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llama.cpp
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add params to `struct common_params_sampling`, add reference to PR

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ddh0 2025-12-11 13:47:51 -06:00
parent 66e2d17c7f
commit 88fb0f3f32
3 changed files with 39 additions and 34 deletions
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58
common/common.h
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@ -116,6 +116,7 @@ enum common_sampler_type {
COMMON_SAMPLER_TYPE_INFILL = 9,
COMMON_SAMPLER_TYPE_PENALTIES = 10,
COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11,
COMMON_SAMPLER_TYPE_POWER_LAW = 12,
};
// dimensionality reduction methods, used by cvector-generator
@ -163,33 +164,36 @@ enum common_params_sampling_config : uint64_t {
struct common_params_sampling {
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float xtc_probability = 0.00f; // 0.0 = disabled
float xtc_threshold = 0.10f; // > 0.5 disables XTC
float typ_p = 1.00f; // typical_p, 1.0 = disabled
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
float dynatemp_range = 0.00f; // 0.0 = disabled
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.00f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition:
float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length)
int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty
int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float top_n_sigma = -1.00f;// -1.0 = disabled
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool ignore_eos = false;
bool no_perf = false; // disable performance metrics
bool timing_per_token = false;
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float xtc_probability = 0.00f; // 0.0 = disabled
float xtc_threshold = 0.10f; // > 0.5 disables XTC
float typ_p = 1.00f; // typical_p, 1.0 = disabled
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
float dynatemp_range = 0.00f; // 0.0 = disabled
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.00f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition:
float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length)
int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty
int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
float power_law_target = 0.5; // target probability (0.0 to 1.0)
float power_law_target_range = 0.5; // adapt the target within this range (target +/- range)
int32_t power_law_queue_size = 10; // rolling history window size for adaptation
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float top_n_sigma = -1.00f; // -1.0 = disabled
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool ignore_eos = false;
bool no_perf = false; // disable performance metrics
bool timing_per_token = false;
uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers

10
include/llama.h
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@ -1291,12 +1291,12 @@ extern "C" {
/// @details power law sampler, reshapes probability distribution to target specific probability ranges
/// ref: https://github.com/MrJackSpade/llama.cpp
/// ref: [PR]
/// ref: https://github.com/ggml-org/llama.cpp/pull/17927
LLAMA_API struct llama_sampler * llama_sampler_init_power_law(
float target, // target probability (0.0 to 1.0)
float target_range, // adaptive target range (±range from target)
int32_t queue_size, // rolling history window size for adaptation
uint32_t seed); // RNG seed
float target, // target probability (0.0 to 1.0)
float target_range, // adaptive target range (±range from target)
int32_t queue_size, // rolling history window size for adaptation
uint32_t seed); // RNG seed
LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias(
int32_t n_vocab,

5
src/llama-sampling.cpp
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@ -2315,7 +2315,7 @@ struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, floa
// power-law
// ref: https://github.com/MrJackSpade/llama.cpp/tree/master
// ref: [PR]
// ref: https://github.com/ggml-org/llama.cpp/pull/17927
struct llama_sampler_power_law {
const float target;
@ -2404,7 +2404,8 @@ static struct llama_sampler * llama_sampler_power_law_clone(const struct llama_s
auto * result = llama_sampler_init_power_law(ctx->target, ctx->target_range, ctx->queue_size, ctx->seed);
auto * result_ctx = (llama_sampler_power_law *) result->ctx;
result_ctx->history = ctx->history;
result_ctx->rng = ctx->rng;
result_ctx->history = ctx->history;
return result;
}