/*
 *  ******************************************************************************
 *  *
 *  *
 *  * 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 (iuriish@yahoo.com)
//
#include <helpers/MKLDNNStream.h>
#include <ops/declarable/OpRegistrator.h>
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/helpers/convolutions.h>
#include <system/platform_boilerplate.h>

#include "mkldnnUtils.h"

using namespace dnnl;

namespace sd {
namespace ops {
namespace platforms {

//////////////////////////////////////////////////////////////////////////
static void depthwiseConv2dMKLDNN(NDArray* input, NDArray* weights, NDArray* bias, NDArray* output,
                                  const sd::LongType kH, const sd::LongType kW, const sd::LongType sH, const sd::LongType sW, const sd::LongType pH, const sd::LongType pW,
                                  const sd::LongType dH, const sd::LongType dW, const int paddingMode, const bool isNCHW,
                                  const int wFormat) {
  // mkl supports only following case: mC = 1, oC = iC

  // input [bS, iC, iH, iW] nchw or [bS, iH, iW, iC] nhwc, since mkl doesn't support nhwc format we'll permute when nhwc
  // is given weights {iC, mC, 1, kH, kW} bias [oC], may be nullptr output [bS, oC, oH, oW] nchw or [bS, oH, oW, oC]
  // nhwc oC = iC*mC

  sd::LongType bS, iC, iH, iW, mC, oC, oH,
      oW;  // batch size, input channels, input height/width, output channels, output height/width;
  sd::LongType indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH;  // corresponding indexes
  ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
                                             indIiH, indWiC, indWmC, indWkH, indOoH);
  mC = weights->sizeAt(indWmC);  // channels multiplier

  const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2
                                                  : pW;  // dH == 1 for causal mode in conv1d

  dnnl::memory::dims strides = {sH, sW};
  dnnl::memory::dims padding = {pH, pW};
  dnnl::memory::dims padding_r = {(oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pWSame};
  dnnl::memory::dims dilation = {dH - 1, dW - 1};

  sd::LongType i0, i1, i2, i3;
  if (0 == wFormat) {
    i0 = 2;
    i1 = 3;
    i2 = 0;
    i3 = 1;  // [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW]
  } else if (1 == wFormat) {
    i0 = 1;
    i1 = 0;
    i2 = 2;
    i3 = 3;  // [mC, iC, kH, kW] -> [iC, mC, 1, kH, kW]
  } else {
    i0 = 3;
    i1 = 0;
    i2 = 1;
    i3 = 2;  // [mC, kH, kW, iC] -> [iC, mC, 1, kH, kW]
  }

  // input type
  dnnl::memory::data_type xType;
  if (input->dataType() == DataType::FLOAT32)
    xType = dnnl::memory::data_type::f32;
  else if (input->dataType() == DataType::HALF)
    xType = dnnl::memory::data_type::f16;
  else if (input->dataType() == DataType::UINT8)
    xType = dnnl::memory::data_type::u8;
  else
    xType = dnnl::memory::data_type::s8;

  // weights type
  dnnl::memory::data_type wType = xType;
  if (xType == dnnl::memory::data_type::u8) wType = dnnl::memory::data_type::s8;

  // output and bias type (have the same types)
  dnnl::memory::data_type zType;
  if (output->dataType() == DataType::FLOAT32)
    zType = dnnl::memory::data_type::f32;
  else if (output->dataType() == DataType::HALF)
    zType = dnnl::memory::data_type::f16;
  else if (output->dataType() == DataType::UINT8)
    zType = dnnl::memory::data_type::u8;
  else if (output->dataType() == DataType::INT8)
    zType = dnnl::memory::data_type::s8;
  else
    zType = dnnl::memory::data_type::s32;

  dnnl::memory::format_tag xzFormatMkl = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
  dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::goihw;

  dnnl::memory::dims xDims = {bS, iC, iH, iW};
  dnnl::memory::dims wDims = {iC, mC, 1, kH, kW};
  dnnl::memory::dims zDims = {bS, oC, oH, oW};

  // memory descriptors for arrays

  // input
  dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any);
  dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xzFormatMkl);
  onednnUtils::setBlockStrides(*input, x_user_md);

