#include "llama.h"
#include "llama-impl.h"
#include "llama-chat.h"
#include "llama-context.h"
#include "llama-mmap.h"
#include "llama-vocab.h"
#include "llama-model-loader.h"
#include "llama-model-saver.h"
#include "llama-model.h"
#include "ggml.h"
#include "ggml-cpp.h"
#include "ggml-backend.h"
#include "gguf.h"
#include <algorithm>
#include <cassert>
#include <cinttypes>
#include <cstddef>
#include <cstdint>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <stdexcept>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
//
// interface implementation
//
const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_type) {
switch (flash_attn_type) {
case LLAMA_FLASH_ATTN_TYPE_AUTO:
return "auto";
case LLAMA_FLASH_ATTN_TYPE_DISABLED:
return "disabled";
case LLAMA_FLASH_ATTN_TYPE_ENABLED:
return "enabled";
}
GGML_ABORT("fatal error");
}
const char * llama_load_mode_name(enum llama_load_mode load_mode) {
switch (load_mode) {
case LLAMA_LOAD_MODE_AUTO:
return "auto";
case LLAMA_LOAD_MODE_NONE:
return "none";
case LLAMA_LOAD_MODE_MMAP:
return "mmap";
case LLAMA_LOAD_MODE_MLOCK:
return "mlock";
case LLAMA_LOAD_MODE_MMAP_MLOCK:
return "mmap+mlock";
case LLAMA_LOAD_MODE_DIRECT_IO:
return "dio";
}
GGML_ABORT("fatal error");
}
enum llama_load_mode llama_load_mode_from_str(const char * str) {
if (std::strcmp(str, "auto") == 0) { return LLAMA_LOAD_MODE_AUTO; }
if (std::strcmp(str, "none") == 0) { return LLAMA_LOAD_MODE_NONE; }
if (std::strcmp(str, "mmap") == 0) { return LLAMA_LOAD_MODE_MMAP; }
if (std::strcmp(str, "mlock") == 0) { return LLAMA_LOAD_MODE_MLOCK; }
if (std::strcmp(str, "mmap+mlock") == 0) { return LLAMA_LOAD_MODE_MMAP_MLOCK; }
if (std::strcmp(str, "dio") == 0) { return LLAMA_LOAD_MODE_DIRECT_IO; }
throw std::invalid_argument(std::string("unknown load mode: ") + str);
}
struct llama_sampler_chain_params llama_sampler_chain_default_params() {
struct llama_sampler_chain_params result = {
/*.no_perf =*/ true,
};
return result;
}
size_t llama_max_devices(void) {
return 16;
}
size_t llama_max_tensor_buft_overrides() {
return 4096;
}
bool llama_supports_mmap(void) {
return llama_mmap::SUPPORTED;
}
bool llama_supports_mlock(void) {
return llama_mlock::SUPPORTED;
}
bool llama_supports_gpu_offload(void) {
if (!ggml_backend_reg_count()) {
ggml_backend_load_all();
}
return ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU) != nullptr ||
ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_IGPU) != nullptr ||
llama_supports_rpc();
}
bool llama_supports_rpc(void) {
if (!ggml_backend_reg_count()) {
ggml_backend_load_all();
}
return ggml_backend_reg_by_name("RPC") != nullptr;
}
const char * llama_version(void) {
return LLAMA_VERSION;
}
void llama_backend_init(void) {
ggml_time_init();
// needed to initialize f16 tables
{
struct ggml_init_params params = { 0, NULL, false };
struct ggml_context * ctx = ggml_init(params);
ggml_free(ctx);
}
if (!ggml_backend_reg_count()) {
ggml_backend_load_all();
}
}
void llama_numa_init(enum ggml_numa_strategy numa) {
if (numa != GGML_NUMA_STRATEGY_DISABLED) {
auto * dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
GGML_ASSERT(dev && "CPU backend is not loaded");
auto * reg = ggml_backend_dev_backend_reg(dev);
auto * numa_init_fn = (decltype(ggml_numa_init) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_numa_init");
if (numa_init_fn) {
numa_init_fn(numa);
}
}
}
void llama_backend_free(void) {
ggml_quantize_free();
}
int64_t llama_time_us(void) {
return ggml_time_us();
}
// returns true on success
static bool llama_prepare_model_devices(const llama_model_params & params, llama_model * model) {
// create list of devices to use with this model
if (params.devices) {
if (params.split_mode == LLAMA_SPLIT_MODE_TENSOR) {
size_t n_devs = 0;
while (params.devices[n_devs]) {
n_devs++;
}
if (n_devs == 0) {
LLAMA_LOG_ERROR("%s: LLAMA_SPLIT_MODE_TENSOR needs >= 1 devices\n", __func__);
return false;
}
LLAMA_LOG_INFO("%s: creating a Meta device with %zu devices\n", __func__, n_devs);
for (size_t i = 0; i < n_devs; ++i) {
LLAMA_LOG_INFO("%s: - device %zu: %s\n", __func__, i, ggml_backend_dev_name(params.devices[i]));
}
model->get_split_state_ud.n_devices = n_devs;
model->get_split_state_ud.model = model;
