use memory margin instead of total size limit, apply to each device separately
This commit is contained in:
parent
d2892543f4
commit
4af1a283a6
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@ -3045,12 +3045,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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}
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).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_MAX"));
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add_opt(common_arg(
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{"--models-memory-max"}, "N",
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string_format("for router server, maximum memory usage in MB (default: %d, 0 = unlimited)", params.models_memory_max),
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{"--models-memory-margin"}, "N",
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string_format("for router server, MB of memory to leave free, per device (default: %d, 0 = unlimited)", params.models_memory_margin),
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[](common_params & params, int value) {
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params.models_memory_max = value;
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params.models_memory_margin = value;
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}
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).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_MEMORY_MAX"));
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).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_MEMORY_MARGIN"));
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add_opt(common_arg(
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{"--models-autoload"},
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{"--no-models-autoload"},
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@ -618,11 +618,11 @@ struct common_params {
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std::vector<std::string> server_tools;
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// router server configs
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std::string models_dir = ""; // directory containing models for the router server
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std::string models_preset = ""; // directory containing model presets for the router server
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int models_max = 4; // maximum number of models to load simultaneously
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int models_memory_max = 0; // maximum memory usage in MB (0 = unlimited, estimated from model files)
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bool models_autoload = true; // automatically load models when requested via the router server
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std::string models_dir = ""; // directory containing models for the router server
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std::string models_preset = ""; // directory containing model presets for the router server
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int models_max = 4; // maximum number of models to load simultaneously
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int models_memory_margin = 1024; // MB of free memory to preserve per device (0 = disabled)
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bool models_autoload = true; // automatically load models when requested via the router server
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bool log_json = false;
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@ -1547,6 +1547,12 @@ extern "C" {
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// print a breakdown of per-device memory use via LLAMA_LOG:
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LLAMA_API void llama_memory_breakdown_print(const struct llama_context * ctx);
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// Returns the projected memory use (model + context + compute) in bytes
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// for the given device within this context. Returns 0 if the device is not used.
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LLAMA_API uint64_t llama_context_device_memory(
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const struct llama_context * ctx,
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ggml_backend_dev_t device);
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//
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// training
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//
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@ -3610,6 +3610,19 @@ void llama_memory_breakdown_print(const struct llama_context * ctx) {
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}
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}
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uint64_t llama_context_device_memory(const llama_context * ctx, ggml_backend_dev_t device) {
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const bool is_host = ggml_backend_dev_type(device) == GGML_BACKEND_DEVICE_TYPE_CPU;
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uint64_t total = 0;
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for (const auto & [buft, mb] : ctx->memory_breakdown()) {
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const bool matches = is_host ? ggml_backend_buft_is_host(buft) :
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ggml_backend_buft_get_device(buft) == device;
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if (matches) {
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total += mb.total();
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}
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}
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return total;
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}
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//
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// training
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//
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@ -178,6 +178,21 @@ server_models::server_models(
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LOG_WRN("failed to get server executable path: %s\n", e.what());
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LOG_WRN("using original argv[0] as fallback: %s\n", argv[0]);
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}
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const uint64_t memory_margin = base_params.models_memory_margin * 1024 * 1024;
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if (memory_margin > 0) {
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const size_t n_devs = ggml_backend_dev_count();
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for (size_t i = 0; i < n_devs; i++) {
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ggml_backend_dev_t dev = ggml_backend_dev_get(i);
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size_t free, total;
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ggml_backend_dev_memory(dev, &free, &total);
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if (total > 0) {
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memory_per_device[dev] = (free > memory_margin) ? free - memory_margin : 0;
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}
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}
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}
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load_models();
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}
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@ -293,17 +308,17 @@ void server_models::load_models() {
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// convert presets to server_model_meta and add to mapping
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for (const auto & preset : final_presets) {
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server_model_meta meta{
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/* preset */ preset.second,
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/* name */ preset.first,
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/* aliases */ {},
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/* tags */ {},
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/* port */ 0,
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/* status */ SERVER_MODEL_STATUS_UNLOADED,
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/* last_used */ 0,
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/* memory_mb */ 0,
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/* args */ std::vector<std::string>(),
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/* exit_code */ 0,
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/* stop_timeout */ DEFAULT_STOP_TIMEOUT,
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/* preset */ preset.second,
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/* name */ preset.first,
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/* aliases */ {},
