text: /*******************************************************************************
  * Copyright 2016-2018 Intel Corporation
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  *******************************************************************************/
  
  #include <assert.h>
  
  #include <chrono>
  #include <iostream>
  #include <numeric>
  #include <string>
  #include <unordered_map>
  #include <vector>
  
  #include "mkldnn.hpp"
  
  using namespace mkldnn;
  
  using namespace std;
  
  memory::dim product(const memory::dims &dims) {
      return std::accumulate(dims.begin(), dims.end(), (memory::dim)1,
              std::multiplies<memory::dim>());
  }
  
  void simple_net(int times = 100) {
      using tag = memory::format_tag;
      using dt = memory::data_type;
  
      engine eng(engine::cpu, 0);
      stream s(eng);
  
      /* Create a vector primitive to hold the network. For efficiency purpose,
       * weights are stored in a separate net to perform reordering only once. */
      std::vector<primitive> net;
      std::vector<std::unordered_map<int, memory>> net_args;
  
      const memory::dim batch = 1;
  
      /* AlexNet: conv1
       * {batch, 3, 227, 227} (x) {96, 3, 11, 11} -> {batch, 96, 55, 55}
       * strides: {4, 4}
       */
      memory::dims conv1_src_tz = { batch, 3, 227, 227 };
      memory::dims conv1_weights_tz = { 96, 3, 11, 11 };
      memory::dims conv1_bias_tz = { 96 };
      memory::dims conv1_dst_tz = { batch, 96, 55, 55 };
      memory::dims conv1_strides = { 4, 4 };
      memory::dims conv1_padding = { 0, 0 };
  
      /* Allocate input and output buffers for user data */
      std::vector<float> user_src(batch * 3 * 227 * 227);
      std::vector<float> user_dst(batch * 1000);
  
      /* Allocate and fill buffers for weights and bias */
      std::vector<float> conv1_weights(product(conv1_weights_tz));
      std::vector<float> conv1_bias(product(conv1_bias_tz));
  
      /* create memory for user data */
      auto user_src_memory = memory(
              { { conv1_src_tz }, dt::f32, tag::nchw }, eng, user_src.data());
      auto user_weights_memory
              = memory({ { conv1_weights_tz }, dt::f32, tag::oihw }, eng,
                      conv1_weights.data());
      auto conv1_user_bias_memory = memory(
              { { conv1_bias_tz }, dt::f32, tag::x }, eng, conv1_bias.data());
  
      /* create memory descriptors for convolution data w/ no specified format
       */
      auto conv1_src_md = memory::desc({ conv1_src_tz }, dt::f32, tag::any);
      auto conv1_bias_md = memory::desc({ conv1_bias_tz }, dt::f32, tag::any);
      auto conv1_weights_md
              = memory::desc({ conv1_weights_tz }, dt::f32, tag::any);
      auto conv1_dst_md = memory::desc({ conv1_dst_tz }, dt::f32, tag::any);
  
      /* create a convolution */
      auto conv1_desc = convolution_forward::desc(prop_kind::forward_inference,
              convolution_direct, conv1_src_md, conv1_weights_md, conv1_bias_md,
              conv1_dst_md, conv1_strides, conv1_padding, conv1_padding,
              padding_kind::zero);
      auto conv1_prim_desc = convolution_forward::primitive_desc(conv1_desc, eng);
  
      /* create reorders for data and weights if layout requested by
       * convolution is different from NCHW/OIHW */
      auto conv1_src_memory = user_src_memory;
      if (conv1_prim_desc.src_desc() != user_src_memory.get_desc()) {
          conv1_src_memory = memory(conv1_prim_desc.src_desc(), eng);
          net.push_back(reorder(user_src_memory, conv1_src_memory));
          net_args.push_back({ { MKLDNN_ARG_FROM, user_src_memory },
                  { MKLDNN_ARG_TO, conv1_src_memory } });
      }
  
      auto conv1_weights_memory = user_weights_memory;
      if (conv1_prim_desc.weights_desc() != user_weights_memory.get_desc()) {
          conv1_weights_memory = memory(conv1_prim_desc.weights_desc(), eng);
          reorder(user_weights_memory, conv1_weights_memory)
                  .execute(s, user_weights_memory, conv1_weights_memory);
      }
  
      auto conv1_dst_memory = memory(conv1_prim_desc.dst_desc(), eng);
  
      /* create convolution primitive and add it to net */
      net.push_back(convolution_forward(conv1_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv1_src_memory },
              { MKLDNN_ARG_WEIGHTS, conv1_weights_memory },
              { MKLDNN_ARG_BIAS, conv1_user_bias_memory },
              { MKLDNN_ARG_DST, conv1_dst_memory } });
  
