mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 21:39:52 +00:00
a803333a4e
* common: use enums for sampler types * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * minor : spaces --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
319 lines
12 KiB
C++
319 lines
12 KiB
C++
#include "sampling.h"
|
|
|
|
struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
|
|
struct llama_sampling_context * result = new llama_sampling_context();
|
|
|
|
result->params = params;
|
|
result->grammar = nullptr;
|
|
|
|
// if there is a grammar, parse it
|
|
if (!params.grammar.empty()) {
|
|
result->parsed_grammar = grammar_parser::parse(params.grammar.c_str());
|
|
|
|
// will be empty (default) if there are parse errors
|
|
if (result->parsed_grammar.rules.empty()) {
|
|
fprintf(stderr, "%s: failed to parse grammar\n", __func__);
|
|
delete result;
|
|
return nullptr;
|
|
}
|
|
|
|
std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
|
|
|
|
result->grammar = llama_grammar_init(
|
|
grammar_rules.data(),
|
|
grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root"));
|
|
}
|
|
|
|
result->prev.resize(params.n_prev);
|
|
|
|
return result;
|
|
}
|
|
|
|
void llama_sampling_free(struct llama_sampling_context * ctx) {
|
|
if (ctx->grammar != NULL) {
|
|
llama_grammar_free(ctx->grammar);
|
|
}
|
|
|
|
delete ctx;
|
|
}
|
|
|
|
void llama_sampling_reset(llama_sampling_context * ctx) {
|
|
if (ctx->grammar != NULL) {
|
|
llama_grammar_free(ctx->grammar);
|
|
ctx->grammar = NULL;
|
|
}
|
|
|
|
if (!ctx->parsed_grammar.rules.empty()) {
|
|
std::vector<const llama_grammar_element *> grammar_rules(ctx->parsed_grammar.c_rules());
|
|
|
|
ctx->grammar = llama_grammar_init(
|
|
grammar_rules.data(),
|
|
grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root"));
|
|
}
|
|
|
|
std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
|
|
ctx->cur.clear();
|
|
}
|
|
|
|
void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
|
|
if (dst->grammar) {
|
|
llama_grammar_free(dst->grammar);
|
|
dst->grammar = nullptr;
|
|
}
|
|
|
|
if (src->grammar) {
|
|
dst->grammar = llama_grammar_copy(src->grammar);
|
|
}
|
|
|
|
dst->prev = src->prev;
|
|
}
|
|
|
|
llama_token llama_sampling_last(llama_sampling_context * ctx) {
|
|
return ctx->prev.back();
|
|
}
|
|
|
|
std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n) {
|
|
const int size = ctx_sampling->prev.size();
|
|
|
|
n = std::min(n, size);
|
|
|
|
std::string result;
|
|
|
|
for (int i = size - n; i < size; i++) {
|
|
result += llama_token_to_piece(ctx_main, ctx_sampling->prev[i]);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
std::string llama_sampling_print(const llama_sampling_params & params) {
|
|
char result[1024];
|
|
|
|
snprintf(result, sizeof(result),
|
|
"\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",
|
|
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);
|
|
|
|
return std::string(result);
|
|
}
|
|
|
|
std::string llama_sampling_order_print(const llama_sampling_params & params) {
|
|
std::string result = "CFG -> Penalties ";
|
|
if (params.mirostat == 0) {
|
|
for (auto sampler_type : params.samplers_sequence) {
|
|
const auto sampler_type_name = sampler_type_to_name_string(sampler_type);
|
|
if (!sampler_type_name.empty()) {
|
|
result += "-> " + sampler_type_name + " ";
|
|
}
|
|
}
|
|
} else {
|
|
result += "-> mirostat ";
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
// no reasons to expose this function in header
|
|
static void sampler_queue(
|
|
struct llama_context * ctx_main,
|
|
const llama_sampling_params & params,
|
|
llama_token_data_array & cur_p,
|
|
size_t & min_keep) {
|
|
const float temp = params.temp;
|
|
const float dynatemp_range = params.dynatemp_range;
|
|
const float dynatemp_exponent = params.dynatemp_exponent;
|
|
const int32_t top_k = params.top_k;
|
|
const float top_p = params.top_p;
|
|
const float min_p = params.min_p;
|
|
const float tfs_z = params.tfs_z;
|
|
const float typical_p = params.typical_p;
|
|
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::TEMP:
|
|
if (dynatemp_range > 0) {
|
|
float dynatemp_min = std::max(0.0f, temp - dynatemp_range);
|
|
float dynatemp_max = std::max(0.0f, temp + dynatemp_range);
|
|
llama_sample_entropy(ctx_main, &cur_p, dynatemp_min, dynatemp_max, dynatemp_exponent);
|
|
} else {
|
|
llama_sample_temp(ctx_main, &cur_p, temp);
|
|
}
|
|
break;
|
|
default : break;
|
|
}
|
|
}
|
|
}
|
|
|
|
static llama_token llama_sampling_sample_impl(
|
|
struct llama_sampling_context * ctx_sampling,
|
|
struct llama_context * ctx_main,
|
|
struct llama_context * ctx_cfg,
|
|
const int idx,
|
|
bool is_resampling) { // Add a parameter to indicate if we are resampling
|
|
const llama_sampling_params & params = ctx_sampling->params;
|
|
|
|
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
|
|
|
|
const float temp = params.temp;
|
|
const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
|
|
const float penalty_repeat = params.penalty_repeat;
|
|
const float penalty_freq = params.penalty_freq;
|
|
const float penalty_present = params.penalty_present;
|
|
const int mirostat = params.mirostat;
|
|
const float mirostat_tau = params.mirostat_tau;
|
|
const float mirostat_eta = params.mirostat_eta;
|
|
const bool penalize_nl = params.penalize_nl;
|
|
|
|
auto & prev = ctx_sampling->prev;
|
|
auto & cur = ctx_sampling->cur;
|
|
|
|
llama_token id = 0;
|
|
|
|
// Get a pointer to the logits
|
|
float * logits = llama_get_logits_ith(ctx_main, idx);
|
|
|
|
// Declare original_logits at the beginning of the function scope
|
|
std::vector<float> original_logits;
|
|
|
|
if (!is_resampling) {
|
|
// Only make a copy of the original logits if we are not in the resampling phase, not sure if I actually have to do this.
