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-rw-r--r--llama.cpp/tools/llama-bench/llama-bench.cpp2291
1 files changed, 2291 insertions, 0 deletions
diff --git a/llama.cpp/tools/llama-bench/llama-bench.cpp b/llama.cpp/tools/llama-bench/llama-bench.cpp
new file mode 100644
index 0000000..7da6c39
--- /dev/null
+++ b/llama.cpp/tools/llama-bench/llama-bench.cpp
@@ -0,0 +1,2291 @@
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <chrono>
+#include <cinttypes>
+#include <clocale>
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <ctime>
+#include <iterator>
+#include <map>
+#include <numeric>
+#include <regex>
+#include <sstream>
+#include <string>
+#include <thread>
+#include <vector>
+#include <unordered_set>
+
+#include "common.h"
+#include "ggml.h"
+#include "llama.h"
+
+#ifdef _WIN32
+# define WIN32_LEAN_AND_MEAN
+# ifndef NOMINMAX
+# define NOMINMAX
+# endif
+# include <windows.h>
+#endif
+
+// utils
+static uint64_t get_time_ns() {
+ using clock = std::chrono::high_resolution_clock;
+ return std::chrono::nanoseconds(clock::now().time_since_epoch()).count();
+}
+
+static bool tensor_buft_override_equal(const llama_model_tensor_buft_override& a, const llama_model_tensor_buft_override& b) {
+ if (a.pattern != b.pattern) {
+ // cString comparison that may be null
+ if (a.pattern == nullptr || b.pattern == nullptr) {
+ return false;
+ }
+ if (strcmp(a.pattern, b.pattern) != 0) {
+ return false;
+ }
+ }
+ if (a.buft != b.buft) {
+ return false;
+ }
+ return true;
+}
+
+static bool vec_tensor_buft_override_equal(const std::vector<llama_model_tensor_buft_override>& a, const std::vector<llama_model_tensor_buft_override>& b) {
+ if (a.size() != b.size()) {
+ return false;
+ }
+ for (size_t i = 0; i < a.size(); i++) {
+ if (!tensor_buft_override_equal(a[i], b[i])) {
+ return false;
+ }
+ }
+ return true;
+}
+
+static bool vec_vec_tensor_buft_override_equal(const std::vector<std::vector<llama_model_tensor_buft_override>>& a, const std::vector<std::vector<llama_model_tensor_buft_override>>& b) {
+ if (a.size() != b.size()) {
+ return false;
+ }
+ for (size_t i = 0; i < a.size(); i++) {
+ if (!vec_tensor_buft_override_equal(a[i], b[i])) {
+ return false;
+ }
+ }
+ return true;
+}
+
+template <class T> static std::string join(const std::vector<T> & values, const std::string & delim) {
+ std::ostringstream str;
+ for (size_t i = 0; i < values.size(); i++) {
+ str << values[i];
+ if (i < values.size() - 1) {
+ str << delim;
+ }
+ }
+ return str.str();
+}
+
+template <typename T, typename F> static std::vector<std::string> transform_to_str(const std::vector<T> & values, F f) {
+ std::vector<std::string> str_values;
+ std::transform(values.begin(), values.end(), std::back_inserter(str_values), f);
+ return str_values;
+}
+
+template <typename T> static T avg(const std::vector<T> & v) {
+ if (v.empty()) {
+ return 0;
+ }
+ T sum = std::accumulate(v.begin(), v.end(), T(0));
+ return sum / (T) v.size();
+}
+
+template <typename T> static T stdev(const std::vector<T> & v) {
+ if (v.size() <= 1) {
+ return 0;
+ }
+ T mean = avg(v);
+ T sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), T(0));
+ T stdev = std::sqrt(sq_sum / (T) (v.size() - 1) - mean * mean * (T) v.size() / (T) (v.size() - 1));
+ return stdev;
+}
+
+static std::string get_cpu_info() {
+ std::vector<std::string> cpu_list;
+ for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
+ auto * dev = ggml_backend_dev_get(i);
+ auto dev_type = ggml_backend_dev_type(dev);
+ if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU || dev_type == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
+ cpu_list.push_back(ggml_backend_dev_description(dev));
+ }
+ }
+ return join(cpu_list, ", ");
+}
+
+static std::string get_gpu_info() {
+ std::vector<std::string> gpu_list;
+ for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
+ auto * dev = ggml_backend_dev_get(i);
+ auto dev_type = ggml_backend_dev_type(dev);
+ if (dev_type == GGML_BACKEND_DEVICE_TYPE_GPU || dev_type == GGML_BACKEND_DEVICE_TYPE_IGPU) {
+ gpu_list.push_back(ggml_backend_dev_description(dev));
+ }
+ }
+ return join(gpu_list, ", ");
+}
+
+static std::vector<ggml_backend_dev_t> parse_devices_arg(const std::string & value) {
+ std::vector<ggml_backend_dev_t> devices;
+ std::string trimmed = string_strip(value);
+ if (trimmed.empty()) {
+ throw std::invalid_argument("no devices specified");
+ }
+ if (trimmed == "auto") {
+ return devices;
+ }
+
+ auto dev_names = string_split<std::string>(trimmed, '/');
+ if (dev_names.size() == 1 && string_strip(dev_names[0]) == "none") {
+ devices.push_back(nullptr);
+ return devices;
+ }
+
+ for (auto & name : dev_names) {
+ std::string dev_name = string_strip(name);
+ if (dev_name.empty()) {
+ throw std::invalid_argument("invalid device specification");
+ }
+ auto * dev = ggml_backend_dev_by_name(dev_name.c_str());
+ if (!dev || ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
+ throw std::invalid_argument(string_format("invalid device: %s", dev_name.c_str()));
+ }
+ devices.push_back(dev);
+ }
+
+ devices.push_back(nullptr);
+ return devices;
+}
+
+static void register_rpc_server_list(const std::string & servers) {
+ auto rpc_servers = string_split<std::string>(servers, ',');
+ if (rpc_servers.empty()) {
+ throw std::invalid_argument("no RPC servers specified");
+ }
+
+ auto * rpc_reg = ggml_backend_reg_by_name("RPC");
+ if (!rpc_reg) {
+ throw std::invalid_argument("failed to find RPC backend");
+ }
+
+ using add_rpc_server_fn = ggml_backend_reg_t (*)(const char * endpoint);
+ auto * ggml_backend_rpc_add_server_fn = (add_rpc_server_fn) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_server");
+ if (!ggml_backend_rpc_add_server_fn) {
+ throw std::invalid_argument("failed to find RPC add server function");
+ }
+ for (const auto & server : rpc_servers) {
+ auto reg = ggml_backend_rpc_add_server_fn(server.c_str());
+ ggml_backend_register(reg);
+ }
+}
+
+static std::string devices_to_string(const std::vector<ggml_backend_dev_t> & devices) {
+ if (devices.empty()) {
+ return "auto";
+ }
+
+ if (devices.size() == 1 && devices[0] == nullptr) {
+ return "none";
+ }
+
+ std::vector<std::string> names;
+ for (auto * dev : devices) {
+ if (dev == nullptr) {
+ break;
+ }
+ names.push_back(ggml_backend_dev_name(dev));
+ }
+
+ return join(names, "/");
+}
+
+// command line params
+enum output_formats { NONE, CSV, JSON, JSONL, MARKDOWN, SQL };
+
+static const char * output_format_str(output_formats format) {
+ switch (format) {
+ case NONE:
+ return "none";
+ case CSV:
+ return "csv";
+ case JSON:
+ return "json";
+ case JSONL:
+ return "jsonl";
+ case MARKDOWN:
+ return "md";
+ case SQL:
+ return "sql";
+ default:
+ GGML_ABORT("invalid output format");
+ }
+}
+
+static bool output_format_from_str(const std::string & s, output_formats & format) {
+ if (s == "none") {
+ format = NONE;
+ } else if (s == "csv") {
+ format = CSV;
+ } else if (s == "json") {
+ format = JSON;
+ } else if (s == "jsonl") {
+ format = JSONL;
+ } else if (s == "md") {
+ format = MARKDOWN;
+ } else if (s == "sql") {
+ format = SQL;
+ } else {
+ return false;
+ }
+ return true;
+}
+
+static const char * split_mode_str(llama_split_mode mode) {
+ switch (mode) {
+ case LLAMA_SPLIT_MODE_NONE:
+ return "none";
+ case LLAMA_SPLIT_MODE_LAYER:
+ return "layer";
+ case LLAMA_SPLIT_MODE_ROW:
+ return "row";
+ default:
+ GGML_ABORT("invalid split mode");
+ }
+}
+
+static std::string pair_str(const std::pair<int, int> & p) {
+ static char buf[32];
+ snprintf(buf, sizeof(buf), "%d,%d", p.first, p.second);
+ return buf;
+}
+
+static std::vector<int> parse_int_range(const std::string & s) {
+ // first[-last[(+|*)step]]
+ std::regex range_regex(R"(^(\d+)(?:-(\d+)(?:([\+|\*])(\d+))?)?(?:,|$))");
+
+ std::smatch match;
+ std::string::const_iterator search_start(s.cbegin());
+ std::vector<int> result;
+ while (std::regex_search(search_start, s.cend(), match, range_regex)) {
+ int first = std::stoi(match[1]);
+ int last = match[2].matched ? std::stoi(match[2]) : first;
+ char op = match[3].matched ? match[3].str()[0] : '+';
+ int step = match[4].matched ? std::stoi(match[4]) : 1;
+
+ for (int i = first; i <= last;) {
+ result.push_back(i);
+
+ int prev_i = i;
+
+ if (op == '+') {
+ i += step;
+ } else if (op == '*') {
+ i *= step;
+ } else {
+ throw std::invalid_argument("invalid range format");
+ }
+
+ if (i <= prev_i) {
+ throw std::invalid_argument("invalid range");
+ }
+ }
+ search_start = match.suffix().first;
+ }
+
+ if (search_start != s.cend()) {
+ throw std::invalid_argument("invalid range format");
+ }
+
+ return result;
+}
+