      /* AlexNet: relu1
       * {batch, 96, 55, 55} -> {batch, 96, 55, 55}
       */
      const float negative1_slope = 1.0f;
  
      /* create relu primitive and add it to net */
      auto relu1_desc = eltwise_forward::desc(prop_kind::forward_inference,
              algorithm::eltwise_relu, conv1_dst_memory.get_desc(),
              negative1_slope);
      auto relu1_prim_desc = eltwise_forward::primitive_desc(relu1_desc, eng);
  
      net.push_back(eltwise_forward(relu1_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv1_dst_memory },
              { MKLDNN_ARG_DST, conv1_dst_memory } });
  
      /* AlexNet: lrn1
       * {batch, 96, 55, 55} -> {batch, 96, 55, 55}
       * local size: 5
       * alpha1: 0.0001
       * beta1: 0.75
       */
      const memory::dim local1_size = 5;
      const float alpha1 = 0.0001f;
      const float beta1 = 0.75f;
      const float k1 = 1.0f;
  
      /* create lrn primitive and add it to net */
      auto lrn1_desc = lrn_forward::desc(prop_kind::forward_inference,
              lrn_across_channels, conv1_dst_memory.get_desc(), local1_size,
              alpha1, beta1, k1);
      auto lrn1_prim_desc = lrn_forward::primitive_desc(lrn1_desc, eng);
      auto lrn1_dst_memory = memory(lrn1_prim_desc.dst_desc(), eng);
  
      net.push_back(lrn_forward(lrn1_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv1_dst_memory },
              { MKLDNN_ARG_DST, lrn1_dst_memory } });
  
      /* AlexNet: pool1
       * {batch, 96, 55, 55} -> {batch, 96, 27, 27}
       * kernel: {3, 3}
       * strides: {2, 2}
       */
  
      memory::dims pool1_dst_tz = { batch, 96, 27, 27 };
      memory::dims pool1_kernel = { 3, 3 };
      memory::dims pool1_strides = { 2, 2 };
      memory::dims pool_padding = { 0, 0 };
  
      auto pool1_dst_md = memory::desc({ pool1_dst_tz }, dt::f32, tag::any);
  
      /* create a pooling */
      auto pool1_desc = pooling_forward::desc(prop_kind::forward_inference,
              pooling_max, lrn1_dst_memory.get_desc(), pool1_dst_md,
              pool1_strides, pool1_kernel, pool_padding, pool_padding,
              padding_kind::zero);
      auto pool1_pd = pooling_forward::primitive_desc(pool1_desc, eng);
      auto pool1_dst_memory = memory(pool1_pd.dst_desc(), eng);
  
      /* create pooling primitive an add it to net */
      net.push_back(pooling_forward(pool1_pd));
      net_args.push_back({ { MKLDNN_ARG_SRC, lrn1_dst_memory },
              { MKLDNN_ARG_DST, pool1_dst_memory } });
  
      /* AlexNet: conv2
       * {batch, 96, 27, 27} (x) {2, 128, 48, 5, 5} -> {batch, 256, 27, 27}
       * strides: {1, 1}
       */
      memory::dims conv2_src_tz = { batch, 96, 27, 27 };
      memory::dims conv2_weights_tz = { 2, 128, 48, 5, 5 };
      memory::dims conv2_bias_tz = { 256 };
      memory::dims conv2_dst_tz = { batch, 256, 27, 27 };
      memory::dims conv2_strides = { 1, 1 };
      memory::dims conv2_padding = { 2, 2 };
  
      std::vector<float> conv2_weights(product(conv2_weights_tz));
      std::vector<float> conv2_bias(product(conv2_bias_tz));
  
      /* create memory for user data */
      auto conv2_user_weights_memory
              = memory({ { conv2_weights_tz }, dt::f32, tag::goihw }, eng,
                      conv2_weights.data());
      auto conv2_user_bias_memory = memory(
              { { conv2_bias_tz }, dt::f32, tag::x }, eng, conv2_bias.data());
  
      /* create memory descriptors for convolution data w/ no specified format
       */
      auto conv2_src_md = memory::desc({ conv2_src_tz }, dt::f32, tag::any);
      auto conv2_bias_md = memory::desc({ conv2_bias_tz }, dt::f32, tag::any);
      auto conv2_weights_md
              = memory::desc({ conv2_weights_tz }, dt::f32, tag::any);
      auto conv2_dst_md = memory::desc({ conv2_dst_tz }, dt::f32, tag::any);
  
