Cleanup: Use std::fill_n and std::copy_n instead of memset and memcpy.
PiperOrigin-RevId: 315811287
Change-Id: Ia3cb960317811f7f9a9f0f24da77880d5fc1b64e
diff --git a/tensorflow/lite/kernels/lstm_eval.cc b/tensorflow/lite/kernels/lstm_eval.cc
index f7422b2..0689158 100644
--- a/tensorflow/lite/kernels/lstm_eval.cc
+++ b/tensorflow/lite/kernels/lstm_eval.cc
@@ -58,61 +58,61 @@
const float* aux_input_ptr) {
// Compute the row sums for dequantization
if (!use_cifg) {
- memset(input_to_input_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(input_to_input_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(input_to_input_weights_ptr,
input_to_input_row_sums, n_cell, n_input);
}
- memset(input_to_forget_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(input_to_forget_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(input_to_forget_weights_ptr,
input_to_forget_row_sums, n_cell, n_input);
- memset(input_to_cell_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(input_to_cell_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(input_to_cell_weights_ptr,
input_to_cell_row_sums, n_cell, n_input);
- memset(input_to_output_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(input_to_output_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(input_to_output_weights_ptr,
input_to_output_row_sums, n_cell, n_input);
if (aux_input_ptr) {
if (!use_cifg) {
- memset(aux_input_to_input_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(aux_input_to_input_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(aux_input_to_input_weights_ptr,
aux_input_to_input_row_sums, n_cell,
n_aux_input);
}
- memset(aux_input_to_forget_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(aux_input_to_forget_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(aux_input_to_forget_weights_ptr,
aux_input_to_forget_row_sums, n_cell,
n_aux_input);
- memset(aux_input_to_cell_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(aux_input_to_cell_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(aux_input_to_cell_weights_ptr,
aux_input_to_cell_row_sums, n_cell,
n_aux_input);
- memset(aux_input_to_output_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(aux_input_to_output_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(aux_input_to_output_weights_ptr,
aux_input_to_output_row_sums, n_cell,
n_aux_input);
}
if (!use_cifg) {
- memset(recurrent_to_input_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(recurrent_to_input_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(recurrent_to_input_weights_ptr,
recurrent_to_input_row_sums, n_cell,
n_output);
}
- memset(recurrent_to_forget_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(recurrent_to_forget_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(recurrent_to_forget_weights_ptr,
recurrent_to_forget_row_sums, n_cell,
n_output);
- memset(recurrent_to_cell_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(recurrent_to_cell_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(recurrent_to_cell_weights_ptr,
recurrent_to_cell_row_sums, n_cell,
n_output);
- memset(recurrent_to_output_row_sums, 0, sizeof(int32_t) * n_cell);
+ std::fill_n(recurrent_to_output_row_sums, n_cell, 0);
tensor_utils::ReductionSumVector(recurrent_to_output_weights_ptr,
recurrent_to_output_row_sums, n_cell,
n_output);
if (projection_weights_ptr != nullptr) {
- memset(projection_weights_row_sums, 0, sizeof(int32_t) * n_output);
+ std::fill_n(projection_weights_row_sums, n_output, 0);
tensor_utils::ReductionSumVector(
projection_weights_ptr, projection_weights_row_sums, n_output, n_cell);
}
@@ -1175,11 +1175,11 @@
// Set scratch to 0.
if (!use_cifg) {
- memset(scratch_0_ptr, 0, n_batch * n_cell * sizeof(int16_t));
+ std::fill_n(scratch_0_ptr, n_batch * n_cell, 0);
}
- memset(scratch_1_ptr, 0, n_batch * n_cell * sizeof(int16_t));
- memset(scratch_2_ptr, 0, n_batch * n_cell * sizeof(int16_t));
- memset(scratch_3_ptr, 0, n_batch * n_cell * sizeof(int16_t));
+ std::fill_n(scratch_1_ptr, n_batch * n_cell, 0);
+ std::fill_n(scratch_2_ptr, n_batch * n_cell, 0);
+ std::fill_n(scratch_3_ptr, n_batch * n_cell, 0);
// Forget gate.
tensor_utils::MatrixBatchVectorMultiplyAccumulate(
@@ -1309,7 +1309,7 @@
scratch_4_ptr);
// Projection.
if (use_projection) {
- memset(output_ptr, 0, n_batch * n_output * sizeof(int8_t));
+ std::fill_n(output_ptr, n_batch * n_output, 0);
tensor_utils::MatrixBatchVectorMultiplyAccumulate(
scratch_4_ptr, projection_effective_bias, proj_weight_ptr,
effective_proj_scale_a, effective_proj_scale_b, n_batch, n_cell,
@@ -1473,8 +1473,8 @@
int16_t* scratch4, int16_t* scratch5, int16_t* scratch6,
int16_t* scratch7) {
// Forget gate.
- memset(scratch0, 0, n_batch * n_cell);
- memset(scratch1, 0, n_batch * n_cell);
+ std::fill_n(scratch0, n_batch * n_cell, 0);
+ std::fill_n(scratch1, n_batch * n_cell, 0);
tensor_utils::MatrixBatchVectorMultiply(
input_ptr, input_zp, input_to_forget_weight_ptr,
effective_input_to_forget_scale_a, effective_input_to_forget_scale_b,
@@ -1500,8 +1500,8 @@
tensor_utils::ApplySigmoidFloat(scratch2, n_batch, n_cell, scratch2);
// Update gate.
- memset(scratch0, 0, n_batch * n_cell);
- memset(scratch1, 0, n_batch * n_cell);
+ std::fill_n(scratch0, n_batch * n_cell, 0);
+ std::fill_n(scratch1, n_batch * n_cell, 0);
tensor_utils::MatrixBatchVectorMultiply(
input_ptr, input_zp, input_to_cell_weight_ptr,
effective_input_to_cell_scale_a, effective_input_to_cell_scale_b, n_batch,
@@ -1526,8 +1526,8 @@
tensor_utils::ApplyTanhFloat(scratch3, n_batch, n_cell, -12, scratch3);
// Output gate.
- memset(scratch0, 0, n_batch * n_cell);
- memset(scratch1, 0, n_batch * n_cell);
+ std::fill_n(scratch0, n_batch * n_cell, 0);
+ std::fill_n(scratch1, n_batch * n_cell, 0);
tensor_utils::MatrixBatchVectorMultiply(
input_ptr, input_zp, input_to_output_weight_ptr,
effective_input_to_output_scale_a, effective_input_to_output_scale_b,
@@ -1587,7 +1587,7 @@
}
// Copy output to activation.
- memcpy(activation_ptr, output_ptr, n_batch * n_output * sizeof(int8_t));
+ std::copy_n(output_ptr, n_batch * n_output, activation_ptr);
}
} // namespace
