text: /*******************************************************************************
   * Copyright (c) 2015-2018 Skymind, Inc.
   *
   * 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.
   *
   * 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 saudet
  // @author raver119@gmail.com
  // @author Yurii Shyrma (iuriish@yahoo.com)
  //
  
  #include <ops/declarable/PlatformHelper.h>
  #include <ops/declarable/OpRegistrator.h>
  #include <platform_boilerplate.h>
  
  #include <helpers/MKLDNNStream.h>
  #include "mkldnnUtils.h"
  #include <ops/declarable/helpers/convolutions.h>
  
  using namespace dnnl;
  
  namespace nd4j      {
  namespace ops       {
  namespace platforms {
  
  //////////////////////////////////////////////////////////////////////
  static void conv2dMKLDNN(const NDArray *input, const NDArray *weights,
                            const NDArray *bias, NDArray *output,
                            const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW,
                            const int paddingMode, const int isNCHW) {
  
      // weights [kH, kW, iC, oC], we'll perform permutation since mkl support [oC, iC, kH, kW]
  
      int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
      int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
      ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
  
      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};
  
      auto xzFrmat = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
      dnnl::memory::format_tag wFormat = dnnl::memory::format_tag::oihw;
  
      dnnl::memory::dims xDims = {bS, iC, iH, iW};
      dnnl::memory::dims wDims = {oC, iC, kH, kW};
      dnnl::memory::dims zDims = {bS, oC, oH, oW};
  
      auto type = dnnl::memory::data_type::f32;
  
      // memory descriptors for arrays
  
      // input
      dnnl::memory::desc x_mkl_md  = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any);
      dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFrmat);
      if(input->ews() != 1 || input->ordering() != 'c') {
          x_user_md.data.format_kind = dnnl_blocked;    // overrides format
          x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0);
          x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1);
          x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2);
          x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3);
      }
  
      // weights
      dnnl::memory::desc w_mkl_md  = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any);
      dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, type, wFormat);
      w_user_md.data.format_kind = dnnl_blocked;    // overrides format
      w_user_md.data.format_desc.blocking.strides[0] = weights->strideAt(3);   // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW]
      w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(2);
      w_user_md.data.format_desc.blocking.strides[2] = weights->strideAt(0);
      w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(1);
  
      // bias
      dnnl::memory::desc b_mkl_md;
      if(bias != nullptr)
          b_mkl_md = dnnl::memory::desc({oC}, type, dnnl::memory::format_tag::x);
  
      // output
      dnnl::memory::desc z_mkl_md  = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any);
      dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, type, xzFrmat);
      if(output->ews() != 1 || output->ordering() != 'c') {
          z_user_md.data.format_kind = dnnl_blocked;    // overrides format
          z_user_md.data.format_desc.blocking.strides[0] = output->strideAt(0);
          z_user_md.data.format_desc.blocking.strides[1] = output->strideAt(1);
          z_user_md.data.format_desc.blocking.strides[2] = output->strideAt(2);
          z_user_md.data.format_desc.blocking.strides[3] = output->strideAt(3);
      }
  
      auto engine = mkldnnUtils::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
      auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer());
      const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc();
      auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem;
      if (xReorder)
          dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
      args[DNNL_ARG_SRC] = x_mkl_mem;
  
      // weights
      auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer());
      const bool wReorder = op_prim_desc.weights_desc() != w_user_mem.get_desc();
      auto w_mkl_mem = wReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : w_user_mem;
      if (wReorder)
          dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem);
      args[DNNL_ARG_WEIGHTS] = w_mkl_mem;
  
      // bias
      if(bias != nullptr) {
          auto b_mkl_mem = dnnl::memory(b_mkl_md, engine, bias->getBuffer());
          args[DNNL_ARG_BIAS] = b_mkl_mem;
      }
  
      // output
      auto z_user_mem = dnnl::memory(z_user_md, engine, output->getBuffer());
      const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc();
      auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem;
      args[DNNL_ARG_DST] = z_mkl_mem;
  
      // run calculations
      dnnl::convolution_forward(op_prim_desc).execute(stream, args);
  
