path: root/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc

/* Copyright 2015 The TensorFlow 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.
==============================================================================*/

// See docs in ../ops/nn_ops.cc.

#ifdef INTEL_MKL

#include <algorithm>
#include <vector>

#include "tensorflow/core/framework/numeric_op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_slice.h"
#include "tensorflow/core/kernels/conv_grad_ops.h"
#include "tensorflow/core/kernels/mkl_conv_ops.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/util/padding.h"
#include "tensorflow/core/util/tensor_format.h"
#include "tensorflow/core/util/use_cudnn.h"
#include "tensorflow/core/util/work_sharder.h"


#ifndef INTEL_MKL_ML
#include "mkldnn.hpp"

using mkldnn::convolution_backward_weights;
using mkldnn::memory;
using mkldnn::prop_kind;
using mkldnn::stream;
#else
#include "mkl_dnn.h"
#include "mkl_dnn_types.h"
#endif

#include "tensorflow/core/util/mkl_util.h"

namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;

#ifndef INTEL_MKL_ML

struct MklConvBwdFilterParams {
  memory::dims src_dims;
  memory::dims diff_filter_dims;
  memory::dims diff_bias_dims;
  memory::dims diff_dst_dims;
  memory::dims strides;
  memory::dims dilations;
  memory::dims padding_left;
  memory::dims padding_right;
  padding_kind padding;

  MklConvBwdFilterParams(memory::dims src_dims,
    memory::dims diff_filter_dims, memory::dims diff_bias_dims,
    memory::dims diff_dst_dims, memory::dims strides,
    memory::dims dilations, memory::dims padding_left,
    memory::dims padding_right, padding_kind padding) :
      src_dims(src_dims), diff_filter_dims(diff_filter_dims),
      diff_bias_dims(diff_bias_dims), diff_dst_dims(diff_dst_dims),
      strides(strides), dilations(dilations),
      padding_left(padding_left), padding_right(padding_right),
      padding(padding) {
  }
};

template <typename T>
class MklConv2DBwdFilterPrimitive : public MklPrimitive {
 public:
  explicit MklConv2DBwdFilterPrimitive(
      const MklConvBwdFilterParams& convBwdFilterDims) :
            cpu_engine_(engine::cpu, 0) {
    context_.bwd_filter_stream.reset(new stream(stream::kind::eager));
    // create conv primitive
    if (context_.conv_bwd_filter == nullptr) {
      Setup(convBwdFilterDims);
    }
  }

  ~MklConv2DBwdFilterPrimitive() {}

  // Convolution backward weights with bias
  //   src_data:         input data buffer of src
  //   diff_filter_data: output data buffer of diff_filter
  //   diff_bias_data:   output data buffer of diff_bias
  //   diff_dst_data:    input data buffer of diff_dst
  void Execute(const T* src_data, const T* diff_filter_data,
      const T* diff_bias_data, const T* diff_dst_data) {
    context_.src_mem->set_data_handle(
        static_cast<void*>(const_cast<T*>(src_data)));
    context_.diff_filter_mem->set_data_handle(
        static_cast<void*>(const_cast<T*>(diff_filter_data)));
    context_.diff_bias_mem->set_data_handle(
        static_cast<void*>(const_cast<T*>(diff_bias_data)));
    context_.diff_dst_mem->set_data_handle(
        static_cast<void*>(const_cast<T*>(diff_dst_data)));

    context_.bwd_filter_stream->submit(context_.bwd_filter_primitives);

    context_.src_mem->set_data_handle(DummyData);
    context_.diff_filter_mem->set_data_handle(DummyData);
    context_.diff_bias_mem->set_data_handle(DummyData);
    context_.diff_dst_mem->set_data_handle(DummyData);
    return;
  }

  // Convolution backward weights without bias
  //   src_data:         input data buffer of src
  //   diff_filter_data: output data buffer of diff_filter
  //   diff_dst_data:    input data buffer of diff_dst
  void Execute(const T* src_data,
      const T* diff_filter_data, const T* diff_dst_data) {
    context_.src_mem->set_data_handle(
        static_cast<void*>(const_cast<T*>(src_data)));
    context_.diff_filter_mem->set_data_handle(
        static_cast<void*>(const_cast<T*>(diff_filter_data)));
    context_.diff_dst_mem->set_data_handle(
        static_cast<void*>(const_cast<T*>(diff_dst_data)));

    context_.bwd_filter_stream->submit(context_.bwd_filter_primitives);

    context_.src_mem->set_data_handle(DummyData);
    context_.diff_filter_mem->set_data_handle(DummyData);
    context_.diff_dst_mem->set_data_handle(DummyData);
    return;
  }

  memory::format GetSrcMemoryFormat() const {
    return context_.src_fmt;
  }

  memory::format GetDiffDstMemoryFormat() const {
    return context_.diff_dst_fmt;
  }

  memory::format GetDiffFilterMemoryFormat() const {
    return context_.diff_filter_fmt;
  }

  // convolution primitive
  std::shared_ptr<mkldnn::convolution_backward_weights::primitive_desc>
  GetPrimitiveDesc() const {
    return context_.bwd_filter_pd;
  }

 private:
  // Primitive reuse context for Conv2D bwd filter op
  struct ConvBwdFilterContext {
    // expected memory format for this primitive instance
    memory::format src_fmt;
    memory::format diff_dst_fmt;
    memory::format diff_filter_fmt;

    // convolution bwd input primitive
    std::shared_ptr<mkldnn::convolution_backward_weights::primitive_desc>
        bwd_filter_pd;
    std::shared_ptr<mkldnn::primitive> conv_bwd_filter;

    // MKLDNN memory
    std::shared_ptr<mkldnn::memory> src_mem;
    std::shared_ptr<mkldnn::memory> diff_filter_mem;
    std::shared_ptr<mkldnn::memory> diff_bias_mem;
    std::shared_ptr<mkldnn::memory> diff_dst_mem;

