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#include <vector>
#include <cstdio>
#include <chrono>
#include "common.h"
#include "llama.h"
#include "llama.cpp"
using namespace std;
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
params.seed = 42;
params.n_threads = 4;
params.repeat_last_n = 64;
params.prompt = "The quick brown fox";
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_parts = params.n_parts;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.use_mmap = params.use_mmap;
lparams.use_mlock = params.use_mlock;
auto n_past = 0;
auto last_n_tokens_data = vector<llama_token>(params.repeat_last_n, 0);
// init
auto ctx = llama_init_from_file(params.model.c_str(), lparams);
auto tokens = vector<llama_token>(params.n_ctx);
auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), tokens.size(), true);
if (n_prompt_tokens < 1) {
fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
return 1;
}
// evaluate prompt
llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past, params.n_threads);
last_n_tokens_data.insert(last_n_tokens_data.end(), tokens.data(), tokens.data() + n_prompt_tokens);
n_past += n_prompt_tokens;
// Save state (rng, logits, embedding and kv_cache) to file
FILE *fp_write = fopen("dump_state.bin", "wb");
auto state_size = llama_get_state_size(ctx);
auto state_mem = new uint8_t[state_size];
llama_copy_state_data(ctx, state_mem); // could also copy directly to memory mapped file
fwrite(state_mem, 1, state_size, fp_write);
fclose(fp_write);
// save state (last tokens)
auto last_n_tokens_data_saved = vector<llama_token>(last_n_tokens_data);
auto n_past_saved = n_past;
// first run
printf("\n%s", params.prompt.c_str());
for (auto i = 0; i < params.n_predict; i++) {
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
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 candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx, &candidates_p);
auto next_token_str = llama_token_to_str(ctx, next_token);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str);
if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
return 1;
}
n_past += 1;
}
printf("\n\n");
// free old model
llama_free(ctx);
// load new model
auto ctx2 = llama_init_from_file(params.model.c_str(), lparams);
// Load state (rng, logits, embedding and kv_cache) from file
FILE *fp_read = fopen("dump_state.bin", "rb");
auto state_size2 = llama_get_state_size(ctx2);
if (state_size != state_size2) {
fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
}
fread(state_mem, 1, state_size, fp_read);
llama_set_state_data(ctx2, state_mem); // could also read directly from memory mapped file
fclose(fp_read);
// restore state (last tokens)
last_n_tokens_data = last_n_tokens_data_saved;
n_past = n_past_saved;
// second run
for (auto i = 0; i < params.n_predict; i++) {
auto logits = llama_get_logits(ctx2);
auto n_vocab = llama_n_vocab(ctx2);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
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 candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx2, &candidates_p);
auto next_token_str = llama_token_to_str(ctx2, next_token);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str);
if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
return 1;
}
n_past += 1;
}
printf("\n\n");
return 0;
}
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