mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-14 06:49:54 +00:00
5be6c803fa
* added `llama_model_token_*` variants to all the `llama_token_*` functions. * added `LLAMA_API` * formatting Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * removed old `llama_token` functions * changed 3 more functions to take in model - `llama_token_get_text` - `llama_token_get_score` - `llama_token_get_type` * added back docs * fixed main.cpp * changed token functions to use new model variants * changed token functions to use new model variants --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
1079 lines
36 KiB
C++
1079 lines
36 KiB
C++
#include <algorithm>
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#include <array>
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#include <cassert>
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#include <chrono>
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#include <cinttypes>
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#include <clocale>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <iterator>
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#include <map>
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#include <numeric>
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#include <regex>
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#include <sstream>
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#include <string>
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#include <vector>
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#include "ggml.h"
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#include "llama.h"
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#include "common.h"
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#include "build-info.h"
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#include "ggml-cuda.h"
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// utils
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static uint64_t get_time_ns() {
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using clock = std::chrono::high_resolution_clock;
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return std::chrono::nanoseconds(clock::now().time_since_epoch()).count();
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}
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template<class T>
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static std::string join(const std::vector<T> & values, const std::string & delim) {
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std::ostringstream str;
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for (size_t i = 0; i < values.size(); i++) {
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str << values[i];
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if (i < values.size() - 1) {
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str << delim;
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}
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}
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return str.str();
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}
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template<class T>
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static std::vector<T> split(const std::string & str, char delim) {
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std::vector<T> values;
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std::istringstream str_stream(str);
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std::string token;
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while (std::getline(str_stream, token, delim)) {
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T value;
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std::istringstream token_stream(token);
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token_stream >> value;
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values.push_back(value);
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}
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return values;
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}
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template<typename T>
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static T avg(const std::vector<T> & v) {
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if (v.empty()) {
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return 0;
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}
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T sum = std::accumulate(v.begin(), v.end(), T(0));
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return sum / (T)v.size();
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}
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template<typename T>
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static T stdev(const std::vector<T> & v) {
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if (v.size() <= 1) {
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return 0;
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}
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T mean = avg(v);
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T sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), T(0));
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T stdev = std::sqrt(sq_sum / (T)(v.size() - 1) - mean * mean * (T)v.size() / (T)(v.size() - 1));
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return stdev;
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}
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static std::string get_cpu_info() {
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std::string id;
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#ifdef __linux__
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FILE * f = fopen("/proc/cpuinfo", "r");
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if (f) {
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char buf[1024];
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while (fgets(buf, sizeof(buf), f)) {
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if (strncmp(buf, "model name", 10) == 0) {
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char * p = strchr(buf, ':');
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if (p) {
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p++;
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while (std::isspace(*p)) {
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p++;
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}
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while (std::isspace(p[strlen(p) - 1])) {
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p[strlen(p) - 1] = '\0';
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}
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id = p;
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break;
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}
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}
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}
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}
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#endif
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// TODO: other platforms
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return id;
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}
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static std::string get_gpu_info() {
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std::string id;
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#ifdef GGML_USE_CUBLAS
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int count = ggml_cuda_get_device_count();
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for (int i = 0; i < count; i++) {
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char buf[128];
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ggml_cuda_get_device_description(i, buf, sizeof(buf));