      // reorder outputs if necessary
      if (zReorder)
          dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem);
  
      stream.wait();
      // shape::printArray(z_mkl_mem.map_data<float>(),8);
  }
  
  //////////////////////////////////////////////////////////////////////
  static void conv2dBpMKLDNN(const NDArray *input, const NDArray *weights, const NDArray *bias, const NDArray *gradO,
                              NDArray *gradI, NDArray *gradW, NDArray *gradB,
                              const int kH, const int kW, const int sH, const int sW, const int pH, const  int pW, const int dH, const int dW,
                              const int paddingMode, const int isNCHW) {
  
      // weights/gradW [kH, kW, iC, oC], we'll perform permutation since mkl support [oC, iC, kH, kW]
  
      int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
      int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
      ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
  
      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};
  
      auto xzFrmat = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
      dnnl::memory::format_tag wFormat = dnnl::memory::format_tag::oihw;
  
      dnnl::memory::dims xDims = {bS, iC, iH, iW};
      dnnl::memory::dims wDims = {oC, iC, kH, kW};
      dnnl::memory::dims zDims = {bS, oC, oH, oW};
  
      auto type = dnnl::memory::data_type::f32;
  
      // memory descriptors for arrays
  
      // input
      dnnl::memory::desc x_mkl_md  = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any);
      dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFrmat);
      if(input->ews() != 1 || input->ordering() != 'c') {
          x_user_md.data.format_kind = dnnl_blocked;    // overrides format
          x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0);
          x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1);
          x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2);
          x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3);
      }
  
      // weights
      dnnl::memory::desc w_mkl_md  = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any);
      dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, type, wFormat);
      w_user_md.data.format_kind = dnnl_blocked;    // overrides format
      w_user_md.data.format_desc.blocking.strides[0] = weights->strideAt(3);   // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW]
      w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(2);
      w_user_md.data.format_desc.blocking.strides[2] = weights->strideAt(0);
      w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(1);
  
      // gradO
      dnnl::memory::desc gradO_mkl_md  = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any);
      dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, type, xzFrmat);
      if(gradO->ews() != 1 || gradO->ordering() != 'c') {
          gradO_user_md.data.format_kind = dnnl_blocked;    // overrides format
          gradO_user_md.data.format_desc.blocking.strides[0] = gradO->strideAt(0);
          gradO_user_md.data.format_desc.blocking.strides[1] = gradO->strideAt(1);
          gradO_user_md.data.format_desc.blocking.strides[2] = gradO->strideAt(2);
          gradO_user_md.data.format_desc.blocking.strides[3] = gradO->strideAt(3);
      }
  
      // gradI
      dnnl::memory::desc gradI_mkl_md  = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any);
      dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, type, xzFrmat);
      if(gradI->ews() != 1 || gradI->ordering() != 'c') {
          gradI_user_md.data.format_kind = dnnl_blocked;    // overrides format
          gradI_user_md.data.format_desc.blocking.strides[0] = gradI->strideAt(0);
          gradI_user_md.data.format_desc.blocking.strides[1] = gradI->strideAt(1);
          gradI_user_md.data.format_desc.blocking.strides[2] = gradI->strideAt(2);
          gradI_user_md.data.format_desc.blocking.strides[3] = gradI->strideAt(3);
      }
  
      // gradW
      dnnl::memory::desc gradW_mkl_md  = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any);
      dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, type, wFormat);
      gradW_user_md.data.format_kind = dnnl_blocked;    // overrides format
      gradW_user_md.data.format_desc.blocking.strides[0] = gradW->strideAt(3);   // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW]
      gradW_user_md.data.format_desc.blocking.strides[1] = gradW->strideAt(2);
      gradW_user_md.data.format_desc.blocking.strides[2] = gradW->strideAt(0);
      gradW_user_md.data.format_desc.blocking.strides[3] = gradW->strideAt(1);
  
      // gradB
      dnnl::memory::desc gradB_mkl_md;
      if(gradB != nullptr)
          gradB_mkl_md = dnnl::memory::desc({oC}, type, dnnl::memory::format_tag::x);
  
      auto engine = mkldnnUtils::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
      auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer());
      const bool xReorder = op_weights_bp_prim_desc.src_desc() != x_user_mem.get_desc();
      auto x_mkl_mem = xReorder ? dnnl::memory(op_weights_bp_prim_desc.src_desc(), engine) : x_user_mem;
      if (xReorder)
          dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
      args[DNNL_ARG_SRC] = x_mkl_mem;
  
