ikawrakow/ik_llama.cpp
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Adding top-n-sigma sampler (#489)

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* Adding top-n-sigma sampler

* Fix typos in XTC PR

* Update README.md for main and server

* More README

* More README

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2025-06-03 17:35:09 +03:00
committed by GitHub
parent accf69b126
commit 1d28b2a9a1
9 changed files with 115 additions and 11 deletions
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9
common/common.cpp
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@@ -659,6 +659,11 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
sparams.xtc_threshold = std::stof(argv[i]);
return true;
}
if (arg == "--top-n-sigma") {
CHECK_ARG
sparams.top_n_sigma = std::stof(argv[i]);
return true;
}
if (arg == "--cfg-negative-prompt") {
CHECK_ARG
sparams.cfg_negative_prompt = argv[i];
@@ -1646,7 +1651,8 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", " --mirostat-lr N", "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta });
options.push_back({ "*", " --mirostat-ent N", "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau });
options.push_back({ "*", " --xtc-probability p", "xtc probability (default: %.1f, 0.0 = disabled)", (double)sparams.xtc_probability });
options.push_back({ "*", " --xtc-threshold t", "xtc threshold (default: %.1f, 0.0 = disabled)", (double)sparams.xtc_threshold});
options.push_back({ "*", " --xtc-threshold t", "xtc threshold (default: %.1f, >0.5 = disabled)", (double)sparams.xtc_threshold});
options.push_back({ "*", " --top-n-sigma t", "top-n-sigma parmeter (default: %.1f, 0.0 = disabled)", (double)sparams.top_n_sigma});
options.push_back({ "*", " -l TOKEN_ID(+/-)BIAS", "modifies the likelihood of token appearing in the completion,\n"
"i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
"or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" });
@@ -3410,6 +3416,7 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
fprintf(stream, "xtc_probability: %f # default: 0.0\n", sparams.xtc_probability);
fprintf(stream, "xtc_threshold: %f # default: 0.0\n", sparams.xtc_threshold);
fprintf(stream, "top_n_sigma: %f # default: 0.0\n", sparams.top_n_sigma);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());

22
common/sampling.cpp
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@@ -122,11 +122,11 @@ 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",
"\txtc_probability = %.3f, xtc_threshold = %.3f, top_n_sigma = %.3f",
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.xtc_probability, params.xtc_threshold, params.top_n_sigma);
return std::string(result);
}
@@ -156,6 +156,7 @@ std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) {
case llama_sampler_type::MIN_P: return "min_p";
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";
default : return "";
}
}
@@ -168,6 +169,7 @@ std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vecto
{"min_p", llama_sampler_type::MIN_P},
{"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}
};
@@ -183,6 +185,7 @@ std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vecto
{"tfs-z", llama_sampler_type::TFS_Z},
{"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}
};
@@ -218,6 +221,7 @@ std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::strin
{'m', llama_sampler_type::MIN_P},
{'f', llama_sampler_type::TFS_Z},
{'x', llama_sampler_type::XTC},
{'n', llama_sampler_type::TOP_N_SIGMA},
{'t', llama_sampler_type::TEMPERATURE}
};
@@ -248,16 +252,18 @@ static void sampler_queue(
const float typical_p = params.typical_p;
const float xtc_probability = params.xtc_probability;
const float xtc_threshold = params.xtc_threshold;
const float top_n_sigma = params.top_n_sigma;
const std::vector<llama_sampler_type> & samplers_sequence = params.samplers_sequence;
for (auto sampler_type : samplers_sequence) {
switch (sampler_type) {
case llama_sampler_type::TOP_K : llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break;
case llama_sampler_type::TFS_Z : llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break;
case llama_sampler_type::TYPICAL_P: llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break;
case llama_sampler_type::TOP_P : llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break;
case llama_sampler_type::MIN_P : llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break;
case llama_sampler_type::XTC : llama_sample_xtc (ctx_main, &cur_p, xtc_probability, xtc_threshold, min_keep); break;
case llama_sampler_type::TOP_K : llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break;
case llama_sampler_type::TFS_Z : llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break;
case llama_sampler_type::TYPICAL_P : llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break;
case llama_sampler_type::TOP_P : llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break;
case llama_sampler_type::MIN_P : llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break;
case llama_sampler_type::XTC : llama_sample_xtc (ctx_main, &cur_p, xtc_probability, xtc_threshold, min_keep); break;
case llama_sampler_type::TOP_N_SIGMA: llama_sample_top_n_sigma(ctx_main, &cur_p, top_n_sigma); break;
case llama_sampler_type::TEMPERATURE:
if (dynatemp_range > 0) {
float dynatemp_min = std::max(0.0f, temp - dynatemp_range);

4
common/sampling.h
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@@ -16,6 +16,7 @@ enum class llama_sampler_type : char {
MIN_P = 'm',
TFS_Z = 'f',
XTC = 'x',
TOP_N_SIGMA = 'n',
TYPICAL_P = 'y',
TEMPERATURE = 't'
};
@@ -41,7 +42,8 @@ typedef struct llama_sampling_params {
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
float xtc_probability = 0.0f; // xtc probability
float xtc_threshold = 1.0f; // xtc threashold, disabled if > 0.5
float xtc_threshold = 1.0f; // xtc threshold, disabled if > 0.5
float top_n_sigma = 0.0f; // top-n-sigma
bool penalize_nl = false; // consider newlines as a repeatable token
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampling_context