mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-06-26 19:55:04 +00:00
e298d2fbd0
* kv-cache : prepare for SWA ggml-ci * kv-cache : initial iSWA implementation ggml-ci * kv-cache : rework error recovery logic ggml-ci * models : fix Phi-3 SWA parameters ggml-ci * model : adjust Granite to rope factor changes ggml-ci * server : check if context can do shifts ggml-ci * iswa : for now, always enable shifts (experiment) ggml-ci * kv-cache : simplify SWA logic ggml-ci * kv-cache : apply defrag when we fail to find slots for the batch ggml-ci * llama : update docs about llama_decode ggml-ci * kv-cache : update warning logs when no space for the batch is available ggml-ci * llama : add llama_kv_self_seq_pos_min() * kv-cache : keep track of partial SWA computes and print warnings * server : disallow use cases involving partial SWA context ggml-ci * llama : add param to control SWA cache size ggml-ci * minor : clean-up ggml-ci
2025 lines
78 KiB
C++
2025 lines
78 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 <cstdlib>
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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 <thread>
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#include <vector>
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#include "common.h"
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#include "ggml.h"
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#include "llama.h"
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#ifdef _WIN32
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# define WIN32_LEAN_AND_MEAN
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# ifndef NOMINMAX
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# define NOMINMAX
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# endif
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# include <windows.h>
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#endif
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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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static bool tensor_buft_override_equal(const llama_model_tensor_buft_override& a, const llama_model_tensor_buft_override& b) {
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if (a.pattern != b.pattern) {
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// cString comparison that may be null
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if (a.pattern == nullptr || b.pattern == nullptr) {
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return false;
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}
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if (strcmp(a.pattern, b.pattern) != 0) {
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return false;
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}
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}
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if (a.buft != b.buft) {
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return false;
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}
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return true;
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}
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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) {
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if (a.size() != b.size()) {
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return false;
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}
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for (size_t i = 0; i < a.size(); i++) {
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if (!tensor_buft_override_equal(a[i], b[i])) {
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return false;
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}
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}
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return true;
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}
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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) {
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if (a.size() != b.size()) {
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return false;
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}
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for (size_t i = 0; i < a.size(); i++) {
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if (!vec_tensor_buft_override_equal(a[i], b[i])) {
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return false;
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}
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}
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return true;
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}
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template <class T> 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 <typename T, typename F> static std::vector<std::string> transform_to_str(const std::vector<T> & values, F f) {
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std::vector<std::string> str_values;
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std::transform(values.begin(), values.end(), std::back_inserter(str_values), f);
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return str_values;
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}
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template <typename T> 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> 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::vector<std::string> cpu_list;
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for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
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auto * dev = ggml_backend_dev_get(i);
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auto dev_type = ggml_backend_dev_type(dev);
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if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU || dev_type == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
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cpu_list.push_back(ggml_backend_dev_description(dev));
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}
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}
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return join(cpu_list, ", ");
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}
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static std::string get_gpu_info() {
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std::vector<std::string> gpu_list;
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for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
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auto * dev = ggml_backend_dev_get(i);
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auto dev_type = ggml_backend_dev_type(dev);
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if (dev_type == GGML_BACKEND_DEVICE_TYPE_GPU) {
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gpu_list.push_back(ggml_backend_dev_description(dev));
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}
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}
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return join(gpu_list, ", ");