model->devices.push_back({
true, ggml_backend_meta_device(
params.devices, n_devs, llama_meta_device_get_split_state, &model->get_split_state_ud)
});
} else {
for (ggml_backend_dev_t * dev = params.devices; *dev; ++dev) {
model->devices.push_back({false, *dev});
}
}
} else {
// default device selection
// build list of available devices
std::vector<llama_device> gpus;
std::vector<llama_device> igpus;
std::vector<llama_device> rpc_servers;
if (params.split_mode == LLAMA_SPLIT_MODE_TENSOR) {
std::vector<ggml_backend_dev_t> devs;
devs.reserve(ggml_backend_dev_count());
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
auto * dev = ggml_backend_dev_get(i);
if (ggml_backend_dev_buffer_type(dev) == ggml_backend_cpu_buffer_type()) {
LLAMA_LOG_INFO("%s: skipping %s (%s) for tensor parallelism\n", __func__, ggml_backend_dev_name(dev), ggml_backend_dev_description(dev));
continue;
}
devs.push_back(dev);
}
if (devs.empty()) {
LLAMA_LOG_ERROR("%s: LLAMA_SPLIT_MODE_TENSOR needs >= 1 devices\n", __func__);
return false;
}
LLAMA_LOG_INFO("%s: creating a Meta device for tensor parallelism from %zu devices:\n", __func__, devs.size());
for (size_t i = 0; i < devs.size(); ++i) {
LLAMA_LOG_INFO("%s: - device %zu: %s (%s)\n", __func__, i, ggml_backend_dev_name(devs[i]), ggml_backend_dev_description(devs[i]));
}
GGML_ASSERT(!devs.empty());
model->get_split_state_ud.n_devices = devs.size();
model->get_split_state_ud.model = model;
gpus.push_back({
true, ggml_backend_meta_device(
devs.data(), devs.size(), llama_meta_device_get_split_state, &model->get_split_state_ud)
});
} else {
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
switch (ggml_backend_dev_type(dev)) {
case GGML_BACKEND_DEVICE_TYPE_CPU:
case GGML_BACKEND_DEVICE_TYPE_ACCEL:
// skip CPU backends since they are handled separately
break;
case GGML_BACKEND_DEVICE_TYPE_GPU: {
ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
if (ggml_backend_reg_name(reg) == std::string("RPC")) {
rpc_servers.push_back({false, dev});
} else {
// check if there is already a GPU with the same device id
ggml_backend_dev_props props;
ggml_backend_dev_get_props(dev, &props);
auto it = std::find_if(gpus.begin(), gpus.end(), [&props](const llama_device & d) {
ggml_backend_dev_props d_props;
ggml_backend_dev_get_props(d.dev, &d_props);
if (props.device_id && d_props.device_id) {
return strcmp(props.device_id, d_props.device_id) == 0;
}
return false;
});
if (it != gpus.end()) {
LLAMA_LOG_INFO("%s: skipping device %s (%s) with id %s - already using device %s (%s) with the same id\n",
__func__,
ggml_backend_dev_name(dev), ggml_backend_dev_description(dev),
props.device_id ? props.device_id : "unknown id",
ggml_backend_dev_name(it->dev), ggml_backend_dev_description(it->dev));
} else {
gpus.push_back({false, dev});
}
}
break;
}
case GGML_BACKEND_DEVICE_TYPE_IGPU:
// igpus.empty() - workaround for integrated devices seen by multiple backends
// ref: https://github.com/ggml-org/llama.cpp/pull/23897
// ggml_backend_dev_backend_reg - allow devices of the same backend regardless if integrated
// ref: https://github.com/ggml-org/llama.cpp/pull/23897#issuecomment-5264222997
if (igpus.empty() || ggml_backend_dev_backend_reg(dev) == ggml_backend_dev_backend_reg(igpus.back().dev)) {
igpus.push_back({false, dev});
}
break;
case GGML_BACKEND_DEVICE_TYPE_META:
GGML_ABORT("fatal error");
}
}
}
// add RPC servers at the front of the list to minimize network transfers
model->devices.insert(model->devices.begin(), rpc_servers.begin(), rpc_servers.end());
// add GPUs
model->devices.insert(model->devices.end(), gpus.begin(), gpus.end());
// add integrated GPUs only if no discrete GPUs were found
// (RPC servers do not count, otherwise the local iGPU would be dropped on iGPU+RPC setups)
if (gpus.empty()) {
model->devices.insert(model->devices.end(), igpus.begin(), igpus.end());
}
}
// if using single GPU mode, remove all except the main GPU
if (params.split_mode == LLAMA_SPLIT_MODE_NONE && !model->devices.empty()) {
if (params.main_gpu < 0) {
model->devices.clear();
} else {
if (params.main_gpu >= (int)model->devices.size()) {
LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %zu)\n", __func__, params.main_gpu, model->devices.size());