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id += buf;
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if (i < count - 1) {
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id += "/";
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}
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}
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#endif
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// TODO: other backends
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return id;
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}
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// command line params
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enum output_formats {CSV, JSON, MARKDOWN, SQL};
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struct cmd_params {
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std::vector<std::string> model;
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std::vector<int> n_prompt;
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std::vector<int> n_gen;
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std::vector<int> n_batch;
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std::vector<bool> f32_kv;
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std::vector<int> n_threads;
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std::vector<int> n_gpu_layers;
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std::vector<int> main_gpu;
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std::vector<bool> mul_mat_q;
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std::vector<std::array<float, LLAMA_MAX_DEVICES>> tensor_split;
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int reps;
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bool verbose;
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output_formats output_format;
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};
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static const cmd_params cmd_params_defaults = {
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/* model */ {"models/7B/ggml-model-q4_0.gguf"},
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/* n_prompt */ {512},
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/* n_gen */ {128},
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/* n_batch */ {512},
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/* f32_kv */ {false},
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/* n_threads */ {get_num_physical_cores()},
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/* n_gpu_layers */ {99},
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/* main_gpu */ {0},
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/* mul_mat_q */ {true},
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/* tensor_split */ {{}},
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/* reps */ 5,
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/* verbose */ false,
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/* output_format */ MARKDOWN
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};
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static void print_usage(int /* argc */, char ** argv) {
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printf("usage: %s [options]\n", argv[0]);
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printf("\n");
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printf("options:\n");
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printf(" -h, --help\n");
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printf(" -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
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printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
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printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
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printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
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printf(" --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str());
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printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
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printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
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printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
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printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
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printf(" -ts, --tensor_split <ts0/ts1/..> \n");
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printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
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printf(" -o, --output <csv|json|md|sql> (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql");
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printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
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printf("\n");
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printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
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}
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static cmd_params parse_cmd_params(int argc, char ** argv) {
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cmd_params params;
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std::string arg;
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bool invalid_param = false;
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const std::string arg_prefix = "--";
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const char split_delim = ',';
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params.verbose = cmd_params_defaults.verbose;
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params.output_format = cmd_params_defaults.output_format;
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params.reps = cmd_params_defaults.reps;
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for (int i = 1; i < argc; i++) {
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arg = argv[i];
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if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
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std::replace(arg.begin(), arg.end(), '_', '-');
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}
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if (arg == "-h" || arg == "--help") {
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print_usage(argc, argv);
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exit(0);
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} else if (arg == "-m" || arg == "--model") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<std::string>(argv[i], split_delim);
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params.model.insert(params.model.end(), p.begin(), p.end());
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} else if (arg == "-p" || arg == "--n-prompt") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<int>(argv[i], split_delim);
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params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end());
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} else if (arg == "-n" || arg == "--n-gen") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<int>(argv[i], split_delim);
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params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());
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} else if (arg == "-b" || arg == "--batch-size") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<int>(argv[i], split_delim);
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params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