      /* create a convolution */
      auto conv2_desc = convolution_forward::desc(prop_kind::forward_inference,
              convolution_direct, conv2_src_md, conv2_weights_md, conv2_bias_md,
              conv2_dst_md, conv2_strides, conv2_padding, conv2_padding,
              padding_kind::zero);
      auto conv2_prim_desc = convolution_forward::primitive_desc(conv2_desc, eng);
  
      auto conv2_src_memory = pool1_dst_memory;
      if (conv2_prim_desc.src_desc() != conv2_src_memory.get_desc()) {
          conv2_src_memory = memory(conv2_prim_desc.src_desc(), eng);
          net.push_back(reorder(pool1_dst_memory, conv2_src_memory));
          net_args.push_back({ { MKLDNN_ARG_FROM, pool1_dst_memory },
                  { MKLDNN_ARG_TO, conv2_src_memory } });
      }
  
      auto conv2_weights_memory = conv2_user_weights_memory;
      if (conv2_prim_desc.weights_desc()
              != conv2_user_weights_memory.get_desc()) {
          conv2_weights_memory = memory(conv2_prim_desc.weights_desc(), eng);
          reorder(conv2_user_weights_memory, conv2_weights_memory)
                  .execute(s, conv2_user_weights_memory, conv2_weights_memory);
      }
  
      auto conv2_dst_memory = memory(conv2_prim_desc.dst_desc(), eng);
  
      /* create convolution primitive and add it to net */
      net.push_back(convolution_forward(conv2_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv2_src_memory },
              { MKLDNN_ARG_WEIGHTS, conv2_weights_memory },
              { MKLDNN_ARG_BIAS, conv2_user_bias_memory },
              { MKLDNN_ARG_DST, conv2_dst_memory } });
  
      /* AlexNet: relu2
       * {batch, 256, 27, 27} -> {batch, 256, 27, 27}
       */
      const float negative2_slope = 1.0f;
  
      /* create relu primitive and add it to net */
      auto relu2_desc = eltwise_forward::desc(prop_kind::forward_inference,
              algorithm::eltwise_relu, conv2_dst_memory.get_desc(),
              negative2_slope);
      auto relu2_prim_desc = eltwise_forward::primitive_desc(relu2_desc, eng);
  
      net.push_back(eltwise_forward(relu2_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv2_dst_memory },
              { MKLDNN_ARG_DST, conv2_dst_memory } });
  
      /* AlexNet: lrn2
       * {batch, 256, 27, 27} -> {batch, 256, 27, 27}
       * local size: 5
       * alpha2: 0.0001
       * beta2: 0.75
       */
      const memory::dim local2_size = 5;
      const float alpha2 = 0.0001f;
      const float beta2 = 0.75f;
      const float k2 = 1.0f;
  
      /* create lrn primitive and add it to net */
      auto lrn2_desc = lrn_forward::desc(prop_kind::forward_inference,
              lrn_across_channels, conv2_prim_desc.dst_desc(), local2_size,
              alpha2, beta2, k2);
      auto lrn2_prim_desc = lrn_forward::primitive_desc(lrn2_desc, eng);
      auto lrn2_dst_memory = memory(lrn2_prim_desc.dst_desc(), eng);
  
      net.push_back(lrn_forward(lrn2_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv2_dst_memory },
              { MKLDNN_ARG_DST, lrn2_dst_memory } });
  
      /* AlexNet: pool2
       * {batch, 256, 27, 27} -> {batch, 256, 13, 13}
       * kernel: {3, 3}
       * strides: {2, 2}
       */
  
      memory::dims pool2_dst_tz = { batch, 256, 13, 13 };
      memory::dims pool2_kernel = { 3, 3 };
      memory::dims pool2_strides = { 2, 2 };
      memory::dims pool2_padding = { 0, 0 };
  
      auto pool2_dst_md = memory::desc({ pool2_dst_tz }, dt::f32, tag::any);
  
      /* create a pooling */
      auto pool2_desc = pooling_forward::desc(prop_kind::forward_inference,
              pooling_max, lrn2_dst_memory.get_desc(), pool2_dst_md,
              pool2_strides, pool2_kernel, pool2_padding, pool2_padding,
              padding_kind::zero);
      auto pool2_pd = pooling_forward::primitive_desc(pool2_desc, eng);
      auto pool2_dst_memory = memory(pool2_pd.dst_desc(), eng);
  
