ikawrakow/ik_llama.cpp
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adaptive p: collect probability before logit bias (#1314)

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dungquixote42
2026-02-24 09:39:17 -05:00
committed by GitHub
parent 38ca19d828
commit aaa545c3dc
5 changed files with 13 additions and 14 deletions
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13
src/llama-sampling.cpp
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@@ -1169,7 +1169,7 @@ void llama_sample_adaptive_p_impl(struct llama_sampling * ctx, llama_token_data_
void llama_prep_adaptive_p_impl(
struct llama_sampling * smpl,
llama_token_data_array * candidates,
float * logits,
struct llama_sampler_adaptive_p * adapt_p_ctx) {
if (adapt_p_ctx->updt_w_cur) {
// update with current probability, original not needed
@@ -1178,16 +1178,11 @@ void llama_prep_adaptive_p_impl(
constexpr float kDelta = 30.0f; //16.6f;
auto t_start = ggml_time_us();
auto & orig_prob = adapt_p_ctx->orig_prob;
if (candidates->size != orig_prob.size() || candidates->sorted) {
LLAMA_LOG_ERROR("%s: this function must be called before any other sampler has been applied\n", __func__);
LLAMA_LOG_ERROR("%s: the sampler has been initialized with a vocabulary of %zu, but is being called with %zu candidates\n",
__func__, orig_prob.size(), candidates->size);
GGML_ABORT("Bad candidates in adaptive_p sampler");
}
std::copy(logits, logits + orig_prob.size(), orig_prob.begin());
float max_logit = -INFINITY;
for (int j = 0; j < int(candidates->size); ++j) {
orig_prob[j] = candidates->data[j].logit;
for (int j = 0; j < int(orig_prob.size()); ++j) {
max_logit = std::max(max_logit, orig_prob[j]);
}
adapt_p_ctx->cum_orig_prob = iqk_exp_with_thresh(orig_prob.size(), orig_prob.data(), max_logit, max_logit - kDelta);