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} else if (arg == "--memory-f32") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<int>(argv[i], split_delim);
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params.f32_kv.insert(params.f32_kv.end(), p.begin(), p.end());
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} else if (arg == "-t" || arg == "--threads") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<int>(argv[i], split_delim);
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params.n_threads.insert(params.n_threads.end(), p.begin(), p.end());
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} else if (arg == "-ngl" || arg == "--n-gpu-layers") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<int>(argv[i], split_delim);
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params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
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} else if (arg == "-mg" || arg == "--main-gpu") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.main_gpu = split<int>(argv[i], split_delim);
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} else if (arg == "-mmq" || arg == "--mul-mat-q") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<bool>(argv[i], split_delim);
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params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end());
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} else if (arg == "-ts" || arg == "--tensor-split") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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for (auto ts : split<std::string>(argv[i], split_delim)) {
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// split string by ; and /
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const std::regex regex{R"([;/]+)"};
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std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1};
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std::vector<std::string> split_arg{it, {}};
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GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
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std::array<float, LLAMA_MAX_DEVICES> tensor_split;
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for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
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if (i < split_arg.size()) {
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tensor_split[i] = std::stof(split_arg[i]);
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} else {
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tensor_split[i] = 0.0f;
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}
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}
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params.tensor_split.push_back(tensor_split);
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}
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} else if (arg == "-r" || arg == "--repetitions") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.reps = std::stoi(argv[i]);
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} else if (arg == "-o" || arg == "--output") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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if (argv[i] == std::string("csv")) {
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params.output_format = CSV;
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} else if (argv[i] == std::string("json")) {
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params.output_format = JSON;
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} else if (argv[i] == std::string("md")) {
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params.output_format = MARKDOWN;
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} else if (argv[i] == std::string("sql")) {
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params.output_format = SQL;
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} else {
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invalid_param = true;
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break;
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}
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} else if (arg == "-v" || arg == "--verbose") {
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params.verbose = true;
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} else {
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invalid_param = true;
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break;
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}
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}
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if (invalid_param) {
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fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
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print_usage(argc, argv);
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exit(1);
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}
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// set defaults
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if (params.model.empty()) { params.model = cmd_params_defaults.model; }
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if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; }
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if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; }
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if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; }
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if (params.f32_kv.empty()) { params.f32_kv = cmd_params_defaults.f32_kv; }
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if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
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if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
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if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
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if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
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if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }
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return params;
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}
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struct cmd_params_instance {
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std::string model;
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int n_prompt;
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int n_gen;
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int n_batch;
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bool f32_kv;
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int n_threads;
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int n_gpu_layers;
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int main_gpu;
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bool mul_mat_q;
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std::array<float, LLAMA_MAX_DEVICES> tensor_split;
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llama_model_params to_llama_mparams() const {
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llama_model_params mparams = llama_model_default_params();
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mparams.n_gpu_layers = n_gpu_layers;
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mparams.main_gpu = main_gpu;
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mparams.tensor_split = tensor_split.data();
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return mparams;
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}
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bool equal_mparams(const cmd_params_instance & other) const {
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return model == other.model &&