    // desc & prmitive desc
    std::shared_ptr<mkldnn::convolution_backward_weights::desc> bwd_filter_desc;
    std::shared_ptr<mkldnn::convolution_forward::desc> fwd_desc;
    std::shared_ptr<mkldnn::convolution_forward::primitive_desc> fwd_pd;

    // memory desc: forward & backward can share same memory desc
    std::shared_ptr<mkldnn::memory::desc> src_md;
    std::shared_ptr<mkldnn::memory::desc> diff_filter_md;
    std::shared_ptr<mkldnn::memory::desc> diff_bias_md;
    std::shared_ptr<mkldnn::memory::desc> diff_dst_md;

    // MKL pipeline
    std::shared_ptr<mkldnn::stream> bwd_filter_stream;
    std::vector<mkldnn::primitive> bwd_filter_primitives;

    ConvBwdFilterContext() :
        src_fmt(memory::format::any),
        diff_dst_fmt(memory::format::any),
        diff_filter_fmt(memory::format::any),
        src_mem(nullptr), diff_filter_mem(nullptr),
        diff_bias_mem(nullptr), diff_dst_mem(nullptr),
        bwd_filter_desc(nullptr), fwd_desc(nullptr), fwd_pd(nullptr),
        src_md(nullptr), diff_filter_md(nullptr),
        diff_bias_md(nullptr), diff_dst_md(nullptr),
        bwd_filter_stream(nullptr) {
    }
  };

  // Setup Conv2d backward filter (weights) primitives.
  void Setup(const MklConvBwdFilterParams& convBwdFilterDims) {
    // create memory descriptors for convolution data w/ no specified format
    context_.src_md.reset(new memory::desc({convBwdFilterDims.src_dims},
        MklDnnType<T>(), memory::format::any));

    context_.diff_dst_md.reset(new memory::desc(
        {convBwdFilterDims.diff_dst_dims},
        MklDnnType<T>(), memory::format::any));

    context_.diff_filter_md.reset(new memory::desc(
        {convBwdFilterDims.diff_filter_dims},
        MklDnnType<T>(), memory::format::any));

    if (!convBwdFilterDims.diff_bias_dims.empty())
      context_.diff_bias_md.reset(new memory::desc(
          {convBwdFilterDims.diff_bias_dims},
          MklDnnType<T>(), memory::format::x));

    // create a convolution
    if (!convBwdFilterDims.diff_bias_dims.empty()) {
      context_.bwd_filter_desc.reset(new convolution_backward_weights::desc(
          convolution_direct, *context_.src_md, *context_.diff_filter_md,
          *context_.diff_bias_md, *context_.diff_dst_md,
          convBwdFilterDims.strides, convBwdFilterDims.dilations,
          convBwdFilterDims.padding_left, convBwdFilterDims.padding_right,
          convBwdFilterDims.padding));
    } else {
      context_.bwd_filter_desc.reset(
          new convolution_backward_weights::desc(
          convolution_direct, *context_.src_md, *context_.diff_filter_md,
          *context_.diff_dst_md, convBwdFilterDims.strides,
          convBwdFilterDims.dilations, convBwdFilterDims.padding_left,
          convBwdFilterDims.padding_right, convBwdFilterDims.padding));
    }

    // create fwd primitive_desc
    context_.fwd_desc.reset(new convolution_forward::desc(
        prop_kind::forward, convolution_direct,
        *context_.src_md, *context_.diff_filter_md, *context_.diff_dst_md,
        convBwdFilterDims.strides,
        convBwdFilterDims.dilations, convBwdFilterDims.padding_left,
        convBwdFilterDims.padding_right, convBwdFilterDims.padding));
    context_.fwd_pd.reset(new convolution_forward::primitive_desc(
        *context_.fwd_desc, cpu_engine_));

    // create backward conv primitive_desc
    context_.bwd_filter_pd.reset(
        new convolution_backward_weights::primitive_desc(
        *context_.bwd_filter_desc, cpu_engine_, *context_.fwd_pd));

    // store the expected memory format
    auto bwd_filter_pd = context_.bwd_filter_pd.get();
    context_.src_fmt = static_cast<mkldnn::memory::format>(
        bwd_filter_pd->src_primitive_desc().desc().data.format);
    context_.diff_filter_fmt = static_cast<mkldnn::memory::format>(
        bwd_filter_pd->diff_weights_primitive_desc().desc().data.format);
    context_.diff_dst_fmt = static_cast<mkldnn::memory::format>(
        bwd_filter_pd->diff_dst_primitive_desc().desc().data.format);

    // create memory primitive based on dummy data
    context_.src_mem.reset(new memory(
        bwd_filter_pd->src_primitive_desc(), DummyData));
    context_.diff_filter_mem.reset(new memory(
        bwd_filter_pd->diff_weights_primitive_desc(), DummyData));
    context_.diff_dst_mem.reset(new memory(
        bwd_filter_pd->diff_dst_primitive_desc(), DummyData));

    // create convolution primitive and add it to net
    if (!convBwdFilterDims.diff_bias_dims.empty()) {
      context_.diff_bias_mem.reset(new memory(
          {{{convBwdFilterDims.diff_bias_dims}, MklDnnType<T>(),
          memory::format::x}, cpu_engine_}, DummyData));
      context_.conv_bwd_filter.reset(new convolution_backward_weights(
          *context_.bwd_filter_pd, *context_.src_mem, *context_.diff_dst_mem,
          *context_.diff_filter_mem, *context_.diff_bias_mem));
    } else {
      context_.conv_bwd_filter.reset(new convolution_backward_weights(
          *context_.bwd_filter_pd, *context_.src_mem,
          *context_.diff_dst_mem, *context_.diff_filter_mem));
    }

    context_.bwd_filter_primitives.push_back(*context_.conv_bwd_filter);
  }

  struct ConvBwdFilterContext context_;
  engine cpu_engine_;
};

template <typename T>
class MklConv2DBwdFilterPrimitiveFactory : public MklPrimitiveFactory<T> {
 public:
  static MklConv2DBwdFilterPrimitive<T>* Get(
      const MklConvBwdFilterParams& convBwdFilterDims) {
    MklConv2DBwdFilterPrimitive<T>* conv2d_bwd_filter = nullptr;

