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binding.cpp
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binding.cpp
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#include "common.h"
#include "llama.h"
#include "binding.h"
#include "grammar-parser.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <sstream>
#include <iostream>
#include <string>
#include <vector>
#include <sstream>
#include <regex>
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined (_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#include <windows.h>
#include <signal.h>
#endif
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
void sigint_handler(int signo) {
if (signo == SIGINT) {
_exit(130);
}
}
#endif
int get_embeddings(void* params_ptr, void* state_pr, float * res_embeddings) {
gpt_params* params_p = (gpt_params*) params_ptr;
llama_binding_state* state = (llama_binding_state*) state_pr;
llama_context* ctx = state->ctx;
gpt_params params = *params_p;
if (params.seed <= 0) {
params.seed = time(NULL);
}
// no need for a rng
// std::mt19937 rng(params.seed);
int n_past = 0;
const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
// tokenize the prompt
auto embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
if (embd_inp.size() > 0) {
if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
}
const int n_embd = llama_n_embd(ctx);
const auto embeddings = llama_get_embeddings(ctx);
for (int i = 0; i < n_embd; i++) {
res_embeddings[i]=embeddings[i];
}
return 0;
}
int get_token_embeddings(void* params_ptr, void* state_pr, int *tokens, int tokenSize, float * res_embeddings) {
gpt_params* params_p = (gpt_params*) params_ptr;
llama_binding_state* state = (llama_binding_state*) state_pr;
llama_context* ctx = state->ctx;
gpt_params params = *params_p;
for (int i = 0; i < tokenSize; i++) {
auto token_str = llama_token_to_piece(ctx, tokens[i]);
std::vector<std::string> my_vector;
std::string str_token(token_str); // create a new std::string from the char*
params_p->prompt += str_token;
}
return get_embeddings(params_ptr,state_pr,res_embeddings);
}
int eval(void* params_ptr,void* state_pr,char *text) {
gpt_params* params_p = (gpt_params*) params_ptr;
llama_binding_state* state = (llama_binding_state*) state_pr;
llama_context* ctx = state->ctx;
auto n_past = 0;
auto last_n_tokens_data = std::vector<llama_token>(params_p->repeat_last_n, 0);
auto tokens = std::vector<llama_token>(params_p->n_ctx);
std::string str = std::string(text);
auto n_prompt_tokens = llama_tokenize(ctx, str.data(), str.length(), tokens.data(), tokens.size(), true);
if (n_prompt_tokens < 1) {
fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
return 1;
}
// evaluate prompt
return llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past, params_p->n_threads);
}
static llama_context ** g_ctx;
static gpt_params * g_params;
static std::vector<llama_token> * g_input_tokens;
static std::ostringstream * g_output_ss;
static std::vector<llama_token> * g_output_tokens;
int llama_predict(void* params_ptr, void* state_pr, char* result, bool debug) {
gpt_params* params_p = (gpt_params*) params_ptr;
llama_binding_state* state = (llama_binding_state*) state_pr;
llama_context* ctx = state->ctx;
gpt_params params = *params_p;
g_params = ¶ms;
const int n_ctx = llama_n_ctx(ctx);
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
// no need for a rng
// std::mt19937 rng(params.seed);
if (params.rope_freq_base != 10000.0) {
fprintf(stderr, "%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base);
}
if (params.rope_freq_scale != 1.0) {
fprintf(stderr, "%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
}
if (params.n_ctx > 2048) {
// TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048
fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx);
} else if (params.n_ctx < 8) {
fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);
params.n_ctx = 8;
}
llama_context * ctx_guidance = NULL;
g_ctx = &ctx;
if (params.cfg_scale > 1.f) {
struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
ctx_guidance = llama_new_context_with_model(state->model, lparams);
}
std::string path_session = params.path_prompt_cache;
std::vector<llama_token> session_tokens;
if (!path_session.empty()) {
if (debug) {
fprintf(stderr, "%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
}
// fopen to check for existing session
FILE * fp = std::fopen(path_session.c_str(), "rb");
if (fp != NULL) {
std::fclose(fp);
session_tokens.resize(n_ctx);
size_t n_token_count_out = 0;