      /* create pooling primitive an add it to net */
      net.push_back(pooling_forward(pool2_pd));
      net_args.push_back({ { MKLDNN_ARG_SRC, lrn2_dst_memory },
              { MKLDNN_ARG_DST, pool2_dst_memory } });
  
      // -------
      /* AlexNet: conv3
       * {batch, 256, 13, 13} (x)  {384, 256, 3, 3}; -> {batch, 384, 13, 13};
       * strides: {1, 1}
       */
      memory::dims conv3_src_tz = { batch, 256, 13, 13 };
      memory::dims conv3_weights_tz = { 384, 256, 3, 3 };
      memory::dims conv3_bias_tz = { 384 };
      memory::dims conv3_dst_tz = { batch, 384, 13, 13 };
      memory::dims conv3_strides = { 1, 1 };
      memory::dims conv3_padding = { 1, 1 };
  
      std::vector<float> conv3_weights(product(conv3_weights_tz));
      std::vector<float> conv3_bias(product(conv3_bias_tz));
  
      /* create memory for user data */
      auto conv3_user_weights_memory
              = memory({ { conv3_weights_tz }, dt::f32, tag::oihw }, eng,
                      conv3_weights.data());
      auto conv3_user_bias_memory = memory(
              { { conv3_bias_tz }, dt::f32, tag::x }, eng, conv3_bias.data());
  
      /* create memory descriptors for convolution data w/ no specified format
       */
      auto conv3_src_md = memory::desc({ conv3_src_tz }, dt::f32, tag::any);
      auto conv3_bias_md = memory::desc({ conv3_bias_tz }, dt::f32, tag::any);
      auto conv3_weights_md
              = memory::desc({ conv3_weights_tz }, dt::f32, tag::any);
      auto conv3_dst_md = memory::desc({ conv3_dst_tz }, dt::f32, tag::any);
  
      /* create a convolution */
      auto conv3_desc = convolution_forward::desc(prop_kind::forward_inference,
              convolution_direct, conv3_src_md, conv3_weights_md, conv3_bias_md,
              conv3_dst_md, conv3_strides, conv3_padding, conv3_padding,
              padding_kind::zero);
      auto conv3_prim_desc = convolution_forward::primitive_desc(conv3_desc, eng);
  
      auto conv3_src_memory = pool2_dst_memory;
      if (conv3_prim_desc.src_desc() != conv3_src_memory.get_desc()) {
          conv3_src_memory = memory(conv3_prim_desc.src_desc(), eng);
          net.push_back(reorder(pool2_dst_memory, conv3_src_memory));
          net_args.push_back({ { MKLDNN_ARG_FROM, pool2_dst_memory },
                  { MKLDNN_ARG_TO, conv3_src_memory } });
      }
  
      auto conv3_weights_memory = conv3_user_weights_memory;
      if (conv3_prim_desc.weights_desc()
              != conv3_user_weights_memory.get_desc()) {
          conv3_weights_memory = memory(conv3_prim_desc.weights_desc(), eng);
          reorder(conv3_user_weights_memory, conv3_weights_memory)
                  .execute(s, conv3_user_weights_memory, conv3_weights_memory);
      }
  
      auto conv3_dst_memory = memory(conv3_prim_desc.dst_desc(), eng);
  
      /* create convolution primitive and add it to net */
      net.push_back(convolution_forward(conv3_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv3_src_memory },
              { MKLDNN_ARG_WEIGHTS, conv3_weights_memory },
              { MKLDNN_ARG_BIAS, conv3_user_bias_memory },
              { MKLDNN_ARG_DST, conv3_dst_memory } });
  
      /* AlexNet: relu3
       * {batch, 384, 13, 13} -> {batch, 384, 13, 13}
       */
      const float negative3_slope = 1.0f;
  
      /* create relu primitive and add it to net */
      auto relu3_desc = eltwise_forward::desc(prop_kind::forward_inference,
              algorithm::eltwise_relu, conv3_dst_memory.get_desc(),
              negative3_slope);
      auto relu3_prim_desc = eltwise_forward::primitive_desc(relu3_desc, eng);
  
      net.push_back(eltwise_forward(relu3_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv3_dst_memory },
              { MKLDNN_ARG_DST, conv3_dst_memory } });
  
      /* AlexNet: conv4
       * {batch, 384, 13, 13} (x)  {2, 192, 192, 3, 3}; -> {batch, 384, 13,
       * 13};
       * strides: {1, 1}
       */
      memory::dims conv4_src_tz = { batch, 384, 13, 13 };
      memory::dims conv4_weights_tz = { 2, 192, 192, 3, 3 };
      memory::dims conv4_bias_tz = { 384 };
      memory::dims conv4_dst_tz = { batch, 384, 13, 13 };
      memory::dims conv4_strides = { 1, 1 };
      memory::dims conv4_padding = { 1, 1 };
  
      std::vector<float> conv4_weights(product(conv4_weights_tz));
      std::vector<float> conv4_bias(product(conv4_bias_tz));
  