  // weights
  dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any);
  dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, wType, wFormatMkl);
  w_user_md.data.format_kind = dnnl_blocked;                               // overrides format
  w_user_md.data.format_desc.blocking.strides[0] = weights->strideAt(i0);  // permute
  w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(i1);
  w_user_md.data.format_desc.blocking.strides[2] = 0;
  w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(i2);
  w_user_md.data.format_desc.blocking.strides[4] = weights->strideAt(i3);

  // bias
  dnnl::memory::desc b_mkl_md;
  if (bias != nullptr) b_mkl_md = dnnl::memory::desc({oC}, zType, dnnl::memory::format_tag::x);

  // output
  dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, zType, dnnl::memory::format_tag::any);
  dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, zType, xzFormatMkl);
  onednnUtils::setBlockStrides(*output, z_user_md);

  auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine());

  // operation primitive description
  dnnl::convolution_forward::desc op_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::convolution_auto,
                                          x_mkl_md, w_mkl_md, b_mkl_md, z_mkl_md, strides, dilation, padding,
                                          padding_r);
  dnnl::convolution_forward::primitive_desc op_prim_desc(op_desc, engine);

  // arguments (memory buffers) necessary for calculations
  std::unordered_map<int, dnnl::memory> args;

  dnnl::stream stream(engine);

  // provide memory buffers and check whether reorder is required

  // input
  onednnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);

  // weights
  onednnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_prim_desc.weights_desc(),
                                   args[DNNL_ARG_WEIGHTS]);

  // bias
  if (bias != nullptr) {
    auto b_mkl_mem = dnnl::memory(b_mkl_md, engine, const_cast<void*>(bias->buffer()));
    args[DNNL_ARG_BIAS] = b_mkl_mem;
  }

  // output
  auto z_user_mem =
      onednnUtils::loadDataToMklStream(*output, engine, stream, z_user_md, op_prim_desc.dst_desc(), args[DNNL_ARG_DST]);

  // run calculations
  dnnl::convolution_forward(op_prim_desc).execute(stream, args);

  // reorder outputs if necessary
  if (op_prim_desc.dst_desc() != z_user_mem.get_desc())
    dnnl::reorder(args[DNNL_ARG_DST], z_user_mem).execute(stream, args[DNNL_ARG_DST], z_user_mem);

  stream.wait();
}

//////////////////////////////////////////////////////////////////////////
static void depthwiseConv2dBpMKLDNN(NDArray* input, NDArray* weights, NDArray* gradO, NDArray* gradI,
                                    NDArray* gradW, NDArray* gradB, const sd::LongType kH, const sd::LongType kW, const sd::LongType sH,
                                    const sd::LongType sW, const sd::LongType pH, const sd::LongType pW, const sd::LongType dH, const sd::LongType dW,
                                    const int paddingMode, const bool isNCHW, const int wFormat) {
  // mkl supports only following case: mC = 1, oC = iC

  // input, gradI [bS, iC, iH, iW] nchw or [bS, iH, iW, iC] nhwc, since mkl doesn't support nhwc format we'll permute
  // when nhwc is given weights/gradW {iC, mC, 1, kH, kW} gradB [oC], may be nullptr gradO [bS, oC, oH, oW] nchw or [bS,
  // oH, oW, oC] nhwc oC = iC*mC

  sd::LongType bS, iC, iH, iW, mC, oC, oH,
      oW;  // batch size, input channels, input height/width, output channels, output height/width;
  sd::LongType indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH;  // corresponding indexes
  ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
                                             indIiH, indWiC, indWmC, indWkH, indOoH);
  mC = weights->sizeAt(indWmC);

  const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2
                                                  : pW;  // dH == 1 for causal mode in conv1d

  dnnl::memory::dims strides = {sH, sW};
  dnnl::memory::dims padding = {pH, pW};
  dnnl::memory::dims padding_r = {(oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pWSame};
  dnnl::memory::dims dilation = {dH - 1, dW - 1};

  sd::LongType i0, i1, i2, i3;
  if (0 == wFormat) {
    i0 = 2;
    i1 = 3;
    i2 = 0;
    i3 = 1;  // [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW]
  } else if (1 == wFormat) {
    i0 = 1;
    i1 = 0;
    i2 = 2;
    i3 = 3;  // [mC, iC, kH, kW] -> [iC, mC, 1, kH, kW]
  } else {
    i0 = 3;
    i1 = 0;
    i2 = 1;
    i3 = 2;  // [mC, kH, kW, iC] -> [iC, mC, 1, kH, kW]
  }