+struct cmd_params {
+ std::vector<std::string> model;
+ std::vector<int> n_prompt;
+ std::vector<int> n_gen;
+ std::vector<std::pair<int, int>> n_pg;
+ std::vector<int> n_depth;
+ std::vector<int> n_batch;
+ std::vector<int> n_ubatch;
+ std::vector<ggml_type> type_k;
+ std::vector<ggml_type> type_v;
+ std::vector<int> n_threads;
+ std::vector<std::string> cpu_mask;
+ std::vector<bool> cpu_strict;
+ std::vector<int> poll;
+ std::vector<int> n_gpu_layers;
+ std::vector<int> n_cpu_moe;
+ std::vector<llama_split_mode> split_mode;
+ std::vector<int> main_gpu;
+ std::vector<bool> no_kv_offload;
+ std::vector<bool> flash_attn;
+ std::vector<std::vector<ggml_backend_dev_t>> devices;
+ std::vector<std::vector<float>> tensor_split;
+ std::vector<std::vector<llama_model_tensor_buft_override>> tensor_buft_overrides;
+ std::vector<bool> use_mmap;
+ std::vector<bool> use_direct_io;
+ std::vector<bool> embeddings;
+ std::vector<bool> no_op_offload;
+ std::vector<bool> no_host;
+ ggml_numa_strategy numa;
+ int reps;
+ ggml_sched_priority prio;
+ int delay;
+ bool verbose;
+ bool progress;
+ bool no_warmup;
+ output_formats output_format;
+ output_formats output_format_stderr;
+};
+
+static const cmd_params cmd_params_defaults = {
+ /* model */ { "models/7B/ggml-model-q4_0.gguf" },
+ /* n_prompt */ { 512 },
+ /* n_gen */ { 128 },
+ /* n_pg */ {},
+ /* n_depth */ { 0 },
+ /* n_batch */ { 2048 },
+ /* n_ubatch */ { 512 },
+ /* type_k */ { GGML_TYPE_F16 },
+ /* type_v */ { GGML_TYPE_F16 },
+ /* n_threads */ { cpu_get_num_math() },
+ /* cpu_mask */ { "0x0" },
+ /* cpu_strict */ { false },
+ /* poll */ { 50 },
+ /* n_gpu_layers */ { 99 },
+ /* n_cpu_moe */ { 0 },
+ /* split_mode */ { LLAMA_SPLIT_MODE_LAYER },
+ /* main_gpu */ { 0 },
+ /* no_kv_offload */ { false },
+ /* flash_attn */ { false },
+ /* devices */ { {} },
+ /* tensor_split */ { std::vector<float>(llama_max_devices(), 0.0f) },
+ /* tensor_buft_overrides*/ { std::vector<llama_model_tensor_buft_override>{ { nullptr, nullptr } } },
+ /* use_mmap */ { false },
+ /* use_direct_io */ { false },
+ /* embeddings */ { false },
+ /* no_op_offload */ { false },
+ /* no_host */ { false },
+ /* numa */ GGML_NUMA_STRATEGY_DISABLED,
+ /* reps */ 5,
+ /* prio */ GGML_SCHED_PRIO_NORMAL,
+ /* delay */ 0,
+ /* verbose */ false,
+ /* progress */ false,
+ /* no_warmup */ false,
+ /* output_format */ MARKDOWN,
+ /* output_format_stderr */ NONE,
+};
+
+static void print_usage(int /* argc */, char ** argv) {
+ printf("usage: %s [options]\n", argv[0]);
+ printf("\n");
+ printf("options:\n");
+ printf(" -h, --help\n");
+ printf(" --numa <distribute|isolate|numactl> numa mode (default: disabled)\n");
+ printf(" -r, --repetitions <n> number of times to repeat each test (default: %d)\n",
+ cmd_params_defaults.reps);
+ printf(" --prio <-1|0|1|2|3> process/thread priority (default: %d)\n",
+ cmd_params_defaults.prio);
+ printf(" --delay <0...N> (seconds) delay between each test (default: %d)\n",
+ cmd_params_defaults.delay);
+ printf(" -o, --output <csv|json|jsonl|md|sql> output format printed to stdout (default: %s)\n",
+ output_format_str(cmd_params_defaults.output_format));
+ printf(" -oe, --output-err <csv|json|jsonl|md|sql> output format printed to stderr (default: %s)\n",
+ output_format_str(cmd_params_defaults.output_format_stderr));
+ printf(" --list-devices list available devices and exit\n");
+ printf(" -v, --verbose verbose output\n");
+ printf(" --progress print test progress indicators\n");
+ printf(" --no-warmup skip warmup runs before benchmarking\n");
+ if (llama_supports_rpc()) {
+ printf(" -rpc, --rpc <rpc_servers> register RPC devices (comma separated)\n");
+ }
+ printf("\n");
+ printf("test parameters:\n");
+ printf(" -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
+ printf(" -p, --n-prompt <n> (default: %s)\n",
+ join(cmd_params_defaults.n_prompt, ",").c_str());
+ printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
+ printf(" -pg <pp,tg> (default: %s)\n",
+ join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str());
+ printf(" -d, --n-depth <n> (default: %s)\n",
+ join(cmd_params_defaults.n_depth, ",").c_str());
+ printf(" -b, --batch-size <n> (default: %s)\n",
+ join(cmd_params_defaults.n_batch, ",").c_str());
+ printf(" -ub, --ubatch-size <n> (default: %s)\n",
+ join(cmd_params_defaults.n_ubatch, ",").c_str());
+ printf(" -ctk, --cache-type-k <t> (default: %s)\n",
+ join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
+ printf(" -ctv, --cache-type-v <t> (default: %s)\n",
+ join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
+ printf(" -t, --threads <n> (default: %s)\n",
+ join(cmd_params_defaults.n_threads, ",").c_str());
+ printf(" -C, --cpu-mask <hex,hex> (default: %s)\n",
+ join(cmd_params_defaults.cpu_mask, ",").c_str());
+ printf(" --cpu-strict <0|1> (default: %s)\n",
+ join(cmd_params_defaults.cpu_strict, ",").c_str());
+ printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str());
+ printf(" -ngl, --n-gpu-layers <n> (default: %s)\n",
+ join(cmd_params_defaults.n_gpu_layers, ",").c_str());
+ printf(" -ncmoe, --n-cpu-moe <n> (default: %s)\n",
+ join(cmd_params_defaults.n_cpu_moe, ",").c_str());
+ printf(" -sm, --split-mode <none|layer|row> (default: %s)\n",
+ join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
+ printf(" -mg, --main-gpu <i> (default: %s)\n",
+ join(cmd_params_defaults.main_gpu, ",").c_str());
+ printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n",
+ join(cmd_params_defaults.no_kv_offload, ",").c_str());
+ printf(" -fa, --flash-attn <0|1> (default: %s)\n",
+ join(cmd_params_defaults.flash_attn, ",").c_str());
+ printf(" -dev, --device <dev0/dev1/...> (default: auto)\n");
+ printf(" -mmp, --mmap <0|1> (default: %s)\n",
+ join(cmd_params_defaults.use_mmap, ",").c_str());
+ printf(" -dio, --direct-io <0|1> (default: %s)\n",
+ join(cmd_params_defaults.use_direct_io, ",").c_str());
+ printf(" -embd, --embeddings <0|1> (default: %s)\n",
+ join(cmd_params_defaults.embeddings, ",").c_str());
+ printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
+ printf(" -ot --override-tensor <tensor name pattern>=<buffer type>;...\n");
+ printf(" (default: disabled)\n");
+ printf(" -nopo, --no-op-offload <0|1> (default: 0)\n");
+ printf(" --no-host <0|1> (default: %s)\n",
+ join(cmd_params_defaults.no_host, ",").c_str());
+ printf("\n");
+ printf(
+ "Multiple values can be given for each parameter by separating them with ','\n"
+ "or by specifying the parameter multiple times. Ranges can be given as\n"
+ "'first-last' or 'first-last+step' or 'first-last*mult'.\n");
+}
+
+static ggml_type ggml_type_from_name(const std::string & s) {
+ if (s == "f16") {
+ return GGML_TYPE_F16;
+ }
+ if (s == "bf16") {
+ return GGML_TYPE_BF16;
+ }
+ if (s == "q8_0") {
+ return GGML_TYPE_Q8_0;
+ }
+ if (s == "q4_0") {
+ return GGML_TYPE_Q4_0;
+ }
+ if (s == "q4_1") {
+ return GGML_TYPE_Q4_1;
+ }
+ if (s == "q5_0") {
+ return GGML_TYPE_Q5_0;
+ }
+ if (s == "q5_1") {
+ return GGML_TYPE_Q5_1;
+ }
+ if (s == "iq4_nl") {
+ return GGML_TYPE_IQ4_NL;
+ }
+
+ return GGML_TYPE_COUNT;
+}
+
+static cmd_params parse_cmd_params(int argc, char ** argv) {
+ cmd_params params;
+ std::string arg;
+ bool invalid_param = false;
+ const std::string arg_prefix = "--";
+ const char split_delim = ',';
+
+ params.verbose = cmd_params_defaults.verbose;
+ params.output_format = cmd_params_defaults.output_format;
+ params.output_format_stderr = cmd_params_defaults.output_format_stderr;
+ params.reps = cmd_params_defaults.reps;
+ params.numa = cmd_params_defaults.numa;
+ params.prio = cmd_params_defaults.prio;
+ params.delay = cmd_params_defaults.delay;
+ params.progress = cmd_params_defaults.progress;
+ params.no_warmup = cmd_params_defaults.no_warmup;
+
+ for (int i = 1; i < argc; i++) {
+ arg = argv[i];
+ if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+ std::replace(arg.begin(), arg.end(), '_', '-');
+ }
+
+ try {
+ if (arg == "-h" || arg == "--help") {
+ print_usage(argc, argv);
+ exit(0);
+ } else if (arg == "-m" || arg == "--model") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<std::string>(argv[i], split_delim);
+ params.model.insert(params.model.end(), p.begin(), p.end());
+ } else if (arg == "-p" || arg == "--n-prompt") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = parse_int_range(argv[i]);
+ params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end());
+ } else if (arg == "-n" || arg == "--n-gen") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = parse_int_range(argv[i]);
+ params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());