    // look into the pool for reusable primitive
    conv2d_bwd_filter = dynamic_cast<MklConv2DBwdFilterPrimitive<T>*> (
      MklConv2DBwdFilterPrimitiveFactory<T>::GetInstance().GetConv2dBwdFilter(
      convBwdFilterDims));

    if (conv2d_bwd_filter == nullptr) {
        conv2d_bwd_filter = new MklConv2DBwdFilterPrimitive<T>(
            convBwdFilterDims);
        MklConv2DBwdFilterPrimitiveFactory<T>::GetInstance().SetConv2dBwdFilter(
            convBwdFilterDims, conv2d_bwd_filter);
    }
    return conv2d_bwd_filter;
  }


 private:
  MklConv2DBwdFilterPrimitiveFactory() {}
  ~MklConv2DBwdFilterPrimitiveFactory() {}

  static MklConv2DBwdFilterPrimitiveFactory& GetInstance() {
    static MklConv2DBwdFilterPrimitiveFactory instance_;
    return instance_;
  }

  static std::string CreateKey(
      const MklConvBwdFilterParams& convBwdFilterDims) {
    std::string prefix = "conv2d_bwd_filter";
    FactoryKeyCreator key_creator;
    key_creator.AddAsKey(prefix);
    key_creator.AddAsKey(convBwdFilterDims.src_dims);
    key_creator.AddAsKey(convBwdFilterDims.diff_filter_dims);
    key_creator.AddAsKey(convBwdFilterDims.diff_bias_dims);
    key_creator.AddAsKey(convBwdFilterDims.diff_dst_dims);
    key_creator.AddAsKey(convBwdFilterDims.strides);
    key_creator.AddAsKey(convBwdFilterDims.dilations);
    key_creator.AddAsKey(convBwdFilterDims.padding_left);
    key_creator.AddAsKey(convBwdFilterDims.padding_right);
    return key_creator.GetKey();
  }

  MklPrimitive* GetConv2dBwdFilter(
      const MklConvBwdFilterParams& convBwdFilterDims) {
    std::string key = CreateKey(convBwdFilterDims);
    return this->GetOp(key);
  }

  void SetConv2dBwdFilter(
      const MklConvBwdFilterParams& convBwdFilterDims, MklPrimitive* op) {
    std::string key = CreateKey(convBwdFilterDims);
    this->SetOp(key, op);
  }
};

#endif

#ifdef INTEL_MKL_ML

template <typename Device, class T>
class MklConv2DCustomBackpropFilterOp : public OpKernel {
 public:
  explicit MklConv2DCustomBackpropFilterOp(OpKernelConstruction* context)
      : OpKernel(context) {
    string data_format;
    OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
    OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
                errors::InvalidArgument("Invalid data format"));

    OP_REQUIRES_OK(context, context->GetAttr("strides", &strides_));
    int stride_n = GetTensorDim(strides_, data_format_, 'N');
    int stride_c = GetTensorDim(strides_, data_format_, 'C');
    OP_REQUIRES(
        context, (stride_n == 1 && stride_c == 1),
        errors::InvalidArgument("Current implementation does not yet support "
                                "strides in the batch and depth dimensions."));
    OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
  }

  void Compute(OpKernelContext* context) override {
    MklConv2DGradFilterOpContext mkl_context;
    const Tensor& input = MklGetInput(context, 0);
    GetMklShape(context, 0, &(mkl_context.input_shape));
    bool input_in_mkl_format = mkl_context.input_shape.IsMklTensor();

    const Tensor& filter_sizes = MklGetInput(context, 1);

    const Tensor& out_backprop = MklGetInput(context, 2);
    GetMklShape(context, 2, &(mkl_context.out_backprop_shape));
    bool out_backprop_in_mkl_format =
        mkl_context.out_backprop_shape.IsMklTensor();

    TensorShape input_shape, filter_shape, out_backprop_shape;

    OP_REQUIRES(
        context, TensorShapeUtils::IsVector(filter_sizes.shape()),
        errors::InvalidArgument(
            "Conv2DCustomBackpropFilter: filter_sizes input must be 1-dim, "
            "not ",
            filter_sizes.dims()));
    OP_REQUIRES_OK(context, TensorShapeUtils::MakeShape(
                                filter_sizes.vec<int32>(), &filter_shape));

    ConvBackpropDimensions backprop_dims;

    // Generate shape for input if input is in MKL format.
    if (input_in_mkl_format) {
      OP_REQUIRES(context, mkl_context.input_shape.GetDimension() == 4,
                  errors::InvalidArgument(
                      "Conv2DCustomBackpropFilter: input size must be 4-dim"));

      MklSizesToTFSizes(context, data_format_, mkl_context.input_shape,
                        &input_shape);
    } else {
      input_shape = input.shape();
    }

    // Generate shape for outback prop if input is in MKL format.
    if (out_backprop_in_mkl_format) {
      OP_REQUIRES(
          context, mkl_context.out_backprop_shape.GetDimension() == 4,
          errors::InvalidArgument(
              "Conv2DCustomBackpropFilter: outbackprop size must be 4-dim"));

      MklSizesToTFSizes(context, data_format_, mkl_context.out_backprop_shape,
                        &out_backprop_shape);
    } else {
      out_backprop_shape = out_backprop.shape();
    }

