llama.cpp/examples/speculative/speculative.cpp
Georgi Gerganov 47068e5170
speculative : PoC for speeding-up inference via speculative sampling (#2926)
* speculative : initial example

* speculative : print encoding speed

* speculative : add --draft CLI arg
2023-09-03 15:12:08 +03:00

235 lines
6.8 KiB
C++

#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "build-info.h"
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
int main(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
if (params.model_draft.empty()) {
fprintf(stderr, "%s: error: --model-draft is required\n", __func__);
return 1;
}
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("speculative", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_model * model_tgt = NULL;
llama_model * model_dft = NULL;
llama_context * ctx_tgt = NULL;
llama_context * ctx_dft = NULL;
// load the target model
params.perplexity = true; // HACK: enable logits_all = true
std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
// load the draft model
params.model = params.model_draft;
std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
// 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;
}
fprintf(stderr, "\n\n");
for (auto id : inp) {
fprintf(stderr, "%s", llama_token_to_piece(ctx_tgt, id).c_str());
}
fflush(stderr);
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;
const auto t_dec_start = ggml_time_us();
while (true) {
LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted));
// sample from the drafted tokens if any
int i_dft = 0;
while (true) {
const llama_token id = llama_sample_token(ctx_tgt, NULL, NULL, params, last_tokens, candidates, i_dft);
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);
printf("%s", token_str.c_str());
fflush(stdout);
if (id == llama_token_eos(ctx_tgt)) {
has_eos = true;
}
++n_predict;
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;
}
// 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;
}
// sample n_draft tokens from the draft model picking the best token
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 };
// 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);
}
// too low probability, stop drafting
if (cur_p.data[0].p < 2*cur_p.data[1].p) {
break;
}
drafted.push_back(cur_p.data[0].id);
++n_drafted;
if (i < n_draft - 1) {
// evaluate the drafted token on the draft model
llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
++n_past_cur;
}
}
// 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;
drafted.erase(drafted.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));
// 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);
llama_free(ctx_tgt);
llama_free_model(model_tgt);
llama_free(ctx_dft);
llama_free_model(model_dft);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}