#include "arg.h"
#include "ggml.h"
#include "common.h"
#include "ngram-cache.h"
#include "sampling.h"
#include "speculative.h"
#include "log.h"
#include "llama.h"
#include <algorithm>
#include <clocale>
#include <cstdint>
#include <cstdio>
#include <fstream>
#include <string>
#include <vector>
int main(int argc, char ** argv){
std::setlocale(LC_NUMERIC, "C");
common_params params;
common_init();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
// max. number of additional tokens to draft if match is found
const int n_draft = params.speculative.draft.n_max;
const auto output_limits = common_speculative_get_output_limits(params.n_batch, params.n_parallel, n_draft);
params.n_outputs_max = output_limits.total;
params.n_outputs_max_per_seq = output_limits.per_seq;
// init llama.cpp
llama_backend_init();
llama_numa_init(params.numa);
// load the model
auto llama_init = common_init_from_params(params);
auto * model = llama_init->model();
auto * ctx = llama_init->context();
const llama_vocab * vocab = llama_model_get_vocab(model);
// tokenize the prompt
std::vector<llama_token> inp;
inp = common_tokenize(ctx, params.prompt, true, true);
common_ngram_cache ngram_cache_context;
common_ngram_cache ngram_cache_dynamic;
common_ngram_cache ngram_cache_static;
int64_t t_draft_flat_us = 0;
int64_t t_draft_us = 0;
{
// Fill up context ngram cache with tokens from user input:
const int64_t t_start_draft_us = ggml_time_us();
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
if (!params.speculative.ngram_cache.lookup_cache_static.empty()) {
try {
ngram_cache_static = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_static);
} catch (std::ifstream::failure const &) {
LOG_ERR("failed to open static lookup cache: %s", params.speculative.ngram_cache.lookup_cache_static.c_str());
exit(1);
}
}
if (!params.speculative.ngram_cache.lookup_cache_dynamic.empty()) {
try {
ngram_cache_dynamic = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_dynamic);
} catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
}
t_draft_flat_us += ggml_time_us() - t_start_draft_us;
}
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) {
LOG_ERR("%s: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
return 1;
}
LOG("\n\n");
for (auto id : inp) {
LOG("%s", common_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));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1));
const auto t_enc_end = ggml_time_us();
int n_predict = 0;
int n_drafted = 0;
int n_accept = 0;
int n_past = inp.size();
bool has_eos = false;
struct common_sampler * smpl = common_sampler_init(model, params.sampling);
std::vector<llama_token> draft;
llama_batch batch_tgt = llama_batch_init(llama_n_ctx(ctx), 0, 1);
const auto t_dec_start = ggml_time_us();
while (true) {
// print current draft sequence
LOG_DBG("drafted %s\n", string_from(ctx, draft).c_str());
int i_dft = 0;
while (true) {
// sample from the target model
llama_token id = common_sampler_sample(smpl, ctx, i_dft);
common_sampler_accept(smpl, id, true);
const std::string token_str = common_token_to_piece(ctx, id);
if (!params.use_color) {
LOG("%s", token_str.c_str());
}
if (llama_vocab_is_eog(vocab, id)) {
has_eos = true;
}
++n_predict;
// check if the target token matches the draft
if (i_dft < (int) draft.size() && id == draft[i_dft]) {
LOG_DBG("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);
{
// Update context ngram cache with the newly accepted token:
const int64_t t_start_draft_us = ggml_time_us();
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
if (params.use_color) {
// color accepted draft token
LOG("\033[34m%s\033[0m", token_str.c_str());
fflush(stdout);
}
continue;
}
if (params.use_color) {
LOG("%s", token_str.c_str());
}
fflush(stdout);
LOG_DBG("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);
{
// Update context ngram cache with the newly accepted token:
const int64_t t_start_draft_us = ggml_time_us();
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
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_memory_seq_rm(llama_get_memory(ctx), 0, n_past, -1);
common_batch_clear(batch_tgt);
common_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
// Draft already contains a single token sampled from the model:
GGML_ASSERT(draft.size() == 1);
GGML_ASSERT(draft[0] == inp.back());
const int64_t t_start_draft_us = ggml_time_us();
common_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
for (size_t i = 1; i < draft.size(); ++i) {
common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
}
t_draft_us += ggml_time_us() - t_start_draft_us;
n_drafted += draft.size() - 1;
llama_decode(ctx, batch_tgt);
++n_past;
draft.erase(draft.begin());
}
auto t_dec_end = ggml_time_us();
// Update dynamic ngram cache with context ngram cache and save it to disk:
common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
common_ngram_cache_save(ngram_cache_dynamic, params.speculative.ngram_cache.lookup_cache_dynamic);
LOG("\n\n");
LOG_INF("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_INF("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_INF("\n");
LOG_INF("n_draft = %d\n", n_draft);
LOG_INF("n_predict = %d\n", n_predict);
LOG_INF("n_drafted = %d\n", n_drafted);
LOG_INF("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
LOG_INF("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_INF("n_accept = %d\n", n_accept);
LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_INF("\ntarget:\n\n");
common_perf_print(ctx, smpl);
common_sampler_free(smpl);
llama_batch_free(batch_tgt);
llama_backend_free();
LOG("\n\n");
return 0;
}