mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-26 17:20:01 +00:00
b6bc5eedad
* server : integrate speculative decoding
* server: Fix field names
* server: fix include, whitespace
* fix compile errors in speculative.cpp
* add llama_sampling_sample_and_accept_n to sampling
* finish porting speculative decoding in server
* port functions from common/speculative, common/sampling
* remove arg
* fix function names
* init params_dft to none
* correct value for n_ctx
* prefix kv cache tensors with model name to avoid conflict
* fix call arguments
* fix spec decoding args
* correct slot.id
* use n_max
* port the rest of sampling funcs
* fix func arguments
* slot.id starts at 1?
* Revert "prefix kv cache tensors with model name to avoid conflict"
This reverts commit fbd5dfd866.
* disable draft logging
* disable logging in speculative.cpp
in mainline, these would be LOG_DEBUG, but since ik_llama doesnt support
it, logging is disabled entirely
* add more draft model parameters
* fix
* pass flash_attn
* add speculative params for parity
* set speculative params in launch_slot_with_task instead
555 lines
21 KiB
C++
555 lines
21 KiB
C++
#define LLAMA_API_INTERNAL
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#include "sampling.h"
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#include "llama-vocab.h"
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#include <random>
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struct llama_sampling_context * llama_sampling_init(const struct llama_vocab* vocab, 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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struct llama_grammar * 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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if (grammar == nullptr) {
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throw std::runtime_error("Failed to initialize llama_grammar");
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}
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result->grammar = grammar;
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}
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result->prev.resize(params.n_prev);
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result->n_valid = 0;
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// init DRY
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for (const auto& cnstr : params.samplers_sequence)
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{
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switch (cnstr)
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{
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case llama_sampler_type::DRY:
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{
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std::vector<const char*> c_breakers;
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c_breakers.reserve(params.dry_sequence_breakers.size());
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for (const auto& str : params.dry_sequence_breakers)
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{
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c_breakers.push_back(str.c_str());
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}
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result->smpl=llama_sampler_init_dry(vocab, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size());
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break;
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}
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default:
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break;
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}
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}
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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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if (ctx->smpl !=NULL)
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llama_sampler_dry_free(ctx->smpl);
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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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struct llama_grammar * 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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if (grammar == nullptr) {
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throw std::runtime_error("Failed to initialize llama_grammar");
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}
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ctx->grammar = grammar;
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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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ctx->n_valid = 0;
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llama_sampler_dry_reset(ctx->smpl);
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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 = std::random_device{}();
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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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dst->smpl = llama_sampler_dry_clone(src->smpl);
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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\n"
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"\txtc_probability = %.3f, xtc_threshold = %.3f, top_n_sigma = %.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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params.xtc_probability, params.xtc_threshold, params.top_n_sigma);
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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 = llama_sampling_type_to_str(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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std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) {
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switch (sampler_type) {
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case llama_sampler_type::DRY: return "dry";
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case llama_sampler_type::TOP_K: return "top_k";
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case llama_sampler_type::TFS_Z: return "tfs_z";
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case llama_sampler_type::TYPICAL_P: return "typical_p";
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case llama_sampler_type::TOP_P: return "top_p";
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case llama_sampler_type::MIN_P: return "min_p";
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case llama_sampler_type::TEMPERATURE: return "temperature";
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case llama_sampler_type::XTC : return "xtc";
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case llama_sampler_type::TOP_N_SIGMA: return "top_n_sigma";
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default : return "";
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}
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}
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std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
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std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
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{"dry", llama_sampler_type::DRY},
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{"top_k", llama_sampler_type::TOP_K},
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{"top_p", llama_sampler_type::TOP_P},
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{"typical_p", llama_sampler_type::TYPICAL_P},
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{"min_p", llama_sampler_type::MIN_P},
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{"tfs_z", llama_sampler_type::TFS_Z},
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{"xtc", llama_sampler_type::XTC},
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{"top_n_sigma", llama_sampler_type::TOP_N_SIGMA},
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{"temperature", llama_sampler_type::TEMPERATURE}
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};
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// since samplers names are written multiple ways
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// make it ready for both system names and input names
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std::unordered_map<std::string, llama_sampler_type> sampler_alt_name_map {
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{"dry", llama_sampler_type::DRY},
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{"top-k", llama_sampler_type::TOP_K},
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{"top-p", llama_sampler_type::TOP_P},
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{"nucleus", llama_sampler_type::TOP_P},
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{"typical-p", llama_sampler_type::TYPICAL_P},
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{"typical", llama_sampler_type::TYPICAL_P},
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{"min-p", llama_sampler_type::MIN_P},
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{"tfs-z", llama_sampler_type::TFS_Z},
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{"tfs", llama_sampler_type::TFS_Z},
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{"xtc", llama_sampler_type::XTC},
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{"top-n-sigma", llama_sampler_type::TOP_N_SIGMA},
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{"temp", llama_sampler_type::TEMPERATURE}
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};