if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
return 1;
}
session_tokens.resize(n_token_count_out);
// no need to set the seed here --- we'll always set it later
// llama_set_rng_seed(ctx, params.seed);
if (debug) {
fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
}
} else {
if (debug) {
fprintf(stderr, "%s: session file does not exist, will create\n", __func__);
}
}
}
const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
std::vector<llama_token> embd_inp;
if ( !params.prompt.empty() || session_tokens.empty() ) {
embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
} else {
embd_inp = session_tokens;
}
// Should not run without any tokens
if (embd_inp.empty()) {
embd_inp.push_back(llama_token_bos(ctx));
}
// Tokenize negative prompt
std::vector<llama_token> guidance_inp;
int guidance_offset = 0;
int original_prompt_len = 0;
if (ctx_guidance) {
guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
original_prompt_len = original_inp.size();
guidance_offset = (int)guidance_inp.size() - original_prompt_len;
}
if ((int) embd_inp.size() > n_ctx - 4) {
fprintf(stderr, "%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
return 1;
}
// debug message about similarity of saved session, if applicable
size_t n_matching_session_tokens = 0;
if (session_tokens.size() > 0) {
for (llama_token id : session_tokens) {
if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
break;
}
n_matching_session_tokens++;
}
if (debug) {
if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
fprintf(stderr, "%s: using full prompt from session file\n", __func__);
} else if (n_matching_session_tokens >= embd_inp.size()) {
fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__);
} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
__func__, n_matching_session_tokens, embd_inp.size());
} else {
fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
__func__, n_matching_session_tokens, embd_inp.size());
}
}
}
// if we will use the cache for the full prompt without reaching the end of the cache, force
// reevaluation of the last token token to recalculate the cached logits
if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
session_tokens.size() > embd_inp.size()) {
session_tokens.resize(embd_inp.size() - 1);
}
// number of tokens to keep when resetting context
if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) {
params.n_keep = (int)embd_inp.size();
}
if (debug && ctx_guidance) {
fprintf(stderr, "\n");
fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
for (int i = 0; i < (int) guidance_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
}
}
struct llama_grammar * grammar = NULL;
grammar_parser::parse_state parsed_grammar;
if (!params.grammar.empty()) {
parsed_grammar = grammar_parser::parse(params.grammar.c_str());
// will be empty (default) if there are parse errors
if (parsed_grammar.rules.empty()) {
return 1;
}
fprintf(stderr, "%s: grammar:\n", __func__);
grammar_parser::print_grammar(stderr, parsed_grammar);
fprintf(stderr, "\n");
{
auto it = params.logit_bias.find(llama_token_eos(ctx));
if (it != params.logit_bias.end() && it->second == -INFINITY) {
fprintf(stderr,
"%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
}
}
std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
// TODO: replace with ring-buffer
std::vector<llama_token> last_tokens(n_ctx);
std::fill(last_tokens.begin(), last_tokens.end(), 0);
bool is_antiprompt = false;
bool input_echo = true;
bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size();
int n_past = 0;
int n_remain = params.n_predict;
int n_consumed = 0;
int n_session_consumed = 0;
int n_past_guidance = 0;
std::vector<int> input_tokens; g_input_tokens = &input_tokens;
std::vector<int> output_tokens; g_output_tokens = &output_tokens;
std::ostringstream output_ss; g_output_ss = &output_ss;
// the first thing we will do is to output the prompt, so set color accordingly
std::vector<llama_token> embd;
std::vector<llama_token> embd_guidance;
const int n_vocab = llama_n_vocab(ctx);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
std::string res = "";
{
const std::vector<llama_token> tmp = { llama_token_bos(ctx), };
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
llama_reset_timings(ctx);
}
// set the seed before actually predicting
llama_set_rng_seed(ctx, params.seed);
while (n_remain != 0) {
// predict
if (embd.size() > 0) {
// Note: n_ctx - 4 here is to match the logic for commandline prompt handling via
// --prompt or --file which uses the same value.
