text: // Tencent is pleased to support the open source community by making ncnn available.
  //
  // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
  //
  // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
  // in compliance with the License. You may obtain a copy of the License at
  //
  // https://opensource.org/licenses/BSD-3-Clause
  //
  // 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 "lstm.h"
  #include <math.h>
  
  namespace ncnn {
  
  DEFINE_LAYER_CREATOR(LSTM)
  
  LSTM::LSTM()
  {
      one_blob_only = false;
      support_inplace = false;
  }
  
  int LSTM::load_param(const ParamDict& pd)
  {
      num_output = pd.get(0, 0);
      weight_data_size = pd.get(1, 0);
  
      return 0;
  }
  
  int LSTM::load_model(const ModelBin& mb)
  {
      int size = weight_data_size / num_output / 4;
  
      // raw weight data
      weight_hc_data = mb.load(size, num_output * 4, 0);
      if (weight_hc_data.empty())
          return -100;
  
      weight_xc_data = mb.load(size, num_output * 4, 0);
      if (weight_xc_data.empty())
          return -100;
  
      bias_c_data = mb.load(4, num_output, 0);
      if (bias_c_data.empty())
          return -100;
  
      return 0;
  }
  
  int LSTM::forward(const std::vector<Mat>& bottom_blobs, std::vector<Mat>& top_blobs, const Option& opt) const
  {
      // size x T
      const Mat& input_blob = bottom_blobs[0];
      size_t elemsize = input_blob.elemsize;
  
      // T, 0 or 1 each
      const Mat& cont_blob = bottom_blobs[1];
  
      int T = input_blob.h;
      int size = input_blob.w;
  
      // initial hidden state
      Mat hidden(num_output, 4u, opt.workspace_allocator);
      if (hidden.empty())
          return -100;
      hidden.fill(0.f);
  
      // internal cell state
      Mat cell(num_output, 4u, opt.workspace_allocator);
      if (cell.empty())
          return -100;
      // 4 x num_output
      Mat gates(4, num_output, 4u, opt.workspace_allocator);
      if (gates.empty())
          return -100;
  
      Mat& top_blob = top_blobs[0];
      top_blob.create(num_output, T, elemsize, opt.blob_allocator);
      if (top_blob.empty())
          return -100;
  
      // unroll
      for (int t=0; t<T; t++)
      {
          // clip hidden by continuation indicator
          // h_cont_{t-1} = cont_t * h_{t-1}
          // h_cont_{t-1} = h_{t-1} if cont_t == 1
          //                0       otherwise
          // calculate hidden
          // gate_input_t := W_hc * h_conted_{t-1} + W_xc * x_t + b_c
          const int cont = ((const int*)cont_blob)[t];
          const float* x = input_blob.row(t);
          for (int q=0; q<num_output; q++)
          {
              float h_cont = cont ? hidden[q] : 0.f;
  
              const float* bias_c_data_ptr = (const float*)bias_c_data + 4 * q;
              float* gates_data = (float*)gates + 4 * q;
  
              // gate I F O G
              const float* weight_hc_data_I = (const float*)weight_hc_data + weight_hc_data.w * q;
              const float* weight_xc_data_I = (const float*)weight_xc_data + weight_xc_data.w * q;
              const float* weight_hc_data_F = (const float*)weight_hc_data + weight_hc_data.w * q + size;
              const float* weight_xc_data_F = (const float*)weight_xc_data + weight_xc_data.w * q + size;
              const float* weight_hc_data_O = (const float*)weight_hc_data + weight_hc_data.w * q + size*2;
              const float* weight_xc_data_O = (const float*)weight_xc_data + weight_xc_data.w * q + size*2;
              const float* weight_hc_data_G = (const float*)weight_hc_data + weight_hc_data.w * q + size*3;
              const float* weight_xc_data_G = (const float*)weight_xc_data + weight_xc_data.w * q + size*3;
  
              float I = bias_c_data_ptr[0];
              float F = bias_c_data_ptr[1];
              float O = bias_c_data_ptr[2];
              float G = bias_c_data_ptr[3];
              for (int i=0; i<size; i++)
              {
                  I += weight_hc_data_I[i] * h_cont + weight_xc_data_I[i] * x[i];
                  F += weight_hc_data_F[i] * h_cont + weight_xc_data_F[i] * x[i];
                  O += weight_hc_data_O[i] * h_cont + weight_xc_data_O[i] * x[i];
                  G += weight_hc_data_G[i] * h_cont + weight_xc_data_G[i] * x[i];
              }
  
              gates_data[0] = I;
              gates_data[1] = F;
              gates_data[2] = O;
              gates_data[3] = G;
          }
  
          // lstm unit
          // sigmoid(I)
          // sigmoid(F)
          // sigmoid(O)
          // tanh(G)
          // c_t := f_t .* c_{t-1} + i_t .* g_t
          // h_t := o_t .* tanh[c_t]
          float* output_data = top_blob.row(t);
          for (int q=0; q<num_output; q++)
          {
              float* gates_data = (float*)gates + 4 * q;
  
              float I = gates_data[0];
              float F = gates_data[1];
              float O = gates_data[2];
              float G = gates_data[3];
  
              I = 1.f / (1.f + exp(-I));
              F = cont ? 1.f / (1.f + exp(-F)) : 0.f;
              O = 1.f / (1.f + exp(-O));
              G = tanh(G);
  
              float cell2 = F * cell[q] + I * G;
              float H = O * tanh(cell2);
  
              cell[q] = cell2;
              hidden[q] = H;
              output_data[q] = H;
          }
  
          // no cell output here
      }
  
      return 0;
  }
  
  } // namespace ncnn
  