conv_mkldnn_op.cc

/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

   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 "paddle/fluid/operators/conv_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#include "paddle/fluid/framework/data_layout_transform.h"
#include <unordered_map>
#include <map>

#include "paddle/fluid/framework/data_layout_transform.h"

namespace paddle {
namespace operators {

using framework::DataLayout;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
using mkldnn::stream;
using platform::to_void_cast;
using platform::GetMKLDNNFormat;

class ConvMKLDNNHandler : public platform::MKLDNNHandler {
 public:
  ConvMKLDNNHandler(
      std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd,
      const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
      const std::string& base_key)
      : platform::MKLDNNHandler(dev_ctx, engine, base_key) {
    conv_pd_ = conv_pd;
  }

  ConvMKLDNNHandler(
      std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd,
      std::shared_ptr<mkldnn::convolution_backward_data::primitive_desc>
          conv_bwd_data_pd,
      std::shared_ptr<mkldnn::convolution_backward_weights::primitive_desc>
          conv_bwd_weights_pd,
      const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
      const std::string& base_key)
      : platform::MKLDNNHandler(dev_ctx, engine, base_key),
        conv_pd_(conv_pd),
        conv_bwd_weights_pd_(conv_bwd_weights_pd),
        conv_bwd_data_pd_(conv_bwd_data_pd) {
    // If we are in Grad operatgor then update a key with BWD suffix to
    // distinguish from FWD memory primitives
    key_ += "-BWD";
  }

  size_t GetDstMemorySize() const {
    return conv_pd_->dst_primitive_desc().get_size();
  }
  
  mkldnn::memory::format GetDstFormat() const {
    return static_cast<mkldnn::memory::format>(
        conv_pd_->dst_primitive_desc().desc().data.format);
  }

  size_t GetDiffWeightsMemorySize() const {
    return conv_bwd_weights_pd_->diff_weights_primitive_desc().get_size();
  }

  size_t GetDiffSourceMemorySize() const {
    return conv_bwd_data_pd_->diff_src_primitive_desc().get_size();
  }

  std::shared_ptr<mkldnn::memory> AcquireSrcMemoryFromWeightsPrimitive(
      const std::shared_ptr<mkldnn::memory> user_memory_p,
      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
    auto src_pd = conv_bwd_weights_pd_->src_primitive_desc();
    auto user_pd = user_memory_p->get_primitive_desc();
    return this->AcquireMemory(src_pd, user_pd, user_memory_p,
                               "@weights-src_mem_p", pipeline);
  }

  std::shared_ptr<mkldnn::memory> AcquireDiffDstMemoryFromWeightsPrimitive(
      const std::shared_ptr<mkldnn::memory> user_memory_p,
      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
    auto diff_dst_pd = conv_bwd_weights_pd_->diff_dst_primitive_desc();
    auto user_pd = user_memory_p->get_primitive_desc();
    return this->AcquireMemory(diff_dst_pd, user_pd, user_memory_p,
                               "@weights-diff_dst_mem_p", pipeline);
  }

  std::shared_ptr<mkldnn::memory> AcquireDiffWeightsMemoryFromWeightsPrimitive(
      void* ptr) {
    return this->AcquireMemoryFromPrimitive(
        conv_bwd_weights_pd_->diff_weights_primitive_desc(), ptr,
        "@diff_weights_mem_p");
  }

  std::shared_ptr<mkldnn::memory> AcquireDiffDstMemoryFromDataPrimitive(
      const std::shared_ptr<mkldnn::memory> user_memory_p,
      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
    auto diff_dst_pd = conv_bwd_data_pd_->diff_dst_primitive_desc();
    auto user_pd = user_memory_p->get_primitive_desc();
    return this->AcquireMemory(diff_dst_pd, user_pd, user_memory_p,
                               "@data-diff_dst_mem_p", pipeline);
  }

  std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryFromDataPrimitive(
      const std::shared_ptr<mkldnn::memory> user_weights_memory_p,
      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
    auto weights_pd = conv_bwd_data_pd_->weights_primitive_desc();
    auto user_pd = user_weights_memory_p->get_primitive_desc();
    return this->AcquireMemory(weights_pd, user_pd, user_weights_memory_p,
                               "@data-weights_mem_p", pipeline);
  }

  std::shared_ptr<mkldnn::memory> AcquireResidualDataMemory(
      const mkldnn::memory::desc& md, void* ptr) {
    return this->AcquireMemory(md, ptr, "@user_residual_data_mem_p");
  }

  std::shared_ptr<mkldnn::memory> AcquireDstMemoryFromResidualDataMemory(
      const std::shared_ptr<mkldnn::memory>& user_residual_memory_p,
      void* dst_ptr,
      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
    return this->AcquireMemory(user_residual_memory_p,
                               this->AcquireDstMemoryFromPrimitive(dst_ptr),
                               "@residual_data_mem_p", pipeline);
  }

  std::shared_ptr<mkldnn::memory> AcquireDiffSrcMemoryFromDataPrimitive(
      void* ptr) {
    return this->AcquireMemoryFromPrimitive(
        conv_bwd_data_pd_->diff_src_primitive_desc(), ptr, "@diff_src_mem_p");
  }

  std::shared_ptr<mkldnn::memory> AcquireDstMemoryFromPrimitive(void* ptr) {
    return this->AcquireMemoryFromPrimitive(conv_pd_->dst_primitive_desc(), ptr,
                                            "@dst_mem_p");
  }

  std::shared_ptr<mkldnn::memory> AcquireSrcMemoryFromPrimitive(
      const std::shared_ptr<mkldnn::memory> user_memory_p,
      std::vector<mkldnn::primitive>& pipeline) {  // NOLINT
    auto src_pd = conv_pd_->src_primitive_desc();
    auto user_pd = user_memory_p->get_primitive_desc();
    return this->AcquireMemory(src_pd, user_pd, user_memory_p, "@src_mem_p",
                               pipeline);
  }

  std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryFromPrimitive(
      const std::shared_ptr<mkldnn::memory> user_weights_memory_p,
      std::vector<mkldnn::primitive>& pipeline,  // NOLINT
      bool is_persistent = false,
      bool is_INT8 = false,
      std::vector<float> scale_data = {1.0f},
      int mask = 0) { 
    auto user_weights_pd = user_weights_memory_p->get_primitive_desc();
    auto weights_pd = conv_pd_->weights_primitive_desc();
    return this->AcquireMemory(weights_pd, user_weights_pd,
                               user_weights_memory_p, "@weights_mem_p",
                               pipeline, is_persistent,
                               is_INT8, scale_data, mask);
  }

