OBJ_COMMON = \
common/common.o \
+ common/arg.o \
common/console.o \
common/ngram-cache.o \
common/sampling.o \
include/llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
+common/arg.o: \
+ common/arg.cpp \
+ common/arg.h
+ $(CXX) $(CXXFLAGS) -c $< -o $@
+
common/sampling.o: \
common/sampling.cpp \
common/sampling.h \
base64.hpp
common.h
common.cpp
+ arg.h
+ arg.cpp
sampling.h
sampling.cpp
console.h
--- /dev/null
+#include "arg.h"
+
+#include "sampling.h"
+
+#include <algorithm>
+#include <string>
+#include <vector>
+#include <set>
+#include <fstream>
+#include <regex>
+#include <cstdarg>
+#include <climits>
+
+#include "json-schema-to-grammar.h"
+
+using json = nlohmann::ordered_json;
+
+llama_arg & llama_arg::set_examples(std::initializer_list<enum llama_example> examples) {
+ this->examples = std::move(examples);
+ return *this;
+}
+
+llama_arg & llama_arg::set_env(const char * env) {
+ help = help + "\n(env: " + env + ")";
+ this->env = env;
+ return *this;
+}
+
+llama_arg & llama_arg::set_sparam() {
+ is_sparam = true;
+ return *this;
+}
+
+bool llama_arg::in_example(enum llama_example ex) {
+ return examples.find(ex) != examples.end();
+}
+
+bool llama_arg::get_value_from_env(std::string & output) {
+ if (env == nullptr) return false;
+ char * value = std::getenv(env);
+ if (value) {
+ output = value;
+ return true;
+ }
+ return false;
+}
+
+bool llama_arg::has_value_from_env() {
+ return env != nullptr && std::getenv(env);
+}
+
+static std::vector<std::string> break_str_into_lines(std::string input, size_t max_char_per_line) {
+ std::vector<std::string> result;
+ std::istringstream iss(input);
+ std::string line;
+ auto add_line = [&](const std::string& l) {
+ if (l.length() <= max_char_per_line) {
+ result.push_back(l);
+ } else {
+ std::istringstream line_stream(l);
+ std::string word, current_line;
+ while (line_stream >> word) {
+ if (current_line.length() + !current_line.empty() + word.length() > max_char_per_line) {
+ if (!current_line.empty()) result.push_back(current_line);
+ current_line = word;
+ } else {
+ current_line += (!current_line.empty() ? " " : "") + word;
+ }
+ }
+ if (!current_line.empty()) result.push_back(current_line);
+ }
+ };
+ while (std::getline(iss, line)) {
+ add_line(line);
+ }
+ return result;
+}
+
+std::string llama_arg::to_string() {
+ // params for printing to console
+ const static int n_leading_spaces = 40;
+ const static int n_char_per_line_help = 70; // TODO: detect this based on current console
+ std::string leading_spaces(n_leading_spaces, ' ');
+
+ std::ostringstream ss;
+ for (const auto arg : args) {
+ if (arg == args.front()) {
+ if (args.size() == 1) {
+ ss << arg;
+ } else {
+ // first arg is usually abbreviation, we need padding to make it more beautiful
+ auto tmp = std::string(arg) + ", ";
+ auto spaces = std::string(std::max(0, 7 - (int)tmp.size()), ' ');
+ ss << tmp << spaces;
+ }
+ } else {
+ ss << arg << (arg != args.back() ? ", " : "");
+ }
+ }
+ if (value_hint) ss << " " << value_hint;
+ if (value_hint_2) ss << " " << value_hint_2;
+ if (ss.tellp() > n_leading_spaces - 3) {
+ // current line is too long, add new line
+ ss << "\n" << leading_spaces;
+ } else {
+ // padding between arg and help, same line
+ ss << std::string(leading_spaces.size() - ss.tellp(), ' ');
+ }
+ const auto help_lines = break_str_into_lines(help, n_char_per_line_help);
+ for (const auto & line : help_lines) {
+ ss << (&line == &help_lines.front() ? "" : leading_spaces) << line << "\n";
+ }
+ return ss.str();
+}
+
+//
+// utils
+//
+
+#ifdef __GNUC__
+#ifdef __MINGW32__
+#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
+#else
+#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
+#endif
+#else
+#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
+#endif
+
+LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
+static std::string format(const char * fmt, ...) {
