vbatts/llama.cpp
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sampling : convert mirostat samplers to constraints

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ggml-ci
This commit is contained in:
Georgi Gerganov 2024-09-04 16:57:43 +03:00
parent 1a0de0b781
commit 0e1378c844
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GPG key ID: 449E073F9DC10735
5 changed files with 304 additions and 214 deletions
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22
common/sampling.cpp
View file

@ -47,9 +47,6 @@ struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const st
lparams.seed = params.seed;
lparams.n_prev = params.n_prev;
lparams.mirostat = params.mirostat;
lparams.mirostat_tau = params.mirostat_tau;
lparams.mirostat_eta = params.mirostat_eta;
auto * result = new gpt_sampler {
/* .params = */ params,
@ -69,6 +66,7 @@ struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const st
/* .smpl = */ llama_sampler_init(model, lparams)
};
if (params.mirostat == 0) {
for (const auto & cnstr : params.constraints) {
switch (cnstr) {
case GPT_CONSTRAINT_TYPE_TOP_K:
@ -93,6 +91,15 @@ struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const st
GGML_ASSERT(false && "unknown constraint type");
}
}
} else if (params.mirostat == 1) {
llama_sampler_add_constraint(result->smpl, llama_constraint_init_temp(params.temp));
llama_sampler_add_constraint(result->smpl, llama_constraint_init_mirostat(model, params.mirostat_tau, params.mirostat_eta));
} else if (params.mirostat == 2) {
llama_sampler_add_constraint(result->smpl, llama_constraint_init_temp(params.temp));
llama_sampler_add_constraint(result->smpl, llama_constraint_init_mirostat_v2(params.mirostat_tau, params.mirostat_eta));
} else {
GGML_ASSERT(false && "unknown mirostat version");
}
return result;
}
@ -153,7 +160,6 @@ static llama_token gpt_sampler_sample(
struct llama_sampler * smpl,
struct llama_token_data_array * cur_p,
float temp,
int mirostat,
int n_probs) {
llama_token res = 0;
@ -167,9 +173,6 @@ static llama_token gpt_sampler_sample(
// apply all sampling constraints and then sample
llama_sampler_apply(smpl, cur_p);
if (mirostat != 0) {
res = llama_sampler_sample_mirostat(smpl, cur_p);
} else {
res = llama_sampler_sample_dist(smpl, cur_p);
//{
@ -185,7 +188,6 @@ static llama_token gpt_sampler_sample(
//LOG("sampled token: %5d: '%s'\n", res, llama_token_to_piece(smpl, res).c_str());
}
}
return res;
}
@ -208,7 +210,7 @@ llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context
llama_constraint_apply(pnlt, cur_p);
// first, sample the token without any grammar constraints
const llama_token id = gpt_sampler_sample(smpl, nullptr, params.temp, params.mirostat, params.n_probs);
const llama_token id = gpt_sampler_sample(smpl, nullptr, params.temp, params.n_probs);
// check if it the sampled token fits the grammar
{
@ -231,7 +233,7 @@ llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context
llama_constraint_apply(pnlt, cur_p);
llama_constraint_apply(grmr, cur_p);
return gpt_sampler_sample(smpl, cur_p, params.temp, params.mirostat, params.n_probs);
return gpt_sampler_sample(smpl, cur_p, params.temp, params.n_probs);
}
void gpt_sampler_apply_grammar(struct gpt_sampler * gsmpl, llama_token_data_array * cur_p) {

29
include/llama.h
View file

@ -369,16 +369,18 @@ extern "C" {
float bias;
} llama_logit_bias;
enum llama_sampler_type {
LLAMA_SAMPLER_TYPE_GREEDY = 0,
LLAMA_SAMPLER_TYPE_DIST = 1,
};
typedef struct llama_sampler_params {
uint32_t seed; // the seed used to initialize the rng of the sampler
int32_t n_prev; // size of ring buffer to keep previous accepted tokens (needed for llama_sampler_prev_ API)
int32_t mirostat; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau; // target entropy
float mirostat_eta; // learning rate
// TODO: add type of sampler: greedy, dist, mirostat, etc.