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}
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// command line params
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enum output_formats { NONE, CSV, JSON, JSONL, MARKDOWN, SQL };
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static const char * output_format_str(output_formats format) {
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switch (format) {
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case NONE:
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return "none";
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case CSV:
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return "csv";
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case JSON:
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return "json";
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case JSONL:
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return "jsonl";
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case MARKDOWN:
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return "md";
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case SQL:
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return "sql";
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default:
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GGML_ABORT("invalid output format");
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}
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}
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static bool output_format_from_str(const std::string & s, output_formats & format) {
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if (s == "none") {
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format = NONE;
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} else if (s == "csv") {
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format = CSV;
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} else if (s == "json") {
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format = JSON;
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} else if (s == "jsonl") {
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format = JSONL;
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} else if (s == "md") {
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format = MARKDOWN;
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} else if (s == "sql") {
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format = SQL;
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} else {
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return false;
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}
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return true;
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}
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static const char * split_mode_str(llama_split_mode mode) {
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switch (mode) {
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case LLAMA_SPLIT_MODE_NONE:
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return "none";
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case LLAMA_SPLIT_MODE_LAYER:
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return "layer";
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case LLAMA_SPLIT_MODE_ROW:
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return "row";
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default:
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GGML_ABORT("invalid split mode");
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}
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}
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static std::string pair_str(const std::pair<int, int> & p) {
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static char buf[32];
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snprintf(buf, sizeof(buf), "%d,%d", p.first, p.second);
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return buf;
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}
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static std::vector<int> parse_int_range(const std::string & s) {
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// first[-last[(+|*)step]]
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std::regex range_regex(R"(^(\d+)(?:-(\d+)(?:([\+|\*])(\d+))?)?(?:,|$))");
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std::smatch match;
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std::string::const_iterator search_start(s.cbegin());
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std::vector<int> result;
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while (std::regex_search(search_start, s.cend(), match, range_regex)) {
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int first = std::stoi(match[1]);
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int last = match[2].matched ? std::stoi(match[2]) : first;
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char op = match[3].matched ? match[3].str()[0] : '+';
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int step = match[4].matched ? std::stoi(match[4]) : 1;
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for (int i = first; i <= last;) {
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result.push_back(i);
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int prev_i = i;
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if (op == '+') {
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i += step;
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} else if (op == '*') {
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i *= step;
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} else {
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throw std::invalid_argument("invalid range format");
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}
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if (i <= prev_i) {
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throw std::invalid_argument("invalid range");
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}
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}
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search_start = match.suffix().first;
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}
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if (search_start != s.cend()) {
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throw std::invalid_argument("invalid range format");
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}
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return result;
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}
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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<std::pair<int, int>> n_pg;
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std::vector<int> n_depth;
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std::vector<int> n_batch;
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std::vector<int> n_ubatch;
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std::vector<ggml_type> type_k;
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std::vector<ggml_type> type_v;
