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fixed adaptive p

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Concedo
2026-09-09 22:17:59 +08:00
parent ad1eee0c5f
commit 9625b44b38
1 changed files with 26 additions and 35 deletions
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gpttype_adapter.cpp
+26 -35
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@@ -1684,18 +1684,25 @@ void sample_dry(int n_ctx, int penalty_range, float penalty_multiplier, float pe
}
}
void sample_adaptive_p(
inline void adaptive_p_update_history(float selected_token_prob, float & weighted_sum, float & total_weight, float adaptive_decay) {
// decay controls how quickly history influence fades (0.0 to 0.99)
weighted_sum = selected_token_prob + adaptive_decay * weighted_sum;
total_weight = 1.0f + adaptive_decay * total_weight;
}
llama_token sample_adaptive_p(
float target, // desired average probability (0..1), <=0 disables
float & weighted_sum, // persistent EMA state
float & total_weight, // persistent EMA state
llama_token_data_array * cur_p)
llama_token_data_array * cur_p,
float adaptive_decay, std::mt19937 & rng)
{
const float width = 0.3; // DISTRIBUTION_WIDTH
const float peak_logit = 5.0; // PEAK_LOGIT_VALUE
const float inv_width = 1.0f / width; // INV_WIDTH
if (target <= 0.0f || cur_p->size == 0) {
return;
return sample_token(cur_p, rng);
}
// target is the desired average probability for selected tokens (0.0 to 1.0)
@@ -1704,6 +1711,11 @@ llama_token_data_array * cur_p)
sample_softmax(cur_p);
// Save the filtered distribution used by Adaptive-P, not the raw vocabulary.
// Keep token IDs because sample_token sorts the transformed logits.
static thread_local std::vector<llama_token_data> original_candidates;
original_candidates.assign(cur_p->data, cur_p->data + cur_p->size);
// compute the adapted target probability for the current sampling step
float computed_target = std::clamp(total_weight == 0.0f ? target : 2.0f * target - (weighted_sum / total_weight),0.0f, 1.0f);
@@ -1716,17 +1728,14 @@ llama_token_data_array * cur_p)
}
cur_p->sorted = false;
sample_softmax(cur_p);
//update EMA history AFTER sampling, update_adaptive_p_history(original_prob[idx])
}
inline void adaptive_p_update_history(float selected_token_prob, float & weighted_sum, float & total_weight, float adaptive_decay) {
// decay controls how quickly history influence fades (0.0 to 0.99)
// lower values = faster adaptation, more reactive to recent tokens
// higher values = slower adaptation, more stable over time
// keep <= 0.99 to prevent unbounded accumulation
weighted_sum = selected_token_prob + adaptive_decay * weighted_sum;
total_weight = 1.0f + adaptive_decay * total_weight;
const llama_token id = sample_token(cur_p, rng);
for (const auto & candidate : original_candidates) {
if (candidate.id == id) {
adaptive_p_update_history(candidate.p, weighted_sum, total_weight, adaptive_decay);
break;
}
}
return id;
}
@@ -2320,7 +2329,7 @@ static int apply_reasoning_budget(int id, const std::vector<int> & start_think,
int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range, float rep_pen, float rep_pen_slope, float presence_penalty, float top_k, float top_a, float top_p, float min_p, float typical_p, float tfs, float nsigma, float temp, std::mt19937 & rng,
int mirostat, float mirostat_tau, float mirostat_eta, float dry_multiplier, float dry_base, int dry_allowed_length, int dry_penalty_last_n, float xtc_threshold, float xtc_probability,
const std::vector<samplers> & sampler_order, llama_grammar * grammar, float dynatemp_range, float dynatemp_exponent, float smoothing_factor, float smoothing_curve, float adaptive_target,
const std::vector<samplers> & sampler_order, llama_grammar * grammar, float dynatemp_range, float dynatemp_exponent, float smoothing_factor, float smoothing_curve, float adaptive_target, float adaptive_decay,
const std::vector<int> & think_start_seq, const std::vector<int> & think_end_seq, std::vector<int> & think_end_phrase_toks, int reasoning_budget)
{
// printf("SampleLogits called with: n_ctx=%d, n_vocab=%d, rep_pen_range=%d, rep_pen=%f, rep_pen_slope=%f, presence_penalty=%f, top_k=%f, top_a=%f, top_p=%f, min_p=%f, typical_p=%f, tfs=%f, nsigma=%f, temp=%f, mirostat=%d, mirostat_tau=%f, mirostat_eta=%f, dry_multiplier=%f, dry_base=%f, dry_allowed_length=%d, dry_penalty_last_n=%d, xtc_threshold=%f, xtc_probability=%f, sampler_order_size=%zu, dynatemp_range=%f, dynatemp_exponent=%f, smoothing_factor=%f\n",
@@ -2441,8 +2450,7 @@ const std::vector<int> & think_start_seq, const std::vector<int> & think_end_seq
//xtc always last
sample_xtc(&candidates_p, xtc_threshold, xtc_probability, rng);
//adaptive p must be last, it messes up all probs
sample_adaptive_p(adaptive_target, adaptive_p_weighted_sum, adaptive_p_total_weight, &candidates_p);
id = sample_token(&candidates_p, rng);
id = sample_adaptive_p(adaptive_target, adaptive_p_weighted_sum, adaptive_p_total_weight, &candidates_p, adaptive_decay, rng);
}
return id;
@@ -6930,18 +6938,6 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
}
}
//if adaptive p sampling is used, we need to cache the original probabilities
std::vector<llama_token_data> original_candidates;
if(adaptive_target > 0.0f)
{
original_candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
original_candidates.emplace_back(llama_token_data{token_id, logitsPtr[token_id], 0.0f});
}
llama_token_data_array original_candidates_p = { original_candidates.data(), original_candidates.size(), false };
sample_softmax(&original_candidates_p,false);
}
if(file_format == FileFormat::GGUF_GENERIC && guidance_ctx && negprompt_tokens.size()>0 && inputs.guidance_scale!=1.0f)
{
sample_guidance(llama_ctx_v4, guidance_ctx, n_vocab, inputs.guidance_scale);
@@ -6986,14 +6982,9 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
kcpp_data->mirostat, kcpp_data->mirostat_tau, kcpp_data->mirostat_eta,
kcpp_data->dry_multiplier, kcpp_data->dry_base,
kcpp_data->dry_allowed_length, kcpp_data->dry_penalty_last_n, kcpp_data->xtc_threshold, kcpp_data->xtc_probability,
sampler_order, grammar, dynatemp_range, dynatemp_exponent, smoothing_factor, smoothing_curve, adaptive_target,
sampler_order, grammar, dynatemp_range, dynatemp_exponent, smoothing_factor, smoothing_curve, adaptive_target, adaptive_decay,
thinking_start_sequence, thinking_end_sequence, thinking_end_phrase_toksleft, kcpp_data->reasoning_budget);
if (adaptive_target > 0.0f) {
float original_prob = original_candidates[id].p;
adaptive_p_update_history(original_prob, adaptive_p_weighted_sum, adaptive_p_total_weight, adaptive_decay);
}
if(draft_used)
{
if(logits_sampled < draft_results.drafted_amount)