      // weights
      auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer());
      const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc();
      auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem;
      if (wReorder)
          dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem);
      args[DNNL_ARG_WEIGHTS] = w_mkl_mem;
  
      // gradO
      auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer());
      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 = dnnl::memory(gradI_user_md, engine, gradI->getBuffer());
      const bool gradIReorder = op_data_bp_prim_desc.diff_src_desc() != gradI_user_mem.get_desc();
      auto gradI_mkl_mem = gradIReorder ? dnnl::memory(op_data_bp_prim_desc.diff_src_desc(), engine) : gradI_user_mem;
      args[DNNL_ARG_DIFF_SRC] = gradI_mkl_mem;
  
      // gradW
      auto gradW_user_mem = dnnl::memory(gradW_user_md, engine, gradW->getBuffer());
      const bool gradWReorder = op_weights_bp_prim_desc.diff_weights_desc() != gradW_user_mem.get_desc();
      auto gradW_mkl_mem = gradWReorder ? dnnl::memory(op_weights_bp_prim_desc.diff_weights_desc(), engine) : gradW_user_mem;
      args[DNNL_ARG_DIFF_WEIGHTS] = gradW_mkl_mem;
  
      // gradB
      if(gradB != nullptr) {
          auto gradB_mkl_mem = dnnl::memory(gradB_mkl_md, engine, gradB->getBuffer());
          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 (gradIReorder)
          dnnl::reorder(gradI_mkl_mem, gradI_user_mem).execute(stream, gradI_mkl_mem, gradI_user_mem);
      if (gradWReorder)
          dnnl::reorder(gradW_mkl_mem, gradW_user_mem).execute(stream, gradW_mkl_mem, gradW_user_mem);
  
      stream.wait();
  
      // shape::printArray(z_mkl_mem.map_data<float>(),8);
  }
  
  /*
  //////////////////////////////////////////////////////////////////////
  static void conv2dMKLDNN(nd4j::graph::Context &block, const NDArray *input, const NDArray *weights,
                            const NDArray *bias, NDArray *output, const int kH, const int kW, const int sH,
                            const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode,
                            const int isNCHW) {
  
      int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
      int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
      ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
  
      ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
  
      dnnl_memory_desc_t empty;
      dnnl::memory::desc x_mkl_md(empty), w_mkl_md(empty), b_mkl_md(empty), z_mkl_md(empty);
      dnnl::memory::desc x_user_md(empty), w_user_md(empty), b_user_md(empty), z_user_md(empty);
  
      dnnl::memory::dims strides, padding, padding_r, dilation;
  
      mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW,
                                             bS, iC, iH, iW, oC, oH, oW, input, nullptr, weights, nullptr,
                                             bias, output,
                                             &x_mkl_md, nullptr, &w_mkl_md, nullptr,
                                             &b_mkl_md, &z_mkl_md,
                                             &x_user_md, nullptr, &w_user_md, nullptr,
                                             &b_user_md, &z_user_md,
                                             strides, padding, padding_r, dilation);
  