+ va_list ap;
+ va_list ap2;
+ va_start(ap, fmt);
+ va_copy(ap2, ap);
+ int size = vsnprintf(NULL, 0, fmt, ap);
+ GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
+ std::vector<char> buf(size + 1);
+ int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+ GGML_ASSERT(size2 == size);
+ va_end(ap2);
+ va_end(ap);
+ return std::string(buf.data(), size);
+}
+
+static void gpt_params_handle_model_default(gpt_params & params) {
+ if (!params.hf_repo.empty()) {
+ // short-hand to avoid specifying --hf-file -> default it to --model
+ if (params.hf_file.empty()) {
+ if (params.model.empty()) {
+ throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
+ }
+ params.hf_file = params.model;
+ } else if (params.model.empty()) {
+ params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
+ }
+ } else if (!params.model_url.empty()) {
+ if (params.model.empty()) {
+ auto f = string_split(params.model_url, '#').front();
+ f = string_split(f, '?').front();
+ params.model = fs_get_cache_file(string_split(f, '/').back());
+ }
+ } else if (params.model.empty()) {
+ params.model = DEFAULT_MODEL_PATH;
+ }
+}
+
+//
+// CLI argument parsing functions
+//
+
+static bool gpt_params_parse_ex(int argc, char ** argv, gpt_params_context & ctx_arg) {
+ std::string arg;
+ const std::string arg_prefix = "--";
+ gpt_params & params = ctx_arg.params;
+ gpt_sampler_params & sparams = params.sparams;
+
+ std::unordered_map<std::string, llama_arg *> arg_to_options;
+ for (auto & opt : ctx_arg.options) {
+ for (const auto & arg : opt.args) {
+ arg_to_options[arg] = &opt;
+ }
+ }
+
+ // handle environment variables
+ for (auto & opt : ctx_arg.options) {
+ std::string value;
+ if (opt.get_value_from_env(value)) {
+ try {
+ if (opt.handler_void && (value == "1" || value == "true")) {
+ opt.handler_void(params);
+ }
+ if (opt.handler_int) {
+ opt.handler_int(params, std::stoi(value));
+ }
+ if (opt.handler_string) {
+ opt.handler_string(params, value);
+ continue;
+ }
+ } catch (std::exception & e) {
+ throw std::invalid_argument(format(
+ "error while handling environment variable \"%s\": %s\n\n", opt.env, e.what()));
+ }
+ }
+ }
+
+ // handle command line arguments
+ auto check_arg = [&](int i) {
+ if (i+1 >= argc) {
+ throw std::invalid_argument("expected value for argument");
+ }
+ };
+
+ for (int i = 1; i < argc; i++) {
+ const std::string arg_prefix = "--";
+
+ std::string arg = argv[i];
+ if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+ std::replace(arg.begin(), arg.end(), '_', '-');
+ }
+ if (arg_to_options.find(arg) == arg_to_options.end()) {
+ throw std::invalid_argument(format("error: invalid argument: %s", arg.c_str()));
+ }
+ auto opt = *arg_to_options[arg];
+ if (opt.has_value_from_env()) {
+ fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
+ }
+ try {
+ if (opt.handler_void) {
+ opt.handler_void(params);
+ continue;
+ }
+
+ // arg with single value
+ check_arg(i);
+ std::string val = argv[++i];
+ if (opt.handler_int) {
+ opt.handler_int(params, std::stoi(val));
+ continue;
+ }
+ if (opt.handler_string) {
+ opt.handler_string(params, val);
+ continue;
+ }
+
+ // arg with 2 values
+ check_arg(i);
+ std::string val2 = argv[++i];
+ if (opt.handler_str_str) {
+ opt.handler_str_str(params, val, val2);
+ continue;
+ }
+ } catch (std::exception & e) {
+ throw std::invalid_argument(format(
+ "error while handling argument \"%s\": %s\n\n"
+ "usage:\n%s\n\nto show complete usage, run with -h",
+ arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str()));
+ }
+ }
+
+ postprocess_cpu_params(params.cpuparams, nullptr);
+ postprocess_cpu_params(params.cpuparams_batch, ¶ms.cpuparams);
+ postprocess_cpu_params(params.draft_cpuparams, ¶ms.cpuparams);
+ postprocess_cpu_params(params.draft_cpuparams_batch, ¶ms.cpuparams_batch);
+
+ if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
+ throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
