0a4ce78681
* common : Changed tuple to struct (TODO fix) Use struct `llama_init_result` to replace the previous std::tuple<struct llama_model *, struct llama_context *> * delete llama_init_default_params() * delete the extra whitespace
486 lines
16 KiB
C++
486 lines
16 KiB
C++
#include "common.h"
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#include "llama.h"
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#include <cmath>
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#include <cstdio>
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#include <string>
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#include <vector>
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struct ngram_data {
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bool active = false;
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llama_seq_id seq_id = -1;
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std::vector<int> i_batch;
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std::vector<llama_token> tokens;
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};
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// n-gram container
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struct ngram_container {
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ngram_container(int n_vocab, int N, int G) {
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cnt.resize(n_vocab);
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head.resize(n_vocab);
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tokens.resize(n_vocab * G * (N - 1));
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}
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int n_total = 0;
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std::vector<int> cnt;
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std::vector<int> head;
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// [n_vocab][G][N - 1]
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// for each token of the vocab, keep a ring-buffer of capacity G of n-grams of size N - 1
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std::vector<llama_token> tokens;
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};
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int main(int argc, char ** argv) {
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gpt_params params;
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if (!gpt_params_parse(argc, argv, params)) {
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gpt_params_print_usage(argc, argv, params);
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return 1;
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}
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const int W = 15; // lookahead window
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const int N = 5; // n-gram size
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const int G = 15; // max verification n-grams
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const bool dump_kv_cache = params.dump_kv_cache;
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#ifndef LOG_DISABLE_LOGS
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log_set_target(log_filename_generator("lookahead", "log"));
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LOG_TEE("Log start\n");
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log_dump_cmdline(argc, argv);
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#endif // LOG_DISABLE_LOGS
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// init llama.cpp
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llama_backend_init();
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llama_numa_init(params.numa);
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// load the target model
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llama_init_result llama_init = llama_init_from_gpt_params(params);
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llama_model * model = llama_init.model;
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llama_context * ctx = llama_init.context;
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// Tokenize the prompt
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std::vector<llama_token> inp;
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std::vector<llama_token> all;
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inp = ::llama_tokenize(ctx, params.prompt, true, true);
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all = inp;
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const int max_context_size = llama_n_ctx(ctx);
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const int max_tokens_list_size = max_context_size - 4;
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if ((int) inp.size() > max_tokens_list_size) {
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fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
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return 1;
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}
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fprintf(stderr, "\n\n");
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for (auto id : inp) {
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fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
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}
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fflush(stderr);
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const int n_input = inp.size();
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const auto t_enc_start = ggml_time_us();
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// eval the prompt
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llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
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llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
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for (int s = 1; s < W + G + 1; ++s) {
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llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
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}
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const auto t_enc_end = ggml_time_us();
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int n_predict = 0;
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int n_accept = 0;
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int n_past = inp.size();
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llama_token id = 0;
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// used to determine end of generation
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bool has_eos = false;
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// for each decoded batch, we have at most W + G + 1 distinct sequences:
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// seq_id == 0 : the current input token
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// seq_id [1, W] : tokens from the past N - 1 Jacobi iterations
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// seq_id [W + 1, W + G] : verification n-grams
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llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
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// target model sampling context
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struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
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// verification n-grams
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std::vector<ngram_data> ngrams_cur(G);
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// tokens for the past N - 1 Jacobi iterations
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std::vector<llama_token> tokens_j_prev(W);
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std::vector<std::vector<llama_token>> tokens_j(N - 1);
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for (int j = 0; j < N - 1; j++) {
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tokens_j[j].resize(W);
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for (int i = 0; i < W; i++) {
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// there are different ways to init these tokens
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if (0) {
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// initialize randomly from the prompt tokens
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tokens_j[j][i] = all[1 + rand() % (all.size() - 1)];
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} else {
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// initialize with a sequence of increasing numbers
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tokens_j[j][i] = 100 + i;
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}
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}
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}
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std::vector<llama_seq_id> seq_id_look;
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// the input token belongs both to all sequences
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std::vector<llama_seq_id> seq_id_all(W + G + 1);
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for (int i = 0; i < W + G + 1; i++) {
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seq_id_all[i] = i;
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}
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// here we keep adding new n-grams as we go
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ngram_container ngrams_observed(llama_n_vocab(model), N, G);
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// debug
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struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
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const auto t_dec_start = ggml_time_us();
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// sample first token
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{
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id = llama_sampling_sample(ctx_sampling, ctx, NULL, 0);
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llama_sampling_accept(ctx_sampling, ctx, id, true);