      auto conv_desc = bias != nullptr ? convolution_forward::desc(prop_kind::forward,
                                                   algorithm::convolution_auto, x_mkl_md,
                                                   w_mkl_md, b_mkl_md,
                                                   z_mkl_md, strides, dilation, padding,
                                                   padding_r)
                                       : convolution_forward::desc(prop_kind::forward,
                                                   algorithm::convolution_auto, x_mkl_md,
                                                   w_mkl_md,
                                                   z_mkl_md, strides, dilation, padding,
                                                   padding_r);
      auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
      dnnl::stream stream(engine);
      auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, engine);
      auto user_src_memory = dnnl::memory(x_user_md, engine, const_cast<NDArray *>(input)->buffer());
      auto user_weights_memory = dnnl::memory(w_user_md, engine,
                                                const_cast<NDArray *>(weights)->buffer());
      auto user_dst_memory = dnnl::memory(z_user_md, engine, output->buffer());
      auto conv_src_memory = user_src_memory;
      if (conv_prim_desc.src_desc() != user_src_memory.get_desc()) {
          conv_src_memory = dnnl::memory(conv_prim_desc.src_desc(), engine);
          reorder(user_src_memory, conv_src_memory).execute(stream, user_src_memory, conv_src_memory);
      }
      auto conv_weights_memory = user_weights_memory;
      if (conv_prim_desc.weights_desc() != user_weights_memory.get_desc()) {
          conv_weights_memory = dnnl::memory(conv_prim_desc.weights_desc(), engine);
          reorder(user_weights_memory, conv_weights_memory).execute(stream, user_weights_memory,
                                                                    conv_weights_memory);
      }
      auto conv_dst_memory = user_dst_memory;
      if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
          conv_dst_memory = dnnl::memory(conv_prim_desc.dst_desc(), engine);
      }
      if (bias != nullptr) {
          auto conv_bias_memory = dnnl::memory(conv_prim_desc.bias_desc(), engine,
                                                 const_cast<NDArray *>(bias)->buffer());
          convolution_forward(conv_prim_desc).execute(stream, {{DNNL_ARG_SRC,     conv_src_memory},
                                                               {DNNL_ARG_WEIGHTS, conv_weights_memory},
                                                               {DNNL_ARG_BIAS,    conv_bias_memory},
                                                               {DNNL_ARG_DST,     conv_dst_memory}});
      } else {
          convolution_forward(conv_prim_desc).execute(stream, {{DNNL_ARG_SRC,     conv_src_memory},
                                                               {DNNL_ARG_WEIGHTS, conv_weights_memory},
                                                               {DNNL_ARG_DST,     conv_dst_memory}});
      }
      if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
          reorder(conv_dst_memory, user_dst_memory).execute(stream, conv_dst_memory, user_dst_memory);
      }
      stream.wait();
  }
  
  //////////////////////////////////////////////////////////////////////
  static void conv2dBpMKLDNN(nd4j::graph::Context &block,
                              const NDArray *input, const NDArray *weights, const NDArray *bias, const NDArray *gradO,
                              NDArray *gradI, NDArray *gradW, NDArray *gradB,
                              const int kH, const int kW, const int sH,const int sW, int pH, int pW, const int dH, const int dW,
                              const int paddingMode, const int isNCHW) {
  
      int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
      int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
      ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
  
      ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
  
      dnnl_memory_desc_t empty;
      dnnl::memory::desc conv_src_md(empty), conv_diff_src_md(empty), conv_weights_md(empty), conv_diff_weights_md(empty), conv_bias_md(empty), conv_dst_md(empty);
      dnnl::memory::desc user_src_md(empty), user_diff_src_md(empty), user_weights_md(empty), user_diff_weights_md(empty), user_bias_md(empty), user_dst_md(empty);
  
      dnnl::memory::dims conv_strides, conv_padding, conv_padding_r, conv_dilation;
  
      mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW,
                                             bS, iC, iH, iW, oC, oH, oW, input, gradI, weights, gradW,
                                             gradB, gradO,
                                             &conv_src_md, &conv_diff_src_md, &conv_weights_md,
                                             &conv_diff_weights_md, &conv_bias_md, &conv_dst_md,
                                             &user_src_md, &user_diff_src_md, &user_weights_md,
                                             &user_diff_weights_md, &user_bias_md, &user_dst_md,
                                             conv_strides, conv_padding, conv_padding_r, conv_dilation);
      auto conv_desc = gradB != nullptr
                       ? convolution_forward::desc(prop_kind::forward, algorithm::convolution_auto, conv_src_md, conv_weights_md, conv_bias_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r)
                       : convolution_forward::desc(prop_kind::forward, algorithm::convolution_auto, conv_src_md, conv_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r);
  
      auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, mkldnnUtils::getEngine( LaunchContext::defaultContext()->engine()));
  
       auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
      dnnl::stream stream(engine);
  