// TODO: will be used by the llama_decode_with_sampler() API in the future
enum llama_sampler_type type;
} llama_sampler_params;
// performance timing information
@ -1005,17 +1007,18 @@ extern "C" {
//
// - Samplers
// The llama_sampler samples a token based on the candidate token probabilities. Before the actual sampling, the
// sampler can apply a sequence of constraints to the candidate tokens.
// sampler can apply a sequence of constraints in order to modify the probabilities of the candidates.
//
// The llama_sampler object contains the entire sampling information:
//
// - RNG state (seed and generator)
// - Custom set of constraints (see llama_sampler_add_constraint)
// - Sampling method (greedy, dist, mirostat)
// - Sampling method (greedy, dist)
// - Previous tokens
//
// In the future, it will be utilized offload the sampling to the backends (e.g. GPU).
//
// TODO: in the future, the entire API should be changed to accept llama_vocab, instead of llama_model
// constraints
@ -1050,6 +1053,15 @@ extern "C" {
LLAMA_API struct llama_constraint * llama_constraint_init_temp (float t);
LLAMA_API struct llama_constraint * llama_constraint_init_temp_ext (float t, float delta, float exponent);
LLAMA_API struct llama_constraint * llama_constraint_init_mirostat(
const struct llama_model * model,
float tau,
float eta);
LLAMA_API struct llama_constraint * llama_constraint_init_mirostat_v2(
float tau,
float eta);
LLAMA_API struct llama_constraint * llama_constraint_init_grammar(
const struct llama_model * model,
const char * grammar_str,
@ -1096,8 +1108,7 @@ extern "C" {
LLAMA_API void llama_sampler_apply (struct llama_sampler * smpl, llama_token_data_array * cur_p);
LLAMA_API llama_token llama_sampler_sample_dist (struct llama_sampler * smpl, llama_token_data_array * cur_p);
LLAMA_API llama_token llama_sampler_sample_greedy (struct llama_sampler * smpl, llama_token_data_array * cur_p, bool probs);
LLAMA_API llama_token llama_sampler_sample_mirostat(struct llama_sampler * smpl, llama_token_data_array * cur_p);
LLAMA_API llama_token llama_sampler_sample_greedy(struct llama_sampler * smpl, llama_token_data_array * cur_p, bool probs);
/// @details Get the number of accepted tokens so far (max of n_prev)
LLAMA_API int llama_sampler_n_prev(const struct llama_sampler * smpl);

280
src/llama-sampling.cpp
View file

@ -450,8 +450,8 @@ struct llama_constraint * llama_constraint_init_softmax_impl() {
// top-k
struct llama_constraint_context_top_k {
int32_t k;
size_t min_keep;
const int32_t k;
const size_t min_keep;
};
static struct llama_constraint_i llama_constraint_top_k_i = {
@ -486,8 +486,8 @@ struct llama_constraint * llama_constraint_init_top_k_impl(int32_t k, size_t min
// top-p
struct llama_constraint_context_top_p {
float p;
size_t min_keep;
const float p;
const size_t min_keep;
};
static struct llama_constraint_i llama_constraint_top_p_i = {
@ -522,8 +522,8 @@ struct llama_constraint * llama_constraint_init_top_p_impl(float p, size_t min_k
// min-p
struct llama_constraint_context_min_p {
float p;
size_t min_keep;
const float p;
const size_t min_keep;
};
static struct llama_constraint_i llama_constraint_min_p_i = {
@ -558,8 +558,8 @@ struct llama_constraint * llama_constraint_init_min_p_impl(float p, size_t min_k
// tail-free
struct llama_constraint_context_tail_free {
float z;
size_t min_keep;
const float z;
const size_t min_keep;
};
static struct llama_constraint_i llama_constraint_tail_free_i = {