|
|
original_logits = std::vector<float>(logits, logits + llama_n_vocab(llama_get_model(ctx_main)));
|
|
}
|
|
|
|
// apply params.logit_bias map
|
|
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
|
|
logits[it->first] += it->second;
|
|
}
|
|
|
|
if (ctx_cfg) {
|
|
float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx);
|
|
llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
|
|
}
|
|
|
|
cur.clear();
|
|
|
|
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
|
cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
|
|
}
|
|
|
|
llama_token_data_array cur_p = { cur.data(), cur.size(), false };
|
|
|
|
// apply penalties
|
|
const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
|
|
const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);
|
|
if (penalty_tokens_used_size) {
|
|
const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
|
|
|
|
llama_sample_repetition_penalties(ctx_main, &cur_p,
|
|
penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size,
|
|
penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present);
|
|
|
|
if (!penalize_nl) {
|
|
for (size_t idx = 0; idx < cur_p.size; idx++) {
|
|
if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
|
|
cur_p.data[idx].logit = nl_logit;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// If we are in the resampling phase, apply grammar checks before sampling logic
|
|
if (is_resampling && ctx_sampling->grammar != NULL) {
|
|
llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
|
|
}
|
|
|
|
if (temp < 0.0) {
|
|
// greedy sampling, with probs
|
|
llama_sample_softmax(ctx_main, &cur_p);
|
|
id = cur_p.data[0].id;
|
|
} else if (temp == 0.0) {
|
|
// greedy sampling, no probs
|
|
id = llama_sample_token_greedy(ctx_main, &cur_p);
|
|
} else {
|
|
if (mirostat == 1) {
|
|
const int mirostat_m = 100;
|
|
llama_sample_temp(ctx_main, &cur_p, temp);
|
|
id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);
|
|
} 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 {
|
|
// temperature sampling
|
|
size_t min_keep = std::max(1, params.n_probs);
|
|
|
|
sampler_queue(ctx_main, params, cur_p, min_keep);
|
|
|
|
id = llama_sample_token(ctx_main, &cur_p);
|
|
|
|
//{
|
|
// const int n_top = 10;
|
|
// LOG("top %d candidates:\n", n_top);
|
|
|
|
// for (int i = 0; i < n_top; i++) {
|
|
// const llama_token id = cur_p.data[i].id;
|
|
// (void)id; // To avoid a warning that id is unused when logging is disabled.
|
|
// LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);
|
|
// }
|
|
//}
|
|
|
|
LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
|
|
}
|
|
}
|
|
|
|
if (ctx_sampling->grammar != NULL && !is_resampling) {
|
|
// Create an array with a single token data element for the sampled id
|
|
llama_token_data single_token_data = {id, logits[id], 0.0f};
|
|
llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
|
|
|
|
// Apply grammar constraints to the single token
|
|
llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar);
|
|
|
|
// Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
|
|
bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
|
|
|
|
// If the token is not valid according to the grammar, perform resampling
|
|
if (!is_valid) {
|
|
LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str());
|
|
|
|
// Restore logits from the copy
|
|
std::copy(original_logits.begin(), original_logits.end(), logits);
|
|
|
|
return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true); // Pass true for is_resampling
|
|
}
|
|
}
|
|
|
|
return id;
|
|
}
|
|
|
|
llama_token llama_sampling_sample(
|
|
struct llama_sampling_context * ctx_sampling,
|
|
struct llama_context * ctx_main,
|
|
struct llama_context * ctx_cfg,
|
|
const int idx) {
|
|
// Call the implementation function with is_resampling set to false by default
|
|
return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
|
|
}
|
|
|
|
void llama_sampling_accept(
|
|
struct llama_sampling_context * ctx_sampling,
|
|
struct llama_context * ctx_main,
|
|
llama_token id,
|
|
bool apply_grammar) {
|
|
ctx_sampling->prev.erase(ctx_sampling->prev.begin());
|
|
ctx_sampling->prev.push_back(id);
|
|
|
|
if (ctx_sampling->grammar != NULL && apply_grammar) {
|
|
llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id);
|
|
}
|
|
}
|