happyz
/
llama.cpp
mirror of https://github.com/ggerganov/llama.cpp.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

remove old unused code from algorithm

This commit is contained in:
ddh0 2025-12-11 16:46:17 -06:00
parent f3457a83e6
commit 93169593b8
1 changed files with 1 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
src/llama-sampling.cpp
View File

@ -2370,22 +2370,9 @@ static void llama_sampler_power_law_apply(struct llama_sampler * smpl, llama_tok
computed_target = std::max(min_target, std::min(next_value, max_target)); computed_target = std::max(min_target, std::min(next_value, max_target));
} }
// find closest token (for degenerate width ~ 0 case)
float min_distance = FLT_MAX;
int closest_token_idx = -1;
for (size_t i = 0; i < cur_p->size; ++i) {
float distance = std::abs(cur_p->data[i].p - computed_target);
if (distance < min_distance) {
min_distance = distance;
closest_token_idx = (int) i;
}
}
// apply power law transformation // apply power law transformation
for (size_t i = 0; i < cur_p->size; ++i) { for (size_t i = 0; i < cur_p->size; ++i) {
float p = cur_p->data[i].p; float p = cur_p->data[i].p;
float normalized_distance = std::abs(p - computed_target) / 0.2f; float normalized_distance = std::abs(p - computed_target) / 0.2f;
cur_p->data[i].logit = 3.0f / (1.0f + std::pow(normalized_distance, 3.0f)); cur_p->data[i].logit = 3.0f / (1.0f + std::pow(normalized_distance, 3.0f));
} }