@ -594,8 +594,8 @@ struct llama_constraint * llama_constraint_init_tail_free_impl(float z, size_t m
// typical
struct llama_constraint_context_typical {
float p;
size_t min_keep;
const float p;
const size_t min_keep;
};
static struct llama_constraint_i llama_constraint_typical_i = {
@ -630,7 +630,7 @@ struct llama_constraint * llama_constraint_init_typical_impl(float p, size_t min
// temp
struct llama_constraint_context_temp {
float temp;
const float temp;
};
static struct llama_constraint_i llama_constraint_temp_i = {
@ -664,9 +664,9 @@ struct llama_constraint * llama_constraint_init_temp_impl(float temp) {
// temp-ext
struct llama_constraint_context_temp_ext {
float temp;
float delta;
float exponent;
const float temp;
const float delta;
const float exponent;
};
static struct llama_constraint_i llama_constraint_temp_ext_i = {
@ -706,6 +706,176 @@ struct llama_constraint * llama_constraint_init_temp_ext_impl(float temp, float
return result;
}
// mirostat
struct llama_constraint_context_mirostat {
const struct llama_vocab * vocab;
const float tau;
const float eta;
const int32_t m;
float mu;
std::vector<llama_token_data> cur;
};
static struct llama_constraint_i llama_constraint_mirostat_i = {
/* .name = */ [](const struct llama_constraint * /*cnstr*/) { return "mirostat"; },
/* .accept = */ [](struct llama_constraint * cnstr, llama_token token) {
auto * ctx = (llama_constraint_context_mirostat *) cnstr->ctx;
int32_t idx = -1;
for (size_t i = 0; i < ctx->cur.size(); ++i) {
if (ctx->cur[i].id == token) {
idx = i;
break;
}
}
float observed_surprise = -log2f(ctx->cur[idx].p);
float e = observed_surprise - ctx->tau;
// Update mu using the learning rate and error
ctx->mu = ctx->mu - ctx->eta * e;
},
/* .apply = */ [](struct llama_constraint * cnstr, llama_token_data_array * cur_p) {
auto * ctx = (llama_constraint_context_mirostat *) cnstr->ctx;
llama_constraint_softmax_impl(cur_p);
// Estimate s_hat using the most probable m tokens
float s_hat = 0.0;
float sum_ti_bi = 0.0;
float sum_ti_sq = 0.0;
for (size_t i = 0; i < size_t(ctx->m - 1) && i < cur_p->size - 1; ++i) {
float t_i = logf(float(i + 2) / float(i + 1));
float b_i = logf(cur_p->data[i].p / cur_p->data[i + 1].p);
sum_ti_bi += t_i * b_i;
sum_ti_sq += t_i * t_i;
}
s_hat = sum_ti_bi / sum_ti_sq;
// Compute k from the estimated s_hat and target surprise value
float epsilon_hat = s_hat - 1;
float k = powf((epsilon_hat * powf(2, ctx->mu)) / (1 - powf(ctx->vocab->n_vocab, -epsilon_hat)), 1 / s_hat);
llama_constraint_top_k_impl(cur_p, int(k), 1);
// remember the order to be able to compute the distance later when accepting the token
ctx->cur.resize(cur_p->size);
for (size_t i = 0; i < cur_p->size; ++i) {
ctx->cur[i] = cur_p->data[i];
}
},
/* .reset = */ [](struct llama_constraint * cnstr) {
auto * ctx = (llama_constraint_context_mirostat *) cnstr->ctx;
ctx->mu = 0.0f;
},
/* .copy = */ [](const struct llama_constraint * cnstr) {
const auto * ctx = (const llama_constraint_context_mirostat *) cnstr->ctx;
return llama_constraint_init_mirostat_impl(*ctx->vocab, ctx->tau, ctx->eta, ctx->m);
},
/* .free = */ [](struct llama_constraint * cnstr) {
delete (llama_constraint_context_mirostat *) cnstr->ctx;
},
};
struct llama_constraint * llama_constraint_init_mirostat_impl(
const struct llama_vocab & vocab,
float tau,
float eta,
int32_t m) {
struct llama_constraint * result = new llama_constraint {
/* .iface = */ &llama_constraint_mirostat_i,
/* .ctx = */ new llama_constraint_context_mirostat {