  // input type
  dnnl::memory::data_type xType =
      input->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
  // weights type
  dnnl::memory::data_type wType =
      weights->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
  // gradO type
  dnnl::memory::data_type gradOType =
      gradO->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
  // gradI type
  dnnl::memory::data_type gradIType =
      gradI->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
  // gradW type
  dnnl::memory::data_type gradWType =
      gradW->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
  // gradB type
  dnnl::memory::data_type gradBType =
      gradB != nullptr
          ? (gradB->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16)
          : dnnl::memory::data_type::f32;

  dnnl::memory::format_tag xzFormatMkl = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
  dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::goihw;

  dnnl::memory::dims xDims = {bS, iC, iH, iW};
  dnnl::memory::dims wDims = {iC, mC, 1, kH, kW};
  dnnl::memory::dims zDims = {bS, oC, oH, oW};

  // memory descriptors for arrays

  // input
  dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any);
  dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xzFormatMkl);
  onednnUtils::setBlockStrides(*input, x_user_md);

  // weights
  dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any);
  dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, wType, wFormatMkl);
  w_user_md.data.format_kind = dnnl_blocked;                               // overrides format
  w_user_md.data.format_desc.blocking.strides[0] = weights->strideAt(i0);  // permute
  w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(i1);
  w_user_md.data.format_desc.blocking.strides[2] = 0;
  w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(i2);
  w_user_md.data.format_desc.blocking.strides[4] = weights->strideAt(i3);

  // gradO
  dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, gradOType, dnnl::memory::format_tag::any);
  dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, gradOType, xzFormatMkl);
  onednnUtils::setBlockStrides(*gradO, gradO_user_md);

  // gradI
  dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, gradIType, dnnl::memory::format_tag::any);
  dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, gradIType, xzFormatMkl);
  onednnUtils::setBlockStrides(*gradI, gradI_user_md);

  // gradW
  dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, gradWType, dnnl::memory::format_tag::any);
  dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, gradWType, wFormatMkl);
  gradW_user_md.data.format_kind = dnnl_blocked;                             // overrides format
  gradW_user_md.data.format_desc.blocking.strides[0] = gradW->strideAt(i0);  // permute
  gradW_user_md.data.format_desc.blocking.strides[1] = gradW->strideAt(i1);
  gradW_user_md.data.format_desc.blocking.strides[2] = 0;
  gradW_user_md.data.format_desc.blocking.strides[3] = gradW->strideAt(i2);
  gradW_user_md.data.format_desc.blocking.strides[4] = gradW->strideAt(i3);

  // gradB
  dnnl::memory::desc gradB_mkl_md;
  if (gradB != nullptr) gradB_mkl_md = dnnl::memory::desc({oC}, gradBType, dnnl::memory::format_tag::x);

  auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine());

  // forward primitive description
  dnnl::convolution_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::convolution_auto,
                                             x_mkl_md, w_mkl_md, gradB_mkl_md, gradO_mkl_md, strides, dilation, padding,
                                             padding_r);
  dnnl::convolution_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);

  // backward data primitive description
  dnnl::convolution_backward_data::desc op_data_bp_desc(dnnl::algorithm::convolution_auto, gradI_mkl_md, w_mkl_md,
                                                        gradO_mkl_md, strides, dilation, padding, padding_r);
  dnnl::convolution_backward_data::primitive_desc op_data_bp_prim_desc(op_data_bp_desc, engine, op_ff_prim_desc);

