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// thread safety test
// - Loads a copy of the same model on each GPU, plus a copy on the CPU
// - Creates n_parallel (--parallel) contexts per model
// - Runs inference in parallel on each context
#include <thread>
#include <vector>
#include <atomic>
#include "llama.h"
#include "arg.h"
#include "common.h"
#include "log.h"
#include "sampling.h"
int main(int argc, char ** argv) {
common_params params;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
common_init();
llama_backend_init();
llama_numa_init(params.numa);
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
//llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
// if (level == GGML_LOG_LEVEL_ERROR) {
// common_log_add(common_log_main(), level, "%s", text);
// }
//}, NULL);
auto cparams = common_context_params_to_llama(params);
// each context has a single sequence
cparams.n_seq_max = 1;
int dev_count = ggml_backend_dev_count();
int gpu_dev_count = 0;
for (int i = 0; i < dev_count; ++i) {
auto * dev = ggml_backend_dev_get(i);
if (dev && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
gpu_dev_count++;
}
}
const int num_models = gpu_dev_count + 1 + 1; // GPUs + 1 CPU model + 1 layer split
//const int num_models = std::max(1, gpu_dev_count);
const int num_contexts = std::max(1, params.n_parallel);
std::vector<llama_model_ptr> models;
std::vector<std::thread> threads;
std::atomic<bool> failed = false;
for (int m = 0; m < num_models; ++m) {
auto mparams = common_model_params_to_llama(params);
if (m < gpu_dev_count) {
mparams.split_mode = LLAMA_SPLIT_MODE_NONE;
mparams.main_gpu = m;
} else if (m == gpu_dev_count) {
mparams.split_mode = LLAMA_SPLIT_MODE_NONE;
mparams.main_gpu = -1; // CPU model
} else {
mparams.split_mode = LLAMA_SPLIT_MODE_LAYER;;
}
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
if (model == NULL) {
LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
return 1;
}
models.emplace_back(model);
}
for (int m = 0; m < num_models; ++m) {
auto * model = models[m].get();
for (int c = 0; c < num_contexts; ++c) {
threads.emplace_back([&, m, c, model]() {
LOG_INF("Creating context %d/%d for model %d/%d\n", c + 1, num_contexts, m + 1, num_models);
llama_context_ptr ctx { llama_init_from_model(model, cparams) };
if (ctx == NULL) {
LOG_ERR("failed to create context\n");
failed.store(true);
return;
}
std::unique_ptr<common_sampler, decltype(&common_sampler_free)> sampler { common_sampler_init(model, params.sampling), common_sampler_free };
if (sampler == NULL) {
LOG_ERR("failed to create sampler\n");
failed.store(true);
return;
}
llama_batch batch = {};
{
auto prompt = common_tokenize(ctx.get(), params.prompt, true);
if (prompt.empty()) {
LOG_ERR("failed to tokenize prompt\n");
failed.store(true);
return;
}
batch = llama_batch_get_one(prompt.data(), prompt.size());
if (llama_decode(ctx.get(), batch)) {
LOG_ERR("failed to decode prompt\n");
failed.store(true);
return;
}
}
const auto * vocab = llama_model_get_vocab(model);
std::string result = params.prompt;
for (int i = 0; i < params.n_predict; i++) {
llama_token token;
if (batch.n_tokens > 0) {
token = common_sampler_sample(sampler.get(), ctx.get(), batch.n_tokens - 1);
} else {
token = llama_vocab_bos(vocab);
}
result += common_token_to_piece(ctx.get(), token);
if (llama_vocab_is_eog(vocab, token)) {
break;
}
batch = llama_batch_get_one(&token, 1);
if (llama_decode(ctx.get(), batch)) {
LOG_ERR("Model %d/%d, Context %d/%d: failed to decode\n", m + 1, num_models, c + 1, num_contexts);
failed.store(true);
return;
}
}
LOG_INF("Model %d/%d, Context %d/%d: %s\n\n", m + 1, num_models, c + 1, num_contexts, result.c_str());
});
}
}
for (auto & thread : threads) {
thread.join();
}
if (failed) {
LOG_ERR("One or more threads failed.\n");
return 1;
}
LOG_INF("All threads finished without errors.\n");
return 0;
}
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