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/* tags */ {},
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/* port */ 0,
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/* status */ SERVER_MODEL_STATUS_UNLOADED,
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/* last_used */ 0,
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/* memory_per_device */ {},
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/* args */ std::vector<std::string>(),
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/* exit_code */ 0,
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/* stop_timeout */ DEFAULT_STOP_TIMEOUT,
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};
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add_model(std::move(meta));
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}
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@ -494,36 +509,63 @@ std::vector<server_model_meta> server_models::get_all_meta() {
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return result;
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}
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void server_models::unload_lru(uint64_t new_model_memory_mb) {
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if (base_params.models_max <= 0 && base_params.models_memory_max <= 0) {
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uint64_t server_models::get_memory_exceeded(const model_memory_map& new_model_memory_per_device) const {
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model_memory_map total_memory_per_device;
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for (const auto & m : mapping) {
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if (m.second.meta.is_running()) {
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for (const auto& [key, value] : m.second.meta.memory_per_device) {
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total_memory_per_device[key] += value;
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}
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}
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}
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auto get = [](const model_memory_map & m, ggml_backend_dev_t k) {
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auto it = m.find(k);
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return it != m.end() ? it->second : 0;
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};
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uint64_t memory_exceeded = 0;
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for (const auto& [key, limit] : memory_per_device) {
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if (get(new_model_memory_per_device, key) + get(total_memory_per_device, key) > limit) {
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memory_exceeded++;
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}
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}
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return memory_exceeded;
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}
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void server_models::unload_lru(const model_memory_map& new_model_memory_per_device) {
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const bool check_memory = base_params.models_memory_margin > 0 && !memory_per_device.empty();
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if (base_params.models_max <= 0 && !check_memory) {
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return; // no limit
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}
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while (true) {
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std::string lru_model_name = "";
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int64_t lru_last_used = ggml_time_ms();
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size_t count_active = 0;
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uint64_t total_memory_mb = 0;
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uint64_t memory_exceeded = 0;
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{
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std::unique_lock<std::mutex> lk(mutex);
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for (const auto & m : mapping) {
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if (m.second.meta.is_running()) {
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count_active++;
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total_memory_mb += m.second.meta.memory_mb;
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if (m.second.meta.last_used < lru_last_used) {
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lru_model_name = m.first;
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lru_last_used = m.second.meta.last_used;
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}
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}
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}
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memory_exceeded = get_memory_exceeded(new_model_memory_per_device);
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}
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bool count_exceeded = base_params.models_max > 0 &&
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(count_active + 1) >= (size_t)base_params.models_max;
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uint64_t projected_memory = total_memory_mb + new_model_memory_mb;
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bool memory_exceeded = base_params.models_memory_max > 0 &&
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projected_memory >= (uint64_t)base_params.models_memory_max;
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if (!lru_model_name.empty() && (count_exceeded || memory_exceeded)) {
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SRV_INF("limits reached (count=%zu, memory=%lu MB + %lu MB new), removing LRU name=%s\n",
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count_active, (unsigned long)total_memory_mb, (unsigned long)new_model_memory_mb, lru_model_name.c_str());
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if (!lru_model_name.empty() && (count_exceeded || memory_exceeded > 0)) {
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SRV_INF("limits reached (count=%zu, memory margin exceeded on %zu device(s)), removing LRU name=%s\n",
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count_active, memory_exceeded, lru_model_name.c_str());
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unload(lru_model_name);
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// wait for unload to complete
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{
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@ -538,12 +580,12 @@ void server_models::unload_lru(uint64_t new_model_memory_mb) {
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}
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}
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static uint64_t get_model_memory_mb(const common_preset& preset) {
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static model_memory_map get_model_memory_per_device(const common_preset& preset) {
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common_params params;
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preset.apply_to_params(params);
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if(params.model.path.empty()) {
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return 0;
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return {};
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}
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struct log_ud_t {
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@ -567,18 +609,32 @@ static uint64_t get_model_memory_mb(const common_preset& preset) {
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mparams.use_mmap = false;
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mparams.use_mlock = false;
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llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
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llama_log_set(log_ud.original.callback, log_ud.original.user_data);
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llama_model_ptr model{llama_model_load_from_file(params.model.path.c_str(), mparams)};
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if (!model) {
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return 0;
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llama_log_set(log_ud.original.callback, log_ud.original.user_data);
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return {};
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}
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uint64_t size_bytes = llama_model_size(model);
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llama_model_free(model);
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llama_context_params cparams = common_context_params_to_llama(params);