return false;
}
llama_device main_gpu = model->devices[params.main_gpu];
model->devices.clear();
model->devices.push_back(main_gpu);
}
}
for (const auto & dev : model->devices) {
ggml_backend_dev_props props;
ggml_backend_dev_get_props(dev.dev, &props);
LLAMA_LOG_INFO("%s: using device %s (%s) (%s) - %zu MiB free\n", __func__,
ggml_backend_dev_name(dev.dev), ggml_backend_dev_description(dev.dev),
props.device_id ? props.device_id : "unknown id",
props.memory_free/1024/1024);
}
return true;
}
// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
static std::pair<int, llama_model *> llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud,
const std::string & fname, std::vector<std::string> & splits, FILE * file, llama_model_params & params) {
try {
llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.load_mode,
params.check_tensors, params.no_alloc, params.load_mtp, params.kv_overrides, params.tensor_buft_overrides);
ml.print_info();
std::unique_ptr<llama_model> model_ptr(llama_model_create(ml, params));
bool ok = llama_prepare_model_devices(params, model_ptr.get());
if (!ok) {
return {-1, nullptr};
}
auto * model = dynamic_cast<llama_model_base *>(model_ptr.get());
if (model == nullptr) {
GGML_ABORT("fatal error: model does not implement llama_model_base");
}
// loading time will be recalculated after the first eval, so
// we take page faults deferred by mmap() into consideration
model->t_load_us = 0;
time_meas tm(model->t_load_us);
model->t_start_us = tm.t_start_us;
model->hparams.vocab_only = params.vocab_only;
model->hparams.no_alloc = params.no_alloc;
try {
model->load_hparams(ml);
} catch(const std::exception & e) {
throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
}
if (model->arch == LLM_ARCH_CLIP) {
throw std::runtime_error("CLIP cannot be used as main model, use it with --mmproj instead");
}
try {
model->load_vocab(ml);
} catch(const std::exception & e) {
throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
}
model->load_stats(ml);
model->print_info();
if (params.vocab_only) {
LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
return {0, model_ptr.release()};
}
if (!model->load_tensors(ml)) {
return {-2, nullptr};
}
return {0, model_ptr.release()};
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
return {-1, nullptr};
}
}
static struct llama_model * llama_model_load_from_file_impl(
struct gguf_context * metadata,
llama_model_set_tensor_data_t set_tensor_data,
void * set_tensor_data_ud,
const std::string & path_model,
std::vector<std::string> & splits,
FILE * file,
struct llama_model_params params) {
{
int n_sources_defined = 0;
if (metadata != nullptr) {
n_sources_defined++;
}
if (!path_model.empty()) {
n_sources_defined++;
}
if (file != nullptr) {
n_sources_defined++;
}
if (n_sources_defined != 1) {
LLAMA_LOG_ERROR("%s: exactly one out metadata, path_model, and file must be defined\n", __func__);
return nullptr;
}
}
ggml_time_init();
if (!params.vocab_only && ggml_backend_reg_count() == 0) {
LLAMA_LOG_ERROR("%s: no backends are loaded. hint: use ggml_backend_load() or ggml_backend_load_all() to load a backend before calling this function\n", __func__);
return nullptr;
}
unsigned cur_percentage = 0;
if (params.progress_callback == NULL) {
params.progress_callback_user_data = &cur_percentage;
params.progress_callback = [](float progress, void * ctx) {
unsigned * cur_percentage_p = (unsigned *) ctx;
unsigned percentage = (unsigned) (100 * progress);
while (percentage > *cur_percentage_p) {
*cur_percentage_p = percentage;
LLAMA_LOG_CONT(".");
if (percentage >= 100) {
LLAMA_LOG_CONT("\n");
}
}
return true;
};
}
const auto [status, model] = llama_model_load(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, file, params);
GGML_ASSERT(status <= 0);
if (status < 0) {
if (status == -1) {
LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
} else if (status == -2) {
LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
}
if (model) {
llama_model_free(model);
}
return nullptr;
}
return model;
}
struct llama_model * llama_model_init_from_user(
struct gguf_context * metadata,
llama_model_set_tensor_data_t set_tensor_data,
void * set_tensor_data_ud,
struct llama_model_params params) {
