW, oD, oH, oW};

  NDArray columns(input->ordering(), colShape, input->dataType(), block.launchContext());
  ConvolutionUtils::vol2col(block, input, &columns, sD, sH, sW, pD, pH, pW, dD, dH,
                            dW);  // [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]
  // [bS, iC, kD, kH, kW, oD, oH, oW] x [kD, kH, kW, iC, oC] = [bS, oD, oH, oW, oC]
  // [bS, iC, kD, kH, kW, oD, oH, oW] x [oC, iC, kD, kH, kW] = [bS, oD, oH, oW, oC]
  // [bS, iC, kD, kH, kW, oD, oH, oW] x [oC, kD, kH, kW, iC] = [bS, oD, oH, oW, oC]
  std::vector<LongType> mulDims = {1,2,3,4};
  MmulHelper::tensorDot(&columns, weights, output, mulDims, wAxes, permutForOutput);

  if (bias)
    helpers::addBias(block, *output, *bias, *output, isNCDHW);

  if (!isNCDHW) delete input;

  return sd::Status::OK;
}

DECLARE_TYPES(conv3dnew) {
  getOpDescriptor()
      ->setAllowedInputTypes(0, sd::DataType::ANY)
      ->setAllowedInputTypes(1, {ALL_FLOATS})
      ->setAllowedInputTypes(2, {ALL_FLOATS})
      ->setAllowedOutputTypes({ALL_FLOATS});
}

DECLARE_SHAPE_FN(conv3dnew) {
  auto inputShapeInfo = inputShape->at(0);    // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
  auto weightsShapeInfo = inputShape->at(1);  // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
  auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr;  // [oC]

  LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0)));  // filter(kernel) depth
  LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1)));  // filter(kernel) height
  LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(2)));  // filter(kernel) width
  LongType sD = INT_ARG(3);                                                                          // strides depth
  LongType sH = INT_ARG(4);                                                                          // strides height
  LongType sW = INT_ARG(5);                                                                          // strides width
  LongType pD = INT_ARG(6);                                                                          // paddings depth
  LongType pH = INT_ARG(7);                                                                          // paddings height
  LongType pW = INT_ARG(8);                                                                          // paddings width
  LongType dD = INT_ARG(9);                                                                          // dilations depth
  LongType dH = INT_ARG(10);                                                                         // dilations height
  LongType dW = INT_ARG(11);                                                                         // dilations width
  int paddingMode = INT_ARG(12);                                                                // 1-SAME,  0-VALID;
  int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;  // INT_ARG(13): 1-NDHWC, 0-NCDHW
  int wFormat = block.getIArguments()->size() > 14
                ? INT_ARG(14)
                : 0;  // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]

  const int rank = 5;
  REQUIRE_TRUE(paddingMode < 2, 0,
               "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
  REQUIRE_TRUE(inputShapeInfo[0] == rank, 0,
               "CUSTOM CONV3D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
  REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
               "CUSTOM CONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank,
               weightsShapeInfo);

  LongType indIOioC, indIiD, indWoC(0 == wFormat ? 4 : 0);
  if (!isNCDHW) {
    indIOioC = 4;
    indIiD = 1;
  } else {
    indIOioC = 1;
    indIiD = 2;
  }

  LongType bS = inputShapeInfo[1];             // batch size
  LongType iD = inputShapeInfo[indIiD + 1];    // input depth
  LongType iH = inputShapeInfo[indIiD + 2];    // input height
  LongType iW = inputShapeInfo[indIiD + 3];    // input width
  LongType iC = inputShapeInfo[indIOioC + 1];  // input channels
  LongType oC = weightsShapeInfo[indWoC + 1];  // output channels

  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
  REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0,
               "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
               ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
  if (biasShapeInfo)
  REQUIRE_TRUE(
      biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0,
      "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !",
      oC, biasShapeInfo[0], shape::length(biasShapeInfo));

  LongType oD, oH, oW;  // output depth, height, width
  ConvolutionUtils::calcOutSizePool3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW,
                                      paddingMode);

  sd::LongType* outputShapeInfo = nullptr;
  ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), sd::LongType);

  outputShapeInfo[0] = rank;
  outputShapeInfo[1] = bS;
  if (isNCDHW) {
    outputShapeInfo[2] = oC;
    outputShapeInfo[3] = oD;
    outputShapeInfo[4] = oH;
    outputShapeInfo[5] = oW;
  } else {
    outputShapeInfo[2] = oD;
    outputShapeInfo[3] = oH;
    outputShapeInfo[4] = oW;
    outputShapeInfo[5] = oC;
  }

  ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(inputShapeInfo));

  return SHAPELIST(CONSTANT(outputShapeInfo));
}

//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) {
  auto input = INPUT_VARIABLE(0);    // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
  auto weights = INPUT_VARIABLE(1);  // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
  auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;  // [oC]
  auto gradO = block.width() > 3
               ? INPUT_VARIABLE(3)
               : INPUT_VARIABLE(2);  // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next

  auto gradI = OUTPUT_VARIABLE(0);  // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
  auto gradW = OUTPUT_VARIABLE(1);  // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
  auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr;  // [oC]

  REQUIRE_TRUE(input->rankOf() == 5, 0,
               "CUSTOM CONV3D_BP OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
  REQUIRE_TRUE(weights->rankOf() == 5, 0,
               "CUSTOM CONV3D_BP OP: rank of weights array must be equal to 5, but got %i instead !",
               weights->rankOf());
  REQUIRE_TRUE(
      gradO->rankOf() == 5, 0,
      "CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to 5, but got %i instead !",
      gradO->rankOf());

  LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0));  // filter(kernel) depth
  LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1));  // filter(kernel) height
  LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(weights->sizeAt(2));  // filter(kernel) width
  LongType sD = INT_ARG(3);                                                          // strides depth
  LongType sH = INT_ARG(4);                                                          // strides height
  LongType sW = INT_ARG(5);                                                          // strides width
  LongType pD = INT_ARG(6);                                                          // paddings depth
  LongType pH = INT_ARG(7);                                                          // paddings height
  LongType pW = INT_ARG(8);                                                          // paddings width
  LongType dD = INT_ARG(9);                                                          // dilations depth
  LongType dH = INT_ARG(10);                                                         // dilations height
  LongType dW = INT_ARG(11);                                                         // dilations width
  int paddingMode = INT_ARG(12);                                                // 1-SAME,  0-VALID
  int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;          // INT_ARG(13): 1-NDHWC, 0-NCDHW
  int wFormat = block.getIArguments()->size() > 14
                ? INT_ARG(14)
                : 0;  // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]

  LongType bS, iC, iD, iH, iW, oC, oD, oH,
      oW;  // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
  LongType indIOioC, indIOioD, indWoC, indWiC, indWkD;  // corresponding indexes
  ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
                                             indIOioC, indIOioD, indWiC, indWoC, indWkD);

  LongType trueoD, trueoH, trueoW;  // true output depth/height/width
  ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH,
                                      iW, paddingMode);

  REQUIRE_TRUE(paddingMode < 2, 0,
               "CUSTOM CONV3D_BP OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
  std::vector<sd::LongType> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx(
      {bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2});
  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
  REQUIRE_TRUE(
      gradO->isSameShape(expectedGradOShape), 0,
      "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !",
      ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
  REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
               "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
  if (bias)
  REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
               "CUSTOM CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
               "%i instead !",
               oC, bias->rankOf(), bias->lengthOf());

  ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);

  sd_debug("MKL-DNN is not used for conv3dnew_bp!\n", 0);

  std::vector<LongType> gradOaxesForDot;

  std::vector<LongType> permute = {0, 4, 1, 2, 3};
  if (!isNCDHW) {
    gradOaxesForDot = {0, 1, 2, 3};                        // bS, oD, oH, oW
    input =input->permute(permute, false, false);  // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
    gradI = gradI->permute(permute, false, false);  // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
  } else {
    gradOaxesForDot = {0, 2, 3, 4};  // bS, oD, oH, oW
  }

  std::vector<LongType> wPermut, colPermut;

  if (0 == wFormat) {
    wPermut = {3, 0, 1, 2, 4};
    colPermut = {2, 3, 4, 1, 0, 5, 6, 7};
  } else if (1 == wFormat) {
    wPermut = {1, 2, 3, 4, 0};
    colPermut = {1, 2, 3, 4, 0, 5, 6, 7};
  } else {
    wPermut = {4, 1, 2, 3, 0};
    colPermut = {2, 3, 4, 1, 0, 5, 6, 7};
  }

  std::vector<sd::LongType> colShape = {bS, iC, kD, kH, kW, oD, oH, oW};
  // ----- calculation of gradW and gradB ----- //
  NDArray columns(input->ordering(), colShape, input->dataType(), block.launchContext());
  ConvolutionUtils::vol2col(block, input, &columns, sD, sH, sW, pD, pH, pW, dD, dH,
                            dW);  // [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]

  std::vector<LongType> mulDims = {0,5,6,7};
  MmulHelper::tensorDot(
      &columns, gradO, gradW, mulDims, gradOaxesForDot,
      wPermut);  // [bS, iC, kD, kH, kW, oD, oH, oW] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [iC, kD, kH, kW, oC]

  //----- calculation of gradO -----//
  if (gradB) {
    std::vector<LongType> bShape = { gradB->lengthOf()};
    if (gradB->rankOf() == 2) gradB =gradB->reshape(gradB->ordering(),bShape, false);
    gradO->reduceAlongDimension(reduce::Sum, gradB, &gradOaxesForDot);  // sum over bS oD oH oW
    if (gradB != OUTPUT_VARIABLE(2)) delete gradB;
  }