+ } else if (arg == "-pg") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<std::string>(argv[i], ',');
+ if (p.size() != 2) {
+ invalid_param = true;
+ break;
+ }
+ params.n_pg.push_back({ std::stoi(p[0]), std::stoi(p[1]) });
+ } else if (arg == "-d" || arg == "--n-depth") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = parse_int_range(argv[i]);
+ params.n_depth.insert(params.n_depth.end(), p.begin(), p.end());
+ } else if (arg == "-b" || arg == "--batch-size") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = parse_int_range(argv[i]);
+ params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
+ } else if (arg == "-ub" || arg == "--ubatch-size") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = parse_int_range(argv[i]);
+ params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
+ } else if (arg == "-ctk" || arg == "--cache-type-k") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<std::string>(argv[i], split_delim);
+
+ std::vector<ggml_type> types;
+ for (const auto & t : p) {
+ ggml_type gt = ggml_type_from_name(t);
+ if (gt == GGML_TYPE_COUNT) {
+ invalid_param = true;
+ break;
+ }
+ types.push_back(gt);
+ }
+ if (invalid_param) {
+ break;
+ }
+ params.type_k.insert(params.type_k.end(), types.begin(), types.end());
+ } else if (arg == "-ctv" || arg == "--cache-type-v") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<std::string>(argv[i], split_delim);
+
+ std::vector<ggml_type> types;
+ for (const auto & t : p) {
+ ggml_type gt = ggml_type_from_name(t);
+ if (gt == GGML_TYPE_COUNT) {
+ invalid_param = true;
+ break;
+ }
+ types.push_back(gt);
+ }
+ if (invalid_param) {
+ break;
+ }
+ params.type_v.insert(params.type_v.end(), types.begin(), types.end());
+ } else if (arg == "-dev" || arg == "--device") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto combos = string_split<std::string>(argv[i], split_delim);
+ for (const auto & combo : combos) {
+ try {
+ params.devices.push_back(parse_devices_arg(combo));
+ } catch (const std::exception & e) {
+ fprintf(stderr, "error: %s\n", e.what());
+ invalid_param = true;
+ break;
+ }
+ }
+ if (invalid_param) {
+ break;
+ }
+ } else if (arg == "--list-devices") {
+ std::vector<ggml_backend_dev_t> devices;
+ for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+ auto * dev = ggml_backend_dev_get(i);
+ if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) {
+ devices.push_back(dev);
+ }
+ }
+ printf("Available devices:\n");
+ if (devices.empty()) {
+ printf(" (none)\n");
+ }
+ for (auto * dev : devices) {
+ size_t free, total;
+ ggml_backend_dev_memory(dev, &free, &total);
+ printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
+ }
+ exit(0);
+ } else if (arg == "-t" || arg == "--threads") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = parse_int_range(argv[i]);
+ params.n_threads.insert(params.n_threads.end(), p.begin(), p.end());
+ } else if (arg == "-C" || arg == "--cpu-mask") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<std::string>(argv[i], split_delim);
+ params.cpu_mask.insert(params.cpu_mask.end(), p.begin(), p.end());
+ } else if (arg == "--cpu-strict") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<bool>(argv[i], split_delim);
+ params.cpu_strict.insert(params.cpu_strict.end(), p.begin(), p.end());
+ } else if (arg == "--poll") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = parse_int_range(argv[i]);
+ params.poll.insert(params.poll.end(), p.begin(), p.end());
+ } else if (arg == "-ngl" || arg == "--n-gpu-layers") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = parse_int_range(argv[i]);
+ params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
+ } else if (arg == "-ncmoe" || arg == "--n-cpu-moe") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = parse_int_range(argv[i]);
+ params.n_cpu_moe.insert(params.n_cpu_moe.end(), p.begin(), p.end());
+ } else if (llama_supports_rpc() && (arg == "-rpc" || arg == "--rpc")) {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ try {
+ register_rpc_server_list(argv[i]);
+ } catch (const std::exception & e) {
+ fprintf(stderr, "error: %s\n", e.what());
+ invalid_param = true;
+ break;
+ }
+ } else if (arg == "-sm" || arg == "--split-mode") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<std::string>(argv[i], split_delim);
+
+ std::vector<llama_split_mode> modes;
+ for (const auto & m : p) {
+ llama_split_mode mode;
+ if (m == "none") {
+ mode = LLAMA_SPLIT_MODE_NONE;
+ } else if (m == "layer") {
+ mode = LLAMA_SPLIT_MODE_LAYER;
+ } else if (m == "row") {
+ mode = LLAMA_SPLIT_MODE_ROW;
+ } else {
+ invalid_param = true;
+ break;
+ }
+ modes.push_back(mode);
+ }
+ if (invalid_param) {
+ break;
+ }
+ params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end());
+ } else if (arg == "-mg" || arg == "--main-gpu") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.main_gpu = parse_int_range(argv[i]);
+ } else if (arg == "-nkvo" || arg == "--no-kv-offload") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<bool>(argv[i], split_delim);
+ params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
+ } else if (arg == "--numa") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ std::string value(argv[i]);
+ if (value == "distribute" || value == "") {
+ params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE;
+ } else if (value == "isolate") {
+ params.numa = GGML_NUMA_STRATEGY_ISOLATE;
+ } else if (value == "numactl") {
+ params.numa = GGML_NUMA_STRATEGY_NUMACTL;
+ } else {
+ invalid_param = true;
+ break;
+ }
+ } else if (arg == "-fa" || arg == "--flash-attn") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<bool>(argv[i], split_delim);
+ params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end());
+ } else if (arg == "-mmp" || arg == "--mmap") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<bool>(argv[i], split_delim);
+ params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end());
+ } else if (arg == "-dio" || arg == "--direct-io") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<bool>(argv[i], split_delim);
+ params.use_direct_io.insert(params.use_direct_io.end(), p.begin(), p.end());
+ } else if (arg == "-embd" || arg == "--embeddings") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<bool>(argv[i], split_delim);
+ params.embeddings.insert(params.embeddings.end(), p.begin(), p.end());
+ } else if (arg == "-nopo" || arg == "--no-op-offload") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<bool>(argv[i], split_delim);
+ params.no_op_offload.insert(params.no_op_offload.end(), p.begin(), p.end());
+ } else if (arg == "--no-host") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<bool>(argv[i], split_delim);
+ params.no_host.insert(params.no_host.end(), p.begin(), p.end());
+ } else if (arg == "-ts" || arg == "--tensor-split") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ for (auto ts : string_split<std::string>(argv[i], split_delim)) {
+ // split string by ; and /
+ const std::regex regex{ R"([;/]+)" };
+ std::sregex_token_iterator it{ ts.begin(), ts.end(), regex, -1 };
+ std::vector<std::string> split_arg{ it, {} };
+ GGML_ASSERT(split_arg.size() <= llama_max_devices());
+
+ std::vector<float> tensor_split(llama_max_devices());
+ for (size_t i = 0; i < llama_max_devices(); ++i) {
+ if (i < split_arg.size()) {
+ tensor_split[i] = std::stof(split_arg[i]);
+ } else {
+ tensor_split[i] = 0.0f;
+ }
+ }
+ params.tensor_split.push_back(tensor_split);
+ }
+ } else if (arg == "-ot" || arg == "--override-tensor") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto * value = argv[i];
+ /* static */ std::map<std::string, ggml_backend_buffer_type_t> buft_list;
+ if (buft_list.empty()) {
+ // enumerate all the devices and add their buffer types to the list
+ for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+ auto * dev = ggml_backend_dev_get(i);
+ auto * buft = ggml_backend_dev_buffer_type(dev);
+ if (buft) {
+ buft_list[ggml_backend_buft_name(buft)] = buft;
+ }
+ }
+ }
+ auto override_group_span_len = std::strcspn(value, ",");
+ bool last_group = false;
+ do {
+ if (override_group_span_len == 0) {
+ // Adds an empty override-tensors for an empty span
+ params.tensor_buft_overrides.push_back({{}});
+ if (value[override_group_span_len] == '\0') {
+ value = &value[override_group_span_len];
+ last_group = true;
+ } else {
+ value = &value[override_group_span_len + 1];
+ override_group_span_len = std::strcspn(value, ",");
+ }
+ continue;
+ }
+ // Stamps null terminators into the argv
+ // value for this option to avoid the
+ // memory leak present in the implementation
+ // over in arg.cpp. Acceptable because we
+ // only parse these args once in this program.
