233 lines
7.1 KiB
C++
233 lines
7.1 KiB
C++
#include "common.h"
|
|
#include "llama.h"
|
|
|
|
#include <cmath>
|
|
#include <cstdio>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
static std::vector<llama_token> heal_last_token(const llama_context * ctx, const std::vector<llama_token> & tokens_list) {
|
|
const llama_token last_token_id = tokens_list.back();
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const int32_t n_vocab = llama_n_vocab(model);
|
|
|
|
// Don't roll back e.g. <|endoftext|> (set parse_special=true in llama_tokenize)
|
|
if (llama_token_get_type(model, last_token_id) != LLAMA_TOKEN_TYPE_NORMAL) {
|
|
return {};
|
|
}
|
|
|
|
const std::string last_piece = llama_token_to_piece(ctx, last_token_id);
|
|
fprintf(stderr, "token_healing: prefix = '%s'\n", last_piece.c_str());
|
|
|
|
fprintf(stderr, "token_healing: candidates:\n");
|
|
fprintf(stderr, " [%6d] '%s'\n", last_token_id, last_piece.c_str());
|
|
std::vector<llama_token> candidates = { last_token_id };
|
|
for (llama_token token_id = 0; token_id < n_vocab; ++token_id) {
|
|
if (token_id == last_token_id) {
|
|
continue;
|
|
}
|
|
std::string token_piece = llama_token_to_piece(ctx, token_id);
|
|
if (token_piece.rfind(last_piece, 0) != std::string::npos) {
|
|
candidates.push_back(token_id);
|
|
fprintf(stderr, " [%6d] '%s'\n", token_id, token_piece.c_str());
|
|
}
|
|
}
|
|
if (candidates.size() == 1) {
|
|
// No healing necessary if the last token is the only candidate.
|
|
return {};
|
|
}
|
|
return candidates;
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
gpt_params params;
|
|
|
|
if (argc == 1 || argv[1][0] == '-') {
|
|
printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
|
|
return 1 ;
|
|
}
|
|
|
|
if (argc >= 2) {
|
|
params.model = argv[1];
|
|
}
|
|
|
|
if (argc >= 3) {
|
|
params.prompt = argv[2];
|
|
}
|
|
|
|
if (params.prompt.empty()) {
|
|
params.prompt = "Hello my name is";
|
|
}
|
|
|
|
// total length of the sequence including the prompt
|
|
const int n_len = 32;
|
|
|
|
// init LLM
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
// initialize the model
|
|
|
|
llama_model_params model_params = llama_model_default_params();
|
|
|
|
// model_params.n_gpu_layers = 99; // offload all layers to the GPU
|
|
|
|
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
|
|
|
|
if (model == NULL) {
|
|
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
|
|
return 1;
|
|
}
|
|
|
|
// initialize the context
|
|
|
|
llama_context_params ctx_params = llama_context_default_params();
|
|
|
|
ctx_params.seed = 1234;
|
|
ctx_params.n_ctx = 2048;
|
|
ctx_params.n_threads = params.n_threads;
|
|
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
|
|
|
|
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
|
|
|
|
if (ctx == NULL) {
|
|
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
|
|
return 1;
|
|
}
|
|
|
|
// tokenize the prompt
|
|
|
|
std::vector<llama_token> tokens_list;
|
|
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
|
|
|
|
// Roll back the last token and constrain tokens to generate in the next step to match the removed last token.
|
|
std::vector<llama_token> token_healing_candidates = heal_last_token(ctx, tokens_list);
|
|
if (!token_healing_candidates.empty()) {
|
|
tokens_list.pop_back();
|
|
}
|
|
if (tokens_list.empty()) {
|
|
// If we remove the first token, llama_decode would crash with an empty sequence, so add bos.
|
|
tokens_list.emplace_back(llama_token_bos(model));
|
|
}
|
|
|
|
const int n_ctx = llama_n_ctx(ctx);
|
|
const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
|
|
|
|
LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_kv_req);
|
|
|
|
// make sure the KV cache is big enough to hold all the prompt and generated tokens
|
|
if (n_kv_req > n_ctx) {
|
|
LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
|
|
LOG_TEE("%s: either reduce n_len or increase n_ctx\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
// print the prompt token-by-token
|
|
|
|
fprintf(stderr, "\n");
|
|
|
|
for (auto id : tokens_list) {
|
|
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
|
|
}
|
|
|
|
fflush(stderr);
|
|
|
|
// create a llama_batch with size 512
|
|
// we use this object to submit token data for decoding
|
|
|
|
llama_batch batch = llama_batch_init(512, 0, 1);
|
|
|
|
// evaluate the initial prompt
|
|
for (size_t i = 0; i < tokens_list.size(); i++) {
|
|
llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
|
|
}
|
|
|
|
// llama_decode will output logits only for the last token of the prompt
|
|
batch.logits[batch.n_tokens - 1] = true;
|
|
|
|
if (llama_decode(ctx, batch) != 0) {
|
|
LOG_TEE("%s: llama_decode() failed\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
// main loop
|
|
|
|
int n_cur = batch.n_tokens;
|
|
int n_decode = 0;
|
|
|
|
const auto t_main_start = ggml_time_us();
|
|
|
|
while (n_cur <= n_len) {
|
|
// sample the next token
|
|
{
|
|
auto n_vocab = llama_n_vocab(model);
|
|
auto * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
|
|
|
|
std::vector<llama_token_data> candidates;
|
|
candidates.reserve(n_vocab);
|
|
|
|
if (n_decode == 0 && !token_healing_candidates.empty()) {
|
|
for (const llama_token token_id : token_healing_candidates) {
|
|
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
|
|
}
|
|
} else {
|
|
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 };
|
|
|
|
// sample the most likely token
|
|
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
|
|
|
|
// is it an end of generation?
|
|
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
|
|
LOG_TEE("\n");
|
|
|
|
break;
|
|
}
|
|
|
|
LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
|
|
fflush(stdout);
|
|
|
|
// prepare the next batch
|
|
llama_batch_clear(batch);
|
|
|
|
// push this new token for next evaluation
|
|
llama_batch_add(batch, new_token_id, n_cur, { 0 }, true);
|
|
|
|
n_decode += 1;
|
|
}
|
|
|
|
n_cur += 1;
|
|
|
|
// evaluate the current batch with the transformer model
|
|
if (llama_decode(ctx, batch)) {
|
|
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
LOG_TEE("\n");
|
|
|
|
const auto t_main_end = ggml_time_us();
|
|
|
|
LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
|
|
__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
|
|
|
|
llama_print_timings(ctx);
|
|
|
|
fprintf(stderr, "\n");
|
|
|
|
llama_batch_free(batch);
|
|
|
|
llama_free(ctx);
|
|
llama_free_model(model);
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|