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std::vector<float> defrag_thold;
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std::vector<int> n_threads;
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std::vector<std::string> cpu_mask;
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std::vector<bool> cpu_strict;
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std::vector<int> poll;
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std::vector<int> n_gpu_layers;
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std::vector<std::string> rpc_servers;
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std::vector<llama_split_mode> split_mode;
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std::vector<int> main_gpu;
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std::vector<bool> no_kv_offload;
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std::vector<bool> flash_attn;
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std::vector<std::vector<float>> tensor_split;
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std::vector<std::vector<llama_model_tensor_buft_override>> tensor_buft_overrides;
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std::vector<bool> use_mmap;
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std::vector<bool> embeddings;
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std::vector<bool> no_op_offload;
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ggml_numa_strategy numa;
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int reps;
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ggml_sched_priority prio;
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int delay;
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bool verbose;
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bool progress;
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output_formats output_format;
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output_formats output_format_stderr;
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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_pg */ {},
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/* n_depth */ { 0 },
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/* n_batch */ { 2048 },
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/* n_ubatch */ { 512 },
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/* type_k */ { GGML_TYPE_F16 },
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/* type_v */ { GGML_TYPE_F16 },
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/* defrag_thold */ { -1.0f },
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/* n_threads */ { cpu_get_num_math() },
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/* cpu_mask */ { "0x0" },
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/* cpu_strict */ { false },
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/* poll */ { 50 },
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/* n_gpu_layers */ { 99 },
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/* rpc_servers */ { "" },
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/* split_mode */ { LLAMA_SPLIT_MODE_LAYER },
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/* main_gpu */ { 0 },
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/* no_kv_offload */ { false },
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/* flash_attn */ { false },
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/* tensor_split */ { std::vector<float>(llama_max_devices(), 0.0f) },
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/* tensor_buft_overrides*/ { std::vector<llama_model_tensor_buft_override>{ { nullptr, nullptr } } },
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/* use_mmap */ { true },
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/* embeddings */ { false },
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/* no_op_offload */ { false },
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/* numa */ GGML_NUMA_STRATEGY_DISABLED,
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/* reps */ 5,
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/* prio */ GGML_SCHED_PRIO_NORMAL,
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/* delay */ 0,
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/* verbose */ false,
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/* progress */ false,
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/* output_format */ MARKDOWN,
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/* output_format_stderr */ NONE,
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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(" --numa <distribute|isolate|numactl> numa mode (default: disabled)\n");
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printf(" -r, --repetitions <n> number of times to repeat each test (default: %d)\n",
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cmd_params_defaults.reps);
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printf(" --prio <0|1|2|3> process/thread priority (default: %d)\n",
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cmd_params_defaults.prio);
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printf(" --delay <0...N> (seconds) delay between each test (default: %d)\n",
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cmd_params_defaults.delay);
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printf(" -o, --output <csv|json|jsonl|md|sql> output format printed to stdout (default: %s)\n",
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output_format_str(cmd_params_defaults.output_format));
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printf(" -oe, --output-err <csv|json|jsonl|md|sql> output format printed to stderr (default: %s)\n",
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output_format_str(cmd_params_defaults.output_format_stderr));
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printf(" -v, --verbose verbose output\n");
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printf(" --progress print test progress indicators\n");
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printf("\n");
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printf("test parameters:\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",
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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(" -pg <pp,tg> (default: %s)\n",
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join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str());
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printf(" -d, --n-depth <n> (default: %s)\n",
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join(cmd_params_defaults.n_depth, ",").c_str());
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printf(" -b, --batch-size <n> (default: %s)\n",
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join(cmd_params_defaults.n_batch, ",").c_str());
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printf(" -ub, --ubatch-size <n> (default: %s)\n",
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join(cmd_params_defaults.n_ubatch, ",").c_str());
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printf(" -ctk, --cache-type-k <t> (default: %s)\n",