+ auto * override_group = value;
+ if (value[override_group_span_len] == '\0') {
+ value = &value[override_group_span_len];
+ last_group = true;
+ } else {
+ value[override_group_span_len] = '\0';
+ value = &value[override_group_span_len + 1];
+ }
+ std::vector<llama_model_tensor_buft_override> group_tensor_buft_overrides{};
+ auto override_span_len = std::strcspn(override_group, ";");
+ while (override_span_len > 0) {
+ auto * override = override_group;
+ if (override_group[override_span_len] != '\0') {
+ override_group[override_span_len] = '\0';
+ override_group = &override_group[override_span_len + 1];
+ } else {
+ override_group = &override_group[override_span_len];
+ }
+ auto tensor_name_span_len = std::strcspn(override, "=");
+ if (tensor_name_span_len >= override_span_len) {
+ invalid_param = true;
+ break;
+ }
+ override[tensor_name_span_len] = '\0';
+ auto * tensor_name = override;
+ auto * buffer_type = &override[tensor_name_span_len + 1];
+ if (buft_list.find(buffer_type) == buft_list.end()) {
+ printf("error: unrecognized buffer type '%s'\n", buffer_type);
+ printf("Available buffer types:\n");
+ for (const auto & it : buft_list) {
+ printf(" %s\n", ggml_backend_buft_name(it.second));
+ }
+ invalid_param = true;
+ break;
+ }
+ group_tensor_buft_overrides.push_back({tensor_name, buft_list.at(buffer_type)});
+ override_span_len = std::strcspn(override_group, ";");
+ }
+ if (invalid_param) {
+ break;
+ }
+ group_tensor_buft_overrides.push_back({nullptr,nullptr});
+ params.tensor_buft_overrides.push_back(group_tensor_buft_overrides);
+ override_group_span_len = std::strcspn(value, ",");
+ } while (!last_group);
+ } else if (arg == "-r" || arg == "--repetitions") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.reps = std::stoi(argv[i]);
+ } else if (arg == "--prio") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.prio = (enum ggml_sched_priority) std::stoi(argv[i]);
+ } else if (arg == "--delay") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.delay = std::stoi(argv[i]);
+ } else if (arg == "-o" || arg == "--output") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ invalid_param = !output_format_from_str(argv[i], params.output_format);
+ } else if (arg == "-oe" || arg == "--output-err") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ invalid_param = !output_format_from_str(argv[i], params.output_format_stderr);
+ } else if (arg == "-v" || arg == "--verbose") {
+ params.verbose = true;
+ } else if (arg == "--progress") {
+ params.progress = true;
+ } else if (arg == "--no-warmup") {
+ params.no_warmup = true;
+ } else {
+ invalid_param = true;
+ break;
+ }
+ } catch (const std::exception & e) {
+ fprintf(stderr, "error: %s\n", e.what());
+ invalid_param = true;
+ break;
+ }
+ }
+
+ if (invalid_param) {
+ fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
+ print_usage(argc, argv);
+ exit(1);
+ }
+
+ // set defaults
+ if (params.model.empty()) {
+ params.model = cmd_params_defaults.model;
+ }
+ if (params.n_prompt.empty()) {
+ params.n_prompt = cmd_params_defaults.n_prompt;
+ }
+ if (params.n_gen.empty()) {
+ params.n_gen = cmd_params_defaults.n_gen;
+ }
+ if (params.n_pg.empty()) {
+ params.n_pg = cmd_params_defaults.n_pg;
+ }
+ if (params.n_depth.empty()) {
+ params.n_depth = cmd_params_defaults.n_depth;
+ }
+ if (params.n_batch.empty()) {
+ params.n_batch = cmd_params_defaults.n_batch;
+ }
+ if (params.n_ubatch.empty()) {
+ params.n_ubatch = cmd_params_defaults.n_ubatch;
+ }
+ if (params.type_k.empty()) {
+ params.type_k = cmd_params_defaults.type_k;
+ }
+ if (params.type_v.empty()) {
+ params.type_v = cmd_params_defaults.type_v;
+ }
+ if (params.n_gpu_layers.empty()) {
+ params.n_gpu_layers = cmd_params_defaults.n_gpu_layers;
+ }
+ if (params.n_cpu_moe.empty()) {
+ params.n_cpu_moe = cmd_params_defaults.n_cpu_moe;
+ }
+ if (params.split_mode.empty()) {
+ params.split_mode = cmd_params_defaults.split_mode;
+ }
+ if (params.main_gpu.empty()) {
+ params.main_gpu = cmd_params_defaults.main_gpu;
+ }
+ if (params.no_kv_offload.empty()) {
+ params.no_kv_offload = cmd_params_defaults.no_kv_offload;
+ }
+ if (params.flash_attn.empty()) {
+ params.flash_attn = cmd_params_defaults.flash_attn;
+ }
+ if (params.devices.empty()) {
+ params.devices = cmd_params_defaults.devices;
+ }
+ if (params.tensor_split.empty()) {
+ params.tensor_split = cmd_params_defaults.tensor_split;
+ }
+ if (params.tensor_buft_overrides.empty()) {
+ params.tensor_buft_overrides = cmd_params_defaults.tensor_buft_overrides;
+ }
+ if (params.use_mmap.empty()) {
+ params.use_mmap = cmd_params_defaults.use_mmap;
+ }
+ if (params.use_direct_io.empty()) {
+ params.use_direct_io = cmd_params_defaults.use_direct_io;
+ }
+ if (params.embeddings.empty()) {
+ params.embeddings = cmd_params_defaults.embeddings;
+ }
+ if (params.no_op_offload.empty()) {
+ params.no_op_offload = cmd_params_defaults.no_op_offload;
+ }
+ if (params.no_host.empty()) {
+ params.no_host = cmd_params_defaults.no_host;
+ }
+ if (params.n_threads.empty()) {
+ params.n_threads = cmd_params_defaults.n_threads;
+ }
+ if (params.cpu_mask.empty()) {
+ params.cpu_mask = cmd_params_defaults.cpu_mask;
+ }
+ if (params.cpu_strict.empty()) {
+ params.cpu_strict = cmd_params_defaults.cpu_strict;
+ }
+ if (params.poll.empty()) {
+ params.poll = cmd_params_defaults.poll;
+ }
+
+ return params;
+}
+
+struct cmd_params_instance {
+ std::string model;
+ int n_prompt;
+ int n_gen;
+ int n_depth;
+ int n_batch;
+ int n_ubatch;
+ ggml_type type_k;
+ ggml_type type_v;
+ int n_threads;
+ std::string cpu_mask;
+ bool cpu_strict;
+ int poll;
+ int n_gpu_layers;
+ int n_cpu_moe;
+ llama_split_mode split_mode;
+ int main_gpu;
+ bool no_kv_offload;
+ bool flash_attn;
+ std::vector<ggml_backend_dev_t> devices;
+ std::vector<float> tensor_split;
+ std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
+ bool use_mmap;
+ bool use_direct_io;
+ bool embeddings;
+ bool no_op_offload;
+ bool no_host;
+
+ llama_model_params to_llama_mparams() const {
+ llama_model_params mparams = llama_model_default_params();
+
+ mparams.n_gpu_layers = n_gpu_layers;
+ if (!devices.empty()) {
+ mparams.devices = const_cast<ggml_backend_dev_t *>(devices.data());
+ }
+ mparams.split_mode = split_mode;
+ mparams.main_gpu = main_gpu;
+ mparams.tensor_split = tensor_split.data();
+ mparams.use_mmap = use_mmap;
+ mparams.use_direct_io = use_direct_io;
+ mparams.no_host = no_host;
+
+ if (n_cpu_moe <= 0) {
+ if (tensor_buft_overrides.empty()) {
+ mparams.tensor_buft_overrides = nullptr;
+ } else {
+ GGML_ASSERT(tensor_buft_overrides.back().pattern == nullptr &&
+ "Tensor buffer overrides not terminated with empty pattern");
+ mparams.tensor_buft_overrides = tensor_buft_overrides.data();
+ }
+ } else {
+ static std::vector<llama_model_tensor_buft_override> merged;
+ static std::vector<std::string> patterns;
+
+ merged.clear();
+ patterns.clear();
+
+ auto first = tensor_buft_overrides.begin();
+ auto last = tensor_buft_overrides.end();
+ if (first != last && (last - 1)->pattern == nullptr) {
+ --last;
+ }
+ merged.insert(merged.end(), first, last);
+
+ patterns.reserve((size_t) n_cpu_moe);
+ merged.reserve(merged.size() + (size_t) n_cpu_moe + 1);
+
+ for (int i = 0; i < n_cpu_moe; ++i) {
+ patterns.push_back(llm_ffn_exps_block_regex(i));
+ merged.push_back({ patterns.back().c_str(),
+ ggml_backend_cpu_buffer_type() });
+ }
+
+ merged.push_back({ nullptr, nullptr });
+
+ mparams.tensor_buft_overrides = merged.data();
+ }
+
+ return mparams;
+ }
+
+ bool equal_mparams(const cmd_params_instance & other) const {
+ return model == other.model && n_gpu_layers == other.n_gpu_layers && n_cpu_moe == other.n_cpu_moe &&
+ split_mode == other.split_mode &&
+ main_gpu == other.main_gpu && tensor_split == other.tensor_split &&
+ use_mmap == other.use_mmap && use_direct_io == other.use_direct_io &&
+ devices == other.devices &&
+ no_host == other.no_host &&
+ vec_tensor_buft_override_equal(tensor_buft_overrides, other.tensor_buft_overrides);
+ }
+
+ llama_context_params to_llama_cparams() const {
+ llama_context_params cparams = llama_context_default_params();
+
+ cparams.n_ctx = n_prompt + n_gen + n_depth;
+ cparams.n_batch = n_batch;
+ cparams.n_ubatch = n_ubatch;
+ cparams.type_k = type_k;
+ cparams.type_v = type_v;
+ cparams.offload_kqv = !no_kv_offload;
+ cparams.flash_attn_type = flash_attn ? LLAMA_FLASH_ATTN_TYPE_ENABLED : LLAMA_FLASH_ATTN_TYPE_DISABLED;
+ cparams.embeddings = embeddings;
+ cparams.op_offload = !no_op_offload;
+ cparams.swa_full = false;
+
+ return cparams;
+ }
+};
+
+static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_params & params) {
+ std::vector<cmd_params_instance> instances;
+
+ // this ordering minimizes the number of times that each model needs to be reloaded
+ // clang-format off
+ for (const auto & m : params.model)
