text: /**
   * Copyright 2020 Huawei Technologies Co., Ltd
   *
   * 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 "backend/kernel_compiler/cpu/mkldnn/softmax_cross_entropy_with_logits_cpu_kernel.h"
  #include <numeric>
  #include <functional>
  #include <cmath>
  #include "backend/kernel_compiler/cpu/mkldnn/mkl_kernel_engine.h"
  #include "runtime/device/cpu/cpu_device_address.h"
  #include "utils/ms_utils.h"
  
  namespace mindspore {
  namespace kernel {
  void SoftmaxCrossEntropyWithLogitsCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
    CPUKernel::InitInputOutputSize(kernel_node);
    MS_EXCEPTION_IF_NULL(kernel_node);
    size_t type_size = sizeof(float);
    std::vector<size_t> shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
    size_t tensor_size = std::accumulate(shape.begin(), shape.end(), type_size, std::multiplies<size_t>());
    workspace_size_list_.emplace_back(tensor_size);
  }
  
  void SoftmaxCrossEntropyWithLogitsCPUKernel::InitKernel(const CNodePtr &kernel_node) {
    MS_EXCEPTION_IF_NULL(kernel_node);
    std::vector<size_t> shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
    dnnl::memory::dims mem_dims;
    mem_dims.insert(mem_dims.end(), shape.begin(), shape.end());
    if (mem_dims.size() != 2) {
      MS_LOG(EXCEPTION) << "SoftmaxCrossEntropyWithLogits kernel dims invalid " << mem_dims.size();
    }
    batch_size_ = shape[0];
    class_num_ = shape[1];
    if (batch_size_ == 0 || class_num_ == 0) {
      MS_LOG(EXCEPTION) << "Invalid batch size or class num input!";
    }
    dnnl::memory::desc mem_desc(mem_dims, dnnl::memory::data_type::f32, dnnl::memory::format_tag::nc);
  
    dnnl::softmax_forward::desc desc = dnnl::softmax_forward::desc(dnnl::prop_kind::forward_training, mem_desc, 1);
    auto prim_desc = dnnl::softmax_forward::primitive_desc(desc, MKLKernelEngine::Get().engine());
    primitive_ = std::make_shared<dnnl::softmax_forward>(prim_desc);
  
    AddArgument(DNNL_ARG_SRC, mem_desc);
    AddArgument(DNNL_ARG_DST, mem_desc);
  }
  
  void SoftmaxCrossEntropyWithLogitsCPUKernel::ForwardPostExecute(const float *logits, const float *labels,
                                                                  float *output1, float *output2) const {
    float epsilon = 1e-6;
    for (size_t i = 0; i < batch_size_; ++i) {
      output1[i] = 0;
      float loss = 0.0;
      for (size_t j = 0; j < class_num_; ++j) {
        float logit = logf(logits[i * class_num_ + j] <= 0.0 ? epsilon : logits[i * class_num_ + j]);
        output2[i * class_num_ + j] = logits[i * class_num_ + j] - labels[i * class_num_ + j];
        loss += labels[i * class_num_ + j] * logit;
      }
      output1[i] = -loss;
    }
  }
  
  bool SoftmaxCrossEntropyWithLogitsCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
                                                      const std::vector<kernel::AddressPtr> &workspace,
                                                      const std::vector<kernel::AddressPtr> &outputs) {
    if (inputs.empty() || workspace.empty() || outputs.empty()) {
      MS_LOG(EXCEPTION) << "Error input output size!";
    }
    size_t batch_float_size = batch_size_ * sizeof(float);
    size_t batch_class_float_size = class_num_ * batch_float_size;
    if (inputs[0]->size != workspace[0]->size || inputs[0]->size != batch_class_float_size ||
        inputs[1]->size != batch_class_float_size) {
      MS_LOG(EXCEPTION) << "Error input data size!";
    }
    if (outputs[1]->size != batch_class_float_size || outputs[0]->size != batch_float_size) {
      MS_LOG(EXCEPTION) << "Error output data size!";
    }
    SetArgumentHandle(DNNL_ARG_SRC, inputs[0]->addr);
    SetArgumentHandle(DNNL_ARG_DST, workspace[0]->addr);
    ExecutePrimitive();
    auto labels = reinterpret_cast<float *>(inputs[1]->addr);
    auto logits = reinterpret_cast<float *>(workspace[0]->addr);
    auto output1 = reinterpret_cast<float *>(outputs[0]->addr);
    auto output2 = reinterpret_cast<float *>(outputs[1]->addr);
    ForwardPostExecute(logits, labels, output1, output2);
    return true;
  }
  }  // namespace kernel
  }  // namespace mindspore
  