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join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
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printf(" -ctv, --cache-type-v <t> (default: %s)\n",
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join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
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printf(" -dt, --defrag-thold <f> (default: %s)\n",
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join(cmd_params_defaults.defrag_thold, ",").c_str());
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printf(" -t, --threads <n> (default: %s)\n",
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join(cmd_params_defaults.n_threads, ",").c_str());
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printf(" -C, --cpu-mask <hex,hex> (default: %s)\n",
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join(cmd_params_defaults.cpu_mask, ",").c_str());
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printf(" --cpu-strict <0|1> (default: %s)\n",
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join(cmd_params_defaults.cpu_strict, ",").c_str());
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printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str());
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printf(" -ngl, --n-gpu-layers <n> (default: %s)\n",
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join(cmd_params_defaults.n_gpu_layers, ",").c_str());
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if (llama_supports_rpc()) {
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printf(" -rpc, --rpc <rpc_servers> (default: %s)\n",
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join(cmd_params_defaults.rpc_servers, ",").c_str());
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}
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printf(" -sm, --split-mode <none|layer|row> (default: %s)\n",
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join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
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printf(" -mg, --main-gpu <i> (default: %s)\n",
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join(cmd_params_defaults.main_gpu, ",").c_str());
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printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n",
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join(cmd_params_defaults.no_kv_offload, ",").c_str());
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printf(" -fa, --flash-attn <0|1> (default: %s)\n",
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join(cmd_params_defaults.flash_attn, ",").c_str());
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printf(" -mmp, --mmap <0|1> (default: %s)\n",
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join(cmd_params_defaults.use_mmap, ",").c_str());
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printf(" -embd, --embeddings <0|1> (default: %s)\n",
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join(cmd_params_defaults.embeddings, ",").c_str());
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printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
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printf(" -ot --override-tensors <tensor name pattern>=<buffer type>;...\n");
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printf(" (default: disabled)\n");
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printf(" -nopo, --no-op-offload <0|1> (default: 0)\n");
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printf("\n");
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printf(
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"Multiple values can be given for each parameter by separating them with ','\n"
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"or by specifying the parameter multiple times. Ranges can be given as\n"
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"'first-last' or 'first-last+step' or 'first-last*mult'.\n");
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}
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static ggml_type ggml_type_from_name(const std::string & s) {
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if (s == "f16") {
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return GGML_TYPE_F16;
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}
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if (s == "bf16") {
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return GGML_TYPE_BF16;
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}
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if (s == "q8_0") {
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return GGML_TYPE_Q8_0;
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}
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if (s == "q4_0") {
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return GGML_TYPE_Q4_0;
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}
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if (s == "q4_1") {
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return GGML_TYPE_Q4_1;
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}
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if (s == "q5_0") {
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return GGML_TYPE_Q5_0;
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}
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if (s == "q5_1") {
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return GGML_TYPE_Q5_1;
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}
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if (s == "iq4_nl") {
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return GGML_TYPE_IQ4_NL;
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}
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return GGML_TYPE_COUNT;
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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.output_format_stderr = cmd_params_defaults.output_format_stderr;
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params.reps = cmd_params_defaults.reps;
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params.numa = cmd_params_defaults.numa;
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params.prio = cmd_params_defaults.prio;
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params.delay = cmd_params_defaults.delay;
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params.progress = cmd_params_defaults.progress;
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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) {
|
|
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 == "-dt" || arg == "--defrag-thold") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
auto p = string_split<float>(argv[i], split_delim);
|
|
params.defrag_thold.insert(params.defrag_thold.end(), p.begin(), p.end());
|
|
} 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 (llama_supports_rpc() && (arg == "-rpc" || arg == "--rpc")) {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rpc_servers.push_back(argv[i]);
|
|
} 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 == "-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 == "-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 {
|
|