+ for (const auto & nl : params.n_gpu_layers)
+ for (const auto & ncmoe : params.n_cpu_moe)
+ for (const auto & sm : params.split_mode)
+ for (const auto & mg : params.main_gpu)
+ for (const auto & devs : params.devices)
+ for (const auto & ts : params.tensor_split)
+ for (const auto & ot : params.tensor_buft_overrides)
+ for (const auto & mmp : params.use_mmap)
+ for (const auto & dio : params.use_direct_io)
+ for (const auto & noh : params.no_host)
+ for (const auto & embd : params.embeddings)
+ for (const auto & nopo : params.no_op_offload)
+ for (const auto & nb : params.n_batch)
+ for (const auto & nub : params.n_ubatch)
+ for (const auto & tk : params.type_k)
+ for (const auto & tv : params.type_v)
+ for (const auto & nkvo : params.no_kv_offload)
+ for (const auto & fa : params.flash_attn)
+ for (const auto & nt : params.n_threads)
+ for (const auto & cm : params.cpu_mask)
+ for (const auto & cs : params.cpu_strict)
+ for (const auto & nd : params.n_depth)
+ for (const auto & pl : params.poll) {
+ for (const auto & n_prompt : params.n_prompt) {
+ if (n_prompt == 0) {
+ continue;
+ }
+ cmd_params_instance instance = {
+ /* .model = */ m,
+ /* .n_prompt = */ n_prompt,
+ /* .n_gen = */ 0,
+ /* .n_depth = */ nd,
+ /* .n_batch = */ nb,
+ /* .n_ubatch = */ nub,
+ /* .type_k = */ tk,
+ /* .type_v = */ tv,
+ /* .n_threads = */ nt,
+ /* .cpu_mask = */ cm,
+ /* .cpu_strict = */ cs,
+ /* .poll = */ pl,
+ /* .n_gpu_layers = */ nl,
+ /* .n_cpu_moe = */ ncmoe,
+ /* .split_mode = */ sm,
+ /* .main_gpu = */ mg,
+ /* .no_kv_offload= */ nkvo,
+ /* .flash_attn = */ fa,
+ /* .devices = */ devs,
+ /* .tensor_split = */ ts,
+ /* .tensor_buft_overrides = */ ot,
+ /* .use_mmap = */ mmp,
+ /* .use_direct_io= */ dio,
+ /* .embeddings = */ embd,
+ /* .no_op_offload= */ nopo,
+ /* .no_host = */ noh,
+ };
+ instances.push_back(instance);
+ }
+
+ for (const auto & n_gen : params.n_gen) {
+ if (n_gen == 0) {
+ continue;
+ }
+ cmd_params_instance instance = {
+ /* .model = */ m,
+ /* .n_prompt = */ 0,
+ /* .n_gen = */ n_gen,
+ /* .n_depth = */ nd,
+ /* .n_batch = */ nb,
+ /* .n_ubatch = */ nub,
+ /* .type_k = */ tk,
+ /* .type_v = */ tv,
+ /* .n_threads = */ nt,
+ /* .cpu_mask = */ cm,
+ /* .cpu_strict = */ cs,
+ /* .poll = */ pl,
+ /* .n_gpu_layers = */ nl,
+ /* .n_cpu_moe = */ ncmoe,
+ /* .split_mode = */ sm,
+ /* .main_gpu = */ mg,
+ /* .no_kv_offload= */ nkvo,
+ /* .flash_attn = */ fa,
+ /* .devices = */ devs,
+ /* .tensor_split = */ ts,
+ /* .tensor_buft_overrides = */ ot,
+ /* .use_mmap = */ mmp,
+ /* .use_direct_io= */ dio,
+ /* .embeddings = */ embd,
+ /* .no_op_offload= */ nopo,
+ /* .no_host = */ noh,
+ };
+ instances.push_back(instance);
+ }
+
+ for (const auto & n_pg : params.n_pg) {
+ if (n_pg.first == 0 && n_pg.second == 0) {
+ continue;
+ }
+ cmd_params_instance instance = {
+ /* .model = */ m,
+ /* .n_prompt = */ n_pg.first,
+ /* .n_gen = */ n_pg.second,
+ /* .n_depth = */ nd,
+ /* .n_batch = */ nb,
+ /* .n_ubatch = */ nub,
+ /* .type_k = */ tk,
+ /* .type_v = */ tv,
+ /* .n_threads = */ nt,
+ /* .cpu_mask = */ cm,
+ /* .cpu_strict = */ cs,
+ /* .poll = */ pl,
+ /* .n_gpu_layers = */ nl,
+ /* .n_cpu_moe = */ ncmoe,
+ /* .split_mode = */ sm,
+ /* .main_gpu = */ mg,
+ /* .no_kv_offload= */ nkvo,
+ /* .flash_attn = */ fa,
+ /* .devices = */ devs,
+ /* .tensor_split = */ ts,
+ /* .tensor_buft_overrides = */ ot,
+ /* .use_mmap = */ mmp,
+ /* .use_direct_io= */ dio,
+ /* .embeddings = */ embd,
+ /* .no_op_offload= */ nopo,
+ /* .no_host = */ noh,
+ };
+ instances.push_back(instance);
+ }
+ }
+ // clang-format on
+
+ return instances;
+}
+
+struct test {
+ static const std::string build_commit;
+ static const int build_number;
+ const std::string cpu_info;
+ const std::string gpu_info;
+ std::string model_filename;
+ std::string model_type;
+ uint64_t model_size;
+ uint64_t model_n_params;
+ int n_batch;
+ int n_ubatch;
+ int n_threads;
+ std::string cpu_mask;
+ bool cpu_strict;
+ int poll;
+ ggml_type type_k;
+ ggml_type type_v;
+ int n_gpu_layers;
+ int n_cpu_moe;
+ llama_split_mode split_mode;
+ int main_gpu;
+ bool no_kv_offload;
+ bool flash_attn;
+ std::vector<ggml_backend_dev_t> devices;
+ std::vector<float> tensor_split;
+ std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
+ bool use_mmap;
+ bool use_direct_io;
+ bool embeddings;
+ bool no_op_offload;
+ bool no_host;
+ int n_prompt;
+ int n_gen;
+ int n_depth;
+ std::string test_time;
+ std::vector<uint64_t> samples_ns;
+
+ test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) :
+ cpu_info(get_cpu_info()),
+ gpu_info(get_gpu_info()) {
+
+ model_filename = inst.model;
+ char buf[128];
+ llama_model_desc(lmodel, buf, sizeof(buf));
+ model_type = buf;
+ model_size = llama_model_size(lmodel);
+ model_n_params = llama_model_n_params(lmodel);
+ n_batch = inst.n_batch;
+ n_ubatch = inst.n_ubatch;
+ n_threads = inst.n_threads;
+ cpu_mask = inst.cpu_mask;
+ cpu_strict = inst.cpu_strict;
+ poll = inst.poll;
+ type_k = inst.type_k;
+ type_v = inst.type_v;
+ n_gpu_layers = inst.n_gpu_layers;
+ n_cpu_moe = inst.n_cpu_moe;
+ split_mode = inst.split_mode;
+ main_gpu = inst.main_gpu;
+ no_kv_offload = inst.no_kv_offload;
+ flash_attn = inst.flash_attn;
+ devices = inst.devices;
+ tensor_split = inst.tensor_split;
+ tensor_buft_overrides = inst.tensor_buft_overrides;
+ use_mmap = inst.use_mmap;
+ use_direct_io = inst.use_direct_io;
+ embeddings = inst.embeddings;
+ no_op_offload = inst.no_op_offload;
+ no_host = inst.no_host;
+ n_prompt = inst.n_prompt;
+ n_gen = inst.n_gen;
+ n_depth = inst.n_depth;
+ // RFC 3339 date-time format
+ time_t t = time(NULL);
+ std::strftime(buf, sizeof(buf), "%FT%TZ", gmtime(&t));
+ test_time = buf;
+
+ (void) ctx;
+ }
+
+ uint64_t avg_ns() const { return ::avg(samples_ns); }
+
+ uint64_t stdev_ns() const { return ::stdev(samples_ns); }
+
+ std::vector<double> get_ts() const {
+ int n_tokens = n_prompt + n_gen;
+ std::vector<double> ts;
+ std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts),
+ [n_tokens](uint64_t t) { return 1e9 * n_tokens / t; });
+ return ts;
+ }
+
+ double avg_ts() const { return ::avg(get_ts()); }
+
+ double stdev_ts() const { return ::stdev(get_ts()); }
+
+ static std::string get_backend() {
+ std::vector<std::string> backends;
+ bool rpc_used = false;
+ for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
+ auto * reg = ggml_backend_reg_get(i);
+ std::string name = ggml_backend_reg_name(reg);
+ if (string_starts_with(name, "RPC")) {
+ if (ggml_backend_reg_dev_count(reg) > 0) {
+ rpc_used = true;
+ }
+ } else {
+ if (name != "CPU") {
+ backends.push_back(ggml_backend_reg_name(reg));
+ }
+ }
+ }
+ if (rpc_used) {
+ backends.push_back("RPC");
+ }
+ return backends.empty() ? "CPU" : join(backends, ",");
+ }
+
+ static const std::vector<std::string> & get_fields() {
+ static const std::vector<std::string> fields = {
+ "build_commit", "build_number", "cpu_info", "gpu_info", "backends",
+ "model_filename", "model_type", "model_size", "model_n_params", "n_batch",
+ "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll",
+ "type_k", "type_v", "n_gpu_layers", "n_cpu_moe", "split_mode",
+ "main_gpu", "no_kv_offload", "flash_attn", "devices", "tensor_split",
+ "tensor_buft_overrides", "use_mmap", "use_direct_io", "embeddings",
+ "no_op_offload", "no_host", "n_prompt", "n_gen", "n_depth",
+ "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts"
+ };
+ return fields;
+ }
+
+ enum field_type { STRING, BOOL, INT, FLOAT };
+
+ static field_type get_field_type(const std::string & field) {
+ if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || field == "n_threads" ||
+ field == "poll" || field == "model_size" || field == "model_n_params" || field == "n_gpu_layers" ||
+ field == "main_gpu" || field == "n_prompt" || field == "n_gen" || field == "n_depth" || field == "avg_ns" ||
+ field == "stddev_ns" || field == "no_op_offload" || field == "n_cpu_moe") {
+ return INT;
+ }
+ if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || field == "flash_attn" ||
+ field == "use_mmap" || field == "use_direct_io" || field == "embeddings" || field == "no_host") {
+ return BOOL;
+ }
+ if (field == "avg_ts" || field == "stddev_ts") {
+ return FLOAT;
+ }
+ return STRING;
+ }
+
+ std::vector<std::string> get_values() const {
+ std::string tensor_split_str;
+ std::string tensor_buft_overrides_str;
+ int max_nonzero = 0;
+ for (size_t i = 0; i < llama_max_devices(); i++) {
+ if (tensor_split[i] > 0) {
+ max_nonzero = i;
+ }
+ }
+ for (int i = 0; i <= max_nonzero; i++) {
+ char buf[32];
+ snprintf(buf, sizeof(buf), "%.2f", tensor_split[i]);
+ tensor_split_str += buf;
+ if (i < max_nonzero) {
+ tensor_split_str += "/";
+ }
+ }
+ if (tensor_buft_overrides.size() == 1) {
+ // Last element of tensor_buft_overrides is always a null pattern
+ // so if it is only one element long, it must be a null pattern.
