text: /*******************************************************************************
   * Copyright (c) 2019-2020 Konduit K.K.
   *
   * This program and the accompanying materials are made available under the
   * terms of the Apache License, Version 2.0 which is available at
   * https://www.apache.org/licenses/LICENSE-2.0.
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
   * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
   * License for the specific language governing permissions and limitations
   * under the License.
   *
   * SPDX-License-Identifier: Apache-2.0
   ******************************************************************************/
  
   //
   //  @author Oleg Semeniv <oleg.semeniv@gmail.com>
   //
   //
  
  #include <ops/declarable/PlatformHelper.h>
  #include <ops/declarable/OpRegistrator.h>
  #include <system/platform_boilerplate.h>
  #include <helpers/MKLDNNStream.h>
  #include "mkldnnUtils.h"
  
  using namespace dnnl;
  
  namespace sd {
      namespace ops {
          namespace platforms {
  
  
              //////////////////////////////////////////////////////////////////////
              static void softmaxMKLDNN(const NDArray* x, NDArray* z, const int axis) {
  
                  const auto xRank = x->rankOf();
                  dnnl::memory::dims xShape, zShape;
  
                  mkldnnUtils::getDims(x, xRank, xShape);
                  mkldnnUtils::getDims(z, xRank, zShape);
  
  
                  dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank);
                  // optimized cases
                  if (2 == xRank && 0 == axis) {
                      format = dnnl::memory::format_tag::ba;
                  }
                  else if (4 == xRank && 1 == axis && (x->sizeAt(2) * x->sizeAt(3)) > 1) {
                      format = dnnl::memory::format_tag::acdb;
                  }
  
                  dnnl::memory::data_type xType = dnnl::memory::data_type::f32;
  
                  dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, xType, format);
                  dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, xType, format);
                  mkldnnUtils::setBlockStrides(x, x_user_md);
  
                  // z
                  dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, xType, format);
                  dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, xType, format);
                  mkldnnUtils::setBlockStrides(z, z_user_md);
  
                  auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
  
                  // Create attributes (to handle alpha and beta if necessary)
                  dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0)
  
                  // operation primitive description
                  dnnl::softmax_forward::desc op_desc(dnnl::prop_kind::forward_inference, x_mkl_md, axis);
  
                  dnnl::softmax_forward::primitive_desc op_prim_desc(op_desc, attr, engine);
  
                  // arguments (memory buffers) necessary for calculations
                  std::unordered_map<int, dnnl::memory> args;
  
                  dnnl::stream stream(engine);
  
                  // provide memory buffers and check whether reorder is required
  
                  // input
                  mkldnnUtils::loadDataToMklStream(x, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
  
                  // z
                  auto z_user_mem = dnnl::memory(z_user_md, engine, z->buffer());
                  const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc();
                  auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem;
                  args[DNNL_ARG_DST] = z_mkl_mem;
  
                  // run calculations
                  dnnl::softmax_forward(op_prim_desc).execute(stream, args);
  
                  // reorder outputs if necessary
                  if (zReorder)
                      dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem);
  
                  stream.wait();
              }
  
  
              PLATFORM_IMPL(softmax, ENGINE_CPU) {
  
                  auto input = INPUT_VARIABLE(0);
                  auto output = OUTPUT_VARIABLE(0);
  
                  const int rank = input->rankOf();
                  int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;
  
                  if (dim < 0) {
                      dim += rank;
                  }
  
                  REQUIRE_TRUE(dim < rank && dim >= 0, 0, "SOFTMAX_MKLDNN OP: the value of input integer parameter (dimension) must be less than input array rank %i, but got dimension = %i instead !", rank, dim);
  
                  REQUIRE_TRUE(rank <= 6, 0, "SOFTMAX_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank);
  
                  // mkldnnSoftMax
                  softmaxMKLDNN(input, output, dim);
  
                  return Status::OK();
              }
  
              PLATFORM_CHECK(softmax, ENGINE_CPU) {
  
                  auto x = INPUT_VARIABLE(0);
                  auto z = OUTPUT_VARIABLE(0);
  
                  const DataType xType = x->dataType();
                  const DataType zType = z->dataType();
  
                  const int xRank = x->rankOf();
                  bool bSupportedRanks = (xRank > 2 && xRank < 7);
                  /*
                  Source     Destination
                  f32 	    f32
                  */
                  return  !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
  
              }
  
              //////////////////////////////////////////////////////////////////////
              static void softmaxBpMKLDNN(const NDArray* x, const NDArray* dLdz, NDArray* dLdx, const int axis) {
  
                  const auto xRank = x->rankOf();
                  const auto dLdzRank = dLdz->rankOf();
  
                  dnnl::memory::dims xShape, dLdxShape, dLdzShape;
  
                  mkldnnUtils::getDims(x, xRank, xShape);
                  mkldnnUtils::getDims(dLdx, xRank, dLdxShape);
                  mkldnnUtils::getDims(dLdz, dLdzRank, dLdzShape);
  
                  dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank);
  
                  // x
                  dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
                  dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
                  mkldnnUtils::setBlockStrides(x, x_user_md);
  