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llama_context_ptr ctx{llama_init_from_model(model.get(), cparams)};
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llama_log_set(log_ud.original.callback, log_ud.original.user_data);
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return size_bytes / (1024 * 1024);
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if (!ctx) {
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return {};
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}
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model_memory_map result;
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const size_t n_devs = ggml_backend_dev_count();
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for (size_t i = 0; i < n_devs; i++) {
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ggml_backend_dev_t dev = ggml_backend_dev_get(i);
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uint64_t bytes = llama_context_device_memory(ctx.get(), dev);
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if (bytes > 0) {
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result[dev] = bytes;
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}
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}
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return result;
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}
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void server_models::load(const std::string & name) {
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@ -586,23 +642,18 @@ void server_models::load(const std::string & name) {
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throw std::runtime_error("model name=" + name + " is not found");
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}
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uint64_t new_model_memory_mb = 0;
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if (base_params.models_memory_max > 0) {
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model_memory_map new_model_memory_per_device;
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if (base_params.models_memory_margin > 0) {
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std::lock_guard<std::mutex> lk(mutex);
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auto & meta = mapping[name].meta;
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if (meta.memory_mb > 0) {
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new_model_memory_mb = meta.memory_mb;
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} else {
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new_model_memory_mb = get_model_memory_mb(meta.preset);
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meta.memory_mb = new_model_memory_mb;
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}
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if (new_model_memory_mb > 0) {
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SRV_INF("model %s memory requirements: %lu MB\n", name.c_str(),
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(unsigned long)new_model_memory_mb);
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if (meta.memory_per_device.empty()) {
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meta.memory_per_device = get_model_memory_per_device(meta.preset);
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}
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new_model_memory_per_device = meta.memory_per_device;
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}
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unload_lru(new_model_memory_mb);
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unload_lru(new_model_memory_per_device);
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std::lock_guard<std::mutex> lk(mutex);
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@ -616,17 +667,15 @@ void server_models::load(const std::string & name) {
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// exceeding models_max. Without this, the window between unload_lru()
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// releasing its lock and this lock_guard acquiring allows multiple
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// threads to each observe capacity and all proceed to load.
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if (base_params.models_max > 0 || base_params.models_memory_max > 0) {
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if (base_params.models_max > 0 || base_params.models_memory_margin > 0) {
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size_t count_active = 0;
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uint64_t total_memory_mb = 0;
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for (const auto & m : mapping) {
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if (m.second.meta.is_running()) {
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count_active++;
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total_memory_mb += m.second.meta.memory_mb;
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}
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}
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bool count_exceeded = base_params.models_max > 0 && count_active >= (size_t)base_params.models_max;
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bool memory_exceeded = base_params.models_memory_max > 0 && total_memory_mb >= (uint64_t)base_params.models_memory_max;
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bool memory_exceeded = get_memory_exceeded(new_model_memory_per_device) > 0;
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if (count_exceeded || memory_exceeded) {
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throw std::runtime_error("model limit reached, try again later");
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}
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@ -54,6 +54,8 @@ static std::string server_model_status_to_string(server_model_status status) {
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}
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}
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using model_memory_map = std::map<ggml_backend_dev_t, uint64_t>;
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struct server_model_meta {
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common_preset preset;
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std::string name;
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@ -62,7 +64,7 @@ struct server_model_meta {
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int port = 0;
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server_model_status status = SERVER_MODEL_STATUS_UNLOADED;
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int64_t last_used = 0; // for LRU unloading
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uint64_t memory_mb = 0; // size in MB
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model_memory_map memory_per_device; // projected bytes per device
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std::vector<std::string> args; // args passed to the model instance, will be populated by render_args()
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int exit_code = 0; // exit code of the model instance process (only valid if status == FAILED)
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int stop_timeout = 0; // seconds to wait before force-killing the model instance during shutdown
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@ -108,14 +110,20 @@ private:
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std::vector<std::string> base_env;
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common_preset base_preset; // base preset from llama-server CLI args
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// available memory per device
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std::map<ggml_backend_dev_t, uint64_t> memory_per_device;
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void update_meta(const std::string & name, const server_model_meta & meta);
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// unload least recently used models if the limit is reached
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void unload_lru(uint64_t new_model_memory_mb = 0);
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void unload_lru(const model_memory_map& new_model_memory_per_device);
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// not thread-safe, caller must hold mutex
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void add_model(server_model_meta && meta);
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// not thread-safe, caller must hold mutex
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uint64_t get_memory_exceeded(const model_memory_map& new_model_memory_per_device) const;
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public:
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server_models(const common_params & params, int argc, char ** argv);
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