+ }
+
+ gpt_params_handle_model_default(params);
+
+ if (params.escape) {
+ string_process_escapes(params.prompt);
+ string_process_escapes(params.input_prefix);
+ string_process_escapes(params.input_suffix);
+ for (auto & antiprompt : params.antiprompt) {
+ string_process_escapes(antiprompt);
+ }
+ }
+
+ if (!params.kv_overrides.empty()) {
+ params.kv_overrides.emplace_back();
+ params.kv_overrides.back().key[0] = 0;
+ }
+
+ if (sparams.seed == LLAMA_DEFAULT_SEED) {
+ sparams.seed = time(NULL);
+ }
+
+ return true;
+}
+
+static void gpt_params_print_usage(gpt_params_context & ctx_arg) {
+ auto print_options = [](std::vector<llama_arg *> & options) {
+ for (llama_arg * opt : options) {
+ printf("%s", opt->to_string().c_str());
+ }
+ };
+
+ std::vector<llama_arg *> common_options;
+ std::vector<llama_arg *> sparam_options;
+ std::vector<llama_arg *> specific_options;
+ for (auto & opt : ctx_arg.options) {
+ // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
+ if (opt.is_sparam) {
+ sparam_options.push_back(&opt);
+ } else if (opt.in_example(ctx_arg.ex)) {
+ specific_options.push_back(&opt);
+ } else {
+ common_options.push_back(&opt);
+ }
+ }
+ printf("----- common params -----\n\n");
+ print_options(common_options);
+ printf("\n\n----- sampling params -----\n\n");
+ print_options(sparam_options);
+ // TODO: maybe convert enum llama_example to string
+ printf("\n\n----- example-specific params -----\n\n");
+ print_options(specific_options);
+}
+
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params, llama_example ex, void(*print_usage)(int, char **)) {
+ auto ctx_arg = gpt_params_parser_init(params, ex, print_usage);
+ const gpt_params params_org = ctx_arg.params; // the example can modify the default params
+
+ try {
+ if (!gpt_params_parse_ex(argc, argv, ctx_arg)) {
+ ctx_arg.params = params_org;
+ return false;
+ }
+ if (ctx_arg.params.usage) {
+ gpt_params_print_usage(ctx_arg);
+ if (ctx_arg.print_usage) {
+ ctx_arg.print_usage(argc, argv);
+ }
+ exit(0);
+ }
+ } catch (const std::invalid_argument & ex) {
+ fprintf(stderr, "%s\n", ex.what());
+ ctx_arg.params = params_org;
+ return false;
+ }
+
+ return true;
+}
+
+gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, void(*print_usage)(int, char **)) {
+ gpt_params_context ctx_arg(params);
+ ctx_arg.print_usage = print_usage;
+ ctx_arg.ex = ex;
+
+ std::string sampler_type_chars;
+ std::string sampler_type_names;
+ for (const auto & sampler : params.sparams.samplers) {
+ sampler_type_chars += gpt_sampler_type_to_chr(sampler);
+ sampler_type_names += gpt_sampler_type_to_str(sampler) + ";";
+ }
+ sampler_type_names.pop_back();
+
+
+ /**
+ * filter options by example
+ * rules:
+ * - all examples inherit options from LLAMA_EXAMPLE_COMMON
+ * - if LLAMA_EXAMPLE_* is set (other than COMMON), we only show the option in the corresponding example
+ * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
+ */
+ auto add_opt = [&](llama_arg arg) {
+ if (arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) {
+ ctx_arg.options.push_back(std::move(arg));
+ }
+ };
+
+
+ add_opt(llama_arg(
+ {"-h", "--help", "--usage"},
+ "print usage and exit",
+ [](gpt_params & params) {
+ params.usage = true;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--version"},
+ "show version and build info",
+ [](gpt_params &) {
+ fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+ fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+ exit(0);
+ }
+ ));
+ add_opt(llama_arg(
+ {"-v", "--verbose"},
+ "print verbose information",
+ [](gpt_params & params) {
+ params.verbosity = 1;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--verbosity"}, "N",
+ format("set specific verbosity level (default: %d)", params.verbosity),
+ [](gpt_params & params, int value) {
+ params.verbosity = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--verbose-prompt"},