                  // dLdx
                  dnnl::memory::desc dLdx_mkl_md = dnnl::memory::desc(dLdxShape, dnnl::memory::data_type::f32, format);
                  dnnl::memory::desc dLdx_user_md = dnnl::memory::desc(dLdxShape, dnnl::memory::data_type::f32, format);
                  mkldnnUtils::setBlockStrides(dLdx, dLdx_user_md);
                  // todo if mkl does not support broadcast we can remove this
                  format = mkldnnUtils::getFormat(dLdzRank);
  
                  // dLdz
                  dnnl::memory::desc dLdz_mkl_md = dnnl::memory::desc(dLdzShape, dnnl::memory::data_type::f32, format);
                  dnnl::memory::desc dLdz_user_md = dnnl::memory::desc(dLdzShape, dnnl::memory::data_type::f32, format);
                  mkldnnUtils::setBlockStrides(dLdz, dLdz_user_md);
  
                  auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
  
                  // operation primitive description
                  // forward description
                  dnnl::softmax_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, x_mkl_md, axis);
                  dnnl::softmax_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
  
                  // backward description
                  dnnl::softmax_backward::desc op_bp_desc(dLdz_mkl_md, dLdx_mkl_md, axis);
                  dnnl::softmax_backward::primitive_desc op_bp_prim_desc(op_bp_desc, engine, op_ff_prim_desc);
  
                  // arguments (memory buffers) necessary for calculations
                  std::unordered_map<int, dnnl::memory> argsbp, argsff;
  
                  dnnl::stream stream(engine);
  
                  // provide memory buffers and check whether reorder is required for forward
                  // input
                  mkldnnUtils::loadDataToMklStream(x, engine, stream, x_user_md, op_ff_prim_desc.src_desc(), argsff[DNNL_ARG_SRC]);
  
                  // dLdx
                  auto dLdx_user_mem = dnnl::memory(dLdx_user_md, engine, dLdx->buffer());
                  const bool dLdxReorder = op_ff_prim_desc.dst_desc() != dLdx_user_mem.get_desc();
                  auto dLdx_mkl_mem = dLdxReorder ? dnnl::memory(op_ff_prim_desc.dst_desc(), engine) : dLdx_user_mem;
                  argsff[DNNL_ARG_DST] = dLdx_mkl_mem;
  
                  // check and arg set for backprob
                  argsbp[DNNL_ARG_DIFF_SRC] = dLdx_mkl_mem;
                  argsbp[DNNL_ARG_DST] = dLdx_mkl_mem;
                  // dLdz
                  mkldnnUtils::loadDataToMklStream(dLdz, engine, stream, dLdz_user_md, op_bp_prim_desc.diff_dst_desc(), argsbp[DNNL_ARG_DIFF_DST]);
  
                  // run calculations forward
                  dnnl::softmax_forward(op_ff_prim_desc).execute(stream, argsff);
  
                  // run calculations backward
                  dnnl::softmax_backward(op_bp_prim_desc).execute(stream, argsbp);
  
                  // reorder outputs if necessary
                  if (dLdxReorder)
                      dnnl::reorder(dLdx_mkl_mem, dLdx_user_mem).execute(stream, dLdx_mkl_mem, dLdx_user_mem);
  
                  stream.wait();
              }
  
  
              PLATFORM_IMPL(softmax_bp, ENGINE_CPU) {
  
                  auto input = INPUT_VARIABLE(0);
                  auto dLdz = INPUT_VARIABLE(1);
                  auto dLdx = OUTPUT_VARIABLE(0);
  
                  const int rank = input->rankOf();
                  const int dLdzRank = dLdz->rankOf();
                  int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;
  
                  if (dim < 0) {
                      dim += rank;
                  }
  
                  REQUIRE_TRUE(dim < rank && dim >= 0, 0, "SOFTMAX_MKLDNN_BP OP: the value of input integer parameter (dimension) must be less than input array rank %i, but got dimension = %i instead !", rank, dim);
  
                  REQUIRE_TRUE(rank <= 6 && dLdzRank <= 6, 0, "SOFTMAX_MKLDNN_BP OP: the rank of input and dLdz must be less or qual 6, but got input rank = %i and dLdz rank rank = %i instead !", rank, dLdzRank);
  
                  // mkldnnSoftMax
                  softmaxBpMKLDNN(input, dLdz, dLdx, dim);
  
                  return Status::OK();
              }
  
              PLATFORM_CHECK(softmax_bp, ENGINE_CPU) {
  
                  auto x = INPUT_VARIABLE(0);
                  auto dLdz = INPUT_VARIABLE(1);
                  auto dLdx = OUTPUT_VARIABLE(0);
  
                  const DataType xType = x->dataType();
                  const DataType dLdzType = dLdz->dataType();
                  const DataType dLdxType = dLdx->dataType();
  
                  const int xRank = x->rankOf();
                  const int dLdzRank = dLdz->rankOf();
  
                  bool bSupportedRanks = xRank < 7 && dLdzRank == xRank && (!x->isEmpty() && !dLdz->isEmpty());
  
                  if (bSupportedRanks) {
                      for (int i = 0; i < xRank; i++) {
                          if (x->sizeAt(i) != dLdz->sizeAt(i)) {
                              bSupportedRanks = false;
                              break;
                          }
                      }
                  }
  
                  //Source     Destination
                  //f32 	    f32
                  return block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && dLdzType == DataType::FLOAT32 && dLdxType == DataType::FLOAT32);
              }
  
          }
      }
  }
  