    OP_REQUIRES_OK(context,
                   ConvBackpropComputeDimensions(
                       "Conv2DCustomBackpropFilter", /*num_spatial_dims=*/2,
                       input_shape, filter_shape, out_backprop_shape, strides_,
                       padding_, data_format_, &backprop_dims));

    int64 pad_top, pad_bottom;
    int64 pad_left, pad_right;
    OP_REQUIRES_OK(context, GetWindowedOutputSizeVerbose(
                                backprop_dims.spatial_dims[0].input_size,
                                backprop_dims.spatial_dims[0].filter_size,
                                backprop_dims.spatial_dims[0].stride, padding_,
                                &backprop_dims.spatial_dims[0].output_size,
                                &pad_top, &pad_bottom));
    OP_REQUIRES_OK(context, GetWindowedOutputSizeVerbose(
                                backprop_dims.spatial_dims[1].input_size,
                                backprop_dims.spatial_dims[1].filter_size,
                                backprop_dims.spatial_dims[1].stride, padding_,
                                &backprop_dims.spatial_dims[1].output_size,
                                &pad_left, &pad_right));

    // Create MKL primitives for convolution filter grad
    mkl_context.in_dims = input_in_mkl_format
                              ? mkl_context.input_shape.GetDimension()
                              : input.dims();
    mkl_context.out_dims = out_backprop_in_mkl_format
                               ? mkl_context.out_backprop_shape.GetDimension()
                               : out_backprop.dims();
    mkl_context.in_sizes[0] =
        static_cast<size_t>(backprop_dims.spatial_dims[1].input_size);
    mkl_context.in_sizes[1] =
        static_cast<size_t>(backprop_dims.spatial_dims[0].input_size);
    mkl_context.in_sizes[2] = static_cast<size_t>(backprop_dims.in_depth);
    mkl_context.in_sizes[3] = static_cast<size_t>(backprop_dims.batch_size);
    mkl_context.out_sizes[0] =
        static_cast<size_t>(backprop_dims.spatial_dims[1].output_size);
    mkl_context.out_sizes[1] =
        static_cast<size_t>(backprop_dims.spatial_dims[0].output_size);
    mkl_context.out_sizes[2] = static_cast<size_t>(backprop_dims.out_depth);
    mkl_context.out_sizes[3] = static_cast<size_t>(backprop_dims.batch_size);
    mkl_context.input_offsets[0] = static_cast<int>(-pad_left);
    mkl_context.input_offsets[1] = static_cast<int>(-pad_top);
    mkl_context.conv_strides[0] =
        static_cast<size_t>(backprop_dims.spatial_dims[1].stride);
    mkl_context.conv_strides[1] =
        static_cast<size_t>(backprop_dims.spatial_dims[0].stride);

    GetStridesFromSizes(data_format_, mkl_context.in_strides,
                        mkl_context.in_sizes);
    GetStridesFromSizes(data_format_, mkl_context.out_strides,
                        mkl_context.out_sizes);

    // MKL understands dimensions in 0, 1, 2, and 3 indices denotes
    // filter cols, rows, input channels, and output depth/channels.
    mkl_context.filter_dims = 4;
    mkl_context.filter_sizes[0] = backprop_dims.spatial_dims[1].filter_size;
    mkl_context.filter_sizes[1] = backprop_dims.spatial_dims[0].filter_size;
    mkl_context.filter_sizes[2] = backprop_dims.in_depth;
    mkl_context.filter_sizes[3] = backprop_dims.out_depth;

    // We want filter grad to be in TF format, so
    // make the strides accordingly to reflect this fact.
    // Note TF filter layout : (rows, cols, in_depth, out_depth),
    // while row is the innermost dimension.
    mkl_context.filter_strides[0] =
        backprop_dims.out_depth * backprop_dims.in_depth;
    mkl_context.filter_strides[1] = backprop_dims.out_depth *
                                    backprop_dims.in_depth *
                                    backprop_dims.spatial_dims[1].filter_size;
    mkl_context.filter_strides[2] = backprop_dims.out_depth;
    mkl_context.filter_strides[3] = 1;

    mkl_context.conv_strides[0] = backprop_dims.spatial_dims[1].stride;
    mkl_context.conv_strides[1] = backprop_dims.spatial_dims[0].stride;

    // Create convolution-grad-filter primitive
    CHECK_EQ(dnnConvolutionCreateBackwardFilter_F32(
                 &mkl_context.prim_conv_bwdfilter, nullptr,
                 dnnAlgorithmConvolutionDirect, mkl_context.in_dims,
                 mkl_context.in_sizes, mkl_context.out_sizes,
                 mkl_context.filter_sizes, mkl_context.conv_strides,
                 mkl_context.input_offsets, dnnBorderZeros),
             E_SUCCESS);

    // Create the layouts for entities in received context.
    mkl_context.MklCreateInputLayouts(context);

    // Mkl needs the entities in its native format.
    // So create temporary tensors along with buffers to
    // convert the received entities.
    Tensor mkl_tmp_input_buf_tensor, mkl_tmp_out_backprop_buf_tensor;
    // This preparation sets (1) dnnResourceSrc (2) dnnResourceDiffDst
    mkl_context.MklPrepareInputs(context, &mkl_tmp_input_buf_tensor,
                                 &mkl_tmp_out_backprop_buf_tensor);

    // Final conv-grad-filter should be in TF layout.
    Tensor* grad_filter;
    mkl_context.grad_filter_shape.SetMklTensor(false);
    mkl_context.grad_filter_shape.SetTfLayout(mkl_context.filter_dims,
                                              mkl_context.filter_sizes,
                                              mkl_context.filter_strides);
    AllocateOutputSetMklShape(context, 0, &grad_filter, filter_shape,
                              mkl_context.grad_filter_shape);

    // Need to set member variable for TF layout
    mkl_context.lt_grad_filter = mkl_context.grad_filter_shape.GetTfLayout();

    // MKL conv-grad-filter might produce grad in its internal layout
    Tensor mkl_tmp_grad_filter_buf_tensor;
    // This preparation sets conversion primitive if required
    // and allocates temporary tensor and its buffer without doing conversions.
    // Also sets (3) dnnResourceDiffFilter accordingly
    mkl_context.MklPrepareGradFilter(context, grad_filter,
                                     &mkl_tmp_grad_filter_buf_tensor);

    // After setting all the required dnnResources, ready for execution!
    CHECK_EQ(
        dnnExecute_F32(mkl_context.prim_conv_bwdfilter, mkl_context.conv_res),
        E_SUCCESS);