/*.vocab =*/ &vocab,
/*.tau =*/ tau,
/*.eta =*/ eta,
/*.m =*/ m,
/*.mu =*/ 0.0f,
/*.cur =*/ {},
},
};
return result;
}
// mirostat v2
struct llama_constraint_context_mirostat_v2 {
const float tau;
const float eta;
float mu;
std::vector<llama_token_data> cur;
};
static struct llama_constraint_i llama_constraint_mirostat_v2_i = {
/* .name = */ [](const struct llama_constraint * /*cnstr*/) { return "mirostat-v2"; },
/* .accept = */ [](struct llama_constraint * cnstr, llama_token token) {
auto * ctx = (llama_constraint_context_mirostat_v2 *) cnstr->ctx;
int32_t idx = -1;
for (size_t i = 0; i < ctx->cur.size(); ++i) {
if (ctx->cur[i].id == token) {
idx = i;
break;
}
}
float observed_surprise = -log2f(ctx->cur[idx].p);
float e = observed_surprise - ctx->tau;
// Update mu using the learning rate and error
ctx->mu = ctx->mu - ctx->eta * e;
},
/* .apply = */ [](struct llama_constraint * cnstr, llama_token_data_array * cur_p) {
auto * ctx = (llama_constraint_context_mirostat_v2 *) cnstr->ctx;
llama_constraint_softmax_impl(cur_p);
// Truncate the words with surprise values greater than mu
cur_p->size = std::distance(cur_p->data, std::find_if(cur_p->data, cur_p->data + cur_p->size, [&](const llama_token_data & candidate) {
return -log2f(candidate.p) > ctx->mu;
}));
if (cur_p->size == 0) {
cur_p->size = 1;
}
// Normalize the probabilities of the remaining words
llama_constraint_softmax_impl(cur_p);
},
/* .reset = */ [](struct llama_constraint * cnstr) {
auto * ctx = (llama_constraint_context_mirostat_v2 *) cnstr->ctx;
ctx->mu = 0.0f;
},
/* .copy = */ [](const struct llama_constraint * cnstr) {
const auto * ctx = (const llama_constraint_context_mirostat_v2 *) cnstr->ctx;
return llama_constraint_init_mirostat_v2_impl(ctx->tau, ctx->eta);
},
/* .free = */ [](struct llama_constraint * cnstr) {
delete (llama_constraint_context_mirostat_v2 *) cnstr->ctx;
},
};
struct llama_constraint * llama_constraint_init_mirostat_v2_impl(float tau, float eta) {
struct llama_constraint * result = new llama_constraint {
/* .iface = */ &llama_constraint_mirostat_v2_i,
/* .ctx = */ new llama_constraint_context_mirostat_v2 {
/*.tau =*/ tau,
/*.eta =*/ eta,
/*.mu =*/ 0.0f,
/*.cur =*/ {},
},
};
return result;
}
// grammar
struct llama_constraint_context_grammar {
@ -796,13 +966,13 @@ struct llama_constraint * llama_constraint_init_grammar_impl(const struct llama_
struct llama_constraint_context_penalties {
const struct llama_vocab * vocab;
int32_t penalty_last_n;
float penalty_repeat;
float penalty_freq;
float penalty_present;
const int32_t penalty_last_n;
const float penalty_repeat;
const float penalty_freq;
const float penalty_present;
bool penalize_nl;
bool ignore_eos;
const bool penalize_nl;
const bool ignore_eos;
ring_buffer<llama_token> prev;
};
@ -980,7 +1150,6 @@ struct llama_sampler * llama_sampler_init_impl(const struct llama_vocab & vocab,
/* .rng = */ std::mt19937(params.seed),
/* .mirostat_mu = */ 0.0f,
/* .prev = */ { (size_t) params.n_prev },
/* .constraints = */ {},
/* .cur = */ {},
@ -1011,7 +1180,6 @@ struct llama_sampler * llama_sampler_cp_impl(const struct llama_sampler & smpl)
/* .rng = */ smpl.rng,
/* .mirostat_mu = */ smpl.mirostat_mu,
/* .prev = */ smpl.prev,
/* .constraints = */ {},
/* .cur = */ {},
@ -1077,74 +1245,6 @@ int llama_sampler_n_prev_impl(const struct llama_sampler & smpl) {
return smpl.prev.size();
}