  // backward weights primitive description
  dnnl::convolution_backward_weights::desc op_weights_bp_desc(dnnl::algorithm::convolution_auto, x_mkl_md, gradW_mkl_md,
                                                              gradB_mkl_md, gradO_mkl_md, strides, dilation, padding,
                                                              padding_r);
  dnnl::convolution_backward_weights::primitive_desc op_weights_bp_prim_desc(op_weights_bp_desc, engine,
                                                                             op_ff_prim_desc);

  // arguments (memory buffers) necessary for calculations
  std::unordered_map<int, dnnl::memory> args;

  dnnl::stream stream(engine);

  // provide memory buffers and check whether reorder is required

  // input
  onednnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_weights_bp_prim_desc.src_desc(),
                                   args[DNNL_ARG_SRC]);

  // weights
  onednnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_data_bp_prim_desc.weights_desc(),
                                   args[DNNL_ARG_WEIGHTS]);

  // gradO
  auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, const_cast<void*>(gradO->buffer()));
  const bool gradOReorderW = op_weights_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
  const bool gradOReorderD = op_data_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
  auto gradO_mkl_memW = gradOReorderW ? dnnl::memory(op_weights_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
  auto gradO_mkl_memD = gradOReorderD ? dnnl::memory(op_data_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
  if (gradOReorderW) dnnl::reorder(gradO_user_mem, gradO_mkl_memW).execute(stream, gradO_user_mem, gradO_mkl_memW);
  if (gradOReorderD) dnnl::reorder(gradO_user_mem, gradO_mkl_memD).execute(stream, gradO_user_mem, gradO_mkl_memD);
  args[DNNL_ARG_DIFF_DST] = gradO_mkl_memD;

  // gradI
  auto gradI_user_mem = onednnUtils::loadDataToMklStream(*gradI, engine, stream, gradI_user_md,
                                                         op_data_bp_prim_desc.diff_src_desc(), args[DNNL_ARG_DIFF_SRC]);

  // gradW
  auto gradW_user_mem = onednnUtils::loadDataToMklStream(
      *gradW, engine, stream, gradW_user_md, op_weights_bp_prim_desc.diff_weights_desc(), args[DNNL_ARG_DIFF_WEIGHTS]);

  // gradB
  if (gradB != nullptr) {
    auto gradB_mkl_mem = dnnl::memory(gradB_mkl_md, engine, gradB->buffer());
    args[DNNL_ARG_DIFF_BIAS] = gradB_mkl_mem;
  }

  // run backward data calculations
  dnnl::convolution_backward_data(op_data_bp_prim_desc).execute(stream, args);

  if (gradOReorderW || gradOReorderD) args[DNNL_ARG_DIFF_DST] = gradO_mkl_memW;

  // run backward weights calculations
  dnnl::convolution_backward_weights(op_weights_bp_prim_desc).execute(stream, args);

  // reorder gradI if necessary
  if (op_data_bp_prim_desc.diff_src_desc() != gradI_user_mem.get_desc())
    dnnl::reorder(args[DNNL_ARG_DIFF_SRC], gradI_user_mem).execute(stream, args[DNNL_ARG_DIFF_SRC], gradI_user_mem);
  if (op_weights_bp_prim_desc.diff_weights_desc() != gradW_user_mem.get_desc())
    dnnl::reorder(args[DNNL_ARG_DIFF_WEIGHTS], gradW_user_mem)
        .execute(stream, args[DNNL_ARG_DIFF_WEIGHTS], gradW_user_mem);

  stream.wait();

}

//////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(depthwise_conv2d, ENGINE_CPU) {
  auto input = INPUT_VARIABLE(0);                               // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
  auto weights = INPUT_VARIABLE(1);                             // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
  auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;  // [oC] = iC*mC

  auto output = OUTPUT_VARIABLE(0);  // [bS, oH, oW, iC*mC] (NHWC) or [bS, iC*mC, oH, oW] (NCHW)