      /* create memory for user data */
      auto conv4_user_weights_memory
              = memory({ { conv4_weights_tz }, dt::f32, tag::goihw }, eng,
                      conv4_weights.data());
      auto conv4_user_bias_memory = memory(
              { { conv4_bias_tz }, dt::f32, tag::x }, eng, conv4_bias.data());
  
      /* create memory descriptors for convolution data w/ no specified format
       */
      auto conv4_src_md = memory::desc({ conv4_src_tz }, dt::f32, tag::any);
      auto conv4_bias_md = memory::desc({ conv4_bias_tz }, dt::f32, tag::any);
      auto conv4_weights_md
              = memory::desc({ conv4_weights_tz }, dt::f32, tag::any);
      auto conv4_dst_md = memory::desc({ conv4_dst_tz }, dt::f32, tag::any);
  
      /* create a convolution */
      auto conv4_desc = convolution_forward::desc(prop_kind::forward_inference,
              convolution_direct, conv4_src_md, conv4_weights_md, conv4_bias_md,
              conv4_dst_md, conv4_strides, conv4_padding, conv4_padding,
              padding_kind::zero);
      auto conv4_prim_desc = convolution_forward::primitive_desc(conv4_desc, eng);
  
      auto conv4_src_memory = conv3_dst_memory;
      if (conv4_prim_desc.src_desc() != conv4_src_memory.get_desc()) {
          conv4_src_memory = memory(conv4_prim_desc.src_desc(), eng);
          net.push_back(reorder(conv3_dst_memory, conv4_src_memory));
          net_args.push_back({ { MKLDNN_ARG_FROM, conv3_dst_memory },
                  { MKLDNN_ARG_TO, conv4_src_memory } });
      }
  
      auto conv4_weights_memory = conv4_user_weights_memory;
      if (conv4_prim_desc.weights_desc()
              != conv4_user_weights_memory.get_desc()) {
          conv4_weights_memory = memory(conv4_prim_desc.weights_desc(), eng);
          reorder(conv4_user_weights_memory, conv4_weights_memory)
                  .execute(s, conv4_user_weights_memory, conv4_weights_memory);
      }
  
      auto conv4_dst_memory = memory(conv4_prim_desc.dst_desc(), eng);
  
      /* create convolution primitive and add it to net */
      net.push_back(convolution_forward(conv4_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv4_src_memory },
              { MKLDNN_ARG_WEIGHTS, conv4_weights_memory },
              { MKLDNN_ARG_BIAS, conv4_user_bias_memory },
              { MKLDNN_ARG_DST, conv4_dst_memory } });
  
      /* AlexNet: relu4
       * {batch, 384, 13, 13} -> {batch, 384, 13, 13}
       */
      const float negative4_slope = 1.0f;
  
      /* create relu primitive and add it to net */
      auto relu4_desc = eltwise_forward::desc(prop_kind::forward_inference,
              algorithm::eltwise_relu, conv4_dst_memory.get_desc(),
              negative4_slope);
      auto relu4_prim_desc = eltwise_forward::primitive_desc(relu4_desc, eng);
  
      net.push_back(eltwise_forward(relu4_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv4_dst_memory },
              { MKLDNN_ARG_DST, conv4_dst_memory } });
  
      /* AlexNet: conv5
       * {batch, 384, 13, 13} (x)  {2, 128, 192, 3, 3}; -> {batch, 256, 13, 13};
       * strides: {1, 1}
       */
      memory::dims conv5_src_tz = { batch, 384, 13, 13 };
      memory::dims conv5_weights_tz = { 2, 128, 192, 3, 3 };
      memory::dims conv5_bias_tz = { 256 };
      memory::dims conv5_dst_tz = { batch, 256, 13, 13 };
      memory::dims conv5_strides = { 1, 1 };
      memory::dims conv5_padding = { 1, 1 };
  
      std::vector<float> conv5_weights(product(conv5_weights_tz));
      std::vector<float> conv5_bias(product(conv5_bias_tz));
  
      /* create memory for user data */
      auto conv5_user_weights_memory
              = memory({ { conv5_weights_tz }, dt::f32, tag::goihw }, eng,
                      conv5_weights.data());
      auto conv5_user_bias_memory = memory(
              { { conv5_bias_tz }, dt::f32, tag::x }, eng, conv5_bias.data());
  
