llama.cpp/examples/lookahead/lookahead.cpp

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#include "common.h"
#include "llama.h"
#include <cstdio>
#include <string>
#include <vector>
struct ngram_data {
bool active = false;
llama_seq_id seq_id = -1;
std::vector<int> i_batch;
std::vector<llama_token> tokens;
};
// n-gram container
struct ngram_container {
ngram_container(int n_vocab, int N, int G) {
cnt.resize(n_vocab);
head.resize(n_vocab);
tokens.resize(n_vocab * G * (N - 1));
}
int n_total = 0;
std::vector<int> cnt;
std::vector<int> head;
// [n_vocab][G][N - 1]
// for each token of the vocab, keep a ring-buffer of capacity G of n-grams of size N - 1
std::vector<llama_token> tokens;
};
int main(int argc, char ** argv) {
gpt_params params;
auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
if (!gpt_params_parse(argc, argv, params, options)) {
return 1;
}
const int W = 15; // lookahead window
const int N = 5; // n-gram size
const int G = 15; // max verification n-grams
const bool dump_kv_cache = params.dump_kv_cache;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("lookahead", "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);
// load the target model
llama_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
// Tokenize the prompt
std::vector<llama_token> inp;
std::vector<llama_token> all;
inp = ::llama_tokenize(ctx, params.prompt, true, true);
all = inp;
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();
// eval the prompt
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));
for (int s = 1; s < W + G + 1; ++s) {
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
}
const auto t_enc_end = ggml_time_us();
int n_predict = 0;
int n_accept = 0;
int n_past = inp.size();
llama_token id = 0;
// used to determine end of generation
bool has_eos = false;
// for each decoded batch, we have at most W + G + 1 distinct sequences:
// seq_id == 0 : the current input token
// seq_id [1, W] : tokens from the past N - 1 Jacobi iterations
// seq_id [W + 1, W + G] : verification n-grams
llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
// target model sampling context
struct gpt_sampler * smpl = gpt_sampler_init(model, params.sparams);
// verification n-grams
std::vector<ngram_data> ngrams_cur(G);
// tokens for the past N - 1 Jacobi iterations
std::vector<llama_token> tokens_j_prev(W);
std::vector<std::vector<llama_token>> tokens_j(N - 1);
for (int j = 0; j < N - 1; j++) {
tokens_j[j].resize(W);
for (int i = 0; i < W; i++) {
// there are different ways to init these tokens
if (0) {
// initialize randomly from the prompt tokens
tokens_j[j][i] = all[1 + rand() % (all.size() - 1)];
} else {
// initialize with a sequence of increasing numbers
tokens_j[j][i] = 100 + i;
}
}
}
std::vector<llama_seq_id> seq_id_look;
// the input token belongs both to all sequences
std::vector<llama_seq_id> seq_id_all(W + G + 1);
for (int i = 0; i < W + G + 1; i++) {
seq_id_all[i] = i;
}
// here we keep adding new n-grams as we go
ngram_container ngrams_observed(llama_n_vocab(model), N, G);
// debug
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
const auto t_dec_start = ggml_time_us();
// sample first token
{
id = gpt_sampler_sample(smpl, ctx, 0);
gpt_sampler_accept(smpl, id, true);
{
const std::string token_str = llama_token_to_piece(ctx, id);
printf("%s", token_str.c_str());
fflush(stdout);
}
}
while (true) {
// debug
if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view);
llama_kv_cache_dump_view_seqs(kvc_view, 40);
}
// build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
//
// Example for W = 5, N = 4, G = 2:
// (I = input, L = lookahead, V = verification)
//
// Batch: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
// T: -2 -2 -2 -2 -1 -1 -1 -1 -1 0 0 0 0 0 0
// Info: I L L L L L L L L L L L L L L V V V V V V
// Pos: 0 1 2 3 4 1 2 3 4 5 2 3 4 5 6 1 2 3 1 2 3 (+ n_past)
// Logits: 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
// ---------------------------------------------------------------------
// Seq: 0
// 1 1 1
// 2 2 2 2
// 3 3 3 3 3
// 4 4 4 4 4 4
// 5 5 5 5 5 5 5
// 6 6 6 6
// 7 7 7 7
// ---------------------------------------------------------------------
// | | | | | | | | | | |
// V V V V V | | | | | |
// j_tokens | | | | | |
// V V V V V V
// id
{
llama_batch_clear(batch);
// current token - first token of the first level
llama_batch_add(batch, id, n_past, seq_id_all, true);
// verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation
{
const int g_cur = ngrams_observed.cnt[id];
ngrams_cur.resize(g_cur);
for (int g = 0; g < g_cur; g++) {
ngrams_cur[g].active = true;
ngrams_cur[g].tokens.resize(N);
ngrams_cur[g].i_batch.resize(N);
ngrams_cur[g].seq_id = W + 1 + g;
ngrams_cur[g].i_batch[0] = 0;
ngrams_cur[g].tokens [0] = id;
}
for (int j = 0; j < N - 1; j++) {
for (int g = 0; g < g_cur; g++) {
const int idx = id*(N - 1)*G + g*(N - 1);
const llama_token t = ngrams_observed.tokens[idx + j];
