mirror of
https://github.com/ggml-org/llama.cpp.git
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365 lines
14 KiB
C++
365 lines
14 KiB
C++
#define LLAMA_API_INTERNAL
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#include "sampling.h"
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#include <random>
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struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
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struct llama_sampling_context * result = new llama_sampling_context();
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result->params = params;
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result->grammar = nullptr;
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// if there is a grammar, parse it
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if (!params.grammar.empty()) {
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result->parsed_grammar = grammar_parser::parse(params.grammar.c_str());
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// will be empty (default) if there are parse errors
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if (result->parsed_grammar.rules.empty()) {
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fprintf(stderr, "%s: failed to parse grammar\n", __func__);
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delete result;
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return nullptr;
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}
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// Ensure that there is a "root" node.
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if (result->parsed_grammar.symbol_ids.find("root") == result->parsed_grammar.symbol_ids.end()) {
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fprintf(stderr, "%s: grammar does not contain a 'root' symbol\n", __func__);
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delete result;
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return nullptr;
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}
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std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
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result->grammar = llama_grammar_init(
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grammar_rules.data(),
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grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root"));
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}
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result->prev.resize(params.n_prev);
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llama_sampling_set_rng_seed(result, params.seed);
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return result;
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}
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void llama_sampling_free(struct llama_sampling_context * ctx) {
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if (ctx->grammar != NULL) {
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llama_grammar_free(ctx->grammar);
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}
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delete ctx;
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}
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void llama_sampling_reset(llama_sampling_context * ctx) {
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if (ctx->grammar != NULL) {
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llama_grammar_free(ctx->grammar);
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ctx->grammar = NULL;
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}
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if (!ctx->parsed_grammar.rules.empty()) {
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std::vector<const llama_grammar_element *> grammar_rules(ctx->parsed_grammar.c_rules());
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ctx->grammar = llama_grammar_init(
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grammar_rules.data(),
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grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root"));
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}
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std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
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ctx->cur.clear();
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}
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void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) {
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if (seed == LLAMA_DEFAULT_SEED) {
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seed = time(NULL);
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}
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ctx->rng.seed(seed);
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}
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void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
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if (dst->grammar) {
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llama_grammar_free(dst->grammar);
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dst->grammar = nullptr;
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}
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if (src->grammar) {
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dst->grammar = llama_grammar_copy(src->grammar);
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}
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dst->prev = src->prev;
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}
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llama_token llama_sampling_last(llama_sampling_context * ctx) {
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return ctx->prev.back();
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}
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std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n) {
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const int size = ctx_sampling->prev.size();
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n = std::min(n, size);
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std::string result;
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for (int i = size - n; i < size; i++) {
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result += llama_token_to_piece(ctx_main, ctx_sampling->prev[i]);
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}
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return result;
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}
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std::string llama_sampling_print(const llama_sampling_params & params) {
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char result[1024];
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snprintf(result, sizeof(result),
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"\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
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"\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
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"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
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params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present,
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params.top_k, params.tfs_z, params.top_p, params.min_p, params.typical_p, params.temp,
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params.mirostat, params.mirostat_eta, params.mirostat_tau);
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return std::string(result);
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}
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std::string llama_sampling_order_print(const llama_sampling_params & params) {
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std::string result = "CFG -> Penalties ";
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if (params.mirostat == 0) {
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for (auto sampler_type : params.samplers_sequence) {
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const auto sampler_type_name = sampler_type_to_name_string(sampler_type);
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if (!sampler_type_name.empty()) {
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result += "-> " + sampler_type_name + " ";
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}
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}
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} else {
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result += "-> mirostat ";
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}
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return result;
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}
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// no reasons to expose this function in header
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static void sampler_queue(
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struct llama_context * ctx_main,
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const llama_sampling_params & params,
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llama_token_data_array & cur_p,
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size_t min_keep) {
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const float temp = params.temp;
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const float dynatemp_range = params.dynatemp_range;
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const float dynatemp_exponent = params.dynatemp_exponent;
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const int32_t top_k = params.top_k;
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const float top_p = params.top_p;
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const float min_p = params.min_p;
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const float tfs_z = params.tfs_z;
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const float typical_p = params.typical_p;
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const std::vector<llama_sampler_type> & samplers_sequence = params.samplers_sequence;
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for (auto sampler_type : samplers_sequence) {
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switch (sampler_type) {
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case llama_sampler_type::TOP_K : llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break;
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case llama_sampler_type::TFS_Z : llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break;
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case llama_sampler_type::TYPICAL_P: llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break;
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case llama_sampler_type::TOP_P : llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break;
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case llama_sampler_type::MIN_P : llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break;
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case llama_sampler_type::TEMPERATURE:
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if (dynatemp_range > 0) {
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float dynatemp_min = std::max(0.0f, temp - dynatemp_range);
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float dynatemp_max = std::max(0.0f, temp + dynatemp_range);
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llama_sample_entropy(ctx_main, &cur_p, dynatemp_min, dynatemp_max, dynatemp_exponent);
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} else {
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llama_sample_temp(ctx_main, &cur_p, temp);
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}
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break;
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default : break;
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}
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}
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}
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static llama_token llama_sampling_sample_impl(
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struct llama_sampling_context * ctx_sampling,
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struct llama_context * ctx_main,
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struct llama_context * ctx_cfg,
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const int idx,
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bool is_resampling) { // Add a parameter to indicate if we are resampling
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const llama_sampling_params & params = ctx_sampling->params;
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const float temp = params.temp;
