Function TEST

test/singa/test_operation_rnn.cc:29–74

#ifdef USE_CUDNN
TEST(OperationRNN, tranining) {
  auto cuda = std::make_shared<singa::CudaGPU>();

  size_t hidden_size = 7;
  int seq_length = 5;
  size_t batch_size = 6;
  size_t feature_size = 3;
  size_t num_layers = 1;
  int bdirect = 0;

  Shape s_s{num_layers * (bdirect ? 2 : 1), batch_size, hidden_size};
  Shape y_s{seq_length, batch_size, hidden_size * (bdirect ? 2 : 1)};

  // x
  Tensor x(Shape{seq_length, batch_size, feature_size}, cuda);
  Gaussian(0.0f, 1.0f, &x);

  // x hidden states and cell states
  Tensor hx(s_s, cuda);
  Tensor cx(s_s, cuda);
  hx.SetValue(0.0f);
  cx.SetValue(0.0f);

  // y dy
  Tensor y(y_s, cuda);
  Tensor dy(y_s, cuda);
  Gaussian(0.0f, 1.0f, &y);
  Gaussian(0.0f, 1.0f, &dy);

  // y hidden states and cell states
  Tensor dhy(s_s, cuda);
  Tensor dcy(s_s, cuda);
  Gaussian(0.0f, 1.0f, &dhy);
  Gaussian(0.0f, 1.0f, &dcy);

  // init handle and weights
  CudnnRNNHandle rnn_handle(x, hidden_size);
  Tensor W(Shape{rnn_handle.weights_size}, cuda);
  Gaussian(0.0f, 1.0f, &W);

  // forward and backward passes
  auto outputs = GpuRNNForwardTraining(x, hx, cx, W, rnn_handle);
  auto outputs2 = GpuRNNForwardInference(x, hx, cx, W, rnn_handle);
  auto output3 = GpuRNNBackwardx(y, dy, dhy, dcy, W, hx, cx, rnn_handle);
  auto dW = GpuRNNBackwardW(x, hx, y, rnn_handle);
}

TEST(OperationRNNEx, tranining) {
  auto cuda = std::make_shared<singa::CudaGPU>();