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std::vector<llama_sampler_type> sampler_types;
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sampler_types.reserve(names.size());
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for (const auto & name : names)
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{
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auto sampler_item = sampler_canonical_name_map.find(name);
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if (sampler_item != sampler_canonical_name_map.end())
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{
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sampler_types.push_back(sampler_item->second);
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}
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else
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{
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if (allow_alt_names)
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{
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sampler_item = sampler_alt_name_map.find(name);
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if (sampler_item != sampler_alt_name_map.end())
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{
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sampler_types.push_back(sampler_item->second);
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}
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}
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}
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}
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return sampler_types;
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}
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std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::string & names_string) {
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std::unordered_map<char, llama_sampler_type> sampler_name_map {
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{'d', llama_sampler_type::DRY},
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{'k', llama_sampler_type::TOP_K},
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{'p', llama_sampler_type::TOP_P},
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{'y', llama_sampler_type::TYPICAL_P},
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{'m', llama_sampler_type::MIN_P},
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{'f', llama_sampler_type::TFS_Z},
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{'x', llama_sampler_type::XTC},
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{'n', llama_sampler_type::TOP_N_SIGMA},
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{'t', llama_sampler_type::TEMPERATURE}
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};
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std::vector<llama_sampler_type> sampler_types;
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sampler_types.reserve(names_string.size());
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for (const auto & c : names_string) {
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const auto sampler_item = sampler_name_map.find(c);
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if (sampler_item != sampler_name_map.end()) {
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sampler_types.push_back(sampler_item->second);
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}
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}
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return sampler_types;
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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_sampling_context * ctx_sampling,
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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 float xtc_probability = params.xtc_probability;
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const float xtc_threshold = params.xtc_threshold;
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const float top_n_sigma = params.top_n_sigma;
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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::DRY : llama_sample_dry (ctx_main, ctx_sampling->smpl, &cur_p); break;
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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::XTC : llama_sample_xtc (ctx_main, &cur_p, xtc_probability, xtc_threshold, min_keep); break;
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case llama_sampler_type::TOP_N_SIGMA: llama_sample_top_n_sigma(ctx_main, &cur_p, top_n_sigma); 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) {
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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, /* apply_grammar= */ is_resampling, &original_logits);
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if (ctx_sampling->grammar != NULL && !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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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,ctx_sampling, 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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// Get a pointer to the logits
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float * logits = llama_get_logits_ith(ctx_main, idx);
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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_grammar_sample(ctx_sampling->grammar, ctx_main, &single_token_data_array);
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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, /* is_resampling= */ true);
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}
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}
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ctx_sampling->n_valid = temp == 0.0f ? 0 : cur_p.size;
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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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|
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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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|
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if (ctx_sampling->grammar != NULL && !apply_grammar) {
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GGML_ASSERT(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 + n_vocab};
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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.resize(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
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}
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|
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ctx_sampling->cur_p = { cur.data(), cur.size(), false };
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|
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llama_token_data_array & cur_p = ctx_sampling->cur_p;
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|
|
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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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|
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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++) {
|
|
if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
|
|
cur_p.data[idx].logit = nl_logit;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// apply grammar checks before sampling logic
|
|
if (apply_grammar && ctx_sampling->grammar != NULL) {
|
|
llama_grammar_sample(ctx_sampling->grammar, ctx_main, &cur_p);
|
|
}
|
|
|
|
return cur_p;
|
|
}
|
|
|
|
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, /* is_resampling= */ false);
|
|
}
|
|
|
|
llama_token_data_array llama_sampling_prepare(
|
|
struct llama_sampling_context * ctx_sampling,
|
|
struct llama_context * ctx_main,
|
|
struct llama_context * ctx_cfg,
|
|
const int idx,
|
|
bool apply_grammar,
|
|
std::vector<float> * original_logits) {
|
|
return llama_sampling_prepare_impl(ctx_sampling,ctx_main, ctx_cfg, idx, apply_grammar, original_logits);
|
|
}
|
|
|
|
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_sampling->grammar, ctx_main, id);
|
|
}
|
|
if (ctx_sampling->smpl) {
|
|
llama_sampler_dry_accept(ctx_sampling->smpl, id);
|
|
}
|
|
}
|
|
|
|
llama_token_data_array * llama_sampling_get_candidates(struct llama_sampling_context * ctx_sampling) {
|
|
return &ctx_sampling->cur_p;
|
|
}
|
|
|
|
std::vector<llama_token> llama_sampling_sample_and_accept_n(struct llama_sampling_context * gsmpl, struct llama_context * ctx, const std::vector<llama_token> & draft) {
|
|
std::vector<int> idxs(draft.size() + 1);
|
|
for (size_t i = 0; i < idxs.size(); ++i) {
|
|
idxs[i] = i;
|
|
}
|
|
|
|
return llama_sampling_sample_and_accept_n(gsmpl, ctx, idxs, draft);
|
|
}
|
|
|
|
std::vector<llama_token> llama_sampling_sample_and_accept_n(struct llama_sampling_context * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const std::vector<llama_token> & draft) {
|
|
GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
|
|
|
|
std::vector<llama_token> result;
|
|
result.reserve(idxs.size());
|
|
|
|
size_t i = 0;
|
|
for (; i < draft.size(); i++) {
|
|
const llama_token id = llama_sampling_sample(gsmpl, ctx, nullptr, idxs[i]);
|
|
|
|
llama_sampling_accept(gsmpl, ctx, id, true);
|
|
|
|
result.push_back(id);
|
|
|
|
if (draft[i] != id) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (i == draft.size()) {
|
|
const llama_token id = llama_sampling_sample(gsmpl, ctx, nullptr, idxs[i]);
|
|
|
|
llama_sampling_accept(gsmpl, ctx, id, true);
|
|
|
|
result.push_back(id);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|