+ GGML_ASSERT(tensor_buft_overrides[0].pattern == nullptr);
+ tensor_buft_overrides_str += "none";
+ } else {
+ for (size_t i = 0; i < tensor_buft_overrides.size()-1; i++) {
+ // Last element of tensor_buft_overrides is always a null pattern
+ if (tensor_buft_overrides[i].pattern == nullptr) {
+ tensor_buft_overrides_str += "none";
+ } else {
+ tensor_buft_overrides_str += tensor_buft_overrides[i].pattern;
+ tensor_buft_overrides_str += "=";
+ tensor_buft_overrides_str += ggml_backend_buft_name(tensor_buft_overrides[i].buft);
+ }
+ if (i + 2 < tensor_buft_overrides.size()) {
+ tensor_buft_overrides_str += ";";
+ }
+ }
+ }
+ std::vector<std::string> values = { build_commit,
+ std::to_string(build_number),
+ cpu_info,
+ gpu_info,
+ get_backend(),
+ model_filename,
+ model_type,
+ std::to_string(model_size),
+ std::to_string(model_n_params),
+ std::to_string(n_batch),
+ std::to_string(n_ubatch),
+ std::to_string(n_threads),
+ cpu_mask,
+ std::to_string(cpu_strict),
+ std::to_string(poll),
+ ggml_type_name(type_k),
+ ggml_type_name(type_v),
+ std::to_string(n_gpu_layers),
+ std::to_string(n_cpu_moe),
+ split_mode_str(split_mode),
+ std::to_string(main_gpu),
+ std::to_string(no_kv_offload),
+ std::to_string(flash_attn),
+ devices_to_string(devices),
+ tensor_split_str,
+ tensor_buft_overrides_str,
+ std::to_string(use_mmap),
+ std::to_string(use_direct_io),
+ std::to_string(embeddings),
+ std::to_string(no_op_offload),
+ std::to_string(no_host),
+ std::to_string(n_prompt),
+ std::to_string(n_gen),
+ std::to_string(n_depth),
+ test_time,
+ std::to_string(avg_ns()),
+ std::to_string(stdev_ns()),
+ std::to_string(avg_ts()),
+ std::to_string(stdev_ts()) };
+ return values;
+ }
+
+ std::map<std::string, std::string> get_map() const {
+ std::map<std::string, std::string> map;
+ auto fields = get_fields();
+ auto values = get_values();
+ std::transform(fields.begin(), fields.end(), values.begin(), std::inserter(map, map.end()),
+ std::make_pair<const std::string &, const std::string &>);
+ return map;
+ }
+};
+
+const std::string test::build_commit = LLAMA_COMMIT;
+const int test::build_number = LLAMA_BUILD_NUMBER;
+
+struct printer {
+ virtual ~printer() {}
+
+ FILE * fout;
+
+ virtual void print_header(const cmd_params & params) { (void) params; }
+
+ virtual void print_test(const test & t) = 0;
+
+ virtual void print_footer() {}
+};
+
+struct csv_printer : public printer {
+ static std::string escape_csv(const std::string & field) {
+ std::string escaped = "\"";
+ for (auto c : field) {
+ if (c == '"') {
+ escaped += "\"";
+ }
+ escaped += c;
+ }
+ escaped += "\"";
+ return escaped;
+ }
+
+ void print_header(const cmd_params & params) override {
+ std::vector<std::string> fields = test::get_fields();
+ fprintf(fout, "%s\n", join(fields, ",").c_str());
+ (void) params;
+ }
+
+ void print_test(const test & t) override {
+ std::vector<std::string> values = t.get_values();
+ std::transform(values.begin(), values.end(), values.begin(), escape_csv);
+ fprintf(fout, "%s\n", join(values, ",").c_str());
+ }
+};
+
+static std::string escape_json(const std::string & value) {
+ std::string escaped;
+ for (auto c : value) {
+ if (c == '"') {
+ escaped += "\\\"";
+ } else if (c == '\\') {
+ escaped += "\\\\";
+ } else if (c <= 0x1f) {
+ char buf[8];
+ snprintf(buf, sizeof(buf), "\\u%04x", c);
+ escaped += buf;
+ } else {
+ escaped += c;
+ }
+ }
+ return escaped;
+}
+
+static std::string format_json_value(const std::string & field, const std::string & value) {
+ switch (test::get_field_type(field)) {
+ case test::STRING:
+ return "\"" + escape_json(value) + "\"";
+ case test::BOOL:
+ return value == "0" ? "false" : "true";
+ default:
+ return value;
+ }
+}
+
+struct json_printer : public printer {
+ bool first = true;
+
+ void print_header(const cmd_params & params) override {
+ fprintf(fout, "[\n");
+ (void) params;
+ }
+
+ void print_fields(const std::vector<std::string> & fields, const std::vector<std::string> & values) {
+ assert(fields.size() == values.size());
+ for (size_t i = 0; i < fields.size(); i++) {
+ fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(),
+ format_json_value(fields.at(i), values.at(i)).c_str());
+ }
+ }
+
+ void print_test(const test & t) override {
+ if (first) {
+ first = false;
+ } else {
+ fprintf(fout, ",\n");
+ }
+ fprintf(fout, " {\n");
+ print_fields(test::get_fields(), t.get_values());
+ fprintf(fout, " \"samples_ns\": [ %s ],\n", join(t.samples_ns, ", ").c_str());
+ fprintf(fout, " \"samples_ts\": [ %s ]\n", join(t.get_ts(), ", ").c_str());
+ fprintf(fout, " }");
+ fflush(fout);
+ }
+
+ void print_footer() override { fprintf(fout, "\n]\n"); }
+};
+
+struct jsonl_printer : public printer {
+ void print_fields(const std::vector<std::string> & fields, const std::vector<std::string> & values) {
+ assert(fields.size() == values.size());
+ for (size_t i = 0; i < fields.size(); i++) {
+ fprintf(fout, "\"%s\": %s, ", fields.at(i).c_str(), format_json_value(fields.at(i), values.at(i)).c_str());
+ }
+ }
+
+ void print_test(const test & t) override {
+ fprintf(fout, "{");
+ print_fields(test::get_fields(), t.get_values());
+ fprintf(fout, "\"samples_ns\": [ %s ],", join(t.samples_ns, ", ").c_str());
+ fprintf(fout, "\"samples_ts\": [ %s ]", join(t.get_ts(), ", ").c_str());
+ fprintf(fout, "}\n");
+ fflush(fout);
+ }
+};
+
+struct markdown_printer : public printer {
+ std::vector<std::string> fields;
+
+ static int get_field_width(const std::string & field) {
+ if (field == "model") {
+ return -30;
+ }
+ if (field == "t/s") {
+ return 20;
+ }
+ if (field == "size" || field == "params") {
+ return 10;
+ }
+ if (field == "n_gpu_layers") {
+ return 3;
+ }
+ if (field == "n_threads") {
+ return 7;
+ }
+ if (field == "n_batch") {
+ return 7;
+ }
+ if (field == "n_ubatch") {
+ return 8;
+ }
+ if (field == "type_k" || field == "type_v") {
+ return 6;
+ }
+ if (field == "split_mode") {
+ return 5;
+ }
+ if (field == "flash_attn") {
+ return 2;
+ }
+ if (field == "devices") {
+ return -12;
+ }
+ if (field == "use_mmap") {
+ return 4;
+ }
+ if (field == "use_direct_io") {
+ return 3;
+ }
+ if (field == "test") {
+ return 15;
+ }
+ if (field == "no_op_offload") {
+ return 4;
+ }
+ if (field == "no_host") {
+ return 4;
+ }
+
+ int width = std::max((int) field.length(), 10);
+
+ if (test::get_field_type(field) == test::STRING) {
+ return -width;
+ }
+ return width;
+ }
+
+ static std::string get_field_display_name(const std::string & field) {
+ if (field == "n_gpu_layers") {
+ return "ngl";
+ }
+ if (field == "split_mode") {
+ return "sm";
+ }
+ if (field == "n_threads") {
+ return "threads";
+ }
+ if (field == "no_kv_offload") {
+ return "nkvo";
+ }
+ if (field == "flash_attn") {
+ return "fa";
+ }
+ if (field == "use_mmap") {
+ return "mmap";
+ }
+ if (field == "use_direct_io") {
+ return "dio";
+ }
+ if (field == "embeddings") {
+ return "embd";
+ }
+ if (field == "no_op_offload") {
+ return "nopo";
+ }
+ if (field == "no_host") {
+ return "noh";
+ }
+ if (field == "devices") {
+ return "dev";
+ }
+ if (field == "tensor_split") {
+ return "ts";
+ }
+ if (field == "tensor_buft_overrides") {
+ return "ot";
+ }
+ return field;
+ }
+
+ void print_header(const cmd_params & params) override {
+ // select fields to print
+ fields.emplace_back("model");
+ fields.emplace_back("size");
+ fields.emplace_back("params");
+ fields.emplace_back("backend");
+ bool is_cpu_backend = test::get_backend().find("CPU") != std::string::npos ||
+ test::get_backend().find("BLAS") != std::string::npos ||
+ test::get_backend().find("ZenDNN") != std::string::npos;
+ if (!is_cpu_backend) {
+ fields.emplace_back("n_gpu_layers");
+ }
+ if (params.n_cpu_moe.size() > 1) {
+ fields.emplace_back("n_cpu_moe");