+ format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
+ [](gpt_params & params) {
+ params.verbose_prompt = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"--no-display-prompt"},
+ format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
+ [](gpt_params & params) {
+ params.display_prompt = false;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"-co", "--color"},
+ format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
+ [](gpt_params & params) {
+ params.use_color = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
+ add_opt(llama_arg(
+ {"-t", "--threads"}, "N",
+ format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
+ [](gpt_params & params, int value) {
+ params.cpuparams.n_threads = value;
+ if (params.cpuparams.n_threads <= 0) {
+ params.cpuparams.n_threads = std::thread::hardware_concurrency();
+ }
+ }
+ ).set_env("LLAMA_ARG_THREADS"));
+ add_opt(llama_arg(
+ {"-tb", "--threads-batch"}, "N",
+ "number of threads to use during batch and prompt processing (default: same as --threads)",
+ [](gpt_params & params, int value) {
+ params.cpuparams_batch.n_threads = value;
+ if (params.cpuparams_batch.n_threads <= 0) {
+ params.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
+ }
+ }
+ ));
+ add_opt(llama_arg(
+ {"-td", "--threads-draft"}, "N",
+ "number of threads to use during generation (default: same as --threads)",
+ [](gpt_params & params, int value) {
+ params.draft_cpuparams.n_threads = value;
+ if (params.draft_cpuparams.n_threads <= 0) {
+ params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
+ }
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"-tbd", "--threads-batch-draft"}, "N",
+ "number of threads to use during batch and prompt processing (default: same as --threads-draft)",
+ [](gpt_params & params, int value) {
+ params.draft_cpuparams_batch.n_threads = value;
+ if (params.draft_cpuparams_batch.n_threads <= 0) {
+ params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
+ }
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"-C", "--cpu-mask"}, "M",
+ "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")",
+ [](gpt_params & params, const std::string & mask) {
+ params.cpuparams.mask_valid = true;
+ if (!parse_cpu_mask(mask, params.cpuparams.cpumask)) {
+ throw std::invalid_argument("invalid cpumask");
+ }
+ }
+ ));
+ add_opt(llama_arg(
+ {"-Cr", "--cpu-range"}, "lo-hi",
+ "range of CPUs for affinity. Complements --cpu-mask",
+ [](gpt_params & params, const std::string & range) {
+ params.cpuparams.mask_valid = true;
+ if (!parse_cpu_range(range, params.cpuparams.cpumask)) {
+ throw std::invalid_argument("invalid range");
+ }
+ }
+ ));
+ add_opt(llama_arg(
+ {"--cpu-strict"}, "<0|1>",
+ format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu),
+ [](gpt_params & params, const std::string & value) {
+ params.cpuparams.strict_cpu = std::stoul(value);
+ }
+ ));
+ add_opt(llama_arg(
+ {"--prio"}, "N",
+ format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority),
+ [](gpt_params & params, int prio) {
+ if (prio < 0 || prio > 3) {
+ throw std::invalid_argument("invalid value");
+ }
+ params.cpuparams.priority = (enum ggml_sched_priority) prio;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--poll"}, "<0...100>",
+ format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll),
+ [](gpt_params & params, const std::string & value) {
+ params.cpuparams.poll = std::stoul(value);
+ }
+ ));
+ add_opt(llama_arg(
+ {"-Cb", "--cpu-mask-batch"}, "M",
+ "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)",
+ [](gpt_params & params, const std::string & mask) {
+ params.cpuparams_batch.mask_valid = true;
+ if (!parse_cpu_mask(mask, params.cpuparams_batch.cpumask)) {
+ throw std::invalid_argument("invalid cpumask");
+ }
+ }
+ ));
+ add_opt(llama_arg(
+ {"-Crb", "--cpu-range-batch"}, "lo-hi",
+ "ranges of CPUs for affinity. Complements --cpu-mask-batch",