      /* create memory descriptors for convolution data w/ no specified format
       */
      auto conv5_src_md = memory::desc({ conv5_src_tz }, dt::f32, tag::any);
      auto conv5_weights_md
              = memory::desc({ conv5_weights_tz }, dt::f32, tag::any);
      auto conv5_bias_md = memory::desc({ conv5_bias_tz }, dt::f32, tag::any);
      auto conv5_dst_md = memory::desc({ conv5_dst_tz }, dt::f32, tag::any);
  
      /* create a convolution */
      auto conv5_desc = convolution_forward::desc(prop_kind::forward_inference,
              convolution_direct, conv5_src_md, conv5_weights_md, conv5_bias_md,
              conv5_dst_md, conv5_strides, conv5_padding, conv5_padding,
              padding_kind::zero);
      auto conv5_prim_desc = convolution_forward::primitive_desc(conv5_desc, eng);
  
      auto conv5_src_memory = conv4_dst_memory;
      if (conv5_prim_desc.src_desc() != conv5_src_memory.get_desc()) {
          conv5_src_memory = memory(conv5_prim_desc.src_desc(), eng);
          net.push_back(reorder(conv4_dst_memory, conv5_src_memory));
          net_args.push_back({ { MKLDNN_ARG_FROM, conv4_dst_memory },
                  { MKLDNN_ARG_TO, conv5_src_memory } });
      }
  
      auto conv5_weights_memory = conv5_user_weights_memory;
      if (conv5_prim_desc.weights_desc()
              != conv5_user_weights_memory.get_desc()) {
          conv5_weights_memory = memory(conv5_prim_desc.weights_desc(), eng);
          reorder(conv5_user_weights_memory, conv5_weights_memory)
                  .execute(s, conv5_user_weights_memory, conv5_weights_memory);
      }
  
      auto conv5_dst_memory = memory(conv5_prim_desc.dst_desc(), eng);
  
      /* create convolution primitive and add it to net */
      net.push_back(convolution_forward(conv5_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv5_src_memory },
              { MKLDNN_ARG_WEIGHTS, conv5_weights_memory },
              { MKLDNN_ARG_BIAS, conv5_user_bias_memory },
              { MKLDNN_ARG_DST, conv5_dst_memory } });
  
      /* AlexNet: relu5
       * {batch, 256, 13, 13} -> {batch, 256, 13, 13}
       */
      const float negative5_slope = 1.0f;
  
      /* create relu primitive and add it to net */
      auto relu5_desc = eltwise_forward::desc(prop_kind::forward_inference,
              algorithm::eltwise_relu, conv5_dst_memory.get_desc(),
              negative5_slope);
      auto relu5_prim_desc = eltwise_forward::primitive_desc(relu5_desc, eng);
  
      net.push_back(eltwise_forward(relu5_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv5_dst_memory },
              { MKLDNN_ARG_DST, conv5_dst_memory } });
  
      /* AlexNet: pool5
       * {batch, 256, 13, 13} -> {batch, 256, 6, 6}
       * kernel: {3, 3}
       * strides: {2, 2}
       */
  
      memory::dims pool5_dst_tz = { batch, 256, 6, 6 };
      memory::dims pool5_kernel = { 3, 3 };
      memory::dims pool5_strides = { 2, 2 };
      memory::dims pool5_padding = { 0, 0 };
  
      std::vector<float> pool5_dst(product(pool5_dst_tz));
  
      auto pool5_dst_md = memory::desc({ pool5_dst_tz }, dt::f32, tag::any);
  
      /* create a pooling */
      auto pool5_desc = pooling_forward::desc(prop_kind::forward_inference,
              pooling_max, conv5_dst_memory.get_desc(), pool5_dst_md,
              pool5_strides, pool5_kernel, pool5_padding, pool5_padding,
              padding_kind::zero);
      auto pool5_pd = pooling_forward::primitive_desc(pool5_desc, eng);
  
      auto pool5_dst_memory = memory(pool5_pd.dst_desc(), eng);
  
      /* create pooling primitive an add it to net */
      net.push_back(pooling_forward(pool5_pd));
      net_args.push_back({ { MKLDNN_ARG_SRC, conv5_dst_memory },
              { MKLDNN_ARG_DST, pool5_dst_memory } });
  