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{
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const std::string token_str = llama_token_to_piece(ctx, id);
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printf("%s", token_str.c_str());
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fflush(stdout);
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}
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}
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while (true) {
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// debug
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if (dump_kv_cache) {
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llama_kv_cache_view_update(ctx, &kvc_view);
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llama_kv_cache_dump_view_seqs(kvc_view, 40);
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}
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// build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
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//
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// Example for W = 5, N = 4, G = 2:
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// (I = input, L = lookahead, V = verification)
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//
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// Batch: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
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// T: -2 -2 -2 -2 -1 -1 -1 -1 -1 0 0 0 0 0 0
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// Info: I L L L L L L L L L L L L L L V V V V V V
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// Pos: 0 1 2 3 4 1 2 3 4 5 2 3 4 5 6 1 2 3 1 2 3 (+ n_past)
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// Logits: 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
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// ---------------------------------------------------------------------
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// Seq: 0
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// 1 1 1
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// 2 2 2 2
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// 3 3 3 3 3
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// 4 4 4 4 4 4
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// 5 5 5 5 5 5 5
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// 6 6 6 6
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// 7 7 7 7
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// ---------------------------------------------------------------------
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// | | | | | | | | | | |
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// V V V V V | | | | | |
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// j_tokens | | | | | |
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// V V V V V V
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// id
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{
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llama_batch_clear(batch);
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// current token - first token of the first level
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llama_batch_add(batch, id, n_past, seq_id_all, true);
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// verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation
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{
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const int g_cur = ngrams_observed.cnt[id];
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ngrams_cur.resize(g_cur);
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for (int g = 0; g < g_cur; g++) {
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ngrams_cur[g].active = true;
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ngrams_cur[g].tokens.resize(N);
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ngrams_cur[g].i_batch.resize(N);
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ngrams_cur[g].seq_id = W + 1 + g;
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ngrams_cur[g].i_batch[0] = 0;
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ngrams_cur[g].tokens [0] = id;
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}
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for (int j = 0; j < N - 1; j++) {
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for (int g = 0; g < g_cur; g++) {
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const int idx = id*(N - 1)*G + g*(N - 1);
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const llama_token t = ngrams_observed.tokens[idx + j];
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ngrams_cur[g].tokens [j + 1] = t;
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ngrams_cur[g].i_batch[j + 1] = batch.n_tokens;
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llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
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}
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}
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}
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// fill the remaining W - 1 tokens for the first level
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for (int i = 1; i < W; i++) {
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seq_id_look.resize(W - i);
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for (int j = 0; j < W - i; j++) {
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seq_id_look[j] = i + j + 1;
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}
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llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
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}
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// fill the rest of the levels
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for (int j = 1; j < N - 1; j++) {
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for (int i = 0; i < W; i++) {
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llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
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}
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}
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}
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if (llama_decode(ctx, batch) != 0) {
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fprintf(stderr, "\n\n%s: error: llama_decode failed - increase KV cache size\n", __func__);
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return 1;
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}
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int seq_id_best = 0;
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for (int v = 0; v < N; ++v) {
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int i_batch = 0;
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// if no active ngrams are left, it means the sampled token does not pass the verification
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if (v > 0) {
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for (int g = 0; g < (int) ngrams_cur.size(); g++) {
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if (ngrams_cur[g].active) {
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i_batch = ngrams_cur[g].i_batch[v];
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seq_id_best = ngrams_cur[g].seq_id;
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++n_accept;
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break;
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}
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}
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// no more matches -> create a new batch
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if (i_batch == 0) {
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break;
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}
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}
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// sample the next token
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id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_batch);
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llama_sampling_accept(ctx_sampling, ctx, id, true);
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// print
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{
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const std::string token_str = llama_token_to_piece(ctx, id);
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if (v == 0) {
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printf("%s", token_str.c_str());
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} else {
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// print light cyan
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printf("\033[0;96m%s\033[0m", token_str.c_str());
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}
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fflush(stdout);
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if (llama_token_is_eog(model, id)) {
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has_eos = true;
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}
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all.push_back(id);
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}
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++n_predict;
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++n_past;
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if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
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break;
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}
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// verify across active n-grams
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for (int g = 0; g < (int) ngrams_cur.size(); g++) {
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if (ngrams_cur[g].active) {
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if (v == N - 1) {
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ngrams_cur[g].active = false;
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} else {