ngrams_cur[g].tokens [j + 1] = t;
ngrams_cur[g].i_batch[j + 1] = batch.n_tokens;
llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
}
}
}
// fill the remaining W - 1 tokens for the first level
for (int i = 1; i < W; i++) {
seq_id_look.resize(W - i);
for (int j = 0; j < W - i; j++) {
seq_id_look[j] = i + j + 1;
}
llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
}
// fill the rest of the levels
for (int j = 1; j < N - 1; j++) {
for (int i = 0; i < W; i++) {
llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
}
}
}
if (llama_decode(ctx, batch) != 0) {
fprintf(stderr, "\n\n%s: error: llama_decode failed - increase KV cache size\n", __func__);
return 1;
}
int seq_id_best = 0;
for (int v = 0; v < N; ++v) {
int i_batch = 0;
// if no active ngrams are left, it means the sampled token does not pass the verification
if (v > 0) {
for (int g = 0; g < (int) ngrams_cur.size(); g++) {
if (ngrams_cur[g].active) {
i_batch = ngrams_cur[g].i_batch[v];
seq_id_best = ngrams_cur[g].seq_id;
++n_accept;
break;
}
}
// no more matches -> create a new batch
if (i_batch == 0) {
break;
}
}
// sample the next token
id = gpt_sampler_sample(smpl, ctx, i_batch);
gpt_sampler_accept(smpl, id, true);
// print
{
const std::string token_str = llama_token_to_piece(ctx, id);
if (v == 0) {
printf("%s", token_str.c_str());
} else {
// print light cyan
printf("\033[0;96m%s\033[0m", token_str.c_str());
}
fflush(stdout);
if (llama_token_is_eog(model, id)) {
has_eos = true;
}
all.push_back(id);
}
++n_predict;
++n_past;
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
break;
}
// verify across active n-grams
for (int g = 0; g < (int) ngrams_cur.size(); g++) {
if (ngrams_cur[g].active) {
if (v == N - 1) {
ngrams_cur[g].active = false;
} else {
if (id != ngrams_cur[g].tokens[v + 1]) {
ngrams_cur[g].active = false;
}
}
}
}
// print known n-grams starting with token id (debug)
if (0 && v == 0) {
if (ngrams_observed.cnt[id] > 0) {
printf("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str());
}
for (int i = 0; i < ngrams_observed.cnt[id]; i++) {
printf(" - ngram %2d: ", i);
const int idx = id*(N - 1)*G + i*(N - 1);
for (int j = 0; j < N - 1; j++) {
const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
printf("%s", token_str.c_str());
}
printf("\n");
}
}
// update lookahead tokens
{
for (int i = 0; i < W; i++) {
tokens_j_prev[i] = tokens_j[0][i];
}
for (int j = 0; j < N - 2; j++) {
tokens_j[j] = tokens_j[j + 1];
}
if (v == 0) {
// sample from the last level
for (int i = 0; i < W; i++) {
tokens_j[N - 2][i] = gpt_sampler_sample(smpl, ctx, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
}
} else {
for (int i = 0; i < W; i++) {
// there are different ways to init these tokens
if (0) {
// random init
tokens_j[N - 2][i] = all[1 + rand() % (all.size() - 1)];
} else {
// init from the previous level
tokens_j[N - 2][i] = tokens_j[0][i];
}
}
}
}
// update observed ngrams
if (v == 0) {
// the first token of the n-gram is determined by the index in the container so it is not stored
std::vector<llama_token> ngram(N - 1);
// n-gram generation
// ref: https://github.com/hao-ai-lab/LookaheadDecoding/issues/14#issuecomment-1826198518
for (int f = 0; f < W; ++f) {
const int ft = tokens_j_prev[f]; // first token of the n-gram
for (int j = 0; j < N - 1; ++j) {
ngram[j] = tokens_j[j][f];
}
// filter-out repeating n-grams
{
bool is_unique = true;
for (int k = 0; k < ngrams_observed.cnt[ft]; ++k) {
const int idx = ft*(N - 1)*G + k*(N - 1);
bool is_match = true;
for (int j = 0; j < N - 1; ++j) {
if (ngrams_observed.tokens[idx + j] != ngram[j]) {
is_match = false;
break;
}
}
if (is_match) {
is_unique = false;
break;
}
}
if (!is_unique) {
continue;
}
}
const int head = ngrams_observed.head[ft];
const int idx = ft*(N - 1)*G + head*(N - 1);
for (int i = 0; i < N - 1; i++) {
ngrams_observed.tokens[idx + i] = ngram[i];
}
ngrams_observed.cnt[ft] = std::min(G, ngrams_observed.cnt[ft] + 1);
ngrams_observed.head[ft] = (head + 1) % G;
ngrams_observed.n_total++;
}
}
}
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
break;
}
// KV cache management
// if no verification token matched, we simply remove all cells from this batch -> no fragmentation
llama_kv_cache_seq_rm(ctx, -1, n_past, -1);
if (seq_id_best != 0) {
// if a verification token matched, we keep the best sequence and remove the rest
// this leads to some KV cache fragmentation
llama_kv_cache_seq_keep(ctx, seq_id_best);
llama_kv_cache_seq_cp (ctx, seq_id_best, 0, -1, -1);
llama_kv_cache_seq_rm (ctx, seq_id_best, -1, -1);
for (int s = 1; s < W + G + 1; ++s) {
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
}
}
}
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("W = %2d\n", W);
LOG_TEE("N = %2d\n", N);
LOG_TEE("G = %2d\n", G);
LOG_TEE("\n");
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("\n");
gpt_perf_print(ctx, smpl);
gpt_sampler_free(smpl);
llama_kv_cache_view_free(&kvc_view);
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}