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.defrag_thold.empty()) {
|
|
params.defrag_thold = cmd_params_defaults.defrag_thold;
|
|
}
|
|
if (params.n_gpu_layers.empty()) {
|
|
params.n_gpu_layers = cmd_params_defaults.n_gpu_layers;
|
|
}
|
|
if (params.rpc_servers.empty()) {
|
|
params.rpc_servers = cmd_params_defaults.rpc_servers;
|
|
}
|
|
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.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.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.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;
|
|
float defrag_thold;
|
|
int n_threads;
|
|
std::string cpu_mask;
|
|
bool cpu_strict;
|
|
int poll;
|
|
int n_gpu_layers;
|
|
std::string rpc_servers_str;
|
|
llama_split_mode split_mode;
|
|
int main_gpu;
|
|
bool no_kv_offload;
|
|
bool flash_attn;
|
|
std::vector<float> tensor_split;
|
|
std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
|
|
bool use_mmap;
|
|
bool embeddings;
|
|
bool no_op_offload;
|
|
|
|
llama_model_params to_llama_mparams() const {
|
|
llama_model_params mparams = llama_model_default_params();
|
|
|
|
mparams.n_gpu_layers = n_gpu_layers;
|
|
if (!rpc_servers_str.empty()) {
|
|
auto rpc_servers = string_split<std::string>(rpc_servers_str, ',');
|
|
|
|
// add RPC devices
|
|
if (!rpc_servers.empty()) {
|
|
ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
|
|
if (!rpc_reg) {
|
|
fprintf(stderr, "%s: failed to find RPC backend\n", __func__);
|
|
exit(1);
|
|
}
|
|
|
|
typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
|
|
ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
|
|
if (!ggml_backend_rpc_add_device_fn) {
|
|
fprintf(stderr, "%s: failed to find RPC device add function\n", __func__);
|
|
exit(1);
|
|
}
|
|
static std::vector<ggml_backend_dev_t> devices;
|
|
devices.clear();
|
|
for (const std::string & server : rpc_servers) {
|
|
ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
|
|
if (dev) {
|
|
devices.push_back(dev);
|
|
} else {
|
|
fprintf(stderr, "%s: failed to add RPC device for server '%s'\n", __func__, server.c_str());
|
|
exit(1);
|
|
}
|
|
}
|
|
devices.push_back(nullptr);
|
|
mparams.devices = devices.data();
|
|
}
|
|
}
|
|
mparams.split_mode = split_mode;
|
|
mparams.main_gpu = main_gpu;
|
|
mparams.tensor_split = tensor_split.data();
|
|
mparams.use_mmap = use_mmap;
|
|
|
|
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();
|
|
}
|
|
|
|
return mparams;
|
|
}
|
|
|
|
bool equal_mparams(const cmd_params_instance & other) const {
|
|
return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers_str == other.rpc_servers_str &&
|
|
split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap &&
|
|
tensor_split == other.tensor_split && 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.defrag_thold = defrag_thold;
|
|
cparams.offload_kqv = !no_kv_offload;
|
|
cparams.flash_attn = flash_attn;
|
|
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 & rpc : params.rpc_servers)
|
|
for (const auto & sm : params.split_mode)
|
|
for (const auto & mg : params.main_gpu)
|
|
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 & 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 & defrag_thold : params.defrag_thold)
|
|
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,
|
|
/* .defrag_thold = */ defrag_thold,
|
|
/* .n_threads = */ nt,
|
|
/* .cpu_mask = */ cm,
|
|
/* .cpu_strict = */ cs,
|
|
/* .poll = */ pl,
|
|
/* .n_gpu_layers = */ nl,
|
|
/* .rpc_servers = */ rpc,
|
|
/* .split_mode = */ sm,
|
|
/* .main_gpu = */ mg,
|
|
/* .no_kv_offload= */ nkvo,
|
|
/* .flash_attn = */ fa,
|
|
/* .tensor_split = */ ts,
|
|
/* .tensor_buft_overrides = */ ot,
|
|
/* .use_mmap = */ mmp,
|
|
/* .embeddings = */ embd,
|
|
/* .no_op_offload= */ nopo,
|
|
};
|
|
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,
|
|
/* .defrag_thold = */ defrag_thold,
|
|
/* .n_threads = */ nt,
|
|
/* .cpu_mask = */ cm,
|
|
/* .cpu_strict = */ cs,
|
|
/* .poll = */ pl,
|
|
/* .n_gpu_layers = */ nl,
|
|
/* .rpc_servers = */ rpc,
|
|
/* .split_mode = */ sm,
|
|
/* .main_gpu = */ mg,
|
|
/* .no_kv_offload= */ nkvo,
|
|
/* .flash_attn = */ fa,
|
|
/* .tensor_split = */ ts,
|
|
/* .tensor_buft_overrides = */ ot,
|
|
/* .use_mmap = */ mmp,
|
|
/* .embeddings = */ embd,
|
|
/* .no_op_offload= */ nopo,
|
|
};
|
|
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,
|
|
/* .defrag_thold = */ defrag_thold,
|
|
/* .n_threads = */ nt,
|
|
/* .cpu_mask = */ cm,
|
|
/* .cpu_strict = */ cs,
|
|
/* .poll = */ pl,
|
|
/* .n_gpu_layers = */ nl,
|
|
/* .rpc_servers = */ rpc,
|
|
/* .split_mode = */ sm,
|
|
/* .main_gpu = */ mg,
|
|
/* .no_kv_offload= */ nkvo,
|
|
/* .flash_attn = */ fa,
|
|
/* .tensor_split = */ ts,
|
|
/* .tensor_buft_overrides = */ ot,
|
|
/* .use_mmap = */ mmp,
|
|
/* .embeddings = */ embd,
|
|
/* .no_op_offload= */ nopo,
|
|
};
|
|
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;
|
|
float defrag_thold;
|
|
int n_gpu_layers;
|
|
llama_split_mode split_mode;
|
|
int main_gpu;
|
|
bool no_kv_offload;
|
|
bool flash_attn;
|
|
std::vector<float> tensor_split;
|
|
std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
|
|
bool use_mmap;
|
|
bool embeddings;
|
|
bool no_op_offload;
|
|
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;
|
|
defrag_thold = inst.defrag_thold;
|
|
n_gpu_layers = inst.n_gpu_layers;
|
|
split_mode = inst.split_mode;
|
|
main_gpu = inst.main_gpu;
|
|
no_kv_offload = inst.no_kv_offload;
|
|
flash_attn = inst.flash_attn;
|
|
tensor_split = inst.tensor_split;
|
|
tensor_buft_overrides = inst.tensor_buft_overrides;
|
|
use_mmap = inst.use_mmap;
|
|
embeddings = inst.embeddings;
|
|
no_op_offload = inst.no_op_offload;
|
|
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;
|
|
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 (name != "CPU") {
|
|
backends.push_back(ggml_backend_reg_name(reg));
|
|
}
|
|
}
|
|
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",
|
|
"split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides",
|
|
"defrag_thold",
|
|
"use_mmap", "embeddings", "no_op_offload", "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") {
|
|
return INT;
|
|
}
|
|
if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || field == "flash_attn" ||
|
|
field == "use_mmap" || field == "embeddings") {
|
|
return BOOL;
|
|
}
|
|
if (field == "avg_ts" || field == "stddev_ts" || field == "defrag_thold") {
|
|
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),
|
|
split_mode_str(split_mode),
|
|
std::to_string(main_gpu),
|
|
std::to_string(no_kv_offload),
|
|
std::to_string(flash_attn),
|
|
tensor_split_str,
|
|
tensor_buft_overrides_str,
|
|
std::to_string(defrag_thold),
|
|
std::to_string(use_mmap),
|
|
std::to_string(embeddings),
|
|
std::to_string(no_op_offload),
|
|
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 == "use_mmap") {
|
|
return 4;
|
|
}
|
|
if (field == "test") {
|
|
return 15;
|
|
}
|
|
if (field == "no_op_offload") {
|
|
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 == "embeddings") {
|
|
return "embd";
|
|
}
|
|
if (field == "no_op_offload") {
|
|
return "nopo";
|
|
}
|
|
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;
|
|
if (!is_cpu_backend) {
|
|
fields.emplace_back("n_gpu_layers");
|
|
}
|
|
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.defrag_thold.size() > 1 || params.defrag_thold != cmd_params_defaults.defrag_thold) {