      /**
       * fc6 inner product {batch, 256, 6, 6} (x) {4096, 256, 6, 6}-> {batch,
       * 4096}
       */
      memory::dims fc6_src_tz = { batch, 256, 6, 6 };
      memory::dims fc6_weights_tz = { 4096, 256, 6, 6 };
      memory::dims fc6_bias_tz = { 4096 };
      memory::dims fc6_dst_tz = { batch, 4096 };
  
      std::vector<float> fc6_weights(product(fc6_weights_tz));
      std::vector<float> fc6_bias(product(fc6_bias_tz));
  
      /* create memory for user data */
      auto fc6_user_weights_memory
              = memory({ { fc6_weights_tz }, dt::f32, tag::oihw }, eng,
                      fc6_weights.data());
      auto fc6_user_bias_memory = memory(
              { { fc6_bias_tz }, dt::f32, tag::x }, eng, fc6_bias.data());
  
      /* create memory descriptors for convolution data w/ no specified format
       */
      auto fc6_src_md = memory::desc({ fc6_src_tz }, dt::f32, tag::any);
      auto fc6_bias_md = memory::desc({ fc6_bias_tz }, dt::f32, tag::any);
      auto fc6_weights_md = memory::desc({ fc6_weights_tz }, dt::f32, tag::any);
      auto fc6_dst_md = memory::desc({ fc6_dst_tz }, dt::f32, tag::any);
  
      /* create a inner_product */
      auto fc6_desc = inner_product_forward::desc(prop_kind::forward_inference,
              fc6_src_md, fc6_weights_md, fc6_bias_md, fc6_dst_md);
      auto fc6_prim_desc = inner_product_forward::primitive_desc(fc6_desc, eng);
  
      auto fc6_src_memory = pool5_dst_memory;
      if (fc6_prim_desc.src_desc() != fc6_src_memory.get_desc()) {
          fc6_src_memory = memory(fc6_prim_desc.src_desc(), eng);
          net.push_back(reorder(pool5_dst_memory, fc6_src_memory));
          net_args.push_back({ { MKLDNN_ARG_FROM, pool5_dst_memory },
                  { MKLDNN_ARG_TO, fc6_src_memory } });
      }
  
      auto fc6_weights_memory = fc6_user_weights_memory;
      if (fc6_prim_desc.weights_desc() != fc6_user_weights_memory.get_desc()) {
          fc6_weights_memory = memory(fc6_prim_desc.weights_desc(), eng);
          reorder(fc6_user_weights_memory, fc6_weights_memory)
                  .execute(s, fc6_user_weights_memory, fc6_weights_memory);
      }
  
      auto fc6_dst_memory = memory(fc6_prim_desc.dst_desc(), eng);
  
      /* create convolution primitive and add it to net */
      net.push_back(inner_product_forward(fc6_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, fc6_src_memory },
              { MKLDNN_ARG_WEIGHTS, fc6_weights_memory },
              { MKLDNN_ARG_BIAS, fc6_user_bias_memory },
              { MKLDNN_ARG_DST, fc6_dst_memory } });
  
      /**
       * fc7 inner product {batch, 4096} (x) {4096, 4096}-> {batch, 4096}
       */
      memory::dims fc7_weights_tz = { 4096, 4096 };
      memory::dims fc7_bias_tz = { 4096 };
      memory::dims fc7_dst_tz = { batch, 4096 };
  
      std::vector<float> fc7_weights(product(fc7_weights_tz));
      std::vector<float> fc7_bias(product(fc7_bias_tz));
  
      /* create memory for user data */
      auto fc7_user_weights_memory = memory(
              { { fc7_weights_tz }, dt::f32, tag::nc }, eng, fc7_weights.data());
  
      auto fc7_user_bias_memory = memory(
              { { fc7_bias_tz }, dt::f32, tag::x }, eng, fc7_bias.data());
  
      /* create memory descriptors for convolution data w/ no specified format
       */
      auto fc7_bias_md = memory::desc({ fc7_bias_tz }, dt::f32, tag::any);
      auto fc7_weights_md = memory::desc({ fc7_weights_tz }, dt::f32, tag::any);
      auto fc7_dst_md = memory::desc({ fc7_dst_tz }, dt::f32, tag::any);
  
      /* create a inner_product */
      auto fc7_desc = inner_product_forward::desc(prop_kind::forward_inference,
              fc6_dst_memory.get_desc(), fc7_weights_md, fc7_bias_md, fc7_dst_md);
      auto fc7_prim_desc = inner_product_forward::primitive_desc(fc7_desc, eng);
  
      auto fc7_weights_memory = fc7_user_weights_memory;
      if (fc7_prim_desc.weights_desc() != fc7_user_weights_memory.get_desc()) {
          fc7_weights_memory = memory(fc7_prim_desc.weights_desc(), eng);
          reorder(fc7_user_weights_memory, fc7_weights_memory)
                  .execute(s, fc7_user_weights_memory, fc7_weights_memory);
      }
  
      auto fc7_dst_memory = memory(fc7_prim_desc.dst_desc(), eng);
  