      if (gradW != nullptr) {
          auto convW_desc = gradB != nullptr ? convolution_backward_weights::desc(algorithm::convolution_auto, conv_src_md, conv_diff_weights_md, conv_bias_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r)
                                             : convolution_backward_weights::desc(algorithm::convolution_auto, conv_src_md, conv_diff_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r);
  
  
          auto convW_prim_desc = convolution_backward_weights::primitive_desc(convW_desc, engine, conv_prim_desc);
  
          auto userW_src_memory = dnnl::memory(user_src_md, engine, const_cast<NDArray *>(input)->buffer());
          auto userW_weights_memory = dnnl::memory(user_diff_weights_md, engine, gradW->buffer());
          auto userW_dst_memory = dnnl::memory(user_dst_md, engine,const_cast<NDArray *>(gradO)->buffer());
  
          auto convW_src_memory = userW_src_memory;
  
          if (convW_prim_desc.src_desc() != userW_src_memory.get_desc()) {
              convW_src_memory = dnnl::memory(convW_prim_desc.src_desc(), engine);
              reorder(userW_src_memory, convW_src_memory).execute(stream, userW_src_memory,convW_src_memory);
          }
  
          auto convW_weights_memory = userW_weights_memory;
          if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) {
              convW_weights_memory = dnnl::memory(convW_prim_desc.diff_weights_desc(), engine);
          }
  
          auto convW_dst_memory = userW_dst_memory;
          if (convW_prim_desc.diff_dst_desc() != userW_dst_memory.get_desc()) {
              convW_dst_memory = dnnl::memory(convW_prim_desc.diff_dst_desc(), engine);
              reorder(userW_dst_memory, convW_dst_memory).execute(stream, userW_dst_memory, convW_dst_memory);
          }
  
          if (gradB != nullptr) {
              auto convW_bias_memory = dnnl::memory(convW_prim_desc.diff_bias_desc(), engine, gradB->buffer());
  
              convolution_backward_weights(convW_prim_desc).execute(stream,
                                                                    {{DNNL_ARG_SRC,          convW_src_memory},
                                                                     {DNNL_ARG_DIFF_DST,     convW_dst_memory},
                                                                     {DNNL_ARG_DIFF_WEIGHTS, convW_weights_memory},
                                                                     {DNNL_ARG_DIFF_BIAS,    convW_bias_memory}});
          }
          else {
              convolution_backward_weights(convW_prim_desc).execute(stream,
                                                                    {{DNNL_ARG_SRC,          convW_src_memory},
                                                                     {DNNL_ARG_DIFF_DST,     convW_dst_memory},
                                                                     {DNNL_ARG_DIFF_WEIGHTS, convW_weights_memory}});
          }
  
          if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) {
              reorder(convW_weights_memory, userW_weights_memory).execute(stream, convW_weights_memory,
                                                                          userW_weights_memory);
          }
  
          stream.wait();
      }
  
      if (gradI != nullptr) {
  
          auto convI_desc = convolution_backward_data::desc(algorithm::convolution_auto, conv_diff_src_md, conv_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r);
  
  
          auto convI_prim_desc = convolution_backward_data::primitive_desc(convI_desc, engine, conv_prim_desc);
          auto userI_src_memory = dnnl::memory(user_diff_src_md, engine, gradI->buffer());
          auto userI_weights_memory = dnnl::memory(user_weights_md, engine,const_cast<NDArray *>(weights)->buffer());
          auto userI_dst_memory = dnnl::memory(user_dst_md, engine, const_cast<NDArray *>(gradO)->buffer());
  
          auto convI_src_memory = userI_src_memory;
          if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) {
              convI_src_memory = dnnl::memory(convI_prim_desc.diff_src_desc(), engine);
          }
  
          auto convI_weights_memory = userI_weights_memory;
          if (convI_prim_desc.weights_desc() != userI_weights_memory.get_desc()) {
              convI_weights_memory = dnnl::memory(convI_prim_desc.weights_desc(), engine);
              reorder(userI_weights_memory, convI_weights_memory).execute(stream, userI_weights_memory, convI_weights_memory);
          }
  