auto max_embd_size = n_ctx - 4;
// Ensure the input doesn't exceed the context size by truncating embd if necessary.
if ((int)embd.size() > max_embd_size) {
const int skipped_tokens = (int) embd.size() - max_embd_size;
printf("<<input too long: skipped %zu token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
embd.resize(max_embd_size);
}
// infinite text generation via context swapping
// if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
const int n_left = n_past - params.n_keep;
// always keep the first token - BOS
n_past = std::max(1, params.n_keep);
n_past_guidance = std::max(1, params.n_keep + guidance_offset);
// insert n_left/2 tokens at the start of embd from last_tokens
embd.insert(embd.begin(), last_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_tokens.end() - embd.size());
// stop saving session if we run out of context
path_session.clear();
}
// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
if (n_session_consumed < (int) session_tokens.size()) {
size_t i = 0;
for ( ; i < embd.size(); i++) {
if (embd[i] != session_tokens[n_session_consumed]) {
session_tokens.resize(n_session_consumed);
break;
}
n_past++;
n_session_consumed++;
if (n_session_consumed >= (int) session_tokens.size()) {
++i;
break;
}
}
if (i > 0) {
embd.erase(embd.begin(), embd.begin() + i);
}
}
// evaluate tokens in batches
// embd is typically prepared beforehand to fit within a batch, but not always
if (ctx_guidance) {
int input_size = 0;
llama_token* input_buf = NULL;
if (n_past_guidance < (int) guidance_inp.size()) {
// Guidance context should have the same data with these modifications:
//
// * Replace the initial prompt
// * Shift everything by guidance_offset
embd_guidance = guidance_inp;
if (embd.begin() + original_prompt_len < embd.end()) {
embd_guidance.insert(
embd_guidance.end(),
embd.begin() + original_prompt_len,
embd.end()
);
}
input_buf = embd_guidance.data();
input_size = embd_guidance.size();
//fprintf(stderr, "\n---------------------\n");
//for (int i = 0; i < (int) embd_guidance.size(); i++) {
//fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_guidance[i]));
//}
//fprintf(stderr, "\n---------------------\n");
} else {
input_buf = embd.data();
input_size = embd.size();
}
for (int i = 0; i < input_size; i += params.n_batch) {
int n_eval = std::min(input_size - i, params.n_batch);
if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
n_past_guidance += n_eval;
}
}
for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
int n_eval = (int) embd.size() - i;
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
n_past += n_eval;
}
if (embd.size() > 0 && !path_session.empty()) {
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
n_session_consumed = session_tokens.size();
}
}
embd.clear();
embd_guidance.clear();
if ((int) embd_inp.size() <= n_consumed) {
// optionally save the session on first sample (for faster prompt loading next time)
if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
need_to_save_session = false;
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
}
const llama_token id = llama_sample_token_binding(ctx, ctx_guidance, grammar, params_p, last_tokens, candidates);
//const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates);
last_tokens.erase(last_tokens.begin());
last_tokens.push_back(id);
// add it to the context
embd.push_back(id);
// decrement remaining sampling budget
--n_remain;
// call the token callback, no need to check if one is actually registered, that will
// be handled on the Go side.