    // Convert grad-filter to TF layout
    if (mkl_context.convert_bwdfilter != nullptr) {
      void* mkl_buf_convert_grad_filter =
          const_cast<void*>(static_cast<const void*>(
              mkl_tmp_grad_filter_buf_tensor.flat<T>().data()));
      void* mkl_buf_grad_filter = const_cast<void*>(
          static_cast<const void*>(grad_filter->flat<T>().data()));
      CHECK_EQ(dnnConversionExecute_F32(mkl_context.convert_bwdfilter,
                                        mkl_buf_convert_grad_filter,
                                        mkl_buf_grad_filter),
               E_SUCCESS);
    }

    mkl_context.MklCleanup();
  }

 private:
  typedef struct {
    int in_dims;
    size_t in_sizes[4];
    size_t in_strides[4];
    int out_dims;
    size_t out_sizes[4];
    size_t out_strides[4];
    int filter_dims;
    size_t filter_sizes[4];
    size_t filter_strides[4];
    int input_offsets[2];
    size_t conv_strides[2];
    MklShape input_shape, grad_filter_shape, out_backprop_shape;
    dnnPrimitive_t prim_conv_bwdfilter = nullptr;
    dnnPrimitive_t convert_bwdfilter = nullptr;
    dnnLayout_t lt_input = nullptr;
    dnnLayout_t lt_grad_filter = nullptr;
    dnnLayout_t lt_out_backprop = nullptr;
    void* conv_res[dnnResourceNumber];

    void MklCleanup() {
      // Cleanup member layouts and primitives except "lt_grad_filter_"
      // which points to MklShape's TFLayout
      bool input_in_mkl_format = input_shape.IsMklTensor();
      bool out_backprop_in_mkl_format = out_backprop_shape.IsMklTensor();
      if (!input_in_mkl_format) dnnLayoutDelete_F32(lt_input);
      if (!out_backprop_in_mkl_format) dnnLayoutDelete_F32(lt_out_backprop);
      if (convert_bwdfilter != nullptr) dnnDelete_F32(convert_bwdfilter);
      dnnDelete_F32(prim_conv_bwdfilter);
    }

    // Create MKL dnnLayout_t objects for tensors coming into the layer
    void MklCreateInputLayouts(OpKernelContext* context) {
      bool input_in_mkl_format = input_shape.IsMklTensor();
      if (input_in_mkl_format) {
        lt_input = static_cast<dnnLayout_t>(input_shape.GetCurLayout());
      } else {
        CHECK_EQ(dnnLayoutCreate_F32(&lt_input, in_dims, in_sizes, in_strides),
                 E_SUCCESS);
      }

      bool out_backprop_in_mkl_format = out_backprop_shape.IsMklTensor();
      if (out_backprop_in_mkl_format) {
        lt_out_backprop =
            static_cast<dnnLayout_t>(out_backprop_shape.GetCurLayout());
      } else {
        CHECK_EQ(dnnLayoutCreate_F32(&lt_out_backprop, out_dims, out_sizes,
                                     out_strides),
                 E_SUCCESS);
      }
    }

    // Compare incoming tensor layouts with MKL preferred layouts and convert
    // data to the preferred layout if necessary
    void MklPrepareInputs(OpKernelContext* context,
                          Tensor* mkl_tmp_input_buf_tensor,
                          Tensor* mkl_tmp_out_backprop_buf_tensor) {
      bool mkl_convert_input, mkl_convert_out_backprop;
      dnnPrimitive_t mkl_prim_convert_input, mkl_prim_convert_out_backprop;
      dnnLayout_t mkl_lt_internal_input, mkl_lt_internal_out_backprop;
      void *mkl_buf_convert_input, *mkl_buf_convert_out_backprop;

      mkl_prim_convert_input = nullptr;
      mkl_prim_convert_out_backprop = nullptr;
      mkl_lt_internal_input = nullptr;
      mkl_lt_internal_out_backprop = nullptr;
      mkl_buf_convert_input = nullptr;
      mkl_buf_convert_out_backprop = nullptr;

      // Compare with internal layouts and convert if needed
      const Tensor& input = MklGetInput(context, 0);
      void* mkl_buf_input =
          const_cast<void*>(static_cast<const void*>(input.flat<T>().data()));
      CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(
                   &mkl_lt_internal_input, prim_conv_bwdfilter, dnnResourceSrc),
               E_SUCCESS);
      mkl_convert_input =
          !dnnLayoutCompare_F32(mkl_lt_internal_input, lt_input);
      if (mkl_convert_input) {
        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, lt_input,
                                         mkl_lt_internal_input),
                 E_SUCCESS);
        AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, mkl_lt_internal_input,
                       &mkl_buf_convert_input);
        CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_input, mkl_buf_input,
                                          mkl_buf_convert_input),
                 E_SUCCESS);
        dnnDelete_F32(mkl_prim_convert_input);
      }
      dnnLayoutDelete_F32(mkl_lt_internal_input);

      conv_res[dnnResourceSrc] =
          (mkl_convert_input) ? mkl_buf_convert_input : mkl_buf_input;

      const Tensor& out_backprop = MklGetInput(context, 2);
      void* mkl_buf_out_backprop = const_cast<void*>(
          static_cast<const void*>(out_backprop.flat<T>().data()));

      CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_out_backprop,
                                                prim_conv_bwdfilter,
                                                dnnResourceDiffDst),
               E_SUCCESS);
      mkl_convert_out_backprop =
          !dnnLayoutCompare_F32(mkl_lt_internal_out_backprop, lt_out_backprop);
      if (mkl_convert_out_backprop) {
        CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_out_backprop,
                                         lt_out_backprop,
                                         mkl_lt_internal_out_backprop),
                 E_SUCCESS);
        AllocTmpBuffer(context, mkl_tmp_out_backprop_buf_tensor,
                       lt_out_backprop, &mkl_buf_convert_out_backprop);
        CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_out_backprop,
                                          mkl_buf_out_backprop,
                                          mkl_buf_convert_out_backprop),
                 E_SUCCESS);
        dnnDelete_F32(mkl_prim_convert_out_backprop);
      }
      dnnLayoutDelete_F32(mkl_lt_internal_out_backprop);

      conv_res[dnnResourceDiffDst] = (mkl_convert_out_backprop)
                                         ? mkl_buf_convert_out_backprop
                                         : mkl_buf_out_backprop;
    }

    void MklPrepareGradFilter(OpKernelContext* context, Tensor* grad_filter,
                              Tensor* mkl_tmp_grad_filter_buf_tensor) {
      bool mkl_convert_grad_filter;
      dnnLayout_t mkl_lt_internal_grad_filter = nullptr;
      void* mkl_buf_convert_grad_filter = nullptr;
      void* mkl_buf_grad_filter = const_cast<void*>(
          static_cast<const void*>(grad_filter->flat<T>().data()));
      CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_grad_filter,
                                                prim_conv_bwdfilter,
                                                dnnResourceDiffFilter),
               E_SUCCESS);
      mkl_convert_grad_filter =
          !dnnLayoutCompare_F32(mkl_lt_internal_grad_filter, lt_grad_filter);
      if (mkl_convert_grad_filter) {
        CHECK_EQ(dnnConversionCreate_F32(&convert_bwdfilter,
                                         mkl_lt_internal_grad_filter,
                                         lt_grad_filter),
                 E_SUCCESS);
        AllocTmpBuffer(context, mkl_tmp_grad_filter_buf_tensor,
                       mkl_lt_internal_grad_filter,
                       &mkl_buf_convert_grad_filter);
      }
      dnnLayoutDelete_F32(mkl_lt_internal_grad_filter);

      conv_res[dnnResourceDiffFilter] = (mkl_convert_grad_filter)
                                            ? mkl_buf_convert_grad_filter
                                            : mkl_buf_grad_filter;
    }
  } MklConv2DGradFilterOpContext;

  std::vector<int32> strides_;
  Padding padding_;
  TensorFormat data_format_;
};

#define REGISTER_MKL_FILTER_KERNELS(T)                              \
  REGISTER_KERNEL_BUILDER(Name("_MklConv2DBackpropFilter")          \
                              .Device(DEVICE_CPU)                   \
                              .TypeConstraint<T>("T")               \
                              .Label(mkl_op_registry::kMklOpLabel), \
                          MklConv2DCustomBackpropFilterOp<CPUDevice, T>);
TF_CALL_float(REGISTER_MKL_FILTER_KERNELS);
#undef REGISTER_MKL_FILTER_KERNELS

#else

template <typename Device, class T, bool biasEnabled>
class MklConv2DCustomBackpropFilterOp
    : public MklConv2DBackpropCommonOp<Device, T> {
 public:
  explicit MklConv2DCustomBackpropFilterOp(OpKernelConstruction* context)
      : MklConv2DBackpropCommonOp<Device, T>(context) {
  }

  ~MklConv2DCustomBackpropFilterOp() {}

  void Compute(OpKernelContext* context) {
    try {
      MklDnnData<T> src(&cpu_engine_);
      MklDnnData<T> diff_dst(&cpu_engine_);
      MklDnnData<T> diff_filter(&cpu_engine_);  // output

      // Input tensors
      const int kInputIdx = 0, kFilterIdx = 1, kOutbpropIdx = 2;
      const Tensor& src_tensor = MklGetInput(context, kInputIdx);
      const Tensor& filter_tensor = MklGetInput(context, kFilterIdx);
      const Tensor& diff_dst_tensor = MklGetInput(context, kOutbpropIdx);

      MklDnnShape src_mkl_shape, filter_mkl_shape, diff_dst_mkl_shape;
      GetMklShape(context, kInputIdx, &src_mkl_shape);
      GetMklShape(context, kFilterIdx, &filter_mkl_shape);
      GetMklShape(context, kOutbpropIdx, &diff_dst_mkl_shape);
      // Allow operator-specific sanity checking of shapes.
      ValidateMklShapes(src_mkl_shape, filter_mkl_shape, diff_dst_mkl_shape);

      // Allow operator-specific generation of shapes.
      // E.g., Conv2DBackpropFilter gets filter as filter_sizes. It is a
      // tensor containing shape of filter. So filter.shape() is not
      // a correct way to get filter shape. These operator-specific calls
      // allow this class to handle this case.
      TensorShape src_tf_shape = MakeInputTfShape(context, src_tensor);
      TensorShape filter_tf_shape = MakeFilterTfShape(context, filter_tensor);
      TensorShape diff_dst_tf_shape = GetTfShape(context, kOutbpropIdx);

      // Corner cases: output with 0 elements and 0 batch size.
      Tensor* diff_filter_tensor = nullptr;
      if (src_tf_shape.num_elements() == 0 ||
          filter_tf_shape.num_elements() == 0 ||
          diff_dst_tf_shape.num_elements() == 0) {
        MklDnnShape diff_filter_mkl_shape;
        diff_filter_mkl_shape.SetMklTensor(false);
        TensorShape diff_filter_tf_shape = GetOutputTfShape(
            src_tf_shape, filter_tf_shape, diff_dst_tf_shape);
        const int kOutputIdx = 0;
        AllocateOutputSetMklShape(context, kOutputIdx, &diff_filter_tensor,
                                  diff_filter_tf_shape, diff_filter_mkl_shape);
        CHECK_NOTNULL(diff_filter_tensor);

        // if output tensor has more than 0 elements, we need to 0 them out.
        auto diff_filter_data = diff_filter_tensor->flat<T>().data();
        for (size_t i = 0; i < diff_filter_tf_shape.num_elements(); ++i) {
          diff_filter_data[i] = 0;
        }
        return;
      }