+ }
+ if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
+ fields.emplace_back("n_threads");
+ }
+ if (params.cpu_mask.size() > 1 || params.cpu_mask != cmd_params_defaults.cpu_mask) {
+ fields.emplace_back("cpu_mask");
+ }
+ if (params.cpu_strict.size() > 1 || params.cpu_strict != cmd_params_defaults.cpu_strict) {
+ fields.emplace_back("cpu_strict");
+ }
+ if (params.poll.size() > 1 || params.poll != cmd_params_defaults.poll) {
+ fields.emplace_back("poll");
+ }
+ if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
+ fields.emplace_back("n_batch");
+ }
+ if (params.n_ubatch.size() > 1 || params.n_ubatch != cmd_params_defaults.n_ubatch) {
+ fields.emplace_back("n_ubatch");
+ }
+ if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
+ fields.emplace_back("type_k");
+ }
+ if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) {
+ fields.emplace_back("type_v");
+ }
+ if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
+ fields.emplace_back("main_gpu");
+ }
+ if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) {
+ fields.emplace_back("split_mode");
+ }
+ if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
+ fields.emplace_back("no_kv_offload");
+ }
+ if (params.flash_attn.size() > 1 || params.flash_attn != cmd_params_defaults.flash_attn) {
+ fields.emplace_back("flash_attn");
+ }
+ if (params.devices.size() > 1 || params.devices != cmd_params_defaults.devices) {
+ fields.emplace_back("devices");
+ }
+ if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
+ fields.emplace_back("tensor_split");
+ }
+ if (params.tensor_buft_overrides.size() > 1 || !vec_vec_tensor_buft_override_equal(params.tensor_buft_overrides, cmd_params_defaults.tensor_buft_overrides)) {
+ fields.emplace_back("tensor_buft_overrides");
+ }
+ if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) {
+ fields.emplace_back("use_mmap");
+ }
+ if (params.use_direct_io.size() > 1 || params.use_direct_io != cmd_params_defaults.use_direct_io) {
+ fields.emplace_back("use_direct_io");
+ }
+ if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) {
+ fields.emplace_back("embeddings");
+ }
+ if (params.no_op_offload.size() > 1 || params.no_op_offload != cmd_params_defaults.no_op_offload) {
+ fields.emplace_back("no_op_offload");
+ }
+ if (params.no_host.size() > 1 || params.no_host != cmd_params_defaults.no_host) {
+ fields.emplace_back("no_host");
+ }
+ fields.emplace_back("test");
+ fields.emplace_back("t/s");
+
+ fprintf(fout, "|");
+ for (const auto & field : fields) {
+ fprintf(fout, " %*s |", get_field_width(field), get_field_display_name(field).c_str());
+ }
+ fprintf(fout, "\n");
+ fprintf(fout, "|");
+ for (const auto & field : fields) {
+ int width = get_field_width(field);
+ fprintf(fout, " %s%s |", std::string(std::abs(width) - 1, '-').c_str(), width > 0 ? ":" : "-");
+ }
+ fprintf(fout, "\n");
+ }
+
+ void print_test(const test & t) override {
+ std::map<std::string, std::string> vmap = t.get_map();
+
+ fprintf(fout, "|");
+ for (const auto & field : fields) {
+ std::string value;
+ char buf[128];
+ if (field == "model") {
+ value = t.model_type;
+ } else if (field == "size") {
+ if (t.model_size < 1024 * 1024 * 1024) {
+ snprintf(buf, sizeof(buf), "%.2f MiB", t.model_size / 1024.0 / 1024.0);
+ } else {
+ snprintf(buf, sizeof(buf), "%.2f GiB", t.model_size / 1024.0 / 1024.0 / 1024.0);
+ }
+ value = buf;
+ } else if (field == "params") {
+ if (t.model_n_params < 1000 * 1000 * 1000) {
+ snprintf(buf, sizeof(buf), "%.2f M", t.model_n_params / 1e6);
+ } else {
+ snprintf(buf, sizeof(buf), "%.2f B", t.model_n_params / 1e9);
+ }
+ value = buf;
+ } else if (field == "backend") {
+ value = test::get_backend();
+ } else if (field == "test") {
+ if (t.n_prompt > 0 && t.n_gen == 0) {
+ snprintf(buf, sizeof(buf), "pp%d", t.n_prompt);
+ } else if (t.n_gen > 0 && t.n_prompt == 0) {
+ snprintf(buf, sizeof(buf), "tg%d", t.n_gen);
+ } else {
+ snprintf(buf, sizeof(buf), "pp%d+tg%d", t.n_prompt, t.n_gen);
+ }
+ if (t.n_depth > 0) {
+ int len = strlen(buf);
+ snprintf(buf + len, sizeof(buf) - len, " @ d%d", t.n_depth);
+ }
+ value = buf;
+ } else if (field == "t/s") {
+ snprintf(buf, sizeof(buf), "%.2f ± %.2f", t.avg_ts(), t.stdev_ts());
+ value = buf;
+ } else if (vmap.find(field) != vmap.end()) {
+ value = vmap.at(field);
+ } else {
+ assert(false);
+ exit(1);
+ }
+
+ int width = get_field_width(field);
+ if (field == "t/s") {
+ // HACK: the utf-8 character is 2 bytes
+ width += 1;
+ }
+ fprintf(fout, " %*s |", width, value.c_str());
+ }
+ fprintf(fout, "\n");
+ }
+
+ void print_footer() override {
+ fprintf(fout, "\nbuild: %s (%d)\n", test::build_commit.c_str(), test::build_number);
+ }
+};
+
+struct sql_printer : public printer {
+ static std::string get_sql_field_type(const std::string & field) {
+ switch (test::get_field_type(field)) {
+ case test::STRING:
+ return "TEXT";
+ case test::BOOL:
+ case test::INT:
+ return "INTEGER";
+ case test::FLOAT:
+ return "REAL";
+ default:
+ assert(false);
+ exit(1);
+ }
+ }
+
+ void print_header(const cmd_params & params) override {
+ std::vector<std::string> fields = test::get_fields();
+ fprintf(fout, "CREATE TABLE IF NOT EXISTS llama_bench (\n");
+ for (size_t i = 0; i < fields.size(); i++) {
+ fprintf(fout, " %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(),
+ i < fields.size() - 1 ? "," : "");
+ }
+ fprintf(fout, ");\n");
+ fprintf(fout, "\n");
+ (void) params;
+ }
+
+ void print_test(const test & t) override {
+ fprintf(fout, "INSERT INTO llama_bench (%s) ", join(test::get_fields(), ", ").c_str());
+ fprintf(fout, "VALUES (");
+ std::vector<std::string> values = t.get_values();
+ for (size_t i = 0; i < values.size(); i++) {
+ fprintf(fout, "'%s'%s", values.at(i).c_str(), i < values.size() - 1 ? ", " : "");
+ }
+ fprintf(fout, ");\n");
+ }
+};
+
+struct ctx_state {
+ int depth = 0; // in tokens
+
+ std::vector<uint8_t> buf; // the llama_context state buffer
+};
+
+static bool test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_threads) {
+ llama_set_n_threads(ctx, n_threads, n_threads);
+
+ const llama_model * model = llama_get_model(ctx);
+ const llama_vocab * vocab = llama_model_get_vocab(model);
+ const int32_t n_vocab = llama_vocab_n_tokens(vocab);
+
+ std::vector<llama_token> tokens(n_batch);
+
+ int n_processed = 0;
+
+ while (n_processed < n_prompt) {
+ int n_tokens = std::min(n_prompt - n_processed, n_batch);
+ tokens[0] = n_processed == 0 && llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab;
+ for (int i = 1; i < n_tokens; i++) {
+ tokens[i] = std::rand() % n_vocab;
+ }
+ int res = llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens));
+ if (res != 0) {
+ fprintf(stderr, "%s: failed to decode prompt batch, res = %d\n", __func__, res);
+ return false;
+ }
+ n_processed += n_tokens;
+ }
+
+ llama_synchronize(ctx);
+ return true;
+}
+
+static bool test_gen(llama_context * ctx, int n_gen, int n_threads) {
+ llama_set_n_threads(ctx, n_threads, n_threads);
+
+ const llama_model * model = llama_get_model(ctx);
+ const llama_vocab * vocab = llama_model_get_vocab(model);
+ const int32_t n_vocab = llama_vocab_n_tokens(vocab);
+
+ llama_token token = llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab;
+
+ for (int i = 0; i < n_gen; i++) {
+ int res = llama_decode(ctx, llama_batch_get_one(&token, 1));
+ if (res != 0) {
+ fprintf(stderr, "%s: failed to decode generation batch, res = %d\n", __func__, res);
+ return false;
+ }
+ llama_synchronize(ctx);
+ token = std::rand() % n_vocab;
+ }
+ return true;
+}
+
+static void llama_null_log_callback(enum ggml_log_level level, const char * text, void * user_data) {
+ (void) level;
+ (void) text;
+ (void) user_data;
+}
+