GGML_ASSERT(metadata != nullptr);
std::string path_model;
std::vector<std::string> splits = {};
params.load_mode = LLAMA_LOAD_MODE_NONE;
params.use_extra_bufts = false;
return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, /*file*/ nullptr, params);
}
// deprecated
struct llama_model * llama_load_model_from_file(
const char * path_model,
struct llama_model_params params) {
return llama_model_load_from_file(path_model, params);
}
struct llama_model * llama_model_load_from_file(
const char * path_model,
struct llama_model_params params) {
std::vector<std::string> splits = {};
return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, path_model, splits, /*file*/ nullptr, params);
}
struct llama_model * llama_model_load_from_splits(
const char ** paths,
size_t n_paths,
struct llama_model_params params) {
std::vector<std::string> splits;
if (n_paths == 0) {
LLAMA_LOG_ERROR("%s: list of splits is empty\n", __func__);
return nullptr;
}
splits.reserve(n_paths);
for (size_t i = 0; i < n_paths; ++i) {
splits.push_back(paths[i]);
}
return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, splits.front(), splits, /*file*/ nullptr, params);
}
struct llama_model * llama_model_load_from_file_ptr(FILE * file, struct llama_model_params params) {
if (!file) {
LLAMA_LOG_ERROR("%s: file is NULL\n", __func__);
return nullptr;
}
std::string path_model;
std::vector<std::string> splits = {};
return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, path_model, splits, file, params);
}
void llama_model_save_to_file(const struct llama_model * model, const char * path_model) {
llama_model_saver ms(model);
ms.add_kv_from_model();
ms.add_tensors_from_model();
ms.save(path_model);
}
//
// chat templates
//
int32_t llama_chat_apply_template(
const char * tmpl,
const struct llama_chat_message * chat,
size_t n_msg,
bool add_ass,
char * buf,
int32_t length) {
const std::string curr_tmpl(tmpl == nullptr ? "chatml" : tmpl);
// format the chat to string
std::vector<const llama_chat_message *> chat_vec;
chat_vec.resize(n_msg);
for (size_t i = 0; i < n_msg; i++) {
chat_vec[i] = &chat[i];
}
std::string formatted_chat;
llm_chat_template detected_tmpl = llm_chat_detect_template(curr_tmpl);
if (detected_tmpl == LLM_CHAT_TEMPLATE_UNKNOWN) {
return -1;
}
int32_t res = llm_chat_apply_template(detected_tmpl, chat_vec, formatted_chat, add_ass);
if (res < 0) {
return res;
}
if (buf && length > 0) {
strncpy(buf, formatted_chat.c_str(), length);
}
return res;
}
//
// model split
//
int32_t llama_split_path(
char * split_path,
size_t maxlen,
const char * path_prefix,
int32_t split_no,
int32_t split_count) {
static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
const int written = snprintf(
split_path,
maxlen,
SPLIT_PATH_FORMAT,
path_prefix,
split_no + 1,
split_count
);
if (written < 0 || (size_t) written >= maxlen) {
return 0;
}
return (int32_t) written;
}
int32_t llama_split_prefix(
char * split_prefix,
size_t maxlen,
const char * split_path,
int32_t split_no,
int32_t split_count) {
const std::string str_split_path(split_path);
char postfix[32];
snprintf(postfix, sizeof(postfix), "-%05d-of-%05d.gguf", split_no + 1, split_count);
const std::string str_postfix(postfix);
if (str_split_path.size() <= str_postfix.size()) {
return 0;
}
const size_t size_prefix = str_split_path.size() - str_postfix.size();
if (str_split_path.compare(size_prefix, std::string::npos, str_postfix) == 0) {
const size_t copy_len = std::min(size_prefix + 1, maxlen);
snprintf(split_prefix, copy_len, "%s", split_path);
return (int32_t) size_prefix;
}
return 0;
}
const char * llama_print_system_info(void) {
static std::string s;
s.clear(); // Clear the string, since it's static, otherwise it will accumulate data from previous calls.
for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
auto * reg = ggml_backend_reg_get(i);
auto * get_features_fn = (ggml_backend_get_features_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_get_features");
if (get_features_fn) {
ggml_backend_feature * features = get_features_fn(reg);
s += ggml_backend_reg_name(reg);
s += " : ";
for (; features->name; features++) {
s += features->name;
s += " = ";
s += features->value;
s += " | ";
}
}
}
return s.c_str();
}