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n_gpu_layers == other.n_gpu_layers &&
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main_gpu == other.main_gpu &&
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tensor_split == other.tensor_split;
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}
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llama_context_params to_llama_cparams() const {
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llama_context_params cparams = llama_context_default_params();
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cparams.n_ctx = n_prompt + n_gen;
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cparams.n_batch = n_batch;
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cparams.f16_kv = !f32_kv;
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cparams.mul_mat_q = mul_mat_q;
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return cparams;
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}
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};
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static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_params & params, int n_gen, int n_prompt) {
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std::vector<cmd_params_instance> instances;
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for (const auto & m : params.model)
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for (const auto & nl : params.n_gpu_layers)
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for (const auto & mg : params.main_gpu)
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for (const auto & ts : params.tensor_split)
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for (const auto & nb : params.n_batch)
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for (const auto & fk : params.f32_kv)
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for (const auto & mmq : params.mul_mat_q)
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for (const auto & nt : params.n_threads) {
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cmd_params_instance instance = {
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/* .model = */ m,
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/* .n_prompt = */ n_prompt,
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/* .n_gen = */ n_gen,
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/* .n_batch = */ nb,
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/* .f32_kv = */ fk,
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/* .n_threads = */ nt,
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/* .n_gpu_layers = */ nl,
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/* .main_gpu = */ mg,
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/* .mul_mat_q = */ mmq,
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/* .tensor_split = */ ts,
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};
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instances.push_back(instance);
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}
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return instances;
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}
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static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_params & params) {
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std::vector<cmd_params_instance> instances;
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#if 1
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// this ordering minimizes the number of times that each model needs to be reloaded
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for (const auto & m : params.model)
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for (const auto & nl : params.n_gpu_layers)
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for (const auto & mg : params.main_gpu)
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for (const auto & ts : params.tensor_split)
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for (const auto & nb : params.n_batch)
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for (const auto & fk : params.f32_kv)
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for (const auto & mmq : params.mul_mat_q)
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for (const auto & nt : params.n_threads) {
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for (const auto & n_prompt : params.n_prompt) {
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if (n_prompt == 0) {
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continue;
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}
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cmd_params_instance instance = {
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/* .model = */ m,
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/* .n_prompt = */ n_prompt,
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/* .n_gen = */ 0,
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/* .n_batch = */ nb,
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/* .f32_kv = */ fk,
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/* .n_threads = */ nt,
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/* .n_gpu_layers = */ nl,
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/* .main_gpu = */ mg,
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/* .mul_mat_q = */ mmq,
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/* .tensor_split = */ ts,
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};
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instances.push_back(instance);
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}
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for (const auto & n_gen : params.n_gen) {
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if (n_gen == 0) {
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continue;
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}
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cmd_params_instance instance = {
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/* .model = */ m,
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/* .n_prompt = */ 0,
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/* .n_gen = */ n_gen,
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/* .n_batch = */ nb,
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/* .f32_kv = */ fk,
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/* .n_threads = */ nt,
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/* .n_gpu_layers = */ nl,
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/* .main_gpu = */ mg,
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/* .mul_mat_q = */ mmq,
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/* .tensor_split = */ ts,
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};
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instances.push_back(instance);
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|
}
|
|
}
|
|
#else
|
|
// this ordering separates the prompt and generation tests
|
|
for (const auto & n_prompt : params.n_prompt) {
|
|
if (n_prompt == 0) {
|
|
continue;
|
|
}
|
|
auto instances_prompt = get_cmd_params_instances_int(params, 0, n_prompt);
|
|
instances.insert(instances.end(), instances_prompt.begin(), instances_prompt.end());
|
|
}
|
|
|
|
for (const auto & n_gen : params.n_gen) {
|
|
if (n_gen == 0) {
|
|
continue;
|
|
}
|
|
auto instances_gen = get_cmd_params_instances_int(params, n_gen, 0);
|
|
instances.insert(instances.end(), instances_gen.begin(), instances_gen.end());
|
|
}
|
|
#endif
|
|
|
|
return instances;
|
|
}
|
|
|
|
struct test {
|
|
static const std::string build_commit;
|
|
static const int build_number;
|
|
static const bool cuda;
|
|
static const bool opencl;
|
|
static const bool metal;
|
|
static const bool gpu_blas;
|
|
static const bool blas;
|
|
static const std::string cpu_info;
|
|
static 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_threads;