  //----- calculation of gradI -----//
  // [kD, kH, kW, iC, oC] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [kD, kH, kW, iC, bS, oD, oH, oW]
  // [oC, iC, kD, kH, kW] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [kD, kH, kW, iC, bS, oD, oH, oW]
  // [oC, kD, kH, kW, iC] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [kD, kH, kW, iC, bS, oD, oH, oW]
  std::vector<LongType> firstDims = {indWoC};
  std::vector<LongType> secondDims = {indIOioC};

  MmulHelper::tensorDot(weights, gradO, &columns, firstDims, secondDims, colPermut);
  ConvolutionUtils::col2vol(block, columns, *gradI, sD, sH, sW, pD, pH, pW, dD, dH,
                            dW);  // columns [bS, iC, kD, kH, kW, oD, oH, oW] is de-convoluted to  [bS, iC, iD, iH, iW]

  if (!isNCDHW) {
    delete input;
    delete gradI;
  }

  return sd::Status::OK;
}

DECLARE_TYPES(conv3dnew_bp) {
  getOpDescriptor()
      ->setAllowedInputTypes(0, sd::DataType::ANY)
      ->setAllowedInputTypes(1, {ALL_FLOATS})
      ->setAllowedInputTypes(2, {ALL_FLOATS})
      ->setAllowedInputTypes(3, {ALL_FLOATS})
      ->setAllowedOutputTypes({ALL_FLOATS});
}

DECLARE_SHAPE_FN(conv3dnew_bp) {
  auto inputShapeInfo = inputShape->at(0);    // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
  auto weightsShapeInfo = inputShape->at(1);  // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
  sd::LongType const* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr;  // [oC]
  sd::LongType const* gradOShapeInfo =
      block.width() > 3
      ? inputShape->at(3)
      : inputShape->at(2);  // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next

  LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0)));  // filter(kernel) depth
  LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1)));  // filter(kernel) height
  LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<sd::LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(2)));  // filter(kernel) width
  LongType sD = INT_ARG(3);                                                                          // strides depth
  LongType sH = INT_ARG(4);                                                                          // strides height
  LongType sW = INT_ARG(5);                                                                          // strides width
  LongType pD = INT_ARG(6);                                                                          // paddings depth
  LongType pH = INT_ARG(7);                                                                          // paddings height
  LongType pW = INT_ARG(8);                                                                          // paddings width
  LongType dD = INT_ARG(9);                                                                          // dilations depth
  LongType dH = INT_ARG(10);                                                                         // dilations height
  LongType dW = INT_ARG(11);                                                                         // dilations width
  int paddingMode = INT_ARG(12);                                                                // 1-SAME,  0-VALID
  int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;  // INT_ARG(13): 1-NDHWC, 0-NCDHW
  int wFormat = block.getIArguments()->size() > 14
                ? INT_ARG(14)
                : 0;  // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]

  const int rank = 5;
  REQUIRE_TRUE(paddingMode < 2, 0,
               "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
  REQUIRE_TRUE(inputShapeInfo[0] == rank, 0,
               "CUSTOM CONV3D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank,
               inputShapeInfo);
  REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
               "CUSTOM CONV3D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank,
               weightsShapeInfo);
  REQUIRE_TRUE(
      gradOShapeInfo[0] == rank, 0,
      "CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !",
      rank, gradOShapeInfo);

  sd::LongType indIOioC, indIiD, indWoC(0 == wFormat ? 4 : 0);
  if (!isNCDHW) {
    indIOioC = 4;
    indIiD = 1;
  } else {
    indIOioC = 1;
    indIiD = 2;
  }

  LongType bS = inputShapeInfo[1];             // batch size
  LongType iD = inputShapeInfo[indIiD + 1];    // input depth
  LongType iH = inputShapeInfo[indIiD + 2];    // input height
  LongType iW = inputShapeInfo[indIiD + 3];    // input width
  LongType iC = inputShapeInfo[indIOioC + 1];  // input channels
  LongType oC = weightsShapeInfo[indWoC + 1];  // output channels

  LongType trueoD, trueoH, trueoW;  // true output depth/height/width
  ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH,
                                      iW, paddingMode);

  std::vector<sd::LongType> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx(
      {bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIiD, indIiD + 1, indIiD + 2});
  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
  REQUIRE_TRUE(
      ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0,
      "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !",
      ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
  REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0,
               "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
               ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
  if (biasShapeInfo)
  REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0,
               "CUSTOM CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
               "%i instead !",
               oC, biasShapeInfo[0], shape::length(biasShapeInfo));

  auto gradIshapeInfo =
      ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
  auto gradWshapeInfo =
      ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());

  if (biasShapeInfo) {
    auto gradBshapeInfo =
        ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
    return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
  }

  return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
}
}  // namespace ops
}  // namespace sd

#endif