  std::shared_ptr<mkldnn::memory> AcquireBiasMemoryFromPrimitive(
      const std::shared_ptr<mkldnn::memory> user_bias_memory_p,
      std::vector<mkldnn::primitive>& pipeline,
      bool is_persistent = false,
      bool is_INT8 = false,
      std::vector<float> scale_data = {1.0f},
      int mask = 0) {  // NOLINT
    auto user_bias_pd = user_bias_memory_p->get_primitive_desc();
    auto bias_pd = conv_pd_->bias_primitive_desc();
    return this->AcquireMemory(bias_pd, user_bias_pd, user_bias_memory_p,
                               "@bias_mem_p", pipeline, is_persistent,
                               is_INT8, scale_data, mask);
  }

  std::shared_ptr<mkldnn::convolution_forward> AcquireConvolution(
      std::shared_ptr<mkldnn::memory> src_memory_p,
      std::shared_ptr<mkldnn::memory> weights_memory_p,
      std::shared_ptr<mkldnn::memory> dst_memory_p) {
    auto prim_key = key_ + "@conv_p";
    auto conv_p = std::static_pointer_cast<mkldnn::convolution_forward>(
        dev_ctx_.GetBlob(prim_key));
    PADDLE_ENFORCE((conv_p != nullptr) || (is_reusing_ == false),
                   "Fail to find convolution primitive in device context");
    if (conv_p == nullptr) {
      conv_p = std::make_shared<mkldnn::convolution_forward>(
          *conv_pd_, *(src_memory_p), *(weights_memory_p.get()),
          *(dst_memory_p.get()));

      dev_ctx_.SetBlob(prim_key, conv_p);
    } else {
      is_reusing_ = true;
    }
    return conv_p;
  }

  std::shared_ptr<mkldnn::convolution_forward> AcquireConvolution(
      std::shared_ptr<mkldnn::memory> src_memory_p,
      std::shared_ptr<mkldnn::memory> weights_memory_p,
      std::shared_ptr<mkldnn::memory> bias_memory_p,
      std::shared_ptr<mkldnn::memory> dst_memory_p) {
    auto prim_key = key_ + "@conv_p";
    auto conv_p = std::static_pointer_cast<mkldnn::convolution_forward>(
        dev_ctx_.GetBlob(prim_key));
    PADDLE_ENFORCE((conv_p != nullptr) || (is_reusing_ == false),
                   "Fail to find convolution primitive in device context");
    if (conv_p == nullptr) {
      conv_p = std::make_shared<mkldnn::convolution_forward>(
          *conv_pd_, *(src_memory_p), *(weights_memory_p.get()),
          *(bias_memory_p.get()), *(dst_memory_p.get()));

      dev_ctx_.SetBlob(prim_key, conv_p);
    } else {
      is_reusing_ = true;
    }
    return conv_p;
  }

  std::shared_ptr<mkldnn::convolution_backward_weights>
  AcquireConvolutionBackwardWeights(
      std::shared_ptr<mkldnn::memory> src_memory_p,
      std::shared_ptr<mkldnn::memory> diff_dst_memory_p,
      std::shared_ptr<mkldnn::memory> diff_weights_memory_p) {
    auto prim_key = key_ + "@conv_bwd_weights_p";
    auto conv_bwd_weights_p =
        std::static_pointer_cast<mkldnn::convolution_backward_weights>(
            dev_ctx_.GetBlob(prim_key));
    PADDLE_ENFORCE(
        (conv_bwd_weights_p != nullptr) || (is_reusing_ == false),
        "Fail to find convolution bwd weights primitive in device context");
    if (conv_bwd_weights_p == nullptr) {
      // create backward conv primitive for weights
      conv_bwd_weights_p =
          std::make_shared<mkldnn::convolution_backward_weights>(
              *conv_bwd_weights_pd_, *src_memory_p, *diff_dst_memory_p,
              *diff_weights_memory_p);
      dev_ctx_.SetBlob(prim_key, conv_bwd_weights_p);
    } else {
      is_reusing_ = true;
    }
    return conv_bwd_weights_p;
  }

  std::shared_ptr<mkldnn::convolution_backward_data>
  AcquireConvolutionBackwardData(
      std::shared_ptr<mkldnn::memory> diff_dst_memory_p,
      std::shared_ptr<mkldnn::memory> weights_memory_p,
      std::shared_ptr<mkldnn::memory> diff_src_memory_p) {
    auto prim_key = key_ + "@conv_bwd_data_p";
    auto conv_bwd_data_p =
        std::static_pointer_cast<mkldnn::convolution_backward_data>(
            dev_ctx_.GetBlob(prim_key));
    PADDLE_ENFORCE(
        (conv_bwd_data_p != nullptr) || (is_reusing_ == false),
        "Fail to find convolution bwd data primitive in device context");
    if (conv_bwd_data_p == nullptr) {
      conv_bwd_data_p = std::make_shared<mkldnn::convolution_backward_data>(
          *conv_bwd_data_pd_, *diff_dst_memory_p, *weights_memory_p,
          *diff_src_memory_p);
      dev_ctx_.SetBlob(prim_key, conv_bwd_data_p);
    } else {
      is_reusing_ = true;
    }
    return conv_bwd_data_p;
  }

  // Generate keys for storing/retriving primitives for this operator
  // TODO(jczaja): Make hashing function more optimial
  static std::string GetHash(memory::dims& input_dims,     // NOLINT
                             memory::dims& weights_dims,   // NOLINT
                             std::vector<int>& strides,    // NOLINT
                             std::vector<int>& paddings,   // NOLINT
                             std::vector<int>& dilations,  // NOLINT
                             int groups, const std::string& suffix) {
    return dims2str(input_dims) + dims2str(weights_dims) + dims2str(strides) +
           dims2str(paddings) + dims2str(dilations) + std::to_string(groups) +
           suffix;
  }

 private:
  std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd_;
  std::shared_ptr<mkldnn::convolution_backward_weights::primitive_desc>
      conv_bwd_weights_pd_;
  std::shared_ptr<mkldnn::convolution_backward_data::primitive_desc>
      conv_bwd_data_pd_;
};

  struct key_desc{
      struct Hash{
          std::size_t operator()(const key_desc &key) const{
              int input_dim = 0;
              int weights_dim = 0;
              int stride_value = 0;
              int padding_value = 0;
              int dilation_value = 0;
              for(size_t i=0; i<key.input_tz.size(); i++){
                 input_dim += key.input_tz[i];
              }
              for(size_t i=0; i<key.weights_tz.size(); i++){
                  weights_dim += key.weights_tz[i];
              }
              for(size_t i=0; i<key.strides.size(); i++){
                  stride_value += key.strides[i];
              }
              for(size_t i=0; i<key.paddings.size(); i++){
                  padding_value += key.paddings[i];
              }
              for(size_t i=0; i<key.dilations.size(); i++){
                  dilation_value += key.dilations[i];
              }
              std::hash<int> hasher;
              return hasher( (input_dim << 8) +
                       (weights_dim << 8 * 2) +
                       (stride_value << 8 * 3) +
                       (padding_value << 8) +
                       (dilation_value << 8 * 2) +
                       (key.groups << 8 * 3));
          }
      };