  sd::LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(weights->sizeAt(0));  // filter(kernel) height
  sd::LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(weights->sizeAt(1));  // filter(kernel) width
  sd::LongType sH = INT_ARG(2);                                                          // strides height
  sd::LongType sW = INT_ARG(3);                                                          // strides width
  sd::LongType pH = INT_ARG(4);                                                          // paddings height
  sd::LongType pW = INT_ARG(5);                                                          // paddings width
  sd::LongType dH = INT_ARG(6);                                                          // dilations height
  sd::LongType dW = INT_ARG(7);                                                          // dilations width
  int paddingMode = INT_ARG(8);                                                 // 0-VALID, 1-SAME
  int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1;             // INT_ARG(9): 0-NCHW,  1-NHWC
  int wFormat = block.getIArguments()->size() > 10
                    ? INT_ARG(10)
                    : 0;  // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]

  sd::LongType bS, iC, iH, iW, mC, oC, oH, oW;  // batch size, input channels, input height/width, channels multiplier(oC =
                                       // iC*mC), output channels, output height/width
  sd::LongType indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH;  // corresponding indexes
  ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
                                             indIiH, indWiC, indWmC, indWkH, indOoH);
  mC = weights->sizeAt(indWmC);  // channels multiplier

  ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);

  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
  REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
               "CUSTOM DEPTHWISECONV2D MKL OP: wrong shape of weights array, expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
  REQUIRE_TRUE(
      output->sizeAt(indIOioC) == iC * mC, 0,
      "CUSTOM DEPTHWISECONV2D MKL OP: the output_channels must be equal to input_channels * channels_multiplier = %i !",
      iC * mC);
  if (bias)
    REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
                 "CUSTOM DEPTHWISECONV2D MKL OP: wrong shape of array with biases, expected rank, length: <=2, %i, but "
                 "got %i, %i instead !",
                 oC, bias->rankOf(), bias->lengthOf());

  depthwiseConv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW, wFormat);

  return sd::Status::OK;
}

//////////////////////////////////////////////////////////////////////
PLATFORM_CHECK(depthwise_conv2d, ENGINE_CPU) {
  auto input = INPUT_VARIABLE(0);
  auto weights = INPUT_VARIABLE(1);
  auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;

  auto output = INPUT_VARIABLE(0);

  Requirements req("ONEDNN DEPTHWISE_CONV2d OP");
  req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
      req.expectEq(makeInfoVariable(weights->sizeAt(3), "weight NdArray size#3"), 1) &&
      req.expectTrue(makeInfoVariable(
                         [input, weights, bias, output] {
                           const DataType xType = input->dataType();
                           const DataType wType = weights->dataType();
                           const DataType zType = output->dataType();
                           const DataType bType = bias != nullptr ? bias->dataType() : zType;
                           return ((xType == DataType::FLOAT32 && wType == DataType::FLOAT32 &&
                                    bType == DataType::FLOAT32 && zType == DataType::FLOAT32) ||
                                   (xType == DataType::BFLOAT16 && wType == DataType::BFLOAT16 &&
                                    bType == DataType::BFLOAT16 && zType == DataType::BFLOAT16) ||
                                   ((xType == DataType::UINT8 || xType == DataType::INT8) && wType == DataType::INT8 &&
                                    (zType == DataType::UINT8 || zType == DataType::INT8 || zType == DataType::INT32 ||
                                     zType == DataType::FLOAT32) &&
                                    bType == zType));
                         },
                         TYPECHECK_MSG),
                     NO_MSG);
  req.logTheSuccess();
  return req;
}

//////////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(depthwise_conv2d_bp, ENGINE_CPU) {
  auto input = INPUT_VARIABLE(0);                               // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
  auto weights = INPUT_VARIABLE(1);                             // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
  auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;  // [oC] = [iC*mC]
  auto gradO = block.width() > 3
                   ? INPUT_VARIABLE(3)
                   : INPUT_VARIABLE(2);  // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next

