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 ccb265c016
commit f6d5fbdc57
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

16
examples/main/README.md
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@@ -239,6 +239,22 @@ The `--mirostat-ent` option sets the Mirostat target entropy (tau), which repres
Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0`
### XTC Sampling (Exclude Top Choices)
The function of this sampler is conrolled by `--xtc-probability` and `--xtc-threshold`. `--xtc-probability` takes values between
0 and 1 (<=0 turns this sampler off) and defines the probability for randomly invoking the sampler. `--xtc-threshold`
defines the token probability threshold. Tokens with probability greater than this threshold will be excluded from the sampling.
The sampler is turned off for `threshold > 0.5`.
- --xtc-probability p: xtc probability (default: 0.0 => disabled)
- --xtc-threshold t : xtc threshold (default: 1.0 => disabled)
### Top-n-sigma Sampling
Sets all logits $L_i$ to $-\infty$ where $L_i < L_{\rm max} - n \sigma$. Here $L_{\rm max}$ is the maximum logit, $\sigma$ is the logit standard deviation, and $n$ (a floating point number) is the top-n-sigma parameter. Increasing $n$ increases the fraction of tokens considered for sampling. In the limit of $n$ close to zero, one effectively gets greedy sampling (only top probability token considered).
- --top-n-sigma t top-n-sigma parmeter (default: 0.0 => disabled)
### Logit Bias
- `-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS`: Modify the likelihood of a token appearing in the generated text completion.

3
examples/server/README.md
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@@ -98,6 +98,9 @@ sampling:
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)
--mirostat-lr N Mirostat learning rate, parameter eta (default: 0.1)
--mirostat-ent N Mirostat target entropy, parameter tau (default: 5.0)
--xtc-probability p xtc probability (default: 0.0 => disabled)
--xtc-threshold t xtc threshold (default: 1.0 => disabled)
--top-n-sigma t top-n-sigma parmeter (default: 0.0 => disabled)
-l TOKEN_ID(+/-)BIAS modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'

7
include/llama.h
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@@ -1216,6 +1216,13 @@ extern "C" {
float threshold,
size_t min_keep);
/// @details Top n sigma sampling as described in academic paper "Top-nσ: Not All Logits Are You Need" https://arxiv.org/pdf/2411.07641
LLAMA_API void llama_sample_top_n_sigma(
struct llama_context * ctx,
llama_token_data_array * candidates_p,
float top_n_sigma);
/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.

60
src/llama-sampling.cpp
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@@ -435,7 +435,7 @@ void llama_sample_temp_impl(struct llama_sampling * smpl, llama_token_data_array
}
void llama_sample_xtc_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float probability, float threshold, size_t min_keep) {
if (probability < 0 || threshold > 0.5f || candidates->size < 2) {
if (probability <= 0 || threshold > 0.5f || candidates->size < 2) {
return;
}
GGML_ASSERT(smpl);
@@ -468,6 +468,64 @@ void llama_sample_xtc_impl(struct llama_sampling * smpl, llama_token_data_array
}
void llama_sample_top_n_sigma_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float top_n_sigma) {
if (top_n_sigma <= 0.0f || candidates->size < 4) {
// top_n_sigma <= 0: disabled
// candidates->size < 4: no point in applying the transformation for fewer than 4 logits.
return;
}
const int64_t t_start_sample_us = ggml_time_us();
float max = candidates->data[0].logit;
float mean = 0;
size_t count = 0;
for (int i = 0; i < (int)candidates->size; ++i) {
// Only count non-negative infinity values
if (candidates->data[i].logit != -INFINITY) {
max = std::max(max, candidates->data[i].logit);
mean += candidates->data[i].logit;
++count;
}
}
if (count < 4) {
return; // again, tandard deviation is not well defined for so few logits (4 is actually pushing it)
}
mean /= count;
float sigma2 = 0;
for (int i = 0; i < (int)candidates->size; ++i) {
if (candidates->data[i].logit != -INFINITY) {
float delta = candidates->data[i].logit - mean;
sigma2 += delta*delta;
}
}
float sigma = sqrtf(sigma2/count);
float thresh = max - top_n_sigma*sigma;
int n_masked = 0;
for (int i = 0; i < (int)candidates->size; ++i) {
if (candidates->data[i].logit != -INFINITY && candidates->data[i].logit < thresh) {
candidates->data[i].logit = -INFINITY;
++n_masked;
}
}
// do we really want to compute softmax unconditionally?
// The following coresponds to mainline implementation with the minor optimization
// that we only call the relativly expensive softmax if we masked away some tokens.
if (n_masked > 0 || !candidates->sorted) {
llama_sample_softmax_impl(nullptr, candidates);
}
if (smpl) {
smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
smpl->n_sample++;
}
}
void llama_sample_repetition_penalties_impl(
struct llama_sampling * smpl,
llama_token_data_array * candidates,

1
src/llama-sampling.h
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@@ -33,6 +33,7 @@ void llama_sample_typical_impl (struct llama_sampling * smpl, llama_token_data_
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);
void llama_sample_repetition_penalties_impl(
struct llama_sampling * smpl,

4
src/llama.cpp
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@@ -23270,6 +23270,10 @@ void llama_sample_xtc(struct llama_context * ctx, llama_token_data_array * candi
llama_sample_xtc_impl(ctx ? &ctx->sampling : nullptr, candidates_p, probability, threshold, min_keep);
}
void llama_sample_top_n_sigma(struct llama_context * ctx, llama_token_data_array * candidates_p, float top_n_sigma) {
llama_sample_top_n_sigma_impl(ctx ? &ctx->sampling : nullptr, candidates_p, top_n_sigma);
}
void llama_sample_repetition_penalties(
struct llama_context * ctx,
llama_token_data_array * candidates,