          auto convI_dst_memory = userI_dst_memory;
          if (convI_prim_desc.diff_dst_desc() != userI_dst_memory.get_desc()) {
              convI_dst_memory = dnnl::memory(convI_prim_desc.diff_dst_desc(), engine);
              reorder(userI_dst_memory, convI_dst_memory).execute(stream, userI_dst_memory, convI_dst_memory);
          }
  
          convolution_backward_data(convI_prim_desc).execute(stream,
                                                             {{DNNL_ARG_DIFF_DST, convI_dst_memory},
                                                              {DNNL_ARG_WEIGHTS,  convI_weights_memory},
                                                              {DNNL_ARG_DIFF_SRC, convI_src_memory}});
  
          if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) {
              reorder(convI_src_memory, userI_src_memory).execute(stream, convI_src_memory, userI_src_memory);
          }
  
          stream.wait();
      }
  }
  
  */
  
  //////////////////////////////////////////////////////////////////////
  PLATFORM_IMPL(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, oC] always
      auto bias    = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;      // [oC]
  
      auto output  = OUTPUT_VARIABLE(0);                                   // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
  
      int sH = INT_ARG(2);                                                        // strides height
      int sW = INT_ARG(3);                                                        // strides width
      int pH = INT_ARG(4);                                                        // paddings height
      int pW = INT_ARG(5);                                                        // paddings width
      int dH = INT_ARG(6);                                                        // dilations height
      int dW = INT_ARG(7);                                                        // dilations width
      int paddingMode = INT_ARG(8);                                                // 0-VALID, 1-SAME
      bool isNCHW    = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1;       // INT_ARG(9): 0-NCHW,  1-NHWC
  
      int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0)); // filter(kernel) height
      int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1)); // filter(kernel) width
  
      int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
      int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
      ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
  
      ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
  
      std::vector<Nd4jLong>  expectedWeightsShape = {kH, kW, iC, oC};
      REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CONV2D MKLDNN 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, "CONV2D MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
  
      conv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW);
  
      return Status::OK();
  }
  
  
  PLATFORM_CHECK(conv2d, ENGINE_CPU) {
      auto input = INPUT_VARIABLE(0);
      auto weights = INPUT_VARIABLE(1);
  
      // conv2d is only available for float32 dtype
      return block.isUseMKLDNN() && input->dataType() == nd4j::DataType::FLOAT32 &&
             weights->dataType() == nd4j::DataType::FLOAT32;
  }
  
  //////////////////////////////////////////////////////////////////////
  PLATFORM_IMPL(conv2d_bp, 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, oC] always
      auto bias    = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;                  // [oC]
      auto gradO   = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2);        // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
  
      auto gradI = OUTPUT_VARIABLE(0);                                                 // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
      auto gradW = OUTPUT_VARIABLE(1);                                                 // [kH, kW, iC, oC] always
      auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr;                   // [oC]
  
      int kH = INT_ARG(0);                                                        // filter(kernel) height
      int kW = INT_ARG(1);                                                        // filter(kernel) width
      int sH = INT_ARG(2);                                                        // strides height
      int sW = INT_ARG(3);                                                        // strides width
      int pH = INT_ARG(4);                                                        // paddings height
      int pW = INT_ARG(5);                                                        // paddings width
      int dH = INT_ARG(6);                                                        // dilations height
      int 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 bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
      int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
      ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
  
      int trueoH, trueoW;          // true output height, width
      ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
  
      if(paddingMode)                       // SAME
          ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
  
      std::vector<Nd4jLong> expectedGradOShape   = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW,  0,indIOioC,indOoH,indOoH+1});
      std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, oC};
      REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,  "CONV2D_BP MKLDNN 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, "CONV2D_BP MKLDNN 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, "CONV2D_BP MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
  
      conv2dBpMKLDNN(input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW);
  
      return Status::OK();
  }
  
  PLATFORM_CHECK(conv2d_bp, 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, oC] always
      auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;                  // [oC]
      auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2);        // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
  
      auto gradI = OUTPUT_VARIABLE(0);                                                 // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
      auto gradW = OUTPUT_VARIABLE(1);                                                 // [kH, kW, iC, oC] always
      auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr;                   // [oC]
  
  
      return block.isUseMKLDNN() &&
             nd4j::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB});
  }
  
  
  
  }
  }
  }
  