message: Remove IsZeroVector calls on cell_state_ptr for hybrid LSTM. It's faster to just execute follow-up computations unconditionally.
diff: @@ -645,13 +645,9 @@ inline void LstmStepHybrid(
  output_gate_scratch, /*result_stride=*/1);
  }
  - // Save quantization and matmul computation for all zero input.
  - bool is_cell_state_all_zeros =
  - tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell);
  -
  // For each batch and cell: update input gate.
  if (!use_cifg) {
  - if (use_peephole && !is_cell_state_all_zeros) {
  + if (use_peephole) {
  tensor_utils::VectorScalarMultiply(cell_to_input_weights_ptr, n_cell,
  cell_to_input_weights_scale,
  recovered_cell_weights);
  @@ -673,7 +669,7 @@ inline void LstmStepHybrid(
  }
  // For each batch and cell: update forget gate.
  - if (use_peephole && !is_cell_state_all_zeros) {
  + if (use_peephole) {
  tensor_utils::VectorScalarMultiply(cell_to_forget_weights_ptr, n_cell,
  cell_to_forget_weights_scale,
  recovered_cell_weights);
  @@ -694,10 +690,8 @@ inline void LstmStepHybrid(
  forget_gate_scratch);
  // For each batch and cell: update the cell.
  - if (!is_cell_state_all_zeros) {
  tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr,
  n_batch * n_cell, cell_state_ptr);
  - }
  if (use_layer_norm_lstm) {
  tensor_utils::MeanStddevNormalization(cell_scratch, cell_scratch, n_cell,
  n_batch);
  @@ -723,10 +717,8 @@ inline void LstmStepHybrid(
  params->cell_clip, cell_state_ptr);
  }
  - is_cell_state_all_zeros =
  - tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell);
  // For each batch and cell: update the output gate.
  - if (use_peephole && !is_cell_state_all_zeros) {
  + if (use_peephole) {
  tensor_utils::VectorScalarMultiply(cell_to_output_weights_ptr, n_cell,
  cell_to_output_weights_scale,
  recovered_cell_weights);
  @@ -810,7 +802,7 @@ inline void LstmStepHybrid(
  // Fully quantized lstm kernel. Currently supports both cifg and non-cifg.
  //
  -// Input activatoin of size n_batch * n_input:
  +// Input activation of size n_batch * n_input:
  // input_ptr
  //
  // LSTM weights:
  