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const int mirostat = params.mirostat;
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const float mirostat_tau = params.mirostat_tau;
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const float mirostat_eta = params.mirostat_eta;
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std::vector<float> original_logits;
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auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, !is_resampling, &original_logits);
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if (!is_resampling) {
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GGML_ASSERT(!original_logits.empty());
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}
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llama_token id = 0;
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// Get a pointer to the logits
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float * logits = llama_get_logits_ith(ctx_main, idx);
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if (temp < 0.0) {
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// greedy sampling, with probs
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llama_sample_softmax(ctx_main, &cur_p);
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id = cur_p.data[0].id;
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} else if (temp == 0.0) {
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// greedy sampling, no probs
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id = llama_sample_token_greedy(ctx_main, &cur_p);
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} else {
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if (mirostat == 1) {
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const int mirostat_m = 100;
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llama_sample_temp(ctx_main, &cur_p, temp);
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id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);
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} else if (mirostat == 2) {
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llama_sample_temp(ctx_main, &cur_p, temp);
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id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
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} else {
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// temperature sampling
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size_t min_keep = std::max(1, params.min_keep);
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sampler_queue(ctx_main, params, cur_p, min_keep);
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id = llama_sample_token_with_rng(ctx_main, &cur_p, ctx_sampling->rng);
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//{
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// const int n_top = 10;
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// LOG("top %d candidates:\n", n_top);
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// for (int i = 0; i < n_top; i++) {
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// const llama_token id = cur_p.data[i].id;
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// (void)id; // To avoid a warning that id is unused when logging is disabled.
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// LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);
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// }
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//}
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//LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
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}
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}
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if (ctx_sampling->grammar != NULL && !is_resampling) {
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// Create an array with a single token data element for the sampled id
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llama_token_data single_token_data = {id, logits[id], 0.0f};
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llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
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// Apply grammar constraints to the single token
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llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar);
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// Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
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bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
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// If the token is not valid according to the grammar, perform resampling
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if (!is_valid) {
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LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str());
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// Restore logits from the copy
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std::copy(original_logits.begin(), original_logits.end(), logits);
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return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true); // Pass true for is_resampling
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}
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}
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return id;
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}
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static llama_token_data_array llama_sampling_prepare_impl(
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struct llama_sampling_context * ctx_sampling,
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struct llama_context * ctx_main,
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struct llama_context * ctx_cfg,
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const int idx,
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bool apply_grammar,
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std::vector<float> * original_logits) {
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const llama_sampling_params & params = ctx_sampling->params;
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const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
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const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
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const float penalty_repeat = params.penalty_repeat;
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const float penalty_freq = params.penalty_freq;
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const float penalty_present = params.penalty_present;
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const bool penalize_nl = params.penalize_nl;
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auto & prev = ctx_sampling->prev;
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auto & cur = ctx_sampling->cur;
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// Get a pointer to the logits
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float * logits = llama_get_logits_ith(ctx_main, idx);
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if (apply_grammar && original_logits != NULL) {
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// Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this.
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*original_logits = {logits, logits + llama_n_vocab(llama_get_model(ctx_main))};
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}
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// apply params.logit_bias map
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for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
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logits[it->first] += it->second;
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}
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if (ctx_cfg) {
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float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx);
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llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
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}
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cur.clear();
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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}
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llama_token_data_array cur_p = { cur.data(), cur.size(), false };
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// apply penalties
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const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
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const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);
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if (penalty_tokens_used_size) {
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const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
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llama_sample_repetition_penalties(ctx_main, &cur_p,
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penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size,
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penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present);
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if (!penalize_nl) {
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for (size_t idx = 0; idx < cur_p.size; idx++) {
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if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
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cur_p.data[idx].logit = nl_logit;
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break;
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}
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}
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}
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}
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// apply grammar checks before sampling logic
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if (apply_grammar && ctx_sampling->grammar != NULL) {
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llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
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}
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return cur_p;
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}
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llama_token llama_sampling_sample(
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struct llama_sampling_context * ctx_sampling,
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struct llama_context * ctx_main,
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struct llama_context * ctx_cfg,
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const int idx) {
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// Call the implementation function with is_resampling set to false by default
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return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
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}
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llama_token_data_array llama_sampling_prepare(
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struct llama_sampling_context * ctx_sampling,
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struct llama_context * ctx_main,
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struct llama_context * ctx_cfg,
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const int idx,
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bool apply_grammar,
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std::vector<float> * original_logits) {
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return llama_sampling_prepare_impl(ctx_sampling,ctx_main, ctx_cfg, idx, apply_grammar, original_logits);
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}
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void llama_sampling_accept(
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struct llama_sampling_context * ctx_sampling,
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struct llama_context * ctx_main,
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llama_token id,
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bool apply_grammar) {
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ctx_sampling->prev.erase(ctx_sampling->prev.begin());
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ctx_sampling->prev.push_back(id);
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if (ctx_sampling->grammar != NULL && apply_grammar) {
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llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id);
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}
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}
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