auto token_str = llama_token_to_piece(ctx, id);
if (!tokenCallback(state_pr, (char*)token_str.c_str())) {
break;
}
} else {
// some user input remains from prompt or interaction, forward it to processing
while ((int) embd_inp.size() > n_consumed) {
embd.push_back(embd_inp[n_consumed]);
last_tokens.erase(last_tokens.begin());
last_tokens.push_back(embd_inp[n_consumed]);
++n_consumed;
if ((int) embd.size() >= params.n_batch) {
break;
}
}
}
for (auto id : embd) {
const std::string token_str = llama_token_to_piece(ctx, id);
if (debug) {
printf("%s", token_str.c_str());
}
if (embd.size() > 1) {
input_tokens.push_back(id);
} else {
output_tokens.push_back(id);
output_ss << token_str;
}
res += llama_token_to_piece(ctx, id).c_str();
}
// if not currently processing queued inputs;
if ((int) embd_inp.size() <= n_consumed) {
// check for reverse prompt
if (params.antiprompt.size()) {
std::string last_output;
for (auto id : last_tokens) {
last_output += llama_token_to_piece(ctx, id);
}
// Check if each of the reverse prompts appears at the end of the output.
// If we're not running interactively, the reverse prompt might be tokenized with some following characters
// so we'll compensate for that by widening the search window a bit.
for (std::string & antiprompt : params.antiprompt) {
size_t extra_padding = params.interactive ? 0 : 2;
size_t search_start_pos = last_output.length() > static_cast<size_t>(antiprompt.length() + extra_padding)
? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding)
: 0;
if (last_output.find(antiprompt, search_start_pos) != std::string::npos) {
is_antiprompt = true;
break;
}
}
}
}
// found antiprompt
if (is_antiprompt) {
break;
}
// end of text token
if (!embd.empty() && embd.back() == llama_token_eos(ctx)) {
break;
}
}
if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
if (debug) {
fprintf(stderr, "\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
}
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
}
end:
#if defined (_WIN32)
signal(SIGINT, SIG_DFL);
#endif
if (debug) {
llama_print_timings(ctx);
llama_reset_timings(ctx);
}
if (grammar != NULL) {
llama_grammar_free(grammar);
}
llama_backend_free();
strcpy(result, res.c_str());
return 0;
}
// this is a bit of a hack now - ideally this should be in the predict function
// and be transparent to the caller, however this now maps 1:1 (mostly) the upstream implementation
// Note: both model have to be loaded with perplexity "true" to enable all logits
int speculative_sampling(void* params_ptr, void* target_model, void* draft_model, char* result, bool debug) {
gpt_params* params_p = (gpt_params*) params_ptr;
llama_binding_state* target_model_state = (llama_binding_state*) target_model;
llama_binding_state* draft_model_state = (llama_binding_state*) draft_model;
gpt_params params = *params_p;
llama_context * ctx_tgt = target_model_state->ctx;
llama_context * ctx_dft = draft_model_state->ctx;
llama_model * model_tgt = target_model_state->model;
llama_model * model_dft = draft_model_state->model;
std::string res = "";
// tokenize the prompt
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx_tgt, params.prompt, true);
const int max_context_size = llama_n_ctx(ctx_tgt);
const int max_tokens_list_size = max_context_size - 4;
if ((int) inp.size() > max_tokens_list_size) {
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
return 1;
}
const int n_input = inp.size();
const auto t_enc_start = ggml_time_us();
// eval the prompt with both models
llama_eval(ctx_tgt, inp.data(), int(inp.size() - 1), 0, params.n_threads);