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

  REQUIRE_TRUE(input->rankOf() == 4, 0,
               "CUSTOM DEPTHWISECONV2D_BP MKL OP: rank of input array must be equal to 4, but got %i instead !",
               input->rankOf());
  REQUIRE_TRUE(weights->rankOf() == 4, 0,
               "CUSTOM DEPTHWISECONV2D_BP MKL OP: rank of weights array must be equal to 4, but got %i instead !",
               weights->rankOf());
  REQUIRE_TRUE(gradO->rankOf() == 4, 0,
               "CUSTOM DEPTHWISECONV2D_BP MKL OP: rank of output gradients (next epsilon) array must be equal to 4, "
               "but got %i instead !",
               gradO->rankOf());

  sd::LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(weights->sizeAt(0));  // filter(kernel) height
  sd::LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(weights->sizeAt(1));  // filter(kernel) width
  sd::LongType sH = INT_ARG(2);                                                          // strides height
  sd::LongType sW = INT_ARG(3);                                                          // strides width
  sd::LongType pH = INT_ARG(4);                                                          // paddings height
  sd::LongType pW = INT_ARG(5);                                                          // paddings width
  sd::LongType dH = INT_ARG(6);                                                          // dilations height
  sd::LongType dW = INT_ARG(7);                                                          // dilations width
  int paddingMode = INT_ARG(8);                                                 // 0-VALID, 1-SAME
  int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1;             // INT_ARG(9): 1-NHWC, 0-NCHW
  int wFormat = block.getIArguments()->size() > 10
                    ? INT_ARG(10)
                    : 0;  // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]

  sd::LongType bS, iC, iH, iW, mC, oC, oH, oW;  // batch size, input channels, input height/width, channels multiplier(oC =
                                       // iC*mC), output channels, output height/width
  sd::LongType indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH;  // corresponding indexes
  ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
                                             indIiH, indWiC, indWmC, indWkH, indOoH);
  mC = weights->sizeAt(indWmC);  // channels multiplier

  sd::LongType trueoH, trueoW;  // correct output height, width
  ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);

  ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);

  std::vector<sd::LongType> expectedGradOShape =
      ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
  REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
               "CUSTOM DEPTHWISECONV2D_BP MKL 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 DEPTHWISECONV2D_BP MKL 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 DEPTHWISECONV2D_BP MKL OP: wrong shape of array with biases, expected rank, length: <=2, %i, "
                 "but got %i, %i instead !",
                 oC, bias->rankOf(), bias->lengthOf());

  depthwiseConv2dBpMKLDNN(input, weights, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode,
                          isNCHW, wFormat);

  return sd::Status::OK;
}

//////////////////////////////////////////////////////////////////////
PLATFORM_CHECK(depthwise_conv2d_bp, ENGINE_CPU) {
  auto input = INPUT_VARIABLE(0);                               // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
  auto weights = INPUT_VARIABLE(1);                             // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
  auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;  // [oC] = [iC*mC]
  auto gradO = block.width() > 3
                   ? INPUT_VARIABLE(3)
                   : INPUT_VARIABLE(2);  // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next

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

  Requirements req("ONEDNN DEPTHWISE_CONV2d_BP OP");
  req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
      req.expectEq(makeInfoVariable(weights->sizeAt(3), "weight NdArray size#3"), 1) &&
      req.expectTrue(makeInfoVariable(
                         [input, weights, gradI, gradW, gradB, gradO] {
                           const DataType xType = input->dataType();
                           const DataType wType = weights->dataType();
                           const DataType gradOType = gradO->dataType();

                           const DataType gradIType = gradI->dataType();
                           const DataType gradWType = gradW->dataType();
                           const DataType gradBType = gradB != nullptr ? gradB->dataType() : DataType::FLOAT32;
                           return ((xType == DataType::FLOAT32 || xType == DataType::BFLOAT16) &&
                                   (wType == DataType::FLOAT32 || wType == DataType::BFLOAT16) &&
                                   (gradOType == DataType::FLOAT32 || gradOType == DataType::BFLOAT16) &&
                                   (gradIType == DataType::FLOAT32 || gradIType == DataType::BFLOAT16) &&
                                   (gradWType == DataType::FLOAT32 || gradWType == DataType::BFLOAT16) &&
                                   (gradBType == DataType::FLOAT32 || gradBType == DataType::BFLOAT16));
                         },
                         TYPECHECK_MSG),
                     NO_MSG);
  req.logTheSuccess();
  return req;
}

}  // namespace platforms
}  // namespace ops
}  // namespace sd