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if (id != ngrams_cur[g].tokens[v + 1]) {
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ngrams_cur[g].active = false;
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}
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}
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}
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}
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// print known n-grams starting with token id (debug)
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if (0 && v == 0) {
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if (ngrams_observed.cnt[id] > 0) {
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printf("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str());
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}
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for (int i = 0; i < ngrams_observed.cnt[id]; i++) {
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printf(" - ngram %2d: ", i);
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const int idx = id*(N - 1)*G + i*(N - 1);
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for (int j = 0; j < N - 1; j++) {
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const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
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printf("%s", token_str.c_str());
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}
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printf("\n");
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}
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}
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// update lookahead tokens
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{
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for (int i = 0; i < W; i++) {
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tokens_j_prev[i] = tokens_j[0][i];
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}
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for (int j = 0; j < N - 2; j++) {
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tokens_j[j] = tokens_j[j + 1];
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}
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if (v == 0) {
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// sample from the last level
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for (int i = 0; i < W; i++) {
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tokens_j[N - 2][i] = llama_sampling_sample(ctx_sampling, ctx, NULL, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
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}
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} else {
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for (int i = 0; i < W; i++) {
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// there are different ways to init these tokens
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if (0) {
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// random init
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tokens_j[N - 2][i] = all[1 + rand() % (all.size() - 1)];
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} else {
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// init from the previous level
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tokens_j[N - 2][i] = tokens_j[0][i];
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}
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}
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}
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}
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// update observed ngrams
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if (v == 0) {
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// the first token of the n-gram is determined by the index in the container so it is not stored
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std::vector<llama_token> ngram(N - 1);
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// n-gram generation
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// ref: https://github.com/hao-ai-lab/LookaheadDecoding/issues/14#issuecomment-1826198518
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for (int f = 0; f < W; ++f) {
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const int ft = tokens_j_prev[f]; // first token of the n-gram
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for (int j = 0; j < N - 1; ++j) {
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ngram[j] = tokens_j[j][f];
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}
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// filter-out repeating n-grams
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{
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bool is_unique = true;
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for (int k = 0; k < ngrams_observed.cnt[ft]; ++k) {
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const int idx = ft*(N - 1)*G + k*(N - 1);
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bool is_match = true;
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for (int j = 0; j < N - 1; ++j) {
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if (ngrams_observed.tokens[idx + j] != ngram[j]) {
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is_match = false;
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break;
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}
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}
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if (is_match) {
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is_unique = false;
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break;
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}
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}
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if (!is_unique) {
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continue;
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}
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}
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const int head = ngrams_observed.head[ft];
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const int idx = ft*(N - 1)*G + head*(N - 1);
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for (int i = 0; i < N - 1; i++) {
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ngrams_observed.tokens[idx + i] = ngram[i];
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}
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ngrams_observed.cnt[ft] = std::min(G, ngrams_observed.cnt[ft] + 1);
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ngrams_observed.head[ft] = (head + 1) % G;
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ngrams_observed.n_total++;
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}
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}
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}
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if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
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break;
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}
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// KV cache management
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// if no verification token matched, we simply remove all cells from this batch -> no fragmentation
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llama_kv_cache_seq_rm(ctx, -1, n_past, -1);
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if (seq_id_best != 0) {
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// if a verification token matched, we keep the best sequence and remove the rest
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// this leads to some KV cache fragmentation
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llama_kv_cache_seq_keep(ctx, seq_id_best);
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llama_kv_cache_seq_cp (ctx, seq_id_best, 0, -1, -1);
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llama_kv_cache_seq_rm (ctx, seq_id_best, -1, -1);
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for (int s = 1; s < W + G + 1; ++s) {
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llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
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}
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}
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}
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auto t_dec_end = ggml_time_us();
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LOG_TEE("\n\n");
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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));
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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));
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LOG_TEE("\n");
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LOG_TEE("W = %2d\n", W);
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LOG_TEE("N = %2d\n", N);
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LOG_TEE("G = %2d\n", G);
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LOG_TEE("\n");
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LOG_TEE("n_predict = %d\n", n_predict);
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LOG_TEE("n_accept = %d\n", n_accept);
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llama_print_timings(ctx);
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llama_kv_cache_view_free(&kvc_view);
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llama_sampling_free(ctx_sampling);
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llama_batch_free(batch);
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llama_free(ctx);
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llama_free_model(model);
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llama_backend_free();
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fprintf(stderr, "\n\n");
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return 0;
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}
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