      std::vector<int> input_tz;
      std::vector<int> weights_tz;
      std::vector<int> strides;
      std::vector<int> paddings;
      std::vector<int> dilations;
      int groups;
      const std::string suffix;
      key_desc(std::vector<int> input_tz, std::vector<int> weights_tz, std::vector<int> strides, std::vector<int> paddings, std::vector<int> dilations,int groups,const std::string suffix): input_tz(input_tz), weights_tz(weights_tz), strides(strides), paddings(paddings), dilations(dilations), groups(groups), suffix(suffix) {}

      bool operator==(const key_desc o) const{
          for(size_t i=0; i<input_tz.size(); i++){
              if(input_tz[i] != o.input_tz[i])
                  return false;
          }

          for(size_t i=0; i<weights_tz.size(); i++){
              if(weights_tz[i] != o.weights_tz[i])
                  return false;
          }

          for(size_t i=0; i<strides.size(); i++){
              if(strides[i] != o.strides[i])
                  return false;
          }

          for(size_t i=0; i<paddings.size(); i++){
              if(paddings[i] != o.paddings[i])
                  return false;
          }

          for(size_t i=0; i<dilations.size(); i++){
              if(dilations[i] != o.dilations[i])
                  return false;
          }
          if(groups != o.groups) return false;
          if(suffix != o.suffix) return false;

          return true;
      }
      bool operator!=(const key_desc& o) const { return !(*this == o); }
  };

class handle_key{
  public:
    void SetKeyMap(std::unordered_map<key_desc, std::string, key_desc::Hash> &key_map, key_desc key_dsr, std::string key){
      auto it = key_map.find(key_dsr);
      if (it == key_map.end()) {
        key_map[key_dsr] = key;  // create new blob
      } else {
        (*it).second = key;  // set data to existing blob
      }
      return;
    }

    std::string GetKeyMap(std::unordered_map<key_desc, std::string, key_desc::Hash> &key_map, key_desc key_dsr){
      auto it = key_map.find(key_dsr);
      if (it != key_map.end()) {
        return (*it).second;
      }
      return "";
    }
};

template <typename T>
class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
 public:
  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
                   "It must use CPUPlace.");
    const bool is_test = ctx.Attr<bool>("is_test");

    auto& dev_ctx =
        ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
    const auto& mkldnn_engine = dev_ctx.GetEngine();

    auto* input = ctx.Input<Tensor>("Input");
    auto* filter = ctx.Input<Tensor>("Filter");
    auto* bias = ctx.HasInput("Bias") ? ctx.Input<Tensor>("Bias") : nullptr;
    auto* output = ctx.Output<Tensor>("Output");

    auto* scale_in = ctx.HasInput("Scale_in") ? ctx.Input<Tensor>("Scale_in") : nullptr;
    auto* scale_in_eltwise = ctx.HasInput("Scale_in_eltwise")? ctx.Input<Tensor>("Scale_in_eltwise") : nullptr;
    auto* scale_weights = ctx.HasInput("Scale_weights")? ctx.Input<Tensor>("Scale_weights") : nullptr;
    auto* scale_out = ctx.HasInput("Scale_out")? ctx.Input<Tensor>("Scale_out") : nullptr;

    bool is_INT8 = ctx.HasInput("Scale_in")? true : false;
    bool is_multi_channel = (is_INT8 && scale_weights->memory_size() > 1) ? true : false;

    PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN &&
                       input->format() != memory::format::format_undef,
                   "Wrong layout/format set for Input tensor");
    PADDLE_ENFORCE(filter->layout() == DataLayout::kMKLDNN &&
                       filter->format() != memory::format::format_undef,
                   "Wrong layout/format set for Filter tensor");
    PADDLE_ENFORCE(input->dims().size() == 4,
                   "Input must be with 4 dimensions, i.e. NCHW");
    PADDLE_ENFORCE(filter->dims().size() == 4,
                   "Filter must be with 4 dimensions, i.e. OIHW");
    if (bias) {
      PADDLE_ENFORCE(bias->layout() == DataLayout::kMKLDNN &&
                         bias->format() != memory::format::format_undef,
                     "Wrong layout/format set for Bias tensor");
      PADDLE_ENFORCE(bias->dims().size() == 1,
                     "Bias must only have 1 dimension, i.e. X");
    }

    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
    bool fuse_relu = ctx.Attr<bool>("fuse_relu");
    bool force_fp32_output = ctx.Attr<bool>("force_fp32_output");
    bool fuse_residual_conn = ctx.Attr<bool>("fuse_residual_connection");
    int groups = ctx.Attr<int>("groups");

    // TODO(tpatejko): add support for dilation
    PADDLE_ENFORCE(
        dilations.size() == 2 && dilations[0] == 1 && dilations[1] == 1,
        "dilation in convolution is not implemented yet");

    const T* input_data = input->data<T>();
    const float* filter_data = filter->data<float>();

    std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
    std::vector<int> weights_tz = 
        paddle::framework::vectorize2int(filter->dims());
    int g = std::max(groups, 1);
    if (g > 1) {
      int o = weights_tz[0];
      int i = weights_tz[1];
      int h = weights_tz[2];
      int w = weights_tz[3];
      weights_tz.resize(5);
      weights_tz[0] = g;
      weights_tz[1] = o / g;
      weights_tz[2] = i;
      weights_tz[3] = h;
      weights_tz[4] = w;
    }
    std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());

    // Get unique name for storing MKLDNN primitives
    handle_key keyhandler;
    key_desc key_dsr = {src_tz, weights_tz, strides, paddings, dilations, groups, ctx.op().Output("Output")};
    
    static std::unordered_map<key_desc, std::string, key_desc::Hash> key_map;
    static std::shared_ptr<std::unordered_map<ConvMKLDNNHandler::key_suffix_desc, std::string, ConvMKLDNNHandler::key_suffix_desc::Hash>> key_suffix_map(new std::unordered_map<ConvMKLDNNHandler::key_suffix_desc, std::string, ConvMKLDNNHandler::key_suffix_desc::Hash>({}));
    bool key_reuse = true;
    std::string none_key = "";
    if(keyhandler.GetKeyMap(key_map, key_dsr) == none_key){
        key_reuse = false;
    }
    std::string key; 
    if(!key_reuse){
        key = ConvMKLDNNHandler::GetHash(
                src_tz, weights_tz, strides, paddings, dilations, groups,
                ctx.op().Output("Output"));
        keyhandler.SetKeyMap(key_map, key_dsr, key);
    } else{
        key = keyhandler.GetKeyMap(key_map, key_dsr);
    }
    const std::string key_conv_pd = key + "@conv_pd";
    static std::unordered_map<std::string, std::vector<std::vector<float>>> scale_map;
    bool scale_reuse = true;
    std::vector<float> scale_in_data;
    std::vector<float> scale_out_data;
    std::vector<float> scale_weights_data;
    std::vector<float> scale_in_eltwise_data = {1.0f};
    std::vector<float> output_shift_scale;
    std::vector<float> sum_scale = {1.0f};
    std::vector<float> scale_bias_data = {1.0f};
    std::vector<std::vector<float>> none_scale = {{0.0f}};
    std::vector<std::vector<float>> scale_datas(7,{1.0f});