llama_token llama_sampler_sample_mirostat_impl(struct llama_token_data_array * cur_p, std::mt19937 & rng, float tau, float eta, int32_t m, int32_t n_vocab, float & mu) {
llama_constraint_softmax_impl(cur_p);
// Estimate s_hat using the most probable m tokens
float s_hat = 0.0;
float sum_ti_bi = 0.0;
float sum_ti_sq = 0.0;
for (size_t i = 0; i < size_t(m - 1) && i < cur_p->size - 1; ++i) {
float t_i = logf(float(i + 2) / float(i + 1));
float b_i = logf(cur_p->data[i].p / cur_p->data[i + 1].p);
sum_ti_bi += t_i * b_i;
sum_ti_sq += t_i * t_i;
}
s_hat = sum_ti_bi / sum_ti_sq;
// Compute k from the estimated s_hat and target surprise value
float epsilon_hat = s_hat - 1;
float k = powf((epsilon_hat * powf(2, mu)) / (1 - powf(n_vocab, -epsilon_hat)), 1 / s_hat);
// Sample the next word X using top-k sampling
llama_constraint_top_k_impl(cur_p, int(k), 1);
llama_token X = llama_sampler_sample_dist_impl(cur_p, rng);
// Compute error as the difference between observed surprise and target surprise value
size_t X_idx = std::distance(cur_p->data, std::find_if(cur_p->data, cur_p->data + cur_p->size, [&](const llama_token_data & candidate) {
return candidate.id == X;
}));
float observed_surprise = -log2f(cur_p->data[X_idx].p);
float e = observed_surprise - tau;
// Update mu using the learning rate and error
mu = mu - eta * e;
return X;
}
llama_token llama_sampler_sample_mirostat_v2_impl(struct llama_token_data_array * cur_p, std::mt19937 & rng, float tau, float eta, float & mu) {
llama_constraint_softmax_impl(cur_p);
// Truncate the words with surprise values greater than mu
cur_p->size = std::distance(cur_p->data, std::find_if(cur_p->data, cur_p->data + cur_p->size, [&](const llama_token_data & candidate) {
return -log2f(candidate.p) > mu;
}));
if (cur_p->size == 0) {
cur_p->size = 1;
}
// Normalize the probabilities of the remaining words
llama_constraint_softmax_impl(cur_p);
// Sample the next word X from the remaining words
llama_token X = llama_sampler_sample_dist_impl(cur_p, rng);
// Compute error as the difference between observed surprise and target surprise value
size_t X_idx = std::distance(cur_p->data, std::find_if(cur_p->data, cur_p->data + cur_p->size, [&](const llama_token_data & candidate) {
return candidate.id == X;
}));
float observed_surprise = -log2f(cur_p->data[X_idx].p);
float e = observed_surprise - tau;
// Update mu using the learning rate and error
mu = mu - eta * e;
return X;
}
llama_token llama_sampler_sample_greedy_impl(llama_token_data_array * cur_p, bool probs) {
if (probs) {
// if probs are needed, we apply softmax to get the probabilities

43
src/llama-sampling.h
View file

@ -24,12 +24,34 @@ struct llama_constraint * llama_constraint_init_softmax_impl ();
struct llama_constraint * llama_constraint_init_top_k_impl (int32_t k, size_t min_keep);
struct llama_constraint * llama_constraint_init_top_p_impl (float p, size_t min_keep);
struct llama_constraint * llama_constraint_init_min_p_impl (float p, size_t min_keep);
struct llama_constraint * llama_constraint_init_tail_free_impl(float z, size_t min_keep);
struct llama_constraint * llama_constraint_init_tail_free_impl (float z, size_t min_keep);
struct llama_constraint * llama_constraint_init_typical_impl (float p, size_t min_keep);
struct llama_constraint * llama_constraint_init_temp_impl (float t);
struct llama_constraint * llama_constraint_init_temp_ext_impl (float t, float delta, float exponent);
struct llama_constraint * llama_constraint_init_grammar_impl (
/// @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.
