Function EvalQuantized

tensorflow/lite/kernels/lstm_eval.cc:1560–1751

}

TfLiteStatus EvalQuantized(
    const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights,
    const TfLiteTensor* input_to_forget_weights,
    const TfLiteTensor* input_to_cell_weights,
    const TfLiteTensor* input_to_output_weights,
    const TfLiteTensor* recurrent_to_input_weights,
    const TfLiteTensor* recurrent_to_forget_weights,
    const TfLiteTensor* recurrent_to_cell_weights,
    const TfLiteTensor* recurrent_to_output_weights,
    const TfLiteTensor* cell_to_input_weights,
    const TfLiteTensor* cell_to_forget_weights,
    const TfLiteTensor* cell_to_output_weights,
    const TfLiteTensor* input_layer_norm_coefficients,
    const TfLiteTensor* forget_layer_norm_coefficients,
    const TfLiteTensor* cell_layer_norm_coefficients,
    const TfLiteTensor* output_layer_norm_coefficients,
    const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias,
    const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias,
    const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias,
    const TfLiteLSTMParams* params,
    const lstm_eval::QuantizedLstmParameter* quantized_lstm_param,
    TfLiteTensor* activation_state, TfLiteTensor* cell_state,
    TfLiteTensor* output, TfLiteTensor* scratch0, TfLiteTensor* scratch1,
    TfLiteTensor* scratch2, TfLiteTensor* scratch3, TfLiteTensor* scratch4) {
  TF_LITE_ASSERT(input->dims->size >= 2 && input->dims->size <= 3);
  const int n_input = input->dims->data[input->dims->size - 1];
  int max_time, n_batch;
  if (input->dims->size == 2) {
    max_time = 1;
    n_batch = input->dims->data[0];
  } else {
    max_time = input->dims->data[0];
    n_batch = input->dims->data[1];
  }

  // n_cell and n_output will be the same size when there is no projection.
  const int n_cell = input_to_output_weights->dims->data[0];
  const int n_output = recurrent_to_output_weights->dims->data[1];

  // Since we have already checked that weights are all there or none, we can
  // check the existence of only one to get the condition.
  const bool use_cifg = (input_to_input_weights == nullptr);
  const bool use_peephole = (cell_to_output_weights != nullptr);
  const bool is_layer_norm_lstm = (forget_layer_norm_coefficients != nullptr);
  const bool use_projection = (projection_weights != nullptr);

  // Weights and states.
  int8_t* input_to_input_weight_ptr = nullptr;
  int8_t* recurrent_to_input_weight_ptr = nullptr;
  int8_t* cell_to_input_weight_ptr = nullptr;
  int8_t* input_to_forget_weight_ptr = nullptr;
  int8_t* recurrent_to_forget_weight_ptr = nullptr;
  int8_t* cell_to_forget_weight_ptr = nullptr;
  int8_t* input_to_cell_weight_ptr = nullptr;
  int8_t* recurrent_to_cell_weight_ptr = nullptr;
  int8_t* input_to_output_weight_ptr = nullptr;
  int8_t* recurrent_to_output_weight_ptr = nullptr;
  int8_t* cell_to_output_weight_ptr = nullptr;