//scale_in_data 0, scale_in_eltwise_data 1, scale_weights_data 2, scale_bias_data 3, scale_out_data 4, output_shift_scale 5, sum_scale 6

    if (is_INT8 && GetScaleMap(scale_map, key) == none_scale){
        scale_reuse = false;
    } else{
        scale_datas = GetScaleMap(scale_map, key);
    }
//std::cout<<"scale_reuse = "<<scale_reuse<<std::endl;
    if(is_INT8){
        if(!scale_reuse){
//std::cout<<"load scale!!!!!!!!"<<std::endl;
            int count = is_multi_channel? (g>1? weights_tz[1]*weights_tz[0] : weights_tz[0]) : 1; 
            scale_in_data = {*(scale_in->data<float>())};
            scale_weights_data.resize(count);
            #pragma omp parallel for if (count > 1)
            for(int i=0; i<count; i++){
                scale_weights_data[i] =*(scale_weights->data<float>() + i);
            }
            scale_out_data = {*(scale_out->data<float>())};
            if(force_fp32_output) 
                scale_out_data[0] = 1.0;
            output_shift_scale.resize(count);
            #pragma omp parallel for if (count > 1)
            for(int i=0; i<count; i++){
                if(scale_weights_data[i] == 0.0)
                    output_shift_scale[i] = scale_out_data[0];
                else 
                    output_shift_scale[i] = scale_out_data[0] / (scale_in_data[0] * scale_weights_data[i]);
            }
            if(fuse_residual_conn){
                scale_in_eltwise_data = {*(scale_in_eltwise->data<float>())};
                sum_scale[0] = scale_out_data[0] / scale_in_eltwise_data[0];
                //SetScaleMap(scale_map, scale_in_eltwise_key, scale_in_eltwise_data);
            }

            //scale reuse
            scale_datas[0] = scale_in_data;
            scale_datas[1] = scale_in_eltwise_data;
            scale_datas[2] = scale_weights_data;
            scale_datas[4] = scale_out_data;
            scale_datas[5] = output_shift_scale;
            scale_datas[6] = sum_scale;
            //SetScaleMap(scale_map, key, scale_datas);
            //SetScaleMap(scale_map, scale_weights_key, scale_weights_data);
            //SetScaleMap(scale_map, scale_out_key, scale_out_data);
            //SetScaleMap(scale_map, output_shift_scale_key, output_shift_scale);
            //SetScaleMap(scale_map, sum_scale_key, sum_scale);
        } else{
            scale_in_data = scale_datas[0];
            scale_out_data = scale_datas[3];
            scale_weights_data = scale_datas[2];
            if(fuse_residual_conn){
                scale_in_eltwise_data = scale_datas[1];
            }
            output_shift_scale = scale_datas[5];
            sum_scale = scale_datas[6]; 
            //printf("pause!!!");
        }

    }

    static std::unordered_map<std::string, std::vector<std::shared_ptr<mkldnn::memory::desc>>> md_map;
    bool md_reuse = true;
    std::vector<std::shared_ptr<mkldnn::memory::desc>> mds(8, nullptr);
    std::vector<std::shared_ptr<mkldnn::memory::desc>> none_mds = {};
    //auto user_src_md_key = key + "@user_src_md";
    if (GetMdMap(md_map, key) == none_mds){
        md_reuse = false;   //we suppose all mds are reused if the first md is in the map.
    } else{
        mds = GetMdMap(md_map, key);
    }
    //auto user_weights_md_key = key + "@user_weights_md";
    std::shared_ptr<mkldnn::memory::desc> user_src_md;
    std::shared_ptr<mkldnn::memory::desc> user_weights_md;
    std::vector<primitive> pipeline;
//std::cout<<"md_reuse = "<<md_reuse<<std::endl;
    if(!md_reuse){
//std::cout<<"create md.......... "<<std::endl;
        user_src_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                {src_tz}, paddle::framework::ToMKLDNNDataType(input->type()), input->format())));
        user_weights_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                {weights_tz}, platform::MKLDNNGetDataType<float>(),
                (g == 1) ? mkldnn::memory::format::oihw : mkldnn::memory::format::goihw)));

        mds[0] = user_src_md;
        mds[1] = user_weights_md;        
        //SetMdMap(md_map, user_src_md_key, user_src_md);
        //SetMdMap(md_map, user_weights_md_key, user_weights_md);
    } else{
        user_src_md = mds[0];
        user_weights_md = mds[1];
        //user_src_md = GetMdMap(md_map, user_src_md_key);
        //user_weights_md = GetMdMap(md_map, user_weights_md_key);
    }

    /* create memory descriptor for convolution without specified format
     * ('any') which lets a primitive (convolution in this case) choose
     * the memory format preferred for best performance
     */
    std::string data_format = ctx.Attr<std::string>("data_format");
    auto chosen_memory_format = 
        platform::data_format_to_memory_format(data_format);

    std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd;
    auto bias_tz = paddle::framework::vectorize2int(bias->dims());

    //auto src_md_key = key + "@src_md";
    //auto weights_md_key = key + "@weights_md_key";
    //auto dst_md_key = key + "@dst_md_key";
    //auto bias_md_key = key + "@bias_md_key";
    std::shared_ptr<mkldnn::memory::desc> src_md;
    std::shared_ptr<mkldnn::memory::desc> weights_md;
    std::shared_ptr<mkldnn::memory::desc> dst_md;

    if(is_INT8){
        if(!md_reuse){
            src_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                src_tz, memory::data_type::u8, chosen_memory_format)));
            weights_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                weights_tz, memory::data_type::s8, chosen_memory_format)));
            auto dst_dt = fuse_relu? paddle::framework::ToMKLDNNDataType(std::type_index(typeid(unsigned char))) : paddle::framework::ToMKLDNNDataType(std::type_index(typeid(signed char)));
            if(fuse_residual_conn){
                auto residual = ctx.Input<Tensor>("ResidualData");
                auto residual_dt = paddle::framework::ToMKLDNNDataType(residual->type());
                if(dst_dt != residual_dt)
                    dst_dt = residual_dt;
            }
            if(force_fp32_output)
                dst_dt = paddle::framework::ToMKLDNNDataType(std::type_index(typeid(float)));
            dst_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(dst_tz, dst_dt, chosen_memory_format)));
            mds[2] = src_md;
            mds[3] = weights_md;
            mds[4] = dst_md;
            //SetMdMap(md_map, src_md_key, src_md);
            //SetMdMap(md_map, weights_md_key, weights_md);
            //SetMdMap(md_map, dst_md_key, dst_md);
        } else{
            src_md = mds[2];
            weights_md = mds[3];
            dst_md = mds[4];
            //src_md = GetMdMap(md_map, src_md_key);
            //weights_md = GetMdMap(md_map, weights_md_key);
            //dst_md = GetMdMap(md_map, dst_md_key);
        }