llama_eval(ctx_tgt, &inp.back(), 1, inp.size() - 1, params.n_threads);
llama_eval(ctx_dft, inp.data(), int(inp.size()), 0, params.n_threads);
const auto t_enc_end = ggml_time_us();
// the 2 models should have the same vocab
const int n_ctx = llama_n_ctx(ctx_tgt);
const int n_vocab = llama_n_vocab(ctx_tgt);
//GGML_ASSERT(n_vocab == llama_n_vocab(ctx_dft));
// how many tokens to draft each time
const int n_draft = params.n_draft;
int n_predict = 0;
int n_drafted = 0;
int n_accept = 0;
int n_past_tgt = inp.size();
int n_past_dft = inp.size();
std::vector<llama_token> drafted;
std::vector<llama_token> last_tokens(n_ctx);
std::fill(last_tokens.begin(), last_tokens.end(), 0);
for (auto & id : inp) {
last_tokens.erase(last_tokens.begin());
last_tokens.push_back(id);
}
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
// used to determine end of generation
bool has_eos = false;
// grammar stuff
struct llama_grammar * grammar_dft = NULL;
struct llama_grammar * grammar_tgt = NULL;
grammar_parser::parse_state parsed_grammar;
// if requested - load the grammar, error checking is omitted for brevity
if (!params.grammar.empty()) {
parsed_grammar = grammar_parser::parse(params.grammar.c_str());
// will be empty (default) if there are parse errors
if (parsed_grammar.rules.empty()) {
return 1;
}
std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
const auto t_dec_start = ggml_time_us();
while (true) {
int i_dft = 0;
while (true) {
// sample from the target model
// const llama_token id = llama_sample_token(ctx_tgt, NULL, grammar_tgt, params, last_tokens, candidates, i_dft);
const llama_token id = llama_sample_token_binding(ctx_tgt, NULL, grammar_tgt, params_p, last_tokens, candidates, i_dft);
// remember which tokens were sampled - used for repetition penalties during sampling
last_tokens.erase(last_tokens.begin());
last_tokens.push_back(id);
//LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens));
const std::string token_str = llama_token_to_piece(ctx_tgt, id);
if (!tokenCallback(draft_model, (char*)token_str.c_str())) {
break;
}
res += token_str.c_str();
if (id == llama_token_eos(ctx_tgt)) {
has_eos = true;
}
++n_predict;
// check if the draft matches the target
if (i_dft < (int) drafted.size() && id == drafted[i_dft]) {
LOG("drafted token %d accepted\n", id);
++n_accept;
++n_past_tgt;
++n_past_dft;
++i_dft;
continue;
}
if (i_dft < (int) drafted.size()) {
LOG("the %dth drafted token (%d, '%s') does not match the sampled target token (%d, '%s') - rejected\n",
i_dft, drafted[i_dft], llama_token_to_piece(ctx_dft, drafted[i_dft]).c_str(), id, token_str.c_str());
} else {
LOG("out of drafted tokens\n");
}
// the drafted token was rejected or we are out of drafted tokens
llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads);
++n_past_dft;
drafted.clear();
drafted.push_back(id);
break;
}
if (n_predict > params.n_predict || has_eos) {
break;
}
if (grammar_tgt) {
if (grammar_dft) {
llama_grammar_free(grammar_dft);
}
grammar_dft = llama_grammar_copy(grammar_tgt);
LOG("copied target grammar to draft grammar\n");
}
// sample n_draft tokens from the draft model using greedy decoding
int n_past_cur = n_past_dft;
for (int i = 0; i < n_draft; ++i) {
float * logits = llama_get_logits(ctx_dft);
candidates.clear();
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
if (grammar_dft != NULL) {
llama_sample_grammar(ctx_dft, &cur_p, grammar_dft);
}
// computes softmax and sorts the candidates
llama_sample_softmax(ctx_dft, &cur_p);
for (int i = 0; i < 3; ++i) {
LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p);
}
// TODO: better logic?