      /* create convolution primitive and add it to net */
      net.push_back(inner_product_forward(fc7_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, fc6_dst_memory },
              { MKLDNN_ARG_WEIGHTS, fc7_weights_memory },
              { MKLDNN_ARG_BIAS, fc7_user_bias_memory },
              { MKLDNN_ARG_DST, fc7_dst_memory } });
  
      /**
       * fc8 inner product {batch, 4096} (x) {1000, 4096}-> {batch, 1000}
       */
      memory::dims fc8_weights_tz = { 1000, 4096 };
      memory::dims fc8_bias_tz = { 1000 };
      memory::dims fc8_dst_tz = { batch, 1000 };
  
      std::vector<float> fc8_weights(product(fc8_weights_tz));
      std::vector<float> fc8_bias(product(fc8_bias_tz));
  
      /* create memory for user data */
      auto fc8_user_weights_memory = memory(
              { { fc8_weights_tz }, dt::f32, tag::nc }, eng, fc8_weights.data());
      auto fc8_user_bias_memory = memory(
              { { fc8_bias_tz }, dt::f32, tag::x }, eng, fc8_bias.data());
      auto user_dst_memory = memory(
              { { fc8_dst_tz }, dt::f32, tag::nc }, eng, user_dst.data());
  
      /* create memory descriptors for convolution data w/ no specified format
       */
      auto fc8_bias_md = memory::desc({ fc8_bias_tz }, dt::f32, tag::any);
      auto fc8_weights_md = memory::desc({ fc8_weights_tz }, dt::f32, tag::any);
      auto fc8_dst_md = memory::desc({ fc8_dst_tz }, dt::f32, tag::any);
  
      /* create a inner_product */
      auto fc8_desc = inner_product_forward::desc(prop_kind::forward_inference,
              fc7_dst_memory.get_desc(), fc8_weights_md, fc8_bias_md, fc8_dst_md);
      auto fc8_prim_desc = inner_product_forward::primitive_desc(fc8_desc, eng);
  
      auto fc8_weights_memory = fc8_user_weights_memory;
      if (fc8_prim_desc.weights_desc() != fc8_user_weights_memory.get_desc()) {
          fc8_weights_memory = memory(fc8_prim_desc.weights_desc(), eng);
          reorder(fc8_user_weights_memory, fc8_weights_memory)
                  .execute(s, fc8_user_weights_memory, fc8_weights_memory);
      }
  
      auto fc8_dst_memory = memory(fc8_prim_desc.dst_desc(), eng);
  
      /* create convolution primitive and add it to net */
      net.push_back(inner_product_forward(fc8_prim_desc));
      net_args.push_back({ { MKLDNN_ARG_SRC, fc7_dst_memory },
              { MKLDNN_ARG_WEIGHTS, fc8_weights_memory },
              { MKLDNN_ARG_BIAS, fc8_user_bias_memory },
              { MKLDNN_ARG_DST, fc8_dst_memory } });
  
      /* create reorder between internal and user data if it is needed and
       *  add it to net after pooling */
      if (fc8_dst_memory != user_dst_memory) {
          net.push_back(reorder(fc8_dst_memory, user_dst_memory));
          net_args.push_back({ { MKLDNN_ARG_FROM, fc8_dst_memory },
                  { MKLDNN_ARG_TO, user_dst_memory } });
      }
  
      for (int j = 0; j < times; ++j) {
          assert(net.size() == net_args.size() && "something is missing");
          for (size_t i = 0; i < net.size(); ++i)
              net.at(i).execute(s, net_args.at(i));
      }
  }
  
  int main(int argc, char **argv) {
      try {
          auto begin = chrono::duration_cast<chrono::milliseconds>(
                  chrono::steady_clock::now().time_since_epoch())
                               .count();
          int times = 100;
          simple_net(times);
          auto end = chrono::duration_cast<chrono::milliseconds>(
                  chrono::steady_clock::now().time_since_epoch())
                             .count();
          cout << "Use time " << (end - begin) / (times + 0.0) << "\n";
      } catch (error &e) {
          std::cerr << "status: " << e.status << std::endl;
          std::cerr << "message: " << e.message << std::endl;
      }
      return 0;
  }
  