      // By default, all dims are in MKL order. Only dims in TF order
      // are those with prefix tf_order.
      memory::dims diff_dst_dims, fwd_src_dims, fwd_filter_dims;
      memory::dims padding_left, padding_right, dilations,
          strides, fwd_dst_dims;
      memory::dims fwd_dst_dims_tf_order;

      // Get forward convolution parameters.
      MklDnnConvUtil conv_utl(context, this->strides_, this->padding_,
          this->data_format_, this->dilations_);
      conv_utl.GetConvFwdSizesInMklOrder(
          src_tf_shape, filter_tf_shape, &fwd_src_dims, &fwd_filter_dims,
          &strides, &dilations, &fwd_dst_dims_tf_order,
          &fwd_dst_dims, &padding_left, &padding_right);
      if (!context->status().ok()) return;

      auto tf_fmt = TFDataFormatToMklDnnDataFormat(this->data_format_);
      auto fwd_src_md =
          src_mkl_shape.IsMklTensor()
              ? src_mkl_shape.GetMklLayout()
              : memory::desc(fwd_src_dims, MklDnnType<T>(), tf_fmt);

      conv_utl.GetInputSizeInMklOrder(diff_dst_tf_shape, &diff_dst_dims);
      if (!context->status().ok()) return;

      auto diff_dst_md = diff_dst_mkl_shape.IsMklTensor()
                       ? diff_dst_mkl_shape.GetMklLayout()
                       : memory::desc(diff_dst_dims,
                           MklDnnType<T>(), tf_fmt);

      memory::dims diff_bias_dims = {};
      int64 depth = 0;
      if (biasEnabled) {
        TensorShape obp_tf_shape = GetTfShape(context, 2);
        depth = (this->data_format_ == FORMAT_NCHW)
              ? obp_tf_shape.dim_size(1)
              : obp_tf_shape.dim_size(3);
        diff_bias_dims = {static_cast<int>(depth)};
      }

      dilations[kDilationH] -= 1;
      dilations[kDilationW] -= 1;

      MklConv2DBwdFilterPrimitive<T> *conv2d_bwd_filter = nullptr;
      MklConvBwdFilterParams convBwdFilterDims(fwd_src_dims, fwd_filter_dims,
          diff_bias_dims, diff_dst_dims, strides, dilations, padding_left,
          padding_right, TFPaddingToMklDnnPadding(this->padding_));
      conv2d_bwd_filter = MklConv2DBwdFilterPrimitiveFactory<T>::Get(
          convBwdFilterDims);
      auto bwd_filter_pd = conv2d_bwd_filter->GetPrimitiveDesc();

      // allocate output tensors: diff_fitler and diff_bias (w bias)
      auto bwd_output_dims = GetOutputDims(fwd_src_dims, fwd_filter_dims);

      // diff_filter
      MklDnnShape diff_filter_mkl_shape;
      diff_filter_mkl_shape.SetMklTensor(false);
      // output_dims_mkl_order is in OIHW format.
      TensorShape diff_filter_tf_shape(
                    {bwd_output_dims[MklDnnDims::Dim_H],
                     bwd_output_dims[MklDnnDims::Dim_W],
                     bwd_output_dims[MklDnnDims::Dim_I],
                     bwd_output_dims[MklDnnDims::Dim_O]});
      AllocateOutputSetMklShape(context, 0, &diff_filter_tensor,
                diff_filter_tf_shape, diff_filter_mkl_shape);

      Tensor* diff_bias_tensor = nullptr;
      if (biasEnabled) {
        TensorShape diff_bias_shape({depth});
        AllocateBiasGradTensor(context, diff_bias_shape, &diff_bias_tensor);
      }

      // check if src and diff_dst need reorder
      T *src_data = nullptr;
      if (fwd_src_md.data.format != conv2d_bwd_filter->GetSrcMemoryFormat()) {
        src.SetUsrMem(fwd_src_md, &src_tensor);
        src.CheckReorderToOpMem(bwd_filter_pd->src_primitive_desc());
        src_data = static_cast<T*>(src.GetOpMem().get_data_handle());
      } else {
        src_data = static_cast<T*>(const_cast<T*>(
            src_tensor.flat<T>().data()));
      }

      T *diff_dst_data = nullptr;
      if (diff_dst_md.data.format !=
          conv2d_bwd_filter->GetDiffDstMemoryFormat()) {
        diff_dst.SetUsrMem(diff_dst_md, &diff_dst_tensor);
        diff_dst.CheckReorderToOpMem(bwd_filter_pd->diff_dst_primitive_desc());
        diff_dst_data = static_cast<T*>(
            diff_dst.GetOpMem().get_data_handle());
      } else {
        diff_dst_data = static_cast<T*>(const_cast<T*>(
            diff_dst_tensor.flat<T>().data()));
      }

      // For backward filter, convert diff_filter back to Tensorflow layout
      // Here we prepare to reorder op memory back to user memory
      bool diff_filter_reorder_required = false;
      T *diff_filter_data = nullptr;
      if (GetOutputFormat(tf_fmt) !=
          conv2d_bwd_filter->GetDiffFilterMemoryFormat()) {
        // Allocate diff filter tensor as Tensorflow layout
        diff_filter.SetUsrMem(bwd_output_dims, GetOutputFormat(tf_fmt),
                              diff_filter_tensor);
        diff_filter_reorder_required = true;
        diff_filter.PrepareReorderToUserMemIfReq(
                bwd_filter_pd->diff_weights_primitive_desc());
        diff_filter_data = static_cast<T*>(
                            diff_filter.GetOpMem().get_data_handle());
      } else {
        diff_filter_data = static_cast<T*>(const_cast<T*>(
                            diff_filter_tensor->flat<T>().data()));
      }

      // Execute convolution filter bwd
      if (biasEnabled) {
        T* diff_bias_data = static_cast<T*>(const_cast<T*>(
                         diff_bias_tensor->flat<T>().data()));
        conv2d_bwd_filter->Execute(src_data, diff_filter_data,
                               diff_bias_data, diff_dst_data);
      } else {
        conv2d_bwd_filter->Execute(src_data, diff_filter_data, diff_dst_data);
      }