if (cur_p.data[0].p < 2*cur_p.data[1].p) {
LOG("stopping drafting, probability too low: %.3f < 2*%.3f\n", cur_p.data[0].p, cur_p.data[1].p);
break;
}
// drafted token
const llama_token id = cur_p.data[0].id;
drafted.push_back(id);
++n_drafted;
// no need to evaluate the last drafted token, since we won't use the result
if (i == n_draft - 1) {
break;
}
// evaluate the drafted token on the draft model
llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
++n_past_cur;
if (grammar_dft != NULL) {
llama_grammar_accept_token(ctx_dft, grammar_dft, id);
}
}
// evaluate the target model on the drafted tokens
llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads);
++n_past_tgt;
// the first token is always proposed by the traget model before the speculation loop
drafted.erase(drafted.begin());
}
if (debug) {
auto t_dec_end = ggml_time_us();
LOG_TEE("\n\n");
LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
// TODO: make sure these numbers are computed correctly
LOG_TEE("\n");
LOG_TEE("n_draft = %d\n", n_draft);
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_drafted = %d\n", n_drafted);
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_TEE("\ndraft:\n");
llama_print_timings(ctx_dft);
LOG_TEE("\ntarget:\n");
llama_print_timings(ctx_tgt);
fprintf(stderr, "\n\n");
}
if (grammar_dft != NULL) {
llama_grammar_free(grammar_dft);
llama_grammar_free(grammar_tgt);
}
strcpy(result, res.c_str());
return 0;
}
void llama_binding_free_model(void *state_ptr) {
llama_binding_state* ctx = (llama_binding_state*) state_ptr;
llama_free(ctx->ctx);
delete ctx->model;
}
void llama_free_params(void* params_ptr) {
gpt_params* params = (gpt_params*) params_ptr;
delete params;
}
int llama_tokenize_string(void* params_ptr, void* state_pr, int* result) {
gpt_params* params_p = (gpt_params*) params_ptr;
llama_binding_state* state = (llama_binding_state*) state_pr;
llama_context* ctx = state->ctx;
const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
return llama_tokenize(ctx, params_p->prompt.data(), params_p->prompt.length(), result, params_p->n_ctx, add_bos);
}
std::vector<std::string> create_vector(const char** strings, int count) {
std::vector<std::string>* vec = new std::vector<std::string>;
for (int i = 0; i < count; i++) {
vec->push_back(std::string(strings[i]));
}
return *vec;
}
void delete_vector(std::vector<std::string>* vec) {
delete vec;
}
int load_state(void *ctx, char *statefile, char*modes) {
llama_context* state = (llama_context*) ctx;
const llama_context* constState = static_cast<const llama_context*>(state);
const size_t state_size = llama_get_state_size(state);
uint8_t * state_mem = new uint8_t[state_size];
{
FILE *fp_read = fopen(statefile, modes);
if (state_size != llama_get_state_size(constState)) {
fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
return 1;
}
const size_t ret = fread(state_mem, 1, state_size, fp_read);
if (ret != state_size) {
fprintf(stderr, "\n%s : failed to read state\n", __func__);
return 1;
}
llama_set_state_data(state, state_mem); // could also read directly from memory mapped file
fclose(fp_read);
}
return 0;
}
void save_state(void *ctx, char *dst, char*modes) {
llama_context* state = (llama_context*) ctx;
const size_t state_size = llama_get_state_size(state);
uint8_t * state_mem = new uint8_t[state_size];
// Save state (rng, logits, embedding and kv_cache) to file
{
FILE *fp_write = fopen(dst, modes);
llama_copy_state_data(state, state_mem); // could also copy directly to memory mapped file
fwrite(state_mem, 1, state_size, fp_write);
fclose(fp_write);
}
}
void* llama_allocate_params(const char *prompt, int seed, int threads, int tokens, int top_k,
float top_p, float temp, float repeat_penalty, int repeat_last_n, bool ignore_eos, bool memory_f16, int n_batch, int n_keep, const char** antiprompt, int antiprompt_count,
float tfs_z, float typical_p, float frequency_penalty, float presence_penalty, int mirostat, float mirostat_eta, float mirostat_tau, bool penalize_nl, const char *logit_bias, const char *session_file, bool prompt_cache_all, bool mlock, bool mmap,
const char *maingpu,const char *tensorsplit , bool prompt_cache_ro, const char *grammar,
