llama.cpp/examples/lookup/lookup.cpp

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#include "common.h"
#include "ggml.h"
#include "llama.h"
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <string>
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
// max/min n-grams size to search for in prompt
const int ngram_max = 4;
const int ngram_min = 1;
// length of the candidate / draft sequence, if match is found
const int n_draft = params.n_draft;
const bool dump_kv_cache = params.dump_kv_cache;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("lookup", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
#endif // LOG_DISABLE_LOGS
// init llama.cpp
ggml : add numa options (#5377) * Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h * Reverted Makefile * Fixed include * Removed sched.h from ggml.h, moved ggml_get_numa_affinity into ggml.c, removed trailing whitespace and fixed up a few inconsistent variables * removed trailing whitespace * Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h * Reverting Makefile * Fixed a number of issues with the move from BOOL to ggml_numa_strategies. Added a note about mirror mode note being implemented yet * Removing MIRROR_MODE code for this PR * Removing last bit of MIRROR_MODE code for this PR * Removing unneeded branch in server.cpp example and moving get_numa_affinity and making it static * Fixed lingering init_llama_backend() bool calls in tests and examples * Remote enum llama_numa_strategies * Revert bad merge with dynatemp flags * add missing enum ggml_numa_strategies declaration and revert sync problem with master * add missing enum ggml_numa_strategies declaration * fixed ggml_init_numa variable * Update ggml.h Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * Update READMEs with info about numa flags, change INTERLEAVE strategy name to DISTRIBUTE everywhere, implement the improved distribution strategy from @rankaiyx, fix a spelling mistake and un-merge some bad merges * split numa init out from llama_backend_init and created llama_numa_init. Updated all code paths and samples * Fix up some boolean vs enum comparisons * Added #ifdefs for non-Linux OS that don't have cpu_set_t datatype * Update ggml.h Align enum values Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml.c Remove whitespace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml.c align paremeters Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/server/server.cpp remove whitespace and align brace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update common/common.cpp Remove whitespace and align brace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * unified ggml_numa_strategy enum and fixed text alignment in server.cpp example * Update ggml.c simplified return for platforms without NUMA support Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * removed redundant else from cli argument processing of --numa * whitespace --------- Co-authored-by: root <root@nenya.lothlorien.ca> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-16 09:31:07 +00:00
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
// tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
LOG("add_bos tgt: %d\n", add_bos);
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
const int max_context_size = llama_n_ctx(ctx);
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;
}
fprintf(stderr, "\n\n");
for (auto id : inp) {
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
const int n_input = inp.size();
const auto t_enc_start = ggml_time_us();
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
const auto t_enc_end = ggml_time_us();
int n_predict = 0;
int n_drafted = 0;
int n_accept = 0;
int64_t t_draft_us = 0;
int n_past = inp.size();
bool has_eos = false;
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
std::vector<llama_token> draft;
llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1);
// debug
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, 1);
const auto t_dec_start = ggml_time_us();
while (true) {
// debug
if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view);
dump_kv_cache_view_seqs(kvc_view, 40);
}
// print current draft sequence
LOG("drafted %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, draft).c_str());
int i_dft = 0;
while (true) {
// sample from the target model
llama_token id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_dft);
llama_sampling_accept(ctx_sampling, ctx, id, true);
const std::string token_str = llama_token_to_piece(ctx, id);
if (!params.use_color) {
printf("%s", token_str.c_str());
}
if (id == llama_token_eos(model)) {
has_eos = true;
}
++n_predict;
// check if the target token matches the draft
if (i_dft < (int) draft.size() && id == draft[i_dft]) {
LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
++n_accept;
++n_past;
++i_dft;
inp.push_back(id);
if (params.use_color) {
// color accepted draft token
printf("\033[34m%s\033[0m", token_str.c_str());
fflush(stdout);
}
continue;
}
if (params.use_color) {
printf("%s", token_str.c_str());
}
fflush(stdout);
LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
draft.clear();
draft.push_back(id);
inp.push_back(id);
break;
}
if ((params.n_predict > 0 && n_predict > params.n_predict) || has_eos) {
break;
}
// KV cache management
// clean the cache of draft tokens that weren't accepted
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
llama_batch_clear(batch_tgt);
llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
// generate n_pred tokens through prompt lookup
auto prompt_lookup = [&]() -> void {
const int inp_size = inp.size();
for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){
const llama_token * ngram = &inp[inp_size - ngram_size];
for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) {
bool match = true;
for (int j = 0; j < ngram_size; ++j) {
if (inp[i + j] != ngram[j]) {
match = false;
break;
}
}
if (match) {
const int startIdx = i + ngram_size;
const int endIdx = startIdx + n_draft;
if (endIdx < inp_size) {
for (int j = startIdx; j < endIdx; ++j) {
LOG(" - draft candidate %d: %d\n", j, inp[j]);
draft.push_back(inp[j]);
llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true);
++n_drafted;
}
return;
}
}
}
}
return;
};
const int64_t t_start_draft_us = ggml_time_us();
prompt_lookup();
t_draft_us += ggml_time_us() - t_start_draft_us;
llama_decode(ctx, batch_tgt);
++n_past;
draft.erase(draft.begin());
}
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));
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("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n",
t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_TEE("\ntarget:\n");
llama_print_timings(ctx);
llama_sampling_free(ctx_sampling);
llama_batch_free(batch_tgt);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}