ikawrakow/ik_llama.cpp
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Implement Adaptive-P Sampler (#1100)

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* 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
This commit is contained in:
dungquixote42
2026-01-10 00:58:53 -05:00
committed by GitHub
parent c91cf84c8f
commit 6695c6c945
8 changed files with 226 additions and 10 deletions
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38
common/sampling.cpp
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@@ -99,7 +99,7 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_vocab* vo
result->n_valid = 0;
}
result->grammar = grmr;
// init DRY
llama_sampling_set_rng_seed(result, params.seed);
for (const auto& cnstr : params.samplers_sequence)
{
switch (cnstr)
@@ -116,11 +116,16 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_vocab* vo
break;
}
case llama_sampler_type::ADAPTIVE_P:
{
result->adapt_p_ctx=llama_sampler_init_adaptive_p(params.adaptive_target, params.adaptive_decay, result->rng());
break;
}
default:
break;
}
}
llama_sampling_set_rng_seed(result, params.seed);
return result;
}
@@ -247,11 +252,13 @@ std::string llama_sampling_print(const llama_sampling_params & params) {
"\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
"\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f\n"
"\txtc_probability = %.3f, xtc_threshold = %.3f, top_n_sigma = %.3f",
"\txtc_probability = %.3f, xtc_threshold = %.3f, top_n_sigma = %.3f\n"
"\tadaptive_target = %.2f, adaptive_decay = %.2f",
params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present,
params.top_k, params.tfs_z, params.top_p, params.min_p, params.typical_p, params.temp,
params.mirostat, params.mirostat_eta, params.mirostat_tau,
params.xtc_probability, params.xtc_threshold, params.top_n_sigma);
params.xtc_probability, params.xtc_threshold, params.top_n_sigma,
params.adaptive_target, params.adaptive_decay);
return std::string(result);
}
@@ -283,6 +290,7 @@ std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) {
case llama_sampler_type::TEMPERATURE: return "temperature";
case llama_sampler_type::XTC : return "xtc";
case llama_sampler_type::TOP_N_SIGMA: return "top_n_sigma";
case llama_sampler_type::ADAPTIVE_P : return "adaptive_p";
default : return "";
}
}
@@ -297,7 +305,8 @@ std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vecto
{"tfs_z", llama_sampler_type::TFS_Z},
{"xtc", llama_sampler_type::XTC},
{"top_n_sigma", llama_sampler_type::TOP_N_SIGMA},
{"temperature", llama_sampler_type::TEMPERATURE}
{"temperature", llama_sampler_type::TEMPERATURE},
{"adaptive_p", llama_sampler_type::ADAPTIVE_P},
};
// since samplers names are written multiple ways
@@ -314,7 +323,8 @@ std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vecto
{"tfs", llama_sampler_type::TFS_Z},
{"xtc", llama_sampler_type::XTC},
{"top-n-sigma", llama_sampler_type::TOP_N_SIGMA},
{"temp", llama_sampler_type::TEMPERATURE}
{"temp", llama_sampler_type::TEMPERATURE},
{"adaptive-p", llama_sampler_type::ADAPTIVE_P},
};
std::vector<llama_sampler_type> sampler_types;
@@ -351,7 +361,8 @@ std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::strin
{'f', llama_sampler_type::TFS_Z},
{'x', llama_sampler_type::XTC},
{'n', llama_sampler_type::TOP_N_SIGMA},
{'t', llama_sampler_type::TEMPERATURE}
{'t', llama_sampler_type::TEMPERATURE},
{'w', llama_sampler_type::ADAPTIVE_P},
};
std::vector<llama_sampler_type> sampler_types;
@@ -405,6 +416,7 @@ static void sampler_queue(
llama_sample_temp(ctx_main, &cur_p, temp);
}
break;
case llama_sampler_type::ADAPTIVE_P: llama_sample_adaptive_p(ctx_main, ctx_sampling->adapt_p_ctx, &cur_p); break;
default : break;
}
}
@@ -422,6 +434,7 @@ static llama_token llama_sampling_sample_impl(
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
const float adaptive_target = params.adaptive_target;
std::vector<float> original_logits;
auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, /* apply_grammar= */ is_resampling, &original_logits);
@@ -451,6 +464,17 @@ static llama_token llama_sampling_sample_impl(
} else if (mirostat == 2) {
llama_sample_temp(ctx_main, &cur_p, temp);
id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
} else if (adaptive_target >= 0.0f) {
// adaptive p sampling
static thread_local std::vector<float> orig_probs;
orig_probs.resize(cur_p.size);
// store original probabilities
for (size_t ii = 0; ii < cur_p.size; ++ii) {
orig_probs[ii] = cur_p.data[ii].p;
}
sampler_queue(ctx_main, params, ctx_sampling, cur_p, std::max(1, params.min_keep));
id = llama_sample_token_adaptive_p(ctx_main, &cur_p, ctx_sampling->adapt_p_ctx, orig_probs.data());
} else {
// temperature sampling
size_t min_keep = std::max(1, params.min_keep);