|
|
bool f32_kv;
|
|
int n_gpu_layers;
|
|
int main_gpu;
|
|
bool mul_mat_q;
|
|
std::array<float, LLAMA_MAX_DEVICES> tensor_split;
|
|
int n_prompt;
|
|
int n_gen;
|
|
std::string test_time;
|
|
std::vector<uint64_t> samples_ns;
|
|
|
|
test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) {
|
|
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_threads = inst.n_threads;
|
|
f32_kv = inst.f32_kv;
|
|
n_gpu_layers = inst.n_gpu_layers;
|
|
main_gpu = inst.main_gpu;
|
|
mul_mat_q = inst.mul_mat_q;
|
|
tensor_split = inst.tensor_split;
|
|
n_prompt = inst.n_prompt;
|
|
n_gen = inst.n_gen;
|
|
// 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() {
|
|
if (cuda) {
|
|
return GGML_CUDA_NAME;
|
|
}
|
|
if (opencl) {
|
|
return "OpenCL";
|
|
}
|
|
if (metal) {
|
|
return "Metal";
|
|
}
|
|
if (gpu_blas) {
|
|
return "GPU BLAS";
|
|
}
|
|
if (blas) {
|
|
return "BLAS";
|
|
}
|
|
return "CPU";
|
|
}
|
|
|
|
static const std::vector<std::string> & get_fields() {
|
|
static const std::vector<std::string> fields = {
|
|
"build_commit", "build_number",
|
|
"cuda", "opencl", "metal", "gpu_blas", "blas",
|
|
"cpu_info", "gpu_info",
|
|
"model_filename", "model_type", "model_size", "model_n_params",
|
|
"n_batch", "n_threads", "f16_kv",
|
|
"n_gpu_layers", "main_gpu", "mul_mat_q", "tensor_split",
|
|
"n_prompt", "n_gen", "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_threads" ||
|
|
field == "model_size" || field == "model_n_params" ||
|
|
field == "n_gpu_layers" || field == "main_gpu" ||
|
|
field == "n_prompt" || field == "n_gen" ||
|
|
field == "avg_ns" || field == "stddev_ns") {
|
|
return INT;
|
|
}
|
|
if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" ||
|
|
field == "f16_kv" || field == "mul_mat_q") {
|
|
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;
|
|
int max_nonzero = 0;
|
|
for (int 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 += "/";
|
|
}
|
|
}
|
|
std::vector<std::string> values = {
|
|
build_commit, std::to_string(build_number),
|
|
std::to_string(cuda), std::to_string(opencl), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas),
|
|
cpu_info, gpu_info,
|
|
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
|
|
std::to_string(n_batch), std::to_string(n_threads), std::to_string(!f32_kv),
|
|
std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), tensor_split_str,
|
|
std::to_string(n_prompt), std::to_string(n_gen), 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 = BUILD_COMMIT;
|
|
const int test::build_number = BUILD_NUMBER;
|
|
const bool test::cuda = !!ggml_cpu_has_cublas();
|
|
const bool test::opencl = !!ggml_cpu_has_clblast();
|
|
const bool test::metal = !!ggml_cpu_has_metal();
|
|
const bool test::gpu_blas = !!ggml_cpu_has_gpublas();
|
|
const bool test::blas = !!ggml_cpu_has_blas();
|
|
const std::string test::cpu_info = get_cpu_info();
|
|
const std::string test::gpu_info = get_gpu_info();
|
|
|
|
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());
|
|
}
|
|
};
|
|
|
|
struct json_printer : public printer {
|
|
bool first = true;
|
|
|
|
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_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;
|
|
}
|
|
}
|
|
|
|
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_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 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 16;
|
|
}
|
|
if (field == "size" || field == "params") {
|
|
return 10;
|
|
}
|
|
if (field == "n_gpu_layers") {
|
|
return 3;
|
|
}
|
|
|
|
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 == "n_threads") {
|
|
return "threads";
|
|
}
|
|
if (field == "mul_mat_q") {
|
|
return "mmq";
|
|
}
|
|
if (field == "tensor_split") {
|
|
return "ts";
|
|
}
|
|
return field;
|
|
}
|
|
|
|
void print_header(const cmd_params & params) override {
|
|
// select fields to print
|
|
fields.push_back("model");
|
|
fields.push_back("size");
|
|
fields.push_back("params");
|
|
fields.push_back("backend");
|
|
bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS";
|
|
if (!is_cpu_backend) {
|
|
fields.push_back("n_gpu_layers");
|
|
}
|
|
if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
|
|
fields.push_back("n_threads");
|
|
}
|
|
if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
|
|
fields.push_back("n_batch");
|
|
}
|
|
if (params.f32_kv.size() > 1 || params.f32_kv != cmd_params_defaults.f32_kv) {
|
|
fields.push_back("f16_kv");
|
|
}
|
|
if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
|
|
fields.push_back("main_gpu");
|
|
}
|
|
if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
|
|
fields.push_back("mul_mat_q");
|
|
}
|
|
if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
|
|
fields.push_back("tensor_split");
|
|
}
|
|
fields.push_back("test");
|
|
fields.push_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 {
|
|
assert(false);
|
|
exit(1);
|
|
}
|
|
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 test (\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 test (%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");
|
|
}
|
|
};
|
|
|
|
static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
|
|
std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
|
|
int n_processed = 0;
|
|
|
|
llama_set_n_threads(ctx, n_threads, n_threads);
|
|
|
|
while (n_processed < n_prompt) {
|
|
int n_tokens = std::min(n_prompt - n_processed, n_batch);
|
|
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
|
|
n_processed += n_tokens;
|
|
}
|
|
}
|
|
|
|
static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
|
|
llama_token token = llama_token_bos(llama_get_model(ctx));
|
|
|
|
llama_set_n_threads(ctx, n_threads, n_threads);
|
|
|
|
for (int i = 0; i < n_gen; i++) {
|
|
llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
|
|
}
|
|
}
|
|
|
|
static void llama_null_log_callback(enum ggml_log_level level, const char * text, void * user_data) {
|
|
(void) level;
|
|
(void) text;
|
|
(void) user_data;
|
|
}
|
|
|
|
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
|
|
|
|
cmd_params params = parse_cmd_params(argc, argv);
|
|
|
|
// initialize llama.cpp
|
|
if (!params.verbose) {
|
|
llama_log_set(llama_null_log_callback, NULL);
|
|
}
|
|
bool numa = false;
|
|
llama_backend_init(numa);
|
|
|
|
// initialize printer
|
|
std::unique_ptr<printer> p;
|
|
switch (params.output_format) {
|
|
case CSV:
|
|
p.reset(new csv_printer());
|
|
break;
|
|
case JSON:
|
|
p.reset(new json_printer());
|
|
break;
|
|
case MARKDOWN:
|
|
p.reset(new markdown_printer());
|
|
break;
|
|
case SQL:
|
|
p.reset(new sql_printer());
|
|
break;
|
|
default:
|
|
assert(false);
|
|
exit(1);
|
|
}
|
|
p->fout = stdout;
|
|
p->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;
|
|
|
|
for (const auto & inst : params_instances) {
|
|
// keep the same model between tests when possible
|
|
if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
|
|
if (lmodel) {
|
|
llama_free_model(lmodel);
|
|
}
|
|
|
|
lmodel = llama_load_model_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_new_context_with_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_free_model(lmodel);
|
|
return 1;
|
|
}
|
|
|
|
test t(inst, lmodel, ctx);
|
|
|
|
llama_kv_cache_tokens_rm(ctx, -1, -1);
|
|
|
|
// warmup run
|
|
if (t.n_prompt > 0) {
|
|
test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);
|
|
}
|
|
if (t.n_gen > 0) {
|
|
test_gen(ctx, 1, 0, t.n_threads);
|
|
}
|
|
|
|
for (int i = 0; i < params.reps; i++) {
|
|
llama_kv_cache_tokens_rm(ctx, -1, -1);
|
|
|
|
uint64_t t_start = get_time_ns();
|
|
if (t.n_prompt > 0) {
|
|
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
|
|
}
|
|
if (t.n_gen > 0) {
|
|
test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
|
|
}
|
|
uint64_t t_ns = get_time_ns() - t_start;
|
|
t.samples_ns.push_back(t_ns);
|
|
}
|
|
|
|
p->print_test(t);
|
|
|
|
llama_print_timings(ctx);
|
|
|
|
llama_free(ctx);
|
|
}
|
|
|
|
llama_free_model(lmodel);
|
|
|
|
p->print_footer();
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|