        // create a conv primitive descriptor and save it for usage in backward
        if (bias) {
            std::shared_ptr<mkldnn::memory::desc> bias_md;
            if(!md_reuse){
                bias_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                    bias_tz, memory::data_type::s32, memory::format::x)));
                mds[5] = bias_md;
                //SetMdMap(md_map, bias_md_key, bias_md);
            } else{
                bias_md = mds[5];
                //bias_md = GetMdMap(md_map, bias_md_key);
            }
             
            conv_pd = ConvFwdPrimitiveDesc(*src_md, *weights_md, *bias_md, *dst_md,
                                           strides, paddings, mkldnn_engine,
                                           fuse_relu, fuse_residual_conn,
                                           output_shift_scale, sum_scale[0], is_test);
        } else {
            conv_pd =
                ConvFwdPrimitiveDesc(*src_md, *weights_md, *dst_md, strides, paddings,
                                     mkldnn_engine, fuse_relu, fuse_residual_conn,
                                     output_shift_scale, sum_scale[0], is_test);
        }
    } else{
        if(!md_reuse){
            src_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                src_tz, platform::MKLDNNGetDataType<float>(), chosen_memory_format)));
            weights_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                weights_tz, platform::MKLDNNGetDataType<float>(), chosen_memory_format)));
            dst_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                dst_tz, platform::MKLDNNGetDataType<float>(), chosen_memory_format)));
            mds[2] = src_md;
            mds[3] = weights_md;
            mds[4] = dst_md;
            //SetMdMap(md_map, src_md_key, src_md);
            //SetMdMap(md_map, weights_md_key, weights_md);
            //SetMdMap(md_map, dst_md_key, dst_md);
        } else{
            src_md = mds[2];
            weights_md = mds[3];
            dst_md = mds[4];
            //src_md = GetMdMap(md_map, src_md_key);
            //weights_md = GetMdMap(md_map, weights_md_key);
            //dst_md = GetMdMap(md_map, dst_md_key);
        }
        // create a conv primitive descriptor and save it for usage in backward
        if (bias) {
            std::shared_ptr<mkldnn::memory::desc> bias_md;
            if(!md_reuse){
                bias_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                    bias_tz, platform::MKLDNNGetDataType<float>(), memory::format::x)));
                mds[5] = bias_md;
                //SetMdMap(md_map, bias_md_key, bias_md);
            } else{
                bias_md = mds[5];
                //bias_md = GetMdMap(md_map, bias_md_key);
            }
            conv_pd = ConvFwdPrimitiveDesc(*src_md, *weights_md, *bias_md, *dst_md,
                                           strides, paddings, mkldnn_engine,
                                           fuse_relu, fuse_residual_conn, is_test);
        } else {
            conv_pd =
                ConvFwdPrimitiveDesc(*src_md, *weights_md, *dst_md, strides, paddings,
                                         mkldnn_engine, fuse_relu, fuse_residual_conn, is_test);
        }
    }
    // Save conv_pd/src_memory/weights_memory for backward pass
    dev_ctx.SetBlob(key_conv_pd, conv_pd);

    ConvMKLDNNHandler handler(conv_pd, dev_ctx, mkldnn_engine, key);
    handler.key_suffix_map_ = key_suffix_map;

    // create mkldnn memory from input tensors (data/weights)
    auto user_src_memory_p =
        handler.AcquireSrcMemory(*user_src_md, to_void_cast<T>(input_data));
    auto user_weights_memory_p = handler.AcquireWeightsMemory(
        *user_weights_md, to_void_cast<float>(filter_data));

    // create reorder primitive if the input format is not the preferred one
    auto src_memory_p =
        handler.AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline);
        
    std::shared_ptr<mkldnn::memory> weights_memory_p;
    if(is_INT8){
        int mask_reorder = is_multi_channel? ((g!= 1) ? (1<<1)+(1<<0) : 1<<0) : 0;
        weights_memory_p = handler.AcquireWeightsMemoryFromPrimitive(
            user_weights_memory_p, pipeline, is_test, is_INT8, scale_weights_data, mask_reorder);
    } else{
        weights_memory_p = handler.AcquireWeightsMemoryFromPrimitive(
            user_weights_memory_p, pipeline, is_test);
    }

    std::shared_ptr<mkldnn::memory> dst_memory_p;
    bool need_s8_to_u8 = false;
    //auto user_residual_md_key = key + "@user_residual_md";
    if(fuse_residual_conn) {
        auto residual_param = ctx.Input<Tensor>("ResidualData");
        PADDLE_ENFORCE_EQ(output->dims(), residual_param->dims(),
              "Output and elementwise parameter need to have the "
              "same dimension sizes");
        auto residual_dt = paddle::framework::ToMKLDNNDataType(residual_param->type());
        if(residual_param->format() != handler.GetDstFormat()) {
            std::shared_ptr<mkldnn::memory::desc> user_residual_md;
            if(!md_reuse){
                auto residual_data_tz =
                    paddle::framework::vectorize2int(residual_param->dims());
                auto residual_data_type =
                    paddle::framework::ToMKLDNNDataType(residual_param->type());
                user_residual_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
                    residual_data_tz, residual_data_type, residual_param->format())));
                mds[6] = user_residual_md;
                //SetMdMap(md_map, user_residual_md_key, user_residual_md);
            } else{
                user_residual_md = mds[6];
                //user_residual_md = GetMdMap(md_map, user_residual_md_key);
            }
            if(is_INT8){
                PADDLE_ENFORCE(
                      force_fp32_output == false,
                      "Conv and sum does not support force_fp32_output");