|
|
fields.emplace_back("defrag_thold");
|
|
}
|
|
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.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.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");
|
|
}
|
|
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 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 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");
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#if !defined(NDEBUG)
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fprintf(stderr, "warning: asserts enabled, performance may be affected\n");
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#endif
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#if (defined(_MSC_VER) && defined(_DEBUG)) || (!defined(_MSC_VER) && !defined(__OPTIMIZE__))
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fprintf(stderr, "warning: debug build, performance may be affected\n");
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#endif
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#if defined(__SANITIZE_ADDRESS__) || defined(__SANITIZE_THREAD__)
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fprintf(stderr, "warning: sanitizer enabled, performance may be affected\n");
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#endif
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// initialize backends
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ggml_backend_load_all();
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cmd_params params = parse_cmd_params(argc, argv);
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auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
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if (!cpu_dev) {
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fprintf(stderr, "%s: error: CPU backend is not loaded\n", __func__);
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return 1;
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}
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auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
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auto * ggml_threadpool_new_fn = (decltype(ggml_threadpool_new) *) ggml_backend_reg_get_proc_address(cpu_reg, "ggml_threadpool_new");
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auto * ggml_threadpool_free_fn = (decltype(ggml_threadpool_free) *) ggml_backend_reg_get_proc_address(cpu_reg, "ggml_threadpool_free");
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// initialize llama.cpp
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if (!params.verbose) {
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llama_log_set(llama_null_log_callback, NULL);
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}
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llama_backend_init();
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llama_numa_init(params.numa);
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set_process_priority(params.prio);
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// initialize printer
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std::unique_ptr<printer> p = create_printer(params.output_format);
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std::unique_ptr<printer> p_err = create_printer(params.output_format_stderr);
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if (p) {
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p->fout = stdout;
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p->print_header(params);
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}
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if (p_err) {
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p_err->fout = stderr;
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p_err->print_header(params);
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}
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std::vector<cmd_params_instance> params_instances = get_cmd_params_instances(params);
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llama_model * lmodel = nullptr;
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const cmd_params_instance * prev_inst = nullptr;
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int params_idx = 0;
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auto params_count = params_instances.size();
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for (const auto & inst : params_instances) {
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params_idx++;
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if (params.progress) {
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fprintf(stderr, "llama-bench: benchmark %d/%zu: starting\n", params_idx, params_count);
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}
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// keep the same model between tests when possible
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if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
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if (lmodel) {
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llama_model_free(lmodel);
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}
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lmodel = llama_model_load_from_file(inst.model.c_str(), inst.to_llama_mparams());
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if (lmodel == NULL) {
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fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, inst.model.c_str());
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return 1;
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}
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prev_inst = &inst;
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}
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llama_context * ctx = llama_init_from_model(lmodel, inst.to_llama_cparams());
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if (ctx == NULL) {
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fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str());
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llama_model_free(lmodel);
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return 1;
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}
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test t(inst, lmodel, ctx);
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llama_kv_self_clear(ctx);
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// cool off before the test
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if (params.delay) {
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std::this_thread::sleep_for(std::chrono::seconds(params.delay));
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}
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struct ggml_threadpool_params tpp = ggml_threadpool_params_default(t.n_threads);
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if (!parse_cpu_mask(t.cpu_mask, tpp.cpumask)) {
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fprintf(stderr, "%s: failed to parse cpu-mask: %s\n", __func__, t.cpu_mask.c_str());
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exit(1);
