mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-02-21 05:34:08 +00:00
52ad1c6421
* initial implementation of adaptive-p sampler * explicitly mark candidates unsorted + cleanup qualifiers * cosmetic update * reorg prototypes * lockstep with mainline * add _impl for _init + reorg * add LLAMA_API to prototypes * update sharpness to 10 * lockstep: rng seed * delete llama_sampling member in llama_sampler_adaptive_p * fix LLAMA_API return type * lockstep: rng seed cont * actually correct implementation * lockstep: sorting behavior * const -> constexpr for known constants * add missing space * fix softmax usage in adaptive p sampler * cosmetic changes * implement do-not-sort version of softmax * simpify rng seed, add static to constexpr * refactor: remove iface + use shared rng + use actually original probabilities * adaptive-p: add dedicated rng back in * fix initial max_logit + add float vector to adaptive p sampler context + stochastic sampling * adaptive-p: fuse first softmax with transformation * adaptive-p: implement binary search selection * adaptive-p: update comment
107 lines
5.0 KiB
C++
107 lines
5.0 KiB
C++
#pragma once
|
|
|
|
#include "llama-impl.h"
|
|
#include <unordered_map>
|
|
struct llama_sampling {
|
|
llama_sampling(int32_t n_vocab) : n_vocab(n_vocab) {}
|
|
|
|
std::mt19937 rng;
|
|
|
|
int32_t n_vocab = 0;
|
|
|
|
mutable int64_t t_sample_us = 0;
|
|
mutable int32_t n_sample = 0;
|
|
|
|
void reset_timings() const {
|
|
t_sample_us = 0;
|
|
n_sample = 0;
|
|
}
|
|
};
|
|
|
|
//
|
|
// internal API
|
|
//
|
|
|
|
void llama_set_rng_seed_impl(struct llama_sampling * smpl, uint32_t seed);
|
|
|
|
void llama_sample_softmax_impl (struct llama_sampling * smpl, llama_token_data_array * candidates);
|
|
void llama_sample_top_k_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, int32_t k, size_t min_keep);
|
|
void llama_sample_top_p_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep);
|
|
void llama_sample_min_p_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep);
|
|
void llama_sample_tail_free_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float z, size_t min_keep);
|
|
void llama_sample_typical_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep);
|
|
void llama_sample_entropy_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, float min_temp, float max_temp, float exponent_val);
|
|
void llama_sample_temp_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, float temp);
|
|
void llama_sample_xtc_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, float probability, float threshold, size_t min_keep);
|
|
void llama_sample_top_n_sigma_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float top_n_sigma);
|
|
|
|
struct llama_sampler_dry {
|
|
int32_t total_context_size;
|
|
|
|
const float dry_multiplier;
|
|
const float dry_base;
|
|
const int32_t dry_allowed_length;
|
|
const int32_t dry_penalty_last_n;
|
|
|
|
std::unordered_multimap<llama_token, std::vector<llama_token>> dry_processed_breakers;
|
|
std::vector<int> dry_repeat_count;
|
|
std::unordered_map<llama_token, int> dry_max_token_repeat;
|
|
ring_buffer<llama_token> last_tokens;
|
|
};
|
|
|
|
struct llama_sampler_dry * llama_sampler_init_dry_impl(
|
|
const struct llama_vocab & vocab,
|
|
int32_t context_size,
|
|
float dry_multiplier,
|
|
float dry_base,
|
|
int32_t dry_allowed_length,
|
|
int32_t dry_penalty_last_n,
|
|
const char ** seq_breakers,
|
|
size_t num_breakers);
|
|
|
|
void llama_sampler_dry_apply(struct llama_sampler_dry* smpl, llama_token_data_array* cur_p);
|
|
|
|
// maintains an exponential moving average of the *ORIGINAL* probabilities of selected tokens
|
|
// used to compute an adapted target at each sampling step.
|
|
// see llama.h for a full description of the sampler
|
|
struct llama_sampler_adaptive_p {
|
|
const float target; // target probability (0.0 - 1.0; negative = disabled)
|
|
const float decay; // EMA decay; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
|
|
std::mt19937 rng; // RNG
|
|
float weighted_sum; // sum(p_n * decay^N)
|
|
float total_weight; // sum(decay^i), converges to 1/(1-decay)
|
|
float max_logit; // maximum logit found during transform
|
|
std::vector<float> probs; // cumulative probabilities
|
|
};
|
|
|
|
void llama_sampler_adaptive_p_apply(
|
|
struct llama_sampler_adaptive_p * adapt_p_ctx,
|
|
llama_token_data_array * candidates);
|
|
|
|
struct llama_sampler_adaptive_p * llama_sampler_init_adaptive_p_impl(const float target, const float decay, const uint32_t seed);
|
|
|
|
|
|
void llama_sample_repetition_penalties_impl(
|
|
struct llama_sampling * smpl,
|
|
llama_token_data_array * candidates,
|
|
const llama_token * last_tokens,
|
|
size_t penalty_last_n,
|
|
float penalty_repeat,
|
|
float penalty_freq,
|
|
float penalty_present);
|
|
|
|
void llama_sample_apply_guidance_impl(
|
|
struct llama_sampling * smpl,
|
|
float * logits,
|
|
float * logits_guidance,
|
|
float scale);
|
|
|
|
llama_token llama_sample_token_mirostat_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu);
|
|
llama_token llama_sample_token_mirostat_v2_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, float * mu);
|
|
llama_token llama_sample_token_greedy_impl (struct llama_sampling * smpl, llama_token_data_array * candidates);
|
|
llama_token llama_sample_token_with_rng_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, std::mt19937 & rng);
|
|
llama_token llama_sample_token_impl (struct llama_sampling * smpl, llama_token_data_array * candidates);
|
|
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, float * orig_probs);
|
|
|
|
|