                if(residual_dt == mkldnn::memory::data_type::u8){
                    auto residual_param_data = residual_param->data<uint8_t>();
                    auto user_residual_memory_p = handler.AcquireResidualDataMemory(
                        *user_residual_md, to_void_cast<uint8_t>(residual_param_data));
                    PADDLE_ENFORCE(
                          residual_param_data != nullptr,
                          "Provide data if you want MKLDNN conv+elementwise_add fusion");
                        uint8_t* output_data = output->mutable_data<uint8_t>(ctx.GetPlace());
                        dst_memory_p =
                            handler.AcquireDstMemoryFromResidualDataMemory(
                                user_residual_memory_p, to_void_cast<uint8_t>(output_data), pipeline);
                } else{
                    auto residual_param_data = residual_param->data<int8_t>();
                    auto user_residual_memory_p = handler.AcquireResidualDataMemory(
                        *user_residual_md, to_void_cast<int8_t>(residual_param_data));
                    PADDLE_ENFORCE(
                          residual_param_data != nullptr,
                          "Provide data if you want MKLDNN conv+elementwise_add fusion");
                        int8_t* output_data = output->mutable_data<int8_t>(ctx.GetPlace());
                        dst_memory_p =
                            handler.AcquireDstMemoryFromResidualDataMemory(
                                user_residual_memory_p, to_void_cast<int8_t>(output_data), pipeline);
                    if(fuse_relu)
                      need_s8_to_u8 = true;
                }
            } else{
                auto residual_param_data = residual_param->data<T>();
                auto user_residual_memory_p = handler.AcquireResidualDataMemory(
                    *user_residual_md, to_void_cast<T>(residual_param_data));
                PADDLE_ENFORCE(
                      residual_param_data != nullptr,
                      "Provide data if you want MKLDNN conv+elementwise_add fusion");
                 auto output_data =
                     output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
                 dst_memory_p = handler.AcquireDstMemoryFromResidualDataMemory(
                      user_residual_memory_p, to_void_cast<T>(output_data), pipeline);
            }
        } else {
             output->ShareDataWith(*residual_param);
             if(is_INT8){
                 if(residual_dt == mkldnn::memory::data_type::u8){
                     uint8_t* output_data = output->mutable_data<uint8_t>(ctx.GetPlace());
                     dst_memory_p =
                         handler.AcquireDstMemoryFromPrimitive(to_void_cast<uint8_t>(output_data));
                 } else{
                     int8_t* output_data = output->mutable_data<int8_t>(ctx.GetPlace());
                     dst_memory_p =
                         handler.AcquireDstMemoryFromPrimitive(to_void_cast<int8_t>(output_data));
                     if(fuse_relu)
                       need_s8_to_u8 = true;
                 }
             } else{
                  auto output_data = output->mutable_data<T>(ctx.GetPlace());
                  dst_memory_p =
                      handler.AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));               
             }
        }
    } else {
        if(is_INT8 && !force_fp32_output){
          if(fuse_relu){
              uint8_t* output_data = output->mutable_data<uint8_t>(ctx.GetPlace(), handler.GetDstMemorySize());
              dst_memory_p =
                  handler.AcquireDstMemoryFromPrimitive(to_void_cast<uint8_t>(output_data));
          } else{
              int8_t* output_data = output->mutable_data<int8_t>(ctx.GetPlace(), handler.GetDstMemorySize());
              dst_memory_p =
                  handler.AcquireDstMemoryFromPrimitive(to_void_cast<int8_t>(output_data));
          }
        } else{
        auto output_data =
            output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
        dst_memory_p =
            handler.AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));
        }
    }

    // create convolution op primitive
    std::shared_ptr<mkldnn::convolution_forward> conv_p;
    //auto scale_bias_key = key + "@scale_bias";
    //auto user_bias_md_key = key + "@user_bias_md";
    if (bias) {
      const float* bias_data = bias->data<float>();
      std::shared_ptr<mkldnn::memory::desc> user_bias_md;
      if(!md_reuse){
          user_bias_md.reset(new mkldnn::memory::desc(platform::MKLDNNMemDesc(
              {bias_tz}, platform::MKLDNNGetDataType<float>(), memory::format::x)));
          mds[7] = user_bias_md;
          //SetMdMap(md_map, user_bias_md_key, user_bias_md);
      } else{
          user_bias_md = mds[7];
          //user_bias_md = GetMdMap(md_map, user_bias_md_key);
      }
      auto user_bias_memory_p =
          handler.AcquireBiasMemory(*user_bias_md, to_void_cast<float>(bias_data));
      std::shared_ptr<mkldnn::memory>  bias_memory_p;
      if(is_INT8){
          int mask_reorder = is_multi_channel? 1<<0 : 1;
          if(!scale_reuse){
              int count = is_multi_channel? (g>1? weights_tz[1]*weights_tz[0] : weights_tz[0]) : 1;
              scale_bias_data.resize(count);
              #pragma omp parallel for if (count > 1)
              for(int i=0; i<count; i++){
                  if (scale_weights_data[i] == 0.0)
                      scale_bias_data[i] = 1.0;
                  else
                      scale_bias_data[i] = scale_in_data[0] * scale_weights_data[i];
              }
              scale_datas[3] = scale_bias_data;
              //SetScaleMap(scale_map, scale_bias_key, scale_bias_data);
          } else{
              scale_bias_data = scale_datas[3];
          }
          bias_memory_p =
              handler.AcquireBiasMemoryFromPrimitive(user_bias_memory_p, pipeline, is_test, is_INT8, scale_bias_data, mask_reorder);
      } else{
          bias_memory_p =
              handler.AcquireBiasMemoryFromPrimitive(user_bias_memory_p, pipeline);
      } 
      conv_p = handler.AcquireConvolution(src_memory_p, weights_memory_p,
                                          bias_memory_p, dst_memory_p);
    } else {
      conv_p = handler.AcquireConvolution(src_memory_p, weights_memory_p,
                                          dst_memory_p);
    }

    SetScaleMap(scale_map, key, scale_datas);
    SetMdMap(md_map, key, mds);

    // push primitive to stream and wait until it's executed
    pipeline.push_back(*conv_p);
    stream(stream::kind::eager).submit(pipeline).wait();

    if(need_s8_to_u8 && !force_fp32_output){
        output->mutable_data<uint8_t>(ctx.GetPlace());
    }

    output->set_layout(DataLayout::kMKLDNN);
    output->set_format(GetMKLDNNFormat(*dst_memory_p));
  }

 private:

    void SetScaleMap(std::unordered_map<std::string, std::vector<std::vector<float>>> &scale_map,
                       const std::string& name, std::vector<std::vector<float>> scale_datas) const {
      auto it = scale_map.find(name);
      if (it == scale_map.end()) {
        scale_map[name] = scale_datas;  // create new blob
      } else {
        (*it).second = scale_datas;  // set data to existing blob
      }
      return;
    }

    std::vector<std::vector<float>> GetScaleMap(std::unordered_map<std::string, std::vector<std::vector<float>>> scale_map,
         const std::string& name) const {
      auto it = scale_map.find(name);
      if (it != scale_map.end()) {
        return (*it).second;
      }
      return {{0.0f}};
    }

    void SetMdMap(std::unordered_map<std::string, std::vector<std::shared_ptr<mkldnn::memory::desc>>> &md_map,
                       const std::string& name, std::vector<std::shared_ptr<mkldnn::memory::desc>> mds) const {
      auto it = md_map.find(name);
      if (it == md_map.end()) {
        md_map[name] = mds;  // create new blob
      } else {
        (*it).second = mds;  // set data to existing blob
      }
      return;
    }

    std::vector<std::shared_ptr<mkldnn::memory::desc>> GetMdMap(std::unordered_map<std::string, std::vector<std::shared_ptr<mkldnn::memory::desc>>> md_map,
         const std::string& name) const {
      auto it = md_map.find(name);
      if (it != md_map.end()) {
        return (*it).second;
      }
      return {};
    }