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}
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tpp.strict_cpu = t.cpu_strict;
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tpp.poll = t.poll;
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tpp.prio = params.prio;
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struct ggml_threadpool * threadpool = ggml_threadpool_new_fn(&tpp);
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if (!threadpool) {
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fprintf(stderr, "%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads);
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exit(1);
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}
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llama_attach_threadpool(ctx, threadpool, NULL);
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// warmup run
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if (t.n_prompt > 0) {
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if (params.progress) {
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fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count);
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}
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//test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
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bool res = test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
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if (!res) {
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fprintf(stderr, "%s: error: failed to run prompt warmup\n", __func__);
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exit(1);
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}
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}
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if (t.n_gen > 0) {
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if (params.progress) {
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fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup generation run\n", params_idx, params_count);
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}
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bool res = test_gen(ctx, 1, t.n_threads);
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if (!res) {
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fprintf(stderr, "%s: error: failed to run gen warmup\n", __func__);
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exit(1);
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}
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}
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for (int i = 0; i < params.reps; i++) {
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llama_kv_self_clear(ctx);
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if (t.n_depth > 0) {
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if (params.progress) {
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fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d\n", params_idx, params_count,
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i + 1, params.reps);
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}
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bool res = test_prompt(ctx, t.n_depth, t.n_batch, t.n_threads);
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if (!res) {
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fprintf(stderr, "%s: error: failed to run depth\n", __func__);
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exit(1);
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}
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}
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uint64_t t_start = get_time_ns();
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if (t.n_prompt > 0) {
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if (params.progress) {
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fprintf(stderr, "llama-bench: benchmark %d/%zu: prompt run %d/%d\n", params_idx, params_count,
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i + 1, params.reps);
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}
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bool res = test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
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if (!res) {
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fprintf(stderr, "%s: error: failed to run prompt\n", __func__);
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exit(1);
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}
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}
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if (t.n_gen > 0) {
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if (params.progress) {
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fprintf(stderr, "llama-bench: benchmark %d/%zu: generation run %d/%d\n", params_idx, params_count,
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i + 1, params.reps);
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}
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bool res = test_gen(ctx, t.n_gen, t.n_threads);
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if (!res) {
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fprintf(stderr, "%s: error: failed to run gen\n", __func__);
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exit(1);
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}
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}
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uint64_t t_ns = get_time_ns() - t_start;
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t.samples_ns.push_back(t_ns);
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}
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if (p) {
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p->print_test(t);
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fflush(p->fout);
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}
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if (p_err) {
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p_err->print_test(t);
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fflush(p_err->fout);
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}
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llama_perf_context_print(ctx);
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llama_free(ctx);
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ggml_threadpool_free_fn(threadpool);
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}
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llama_model_free(lmodel);
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if (p) {
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p->print_footer();
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}
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if (p_err) {
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p_err->print_footer();
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}
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llama_backend_free();
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return 0;
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}
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