ikawrakow/ik_llama.cpp
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A hopefully more efficient adaptive_p sampling

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Kawrakow
2026-01-18 14:56:30 +00:00
parent 0c0b6e4b8b
commit 6c65430257
1 changed files with 71 additions and 32 deletions
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103
src/llama-sampling.cpp
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@@ -1038,8 +1038,7 @@ struct llama_sampler_dry* llama_sampler_init_dry_impl(const struct llama_vocab&
llama_token llama_sample_token_adaptive_p_impl(
struct llama_sampling * smpl,
llama_token_data_array * candidates,
struct llama_sampler_adaptive_p * adapt_p_ctx)
{
struct llama_sampler_adaptive_p * adapt_p_ctx) {
GGML_ASSERT(candidates->size > 0);
const int64_t t_start_sample_us = ggml_time_us();
@@ -1065,15 +1064,21 @@ llama_token llama_sample_token_adaptive_p_impl(
smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
smpl->n_sample++;
float update_prob = candidates->data[idx].p; // not ideal
if (ctx->orig_prob_map.contains(id)) {
// selected token id is among tracked ids
update_prob = ctx->orig_prob_map[id] / ctx->cum_orig_prob;
if (auto it = ctx->orig_prob_map.find(id); it != ctx->orig_prob_map.end()) {
float update_prob = it->second / ctx->cum_orig_prob;
ctx->weighted_sum = ctx->decay * ctx->weighted_sum + update_prob;
ctx->total_weight = ctx->decay * ctx->total_weight + 1.0f;
}
// update history with original probability of selected token
ctx->weighted_sum = ctx->decay * ctx->weighted_sum + update_prob;
ctx->total_weight = ctx->decay * ctx->total_weight + 1.0f;
//float update_prob = candidates->data[idx].p; // not ideal
//if (ctx->orig_prob_map.contains(id)) {
// // selected token id is among tracked ids
// update_prob = ctx->orig_prob_map[id] / ctx->cum_orig_prob;
//}
//// update history with original probability of selected token
//ctx->weighted_sum = ctx->decay * ctx->weighted_sum + update_prob;
//ctx->total_weight = ctx->decay * ctx->total_weight + 1.0f;
return id;
}
@@ -1130,37 +1135,71 @@ void llama_sample_adaptive_p_impl(llama_token_data_array * candidates, struct ll
void llama_prep_adaptive_p_impl(
llama_token_data_array * candidates,
struct llama_sampler_adaptive_p * adapt_p_ctx)
{
struct llama_sampler_adaptive_p * adapt_p_ctx) {
constexpr float kDelta = 16.6f;
if (!candidates->sorted) {
std::sort(candidates->data, candidates->data + candidates->size,
[](const llama_token_data & a, const llama_token_data & b) {
return a.logit > b.logit;
});
candidates->sorted = true;
float max_logit = candidates->data[0].logit;
for (int j = 1; j < int(candidates->size); ++j) {
max_logit = std::max(max_logit, candidates->data[j].logit);
}
float min_logit = max_logit - kDelta;
float cum_prob = 0.0f;
adapt_p_ctx->orig_prob_map.clear();
for (int j = 0; j < int(candidates->size); ++j) {
if (candidates->data[j].logit > min_logit) {
float prob = expf(candidates->data[j].logit - max_logit);
cum_prob += prob;
adapt_p_ctx->orig_prob_map[candidates->data[j].id] = prob;
}
}
adapt_p_ctx->cum_orig_prob = cum_prob;
return;
}
const float max_logit = candidates->data[0].logit;
// decide how many tokens to track based on logit delta
// i.e. do not track unlikely tokens
auto iter = std::lower_bound(
candidates->data,
candidates->data + candidates->size,
max_logit - 16.6f, // delta
[](const llama_token_data & data, const float delta) {
return data.logit > delta;
});
const size_t n_track = std::distance(candidates->data, iter);
// store orig_prob_map and cum_orig_prob to estimate original probability later
float max_logit = candidates->data[0].logit;
float min_logit = max_logit - kDelta;
float cum_prob = 0.0f;
adapt_p_ctx->orig_prob_map.clear();
for (size_t i = 0; i < n_track; ++i) {
const float prob = expf(candidates->data[i].logit - max_logit);
for (int j = 0; j < int(candidates->size); ++j) {
auto logit = candidates->data[j].logit;
if (logit <= min_logit) {
break;
}
float prob = expf(logit - max_logit);
cum_prob += prob;
adapt_p_ctx->orig_prob_map[candidates->data[i].id] = prob;
adapt_p_ctx->orig_prob_map[candidates->data[j].id] = prob;
}
adapt_p_ctx->cum_orig_prob = cum_prob;
//if (!candidates->sorted) {
// std::sort(candidates->data, candidates->data + candidates->size,
// [](const llama_token_data & a, const llama_token_data & b) {
// return a.logit > b.logit;
// });
// candidates->sorted = true;
//}
//const float max_logit = candidates->data[0].logit;
//// decide how many tokens to track based on logit delta
//// i.e. do not track unlikely tokens
//auto iter = std::lower_bound(
// candidates->data,
// candidates->data + candidates->size,
// max_logit - kDelta, // delta
// [](const llama_token_data & data, const float delta) {
// return data.logit > delta;
// });
//const size_t n_track = std::distance(candidates->data, iter);
//// store orig_prob_map and cum_orig_prob to estimate original probability later
//float cum_prob = 0.0f;
//adapt_p_ctx->orig_prob_map.clear();
//for (size_t i = 0; i < n_track; ++i) {
// const float prob = expf(candidates->data[i].logit - max_logit);
// cum_prob += prob;
// adapt_p_ctx->orig_prob_map[candidates->data[i].id] = prob;
//}
//adapt_p_ctx->cum_orig_prob = cum_prob;
}
struct llama_sampler_adaptive_p * llama_init_adaptive_p_impl(