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
struct llama_constraint * llama_constraint_init_mirostat_impl(
const struct llama_vocab & vocab,
float tau,
float eta,
int32_t m);
/// @details Mirostat 2.0 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.
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
struct llama_constraint * llama_constraint_init_mirostat_v2_impl(
float tau,
float eta);
struct llama_constraint * llama_constraint_init_grammar_impl(
const struct llama_vocab & vocab,
const char * grammar_str,
const char * grammar_root);
@ -67,8 +89,6 @@ struct llama_sampler {
std::mt19937 rng;
float mirostat_mu;
ring_buffer<llama_token> prev;
std::vector<llama_constraint *> constraints;
@ -97,20 +117,5 @@ void llama_sampler_apply_impl (struct llama_sampler & smpl, struct llama_token_d
llama_token llama_sampler_prev_impl (const struct llama_sampler & smpl, int ith);
int llama_sampler_n_prev_impl(const struct llama_sampler & smpl);
/// @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.
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
llama_token llama_sampler_sample_mirostat_impl (struct llama_token_data_array * cur_p, std::mt19937 & rng, float tau, float eta, int32_t m, int32_t n_vocab, float & mu);
/// @details Mirostat 2.0 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.
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
llama_token llama_sampler_sample_mirostat_v2_impl(struct llama_token_data_array * cur_p, std::mt19937 & rng, float tau, float eta, float & mu);
llama_token llama_sampler_sample_greedy_impl(struct llama_token_data_array * cur_p, bool probs);
llama_token llama_sampler_sample_dist_impl (struct llama_token_data_array * cur_p, std::mt19937 & rng);

46
src/llama.cpp
View file

@ -17939,9 +17939,7 @@ struct llama_sampler_params llama_sampler_default_params() {
struct llama_sampler_params result = {
/*.seed =*/ LLAMA_DEFAULT_SEED,
/*.n_prev =*/ 256,
/*.mirostat =*/ 0,
/*.mirostat_tau =*/ 5.00f,
/*.mirostat_eta =*/ 0.10f,
/*.type =*/ LLAMA_SAMPLER_TYPE_GREEDY,
};
return result;
@ -20641,6 +20639,14 @@ struct llama_constraint * llama_constraint_init_temp_ext(float temp, float delta
return llama_constraint_init_temp_ext_impl(temp, delta, exponent);
}
struct llama_constraint * llama_constraint_init_mirostat(const struct llama_model * model, float tau, float eta) {
return llama_constraint_init_mirostat_impl(model->vocab, tau, eta, 100);
}
struct llama_constraint * llama_constraint_init_mirostat_v2(float tau, float eta) {
return llama_constraint_init_mirostat_v2_impl(tau, eta);
}
struct llama_constraint * llama_constraint_init_grammar(const struct llama_model * model, const char * grammar_str, const char * grammar_root) {
return llama_constraint_init_grammar_impl(model->vocab, grammar_str, grammar_root);
}
@ -20741,40 +20747,6 @@ void llama_sampler_apply(struct llama_sampler * smpl, llama_token_data_array * c
llama_sampler_apply_impl(*smpl, cur_p);
}
llama_token llama_sampler_sample_mirostat(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
time_meas tm(smpl->t_sample_us);
if (cur_p == nullptr) {
cur_p = &smpl->cur_p;
}
const auto type = smpl->params.mirostat;
llama_token res;
if (type == 1) {
res = llama_sampler_sample_mirostat_impl(cur_p,
smpl->rng,
smpl->params.mirostat_tau,
smpl->params.mirostat_eta,
100,
smpl->vocab->n_vocab,
smpl->mirostat_mu);
} else if (type == 2) {
res = llama_sampler_sample_mirostat_v2_impl(cur_p,
smpl->rng,
smpl->params.mirostat_tau,
smpl->params.mirostat_eta,
smpl->mirostat_mu);
} else {
GGML_ABORT("invalid mirostat type: %d", type);
}
smpl->n_sample++;
return res;
}
llama_token llama_sampler_sample_greedy(struct llama_sampler * smpl, llama_token_data_array * cur_p, bool probs) {
time_meas tm(smpl->t_sample_us);