      // Reorder diff_filter back to Tensorflow layout if necessary
      if (diff_filter_reorder_required) {
        diff_filter.InsertReorderToUserMem();
      }
    } catch (mkldnn::error& e) {
      string error_msg = "Status: " + std::to_string(e.status) +
                         ", message: " + string(e.message) + ", in file " +
                         string(__FILE__) + ":" + std::to_string(__LINE__);
      OP_REQUIRES_OK(
          context,
          errors::Aborted("Operation received an exception:", error_msg));
    }
  }

 private:
  const int kInputIndex_Filter = 1;
  const int kInputIndex_InputSizes = 0;
  const int kDilationH = 0, kDilationW = 1;
  engine cpu_engine_ = engine(engine::cpu, 0);

  // Validate input shapes.
  // Function asserts that input shapes are valid.
  void ValidateMklShapes(const MklDnnShape& input_mkl_shape,
                         const MklDnnShape& filter_mkl_shape,
                         const MklDnnShape& obp_mkl_shape) {
    CHECK(!filter_mkl_shape.IsMklTensor())
        << "Conv2DBackpropFilter: filter should not be in MKL Layout";
  }

  // Get TensorFlow shape of input tensor.
  TensorShape MakeInputTfShape(OpKernelContext* context,
                               const Tensor& input_tensor) {
    size_t input_idx = 0;
    return GetTfShape(context, input_idx);
  }

  // Get TensorFlow shape of filter tensor.
  TensorShape MakeFilterTfShape(OpKernelContext* context,
                                const Tensor& filter_tensor) {
    TensorShape filter_tf_shape;
    CHECK_EQ(TensorShapeUtils::IsVector(filter_tensor.shape()), true);
    CHECK_EQ(TensorShapeUtils::MakeShape(filter_tensor.vec<int32>(),
             &filter_tf_shape).ok(), true);
    return filter_tf_shape;
  }

  // Get Tensorflow shape of output tensor (diff_filter),
  // which is same as shape of filter.
  TensorShape GetOutputTfShape(const TensorShape& input_shape,
                               const TensorShape& filter_shape,
                               const TensorShape& outbprop_shape) {
    return filter_shape;
  }

  // Get the shape of output (diff_filter) in MKL-DNN order.
  // Computes shape of output from input shape (fwd_input_dims)
  // and filter shape (fwd_filter_dims).
  const memory::dims& GetOutputDims(const memory::dims& fwd_input_dims,
                                    const memory::dims& fwd_filter_dims) {
    return fwd_filter_dims;
  }

  // Output layout is Tensorflow's filter layout (HWIO).
  memory::format GetOutputFormat(const memory::format data_format) {
    return memory::format::hwio;
  }

  // Allocate output tensor.
  void AllocateOutputTensor(
      OpKernelContext* context,
      const convolution_backward_weights::primitive_desc& conv_pd,
      const memory::dims& output_dims_mkl_order,
      memory::format output_tf_format, Tensor** output_tensor) {
    CHECK_NOTNULL(output_tensor);

    // For BackpropFilter, we convert the output tensor back in Tensorflow
    // layout. Because typically, BackpropFilter is the last operator in the
    // graph that emit filter gradient that is provided to ApplyGradient
    // method to update the filter. But it may be possible to eliminate this
    // by forwarding filter in MKL layout if we support ApplyGradient method
    // for MKL layout propagation.
    MklDnnShape output_mkl_shape;
    output_mkl_shape.SetMklTensor(false);
    // output_dims_mkl_order is in OIHW format.
    // Allocate shape of TF tensor in HWIO format.
    TensorShape output_tf_shape({output_dims_mkl_order[MklDnnDims::Dim_H],
                                 output_dims_mkl_order[MklDnnDims::Dim_W],
                                 output_dims_mkl_order[MklDnnDims::Dim_I],
                                 output_dims_mkl_order[MklDnnDims::Dim_O]});
    AllocateOutputSetMklShape(context, 0, output_tensor, output_tf_shape,
                              output_mkl_shape);
  }

  // Allocate tensor for bias grad
  void AllocateBiasGradTensor(OpKernelContext* context,
                              const TensorShape& bias_grad_shape,
                              Tensor** bias_grad_tensor) {
    CHECK_NOTNULL(bias_grad_tensor);

    MklDnnShape bias_grad_mkl_shape;
    bias_grad_mkl_shape.SetMklTensor(false);
    AllocateOutputSetMklShape(context, 1, bias_grad_tensor,
        bias_grad_shape, bias_grad_mkl_shape);
  }
};

#define REGISTER_MKL_FILTER_KERNELS(T)                                   \
  REGISTER_KERNEL_BUILDER(                                               \
      Name("_MklConv2DBackpropFilter")                                   \
          .Device(DEVICE_CPU)                                            \
          .TypeConstraint<T>("T")                                        \
          .Label(mkl_op_registry::kMklOpLabel),                          \
      MklConv2DCustomBackpropFilterOp<CPUDevice, T, false>);             \
  REGISTER_KERNEL_BUILDER(                                               \
      Name("_MklConv2DBackpropFilterWithBias")                           \
          .Device(DEVICE_CPU)                                            \
          .TypeConstraint<T>("T")                                        \
          .Label(mkl_op_registry::kMklOpLabel),                          \
      MklConv2DCustomBackpropFilterOp<CPUDevice, T, true>);              \
  REGISTER_KERNEL_BUILDER(Name("__MklDummyConv2DBackpropFilterWithBias") \
                              .Device(DEVICE_CPU)                        \
                              .TypeConstraint<T>("T")                    \
                              .Label(mkl_op_registry::kMklOpLabel),      \
                          MklDummyOp<CPUDevice, T>);

TF_CALL_float(REGISTER_MKL_FILTER_KERNELS);
#undef REGISTER_MKL_FILTER_KERNELS

#endif  // INTEL_MKL_ML

}  // namespace tensorflow

#endif  // INTEL_MKL