#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama.h"
#include "sampling.h"
#include <algorithm>
#include <clocale>
#include <cstdio>
#include <string>
#include <vector>
static void print_usage(int, char ** argv) {
LOG("\nexample usage:\n");
LOG("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]);
LOG("\n");
}
int main(int argc, char ** argv) {
std::setlocale(LC_NUMERIC, "C");
common_params params;
params.prompt = "Hello my name is";
params.n_predict = 32;
common_init();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BATCHED, print_usage)) {
return 1;
}
// number of parallel batches
int n_parallel = params.n_parallel;
// total length of the sequences including the prompt
int n_predict = params.n_predict;
// init LLM
llama_backend_init();
llama_numa_init(params.numa);
// initialize the model
llama_model_params model_params = common_model_params_to_llama(params);
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params);
if (model == NULL) {
LOG_ERR("%s: error: unable to load model\n" , __func__);
return 1;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = common_tokenize(vocab, params.prompt, true);
const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
// initialize the context
llama_context_params ctx_params = common_context_params_to_llama(params);
ctx_params.n_ctx = n_kv_req;
ctx_params.n_batch = std::max(n_predict, n_parallel);
auto sparams = llama_sampler_chain_default_params();
sparams.no_perf = false;
std::vector<llama_sampler_seq_config> sampler_configs;
for (int32_t i = 0; i < n_parallel; ++i) {
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
sampler_configs.push_back({ i, smpl });
}
if (params.sampling.backend_sampling) {
ctx_params.samplers = sampler_configs.data();
ctx_params.n_samplers = sampler_configs.size();
}
llama_context * ctx = llama_init_from_model(model, ctx_params);
if (ctx == NULL) {
LOG_ERR("%s: error: failed to create the llama_context\n" , __func__);
return 1;
}
const int n_ctx = llama_n_ctx(ctx);
LOG_INF("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, 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_ERR("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__, n_kv_req);
LOG_ERR("%s: either reduce n_parallel or increase n_ctx\n", __func__);
return 1;
}
// print the prompt token-by-token
LOG("\n");
for (auto id : tokens_list) {
LOG("%s", common_token_to_piece(ctx, id).c_str());
}
// create a llama_batch
// we use this object to submit token data for decoding
llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel);
std::vector<llama_seq_id> seq_ids(n_parallel, 0);
for (int32_t i = 0; i < n_parallel; ++i) {
seq_ids[i] = i;
}
// evaluate the initial prompt
for (size_t i = 0; i < tokens_list.size(); ++i) {
common_batch_add(batch, tokens_list[i], i, seq_ids, false);
}
GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
if (llama_model_has_encoder(model)) {
if (llama_encode(ctx, batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return 1;
}
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
decoder_start_token_id = llama_vocab_bos(vocab);
}
common_batch_clear(batch);
common_batch_add(batch, decoder_start_token_id, 0, seq_ids, 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_ERR("%s: llama_decode() failed\n", __func__);
return 1;
}
//// assign the system KV cache to all parallel sequences
//// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
//for (int32_t i = 1; i < n_parallel; ++i) {
// llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
//}
if (n_parallel > 1) {
LOG("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
}
// main loop
// we will store the parallel decoded sequences in this vector
std::vector<std::string> streams(n_parallel);
// remember the batch index of the last token for each parallel sequence
// we need this to determine which logits to sample from
std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
int n_cur = batch.n_tokens;
int n_decode = 0;
const auto t_main_start = ggml_time_us();
while (n_cur <= n_predict) {
// prepare the next batch
common_batch_clear(batch);
// sample the next token for each parallel sequence / stream
for (int32_t i = 0; i < n_parallel; ++i) {
if (i_batch[i] < 0) {
// the stream has already finished
continue;
}
const llama_token new_token_id = llama_sampler_sample(sampler_configs[i].sampler, ctx, i_batch[i]);
// is it an end of generation? -> mark the stream as finished
if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
i_batch[i] = -1;
LOG("\n");
if (n_parallel > 1) {
LOG_INF("%s: stream %d finished at n_cur = %d", __func__, i, n_cur);
}
continue;
}
// if there is only one stream, we print immediately to stdout
if (n_parallel == 1) {
LOG("%s", common_token_to_piece(ctx, new_token_id).c_str());
}
streams[i] += common_token_to_piece(ctx, new_token_id);
i_batch[i] = batch.n_tokens;
// push this new token for next evaluation
common_batch_add(batch, new_token_id, n_cur, { i }, true);
n_decode += 1;
}
// all streams are finished
if (batch.n_tokens == 0) {
break;
}
n_cur += 1;
// evaluate the current batch with the transformer model
if (llama_decode(ctx, batch)) {
LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
return 1;
}
}
if (n_parallel > 1) {
LOG("\n");
for (int32_t i = 0; i < n_parallel; ++i) {
LOG("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str());
}
}
const auto t_main_end = ggml_time_us();
LOG_INF("%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));
LOG("\n");
llama_perf_sampler_print(sampler_configs[0].sampler);
llama_perf_context_print(ctx);
fprintf(stderr, "\n");
llama_batch_free(batch);
for (auto & sampler_config : sampler_configs) {
llama_sampler_free(sampler_config.sampler);
}
llama_free(ctx);
llama_model_free(model);
llama_backend_free();
return 0;
}