+static std::unique_ptr<printer> create_printer(output_formats format) {
+ switch (format) {
+ case NONE:
+ return nullptr;
+ case CSV:
+ return std::unique_ptr<printer>(new csv_printer());
+ case JSON:
+ return std::unique_ptr<printer>(new json_printer());
+ case JSONL:
+ return std::unique_ptr<printer>(new jsonl_printer());
+ case MARKDOWN:
+ return std::unique_ptr<printer>(new markdown_printer());
+ case SQL:
+ return std::unique_ptr<printer>(new sql_printer());
+ }
+ GGML_ABORT("fatal error");
+}
+
+int main(int argc, char ** argv) {
+ // try to set locale for unicode characters in markdown
+ setlocale(LC_CTYPE, ".UTF-8");
+
+#if !defined(NDEBUG)
+ fprintf(stderr, "warning: asserts enabled, performance may be affected\n");
+#endif
+
+#if (defined(_MSC_VER) && defined(_DEBUG)) || (!defined(_MSC_VER) && !defined(__OPTIMIZE__))
+ fprintf(stderr, "warning: debug build, performance may be affected\n");
+#endif
+
+#if defined(__SANITIZE_ADDRESS__) || defined(__SANITIZE_THREAD__)
+ fprintf(stderr, "warning: sanitizer enabled, performance may be affected\n");
+#endif
+
+ // initialize backends
+ ggml_backend_load_all();
+
+ cmd_params params = parse_cmd_params(argc, argv);
+
+ auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+ if (!cpu_dev) {
+ fprintf(stderr, "%s: error: CPU backend is not loaded\n", __func__);
+ return 1;
+ }
+ auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
+ auto * ggml_threadpool_new_fn = (decltype(ggml_threadpool_new) *) ggml_backend_reg_get_proc_address(cpu_reg, "ggml_threadpool_new");
+ auto * ggml_threadpool_free_fn = (decltype(ggml_threadpool_free) *) ggml_backend_reg_get_proc_address(cpu_reg, "ggml_threadpool_free");
+
+ // initialize llama.cpp
+ if (!params.verbose) {
+ llama_log_set(llama_null_log_callback, NULL);
+ }
+ llama_backend_init();
+ llama_numa_init(params.numa);
+
+ if (!set_process_priority(params.prio)) {
+ fprintf(stderr, "%s: error: failed to set process priority\n", __func__);
+ return 1;
+ }
+
+ // initialize printer
+ std::unique_ptr<printer> p = create_printer(params.output_format);
+ std::unique_ptr<printer> p_err = create_printer(params.output_format_stderr);
+
+ if (p) {
+ p->fout = stdout;
+ p->print_header(params);
+ }
+
+ if (p_err) {
+ p_err->fout = stderr;
+ p_err->print_header(params);
+ }
+
+ std::vector<cmd_params_instance> params_instances = get_cmd_params_instances(params);
+
+ llama_model * lmodel = nullptr;
+ const cmd_params_instance * prev_inst = nullptr;
+
+ // store the llama_context state at the previous depth that we performed a test
+ // ref: https://github.com/ggml-org/llama.cpp/pull/16944#issuecomment-3478151721
+ ctx_state cstate;
+
+ int params_idx = 0;
+ auto params_count = params_instances.size();
+ for (const auto & inst : params_instances) {
+ params_idx++;
+ if (params.progress) {
+ fprintf(stderr, "llama-bench: benchmark %d/%zu: starting\n", params_idx, params_count);
+ }
+ // keep the same model between tests when possible
+ if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
+ if (lmodel) {
+ llama_model_free(lmodel);
+ }
+
+ lmodel = llama_model_load_from_file(inst.model.c_str(), inst.to_llama_mparams());
+ if (lmodel == NULL) {
+ fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, inst.model.c_str());
+ return 1;
+ }
+ prev_inst = &inst;
+ }
+
+ llama_context * ctx = llama_init_from_model(lmodel, inst.to_llama_cparams());
+ if (ctx == NULL) {
+ fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str());
+ llama_model_free(lmodel);
+ return 1;
+ }
+
+ test t(inst, lmodel, ctx);
+
+ llama_memory_clear(llama_get_memory(ctx), false);
+
+ // cool off before the test
+ if (params.delay) {
+ std::this_thread::sleep_for(std::chrono::seconds(params.delay));
+ }
+
+ struct ggml_threadpool_params tpp = ggml_threadpool_params_default(t.n_threads);
+ if (!parse_cpu_mask(t.cpu_mask, tpp.cpumask)) {
+ fprintf(stderr, "%s: failed to parse cpu-mask: %s\n", __func__, t.cpu_mask.c_str());
+ llama_free(ctx);
+ llama_model_free(lmodel);
+ exit(1);
+ }
+ tpp.strict_cpu = t.cpu_strict;
+ tpp.poll = t.poll;
+ tpp.prio = params.prio;
+
+ struct ggml_threadpool * threadpool = ggml_threadpool_new_fn(&tpp);
+ if (!threadpool) {
+ fprintf(stderr, "%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads);
+ llama_free(ctx);
+ llama_model_free(lmodel);
+ exit(1);
+ }
+
+ llama_attach_threadpool(ctx, threadpool, NULL);
+
+ // warmup run
+ if (!params.no_warmup) {
+ if (t.n_prompt > 0) {
+ if (params.progress) {
+ fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count);
+ }
+ //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
+ bool res = test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
+ if (!res) {
+ fprintf(stderr, "%s: error: failed to run prompt warmup\n", __func__);
+ llama_free(ctx);
+ llama_model_free(lmodel);
+ exit(1);
+ }
+ }
+ if (t.n_gen > 0) {
+ if (params.progress) {
+ fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup generation run\n", params_idx, params_count);
+ }
+ bool res = test_gen(ctx, 1, t.n_threads);
+ if (!res) {
+ fprintf(stderr, "%s: error: failed to run gen warmup\n", __func__);
+ llama_free(ctx);
+ llama_model_free(lmodel);
+ exit(1);
+ }
+ }
+ }
+
+ for (int i = 0; i < params.reps; i++) {
+ llama_memory_clear(llama_get_memory(ctx), false);
+
+ if (t.n_depth > 0) {
+ bool is_cached = t.n_depth == cstate.depth;
+
+ if (is_cached) {
+ // if previously we have computed at this depth, just restore the state
+ const size_t ret = llama_state_seq_set_data(ctx, cstate.buf.data(), cstate.buf.size(), 0);
+ if (ret == 0) {
+ // if the old state is incompatible with the current context - reprocess from scratch
+ is_cached = false;
+ }
+ }
+
+ if (!is_cached) {
+ if (params.progress) {
+ fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d\n", params_idx, params_count,
+ i + 1, params.reps);
+ }
+ bool res = test_prompt(ctx, t.n_depth, t.n_batch, t.n_threads);
+ if (!res) {
+ fprintf(stderr, "%s: error: failed to run depth\n", __func__);
+ llama_free(ctx);
+ llama_model_free(lmodel);
+ exit(1);
+ }
+
+ // store the context state for reuse in later runs
+ cstate.depth = t.n_depth;
+ cstate.buf.resize(llama_state_seq_get_size(ctx, 0));
+ llama_state_seq_get_data(ctx, cstate.buf.data(), cstate.buf.size(), 0);
+ } else {
+ if (params.progress) {
+ fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d (cached)\n", params_idx, params_count,
+ i + 1, params.reps);
+ }
+ }
+ }
+
+ uint64_t t_start = get_time_ns();
+
+ if (t.n_prompt > 0) {
+ if (params.progress) {
+ fprintf(stderr, "llama-bench: benchmark %d/%zu: prompt run %d/%d\n", params_idx, params_count,
+ i + 1, params.reps);
+ }
+ bool res = test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
+ if (!res) {
+ fprintf(stderr, "%s: error: failed to run prompt\n", __func__);
+ llama_free(ctx);
+ llama_model_free(lmodel);
+ exit(1);
+ }
+ }
+ if (t.n_gen > 0) {
+ if (params.progress) {
+ fprintf(stderr, "llama-bench: benchmark %d/%zu: generation run %d/%d\n", params_idx, params_count,
+ i + 1, params.reps);
+ }
+ bool res = test_gen(ctx, t.n_gen, t.n_threads);
+ if (!res) {
+ fprintf(stderr, "%s: error: failed to run gen\n", __func__);
+ llama_free(ctx);
+ llama_model_free(lmodel);
+ exit(1);
+ }
+ }
+
+ uint64_t t_ns = get_time_ns() - t_start;
+ t.samples_ns.push_back(t_ns);
+ }
+
+ if (p) {
+ p->print_test(t);
+ fflush(p->fout);
+ }
+
+ if (p_err) {
+ p_err->print_test(t);
+ fflush(p_err->fout);
+ }
+
+ llama_perf_context_print(ctx);
+
+ llama_free(ctx);
+
+ ggml_threadpool_free_fn(threadpool);
+ }
+
+ llama_model_free(lmodel);
+
+ if (p) {
+ p->print_footer();
+ }
+
+ if (p_err) {
+ p_err->print_footer();
+ }
+
+ llama_backend_free();
+
+ return 0;
+}
generated by cgit v1.2.3 (git 2.46.0) at 2026-04-29 12:13:47 +0000