    mkldnn::primitive_attr CreatePostOps(bool fuse_relu, bool fuse_residual_conn,
                          const std::vector<float> output_shift_scale, float sum_scale) const {
      mkldnn::primitive_attr conv_attr;
      mkldnn::post_ops post_operations;
    // Fusion with Elementwise layer relies on adding a sum post-operation with
    // the scale parameter. It is assumed that when fuse_residual_connection is
    // true, the output tensor contains the data coming from residual
    // connection. The result of this post_op is:
    // Output = scale * Output + Conv_Out.
      int mask = output_shift_scale.size() > 1 ? 1<<1 : 0;
      conv_attr.set_output_scales(mask, output_shift_scale);
      if (fuse_residual_conn) {
        post_operations.append_sum(sum_scale);
      }
      if (fuse_relu) {
        constexpr float scale = 1.0f;
        constexpr float negative_slope = 0.0f;
        constexpr float placeholder = 1.0f; //beta
        post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
                                       negative_slope, placeholder);
      }
      conv_attr.set_post_ops(post_operations);
      return conv_attr;
    }

      mkldnn::primitive_attr CreatePostOps(bool fuse_relu, bool fuse_residual_conn) const {

      mkldnn::primitive_attr conv_attr;
      mkldnn::post_ops post_operations;
      // Fusion with Elementwise layer relies on adding a sum post-operation with
      // the scale parameter. It is assumed that when fuse_residual_conn is true, the
      // Output tensor contains the data coming from residual connection. The
      // result of this post_op is: Output = scale * Output + Conv_Out.
      conv_attr.set_output_scales(0, {1.0f});
      if (fuse_residual_conn) {
        post_operations.append_sum(1.0f);
      }
      // Fusion with ReLU layer is executed through the PostOps feature. Create a
      // PostOps object and configure it to execute an eltwise relu operation.
      if (fuse_relu) {
        constexpr float scale = 1.0f;
        constexpr float negative_slope = 0.0f;
        constexpr float placeholder = 0.0f;
        post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
                                       negative_slope, placeholder);
      }
      conv_attr.set_post_ops(post_operations);
      return conv_attr;
    }

    std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
    ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
                         const memory::desc& dst, const std::vector<int>& strides,
                         const std::vector<int>& paddings,
                         const mkldnn::engine& engine, const bool fuse_relu,
                         const bool fuse_residual_conn,
                         const std::vector<float> output_shift_scale, const float sum_scale, bool is_test) const {
      memory::dims stride_dims = {strides[0], strides[1]};
      memory::dims padding_dims = {paddings[0], paddings[1]};

      auto propagation = is_test ? mkldnn::prop_kind::forward_scoring : mkldnn::prop_kind::forward_training;

      auto conv_desc = mkldnn::convolution_forward::desc(
          propagation, mkldnn::convolution_direct, src, weights,
          dst, stride_dims, padding_dims, padding_dims,
          mkldnn::padding_kind::zero);

      mkldnn::primitive_attr conv_attr =
          CreatePostOps(fuse_relu, fuse_residual_conn, output_shift_scale, sum_scale);

      auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
          conv_desc, conv_attr, engine);

      return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
          p_conv_pd);
    }

  std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
    ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
                         const memory::desc& dst, const std::vector<int>& strides,
                         const std::vector<int>& paddings,
                         const mkldnn::engine& engine, const bool fuse_relu,
                         const bool fuse_residual_conn, bool is_test) const{
      memory::dims stride_dims = {strides[0], strides[1]};
      memory::dims padding_dims = {paddings[0], paddings[1]};
 
      auto propagation = is_test ? mkldnn::prop_kind::forward_scoring : mkldnn::prop_kind::forward_training;
 
      auto conv_desc = mkldnn::convolution_forward::desc(
          propagation, mkldnn::convolution_direct, src, weights,
          dst, stride_dims, padding_dims, padding_dims,
          mkldnn::padding_kind::zero);
  
      mkldnn::primitive_attr conv_attr = CreatePostOps(fuse_relu, fuse_residual_conn);
  
      auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
          conv_desc, conv_attr, engine);
  
      return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
          p_conv_pd);
    }

  std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
    ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
                         const memory::desc& bias, const memory::desc& dst,
                         const std::vector<int>& strides,
                         const std::vector<int>& paddings,
                         const mkldnn::engine& engine, const bool fuse_relu,
                         const bool fuse_residual_conn,
                         const std::vector<float> output_shift_scale, const float sum_scale, bool is_test) const {
      memory::dims stride_dims = {strides[0], strides[1]};
      memory::dims padding_dims = {paddings[0], paddings[1]};

      auto propagation = is_test ? mkldnn::prop_kind::forward_scoring : mkldnn::prop_kind::forward_training;

      auto conv_desc = mkldnn::convolution_forward::desc(
          propagation, mkldnn::convolution_direct, src, weights,
          bias, dst, stride_dims, padding_dims, padding_dims,
          mkldnn::padding_kind::zero);

      mkldnn::primitive_attr conv_attr = 
          CreatePostOps(fuse_relu, fuse_residual_conn, output_shift_scale, sum_scale);

      auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
          conv_desc, conv_attr, engine);

      return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
          p_conv_pd);
    }

  std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
    ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
                         const memory::desc& bias, const memory::desc& dst,
                         const std::vector<int>& strides,
                         const std::vector<int>& paddings,
                         const mkldnn::engine& engine, const bool fuse_relu,
                         const bool fuse_residual_conn, bool is_test) const{
      memory::dims stride_dims = {strides[0], strides[1]};
      memory::dims padding_dims = {paddings[0], paddings[1]};

      auto propagation = is_test ? mkldnn::prop_kind::forward_scoring : mkldnn::prop_kind::forward_training;

      auto conv_desc = mkldnn::convolution_forward::desc(
          propagation, mkldnn::convolution_direct, src, weights,
          bias, dst, stride_dims, padding_dims, padding_dims,
          mkldnn::padding_kind::zero);

      mkldnn::primitive_attr conv_attr = CreatePostOps(fuse_relu, fuse_residual_conn);

      auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
          conv_desc, conv_attr, engine);

      return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
          p_conv_pd);
    }

};

template <typename T>
class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
 public:
  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
                   "It must use CPUPlace.");

    auto& dev_ctx =
        ctx.template device_context<platform::MKLDNNDeviceContext>();
    const auto& mkldnn_engine = dev_ctx.GetEngine();

    const Tensor* input = ctx.Input<Tensor>("Input");
    const Tensor* filter = ctx.Input<Tensor>("Filter");
    const Tensor* output = ctx.Input<Tensor>("Output");
    const Tensor* output_grad =
        ctx.Input<Tensor>(framework::GradVarName("Output"));
    Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
    Tensor* filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));

    PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN &&
                       input->format() != memory::format::format_undef,
                   "Wrong layout/format set for Input tensor");
    PADDLE_ENFORCE(filter->layout() == DataLayout::kMKLDNN &&
                       filter->format() != memory::format::format_undef,
                   "Wrong layout/format set for Filter tensor");
    PADDLE_ENFORCE(output->layout() == DataLayout::kMKLDNN &&
                       output->format() != memory::format::format_undef,
                   "Wrong layout/format set for Output tensor");
    PADDLE_ENFORCE(output_grad->layout() == DataLayout::kMKLDNN &&
                       output_grad->format() != memory::format::format_undef,
                   "Wrong layout/format set for output_grad tensor");

    if (!input_grad && !filter_grad) return;

    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
    int groups = ctx.Attr<int>("groups");

    const T* input_data = input->data<T>();
    const T* filter_data = filter->data<T>();
    const T* output_grad_data = output_grad->data<T>();
    T* input_grad_data = nullptr;
    T* filter_grad_data = nullptr;

    std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
    std::vector<int> weights_tz =
        paddle::framework::vectorize2int(filter->dims());
    std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());

    // Get an unique name from "argument" name of "Output" variable
    // as well as attributes of primitive to be created
    // This name will be used as key when saving info into device context
    const std::string key =
        ConvMKLDNNHandler::GetHash(src_tz, weights_tz, strides, paddings,
                                   dilations, groups, ctx.op().Input("Output"));

    const std::string key_conv_pd = key + "@conv_pd";
    std::vector<primitive> pipeline;

    // Create user memory descriptors
    auto user_src_md = platform::MKLDNNMemDesc(
        {src_tz}, platform::MKLDNNGetDataType<T>(), input->format());
    auto user_weights_md = platform::MKLDNNMemDesc(
        {weights_tz}, platform::MKLDNNGetDataType<T>(), filter->format());
    auto user_diff_dst_md = platform::MKLDNNMemDesc(
        {dst_tz}, platform::MKLDNNGetDataType<T>(), output_grad->format());

    /* create memory descriptor for conv backward without specified format
     * ('any') which lets a primitive (conv backward in this case) choose
     * the memory format preferred for best performance
     */
    std::string data_format = ctx.Attr<std::string>("data_format");
    auto chosen_memory_format =
        platform::data_format_to_memory_format(data_format);

    auto src_md = platform::MKLDNNMemDesc(
        src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
    auto diff_src_md = platform::MKLDNNMemDesc(
        src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
    auto weights_md = platform::MKLDNNMemDesc(
        weights_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
    auto diff_weights_md = platform::MKLDNNMemDesc(
        weights_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
    auto diff_dst_md = platform::MKLDNNMemDesc(
        dst_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);

    // Retrieve conv_pd from device context
    auto conv_pd =
        std::static_pointer_cast<mkldnn::convolution_forward::primitive_desc>(
            dev_ctx.GetBlob(key_conv_pd));
    PADDLE_ENFORCE(conv_pd != nullptr,
                   "Fail to find conv_pd in device context");

    // create backward convolution weights primitive descriptor
    auto conv_bwd_weights_desc = mkldnn::convolution_backward_weights::desc(
        mkldnn::convolution_direct, src_md, diff_weights_md, diff_dst_md,
        strides, paddings, paddings, mkldnn::padding_kind::zero);
    auto conv_bwd_weights_pd =
        std::make_shared<mkldnn::convolution_backward_weights::primitive_desc>(
            conv_bwd_weights_desc, mkldnn_engine, *conv_pd);

    // create backward convolution data primitive descriptor
    auto conv_bwd_data_desc = mkldnn::convolution_backward_data::desc(
        mkldnn::convolution_direct, diff_src_md, weights_md, diff_dst_md,
        strides, paddings, paddings, mkldnn::padding_kind::zero);
    auto conv_bwd_data_pd =
        std::make_shared<mkldnn::convolution_backward_data::primitive_desc>(
            conv_bwd_data_desc, mkldnn_engine, *conv_pd);

    ConvMKLDNNHandler handler(conv_pd, conv_bwd_data_pd, conv_bwd_weights_pd,
                              dev_ctx, mkldnn_engine, key);

    // create mkldnn memory from input tensors (data/weights)
    auto user_src_memory_p =
        handler.AcquireSrcMemory(user_src_md, to_void_cast<T>(input_data));
    auto user_weights_memory_p = handler.AcquireWeightsMemory(
        user_weights_md, to_void_cast<T>(filter_data));
    auto user_diff_dst_memory_p = handler.AcquireDiffDstMemory(
        user_diff_dst_md, to_void_cast<T>(output_grad_data));
    // create backward conv primitive for weights
    if (filter_grad) {
      auto src_memory_p = handler.AcquireSrcMemoryFromWeightsPrimitive(
          user_src_memory_p, pipeline);

      auto diff_dst_memory_4filter_p =
          handler.AcquireDiffDstMemoryFromWeightsPrimitive(
              user_diff_dst_memory_p, pipeline);

      const size_t size = handler.GetDiffWeightsMemorySize();
      filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace(), size);

      auto diff_weights_memory_p =
          handler.AcquireDiffWeightsMemoryFromWeightsPrimitive(
              reinterpret_cast<void*>(filter_grad_data));

      auto conv_bwd_weights_p = handler.AcquireConvolutionBackwardWeights(
          src_memory_p, diff_dst_memory_4filter_p, diff_weights_memory_p);

      // push primitive to stream and wait until it's executed
      pipeline.push_back(*conv_bwd_weights_p);

      filter_grad->set_layout(DataLayout::kMKLDNN);
      filter_grad->set_format(GetMKLDNNFormat(*diff_weights_memory_p));
    }

    if (input_grad) {
      auto weights_memory_p = handler.AcquireWeightsMemoryFromDataPrimitive(
          user_weights_memory_p, pipeline);

      auto diff_dst_memory_4data_p =
          handler.AcquireDiffDstMemoryFromDataPrimitive(user_diff_dst_memory_p,
                                                        pipeline);

      const size_t size = handler.GetDiffSourceMemorySize();
      input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace(), size);

      auto diff_src_memory_p = handler.AcquireDiffSrcMemoryFromDataPrimitive(
          reinterpret_cast<void*>(input_grad_data));

      auto conv_bwd_data_p = handler.AcquireConvolutionBackwardData(
          diff_dst_memory_4data_p, weights_memory_p, diff_src_memory_p);

      pipeline.push_back(*conv_bwd_data_p);

      input_grad->set_layout(DataLayout::kMKLDNN);
      input_grad->set_format(GetMKLDNNFormat(*diff_src_memory_p));
    }
    stream(stream::kind::eager).submit(pipeline).wait();
  }  // Compute()
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP_KERNEL(conv2d, MKLDNN, ::paddle::platform::CPUPlace,
                   ops::ConvMKLDNNOpKernel<float>,
                   ops::ConvMKLDNNOpKernel<uint8_t>);

REGISTER_OP_KERNEL(conv2d_grad, MKLDNN, ::paddle::platform::CPUPlace,
                   ops::ConvMKLDNNGradOpKernel<float>);