float rope_freq_base, float rope_freq_scale, float negative_prompt_scale, const char* negative_prompt, int n_draft) {
gpt_params* params = new gpt_params;
params->seed = seed;
params->n_threads = threads;
params->n_predict = tokens;
params->repeat_last_n = repeat_last_n;
params->prompt_cache_ro = prompt_cache_ro;
params->top_k = top_k;
params->top_p = top_p;
params->memory_f16 = memory_f16;
params->temp = temp;
params->use_mmap = mmap;
params->use_mlock = mlock;
params->repeat_penalty = repeat_penalty;
params->n_batch = n_batch;
params->n_keep = n_keep;
params->grammar = std::string(grammar);
params->rope_freq_base = rope_freq_base;
params->rope_freq_scale = rope_freq_scale;
params->cfg_scale = negative_prompt_scale;
params->cfg_negative_prompt = std::string(negative_prompt);
params->n_draft = n_draft;
if (maingpu[0] != '\0') {
params->main_gpu = std::stoi(maingpu);
}
if (tensorsplit[0] != '\0') {
std::string arg_next = tensorsplit;
// split string by , and /
const std::regex regex{R"([,/]+)"};
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
std::vector<std::string> split_arg{it, {}};
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
if (i < split_arg.size()) {
params->tensor_split[i] = std::stof(split_arg[i]);
} else {
params->tensor_split[i] = 0.0f;
}
}
}
params->prompt_cache_all = prompt_cache_all;
params->path_prompt_cache = session_file;
if (ignore_eos) {
params->ignore_eos = true;
}
if(antiprompt_count > 0) {
params->antiprompt = create_vector(antiprompt, antiprompt_count);
}
params->tfs_z = tfs_z;
params->typical_p = typical_p;
params->presence_penalty = presence_penalty;
params->mirostat = mirostat;
params->mirostat_eta = mirostat_eta;
params->mirostat_tau = mirostat_tau;
params->penalize_nl = penalize_nl;
std::stringstream ss(logit_bias);
llama_token key;
char sign;
std::string value_str;
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
params->logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
}
params->frequency_penalty = frequency_penalty;
params->prompt = prompt;
return params;
}
void* load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16, bool mlock, bool embeddings, bool mmap, bool low_vram, int n_gpu_layers, int n_batch, const char *maingpu, const char *tensorsplit, bool numa, float rope_freq_base, float rope_freq_scale, bool mul_mat_q, const char *lora, const char *lora_base, bool perplexity) {
return load_binding_model(fname, n_ctx, n_seed, memory_f16, mlock, embeddings, mmap, low_vram, n_gpu_layers, n_batch, maingpu, tensorsplit, numa, rope_freq_base, rope_freq_scale, mul_mat_q, lora, lora_base, perplexity);
}
/*
Currently we hard patch the following functions to common.cpp and common.h into the llama library due to a bug into the nvcc/gcc compiler.
It seems that copying by value lead to a misalignment of structure and copy - resulting in a mixed up values that we pass by.
See also: https://github.com/ggerganov/llama.cpp/pull/1902
Keeping them here in sync to generate again patches if needed.
common.h:
struct llama_binding_state {
llama_context * ctx;
llama_model * model;
};
void* load_binding_model(const char *fname, int n_ctx, int n_seed, bool memory_f16, bool mlock, bool embeddings, bool mmap, bool low_vram, int n_gpu_layers, int n_batch, const char *maingpu, const char *tensorsplit, bool numa, float rope_freq_base, float rope_freq_scale, bool mul_mat_q, const char *lora, const char *lora_base, bool perplexity);
llama_token llama_sample_token_binding(
struct llama_context * ctx,
struct llama_context * ctx_guidance,
struct llama_grammar * grammar,
const struct gpt_params * g_params,
const std::vector<llama_token> & last_tokens,
std::vector<llama_token_data> & candidates,
int idx = 0);
common.cpp:
gpt_params* create_gpt_params(const std::string& fname,const std::string& lora,const std::string& lora_base) {
gpt_params* lparams = new gpt_params;
fprintf(stderr, "%s: loading model %s\n", __func__, fname.c_str());
// Initialize the 'model' member with the 'fname' parameter
lparams->model = fname;
lparams->lora_base = lora_base;
lparams->lora_adapter = lora;
if (lparams->lora_adapter.empty()) {
lparams->use_mmap = false;
}
return lparams;