+ [](gpt_params & params, const std::string & range) {
+ params.cpuparams_batch.mask_valid = true;
+ if (!parse_cpu_range(range, params.cpuparams_batch.cpumask)) {
+ throw std::invalid_argument("invalid range");
+ }
+ }
+ ));
+ add_opt(llama_arg(
+ {"--cpu-strict-batch"}, "<0|1>",
+ "use strict CPU placement (default: same as --cpu-strict)",
+ [](gpt_params & params, int value) {
+ params.cpuparams_batch.strict_cpu = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--prio-batch"}, "N",
+ format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority),
+ [](gpt_params & params, int prio) {
+ if (prio < 0 || prio > 3) {
+ throw std::invalid_argument("invalid value");
+ }
+ params.cpuparams_batch.priority = (enum ggml_sched_priority) prio;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--poll-batch"}, "<0|1>",
+ "use polling to wait for work (default: same as --poll)",
+ [](gpt_params & params, int value) {
+ params.cpuparams_batch.poll = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"-Cd", "--cpu-mask-draft"}, "M",
+ "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
+ [](gpt_params & params, const std::string & mask) {
+ params.draft_cpuparams.mask_valid = true;
+ if (!parse_cpu_mask(mask, params.draft_cpuparams.cpumask)) {
+ throw std::invalid_argument("invalid cpumask");
+ }
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"-Crd", "--cpu-range-draft"}, "lo-hi",
+ "Ranges of CPUs for affinity. Complements --cpu-mask-draft",
+ [](gpt_params & params, const std::string & range) {
+ params.draft_cpuparams.mask_valid = true;
+ if (!parse_cpu_range(range, params.draft_cpuparams.cpumask)) {
+ throw std::invalid_argument("invalid range");
+ }
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"--cpu-strict-draft"}, "<0|1>",
+ "Use strict CPU placement for draft model (default: same as --cpu-strict)",
+ [](gpt_params & params, int value) {
+ params.draft_cpuparams.strict_cpu = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"--prio-draft"}, "N",
+ format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams.priority),
+ [](gpt_params & params, int prio) {
+ if (prio < 0 || prio > 3) {
+ throw std::invalid_argument("invalid value");
+ }
+ params.draft_cpuparams.priority = (enum ggml_sched_priority) prio;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"--poll-draft"}, "<0|1>",
+ "Use polling to wait for draft model work (default: same as --poll])",
+ [](gpt_params & params, int value) {
+ params.draft_cpuparams.poll = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"-Cbd", "--cpu-mask-batch-draft"}, "M",
+ "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
+ [](gpt_params & params, const std::string & mask) {
+ params.draft_cpuparams_batch.mask_valid = true;
+ if (!parse_cpu_mask(mask, params.draft_cpuparams_batch.cpumask)) {
+ throw std::invalid_argument("invalid cpumask");
+ }
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
+ "Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
+ [](gpt_params & params, const std::string & range) {
+ params.draft_cpuparams_batch.mask_valid = true;
+ if (!parse_cpu_range(range, params.draft_cpuparams_batch.cpumask)) {
+ throw std::invalid_argument("invalid cpumask");
+ }
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"--cpu-strict-batch-draft"}, "<0|1>",
+ "Use strict CPU placement for draft model (default: --cpu-strict-draft)",
+ [](gpt_params & params, int value) {
+ params.draft_cpuparams_batch.strict_cpu = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"--prio-batch-draft"}, "N",
+ format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams_batch.priority),
+ [](gpt_params & params, int prio) {
+ if (prio < 0 || prio > 3) {
+ throw std::invalid_argument("invalid value");
+ }
+ params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) prio;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"--poll-batch-draft"}, "<0|1>",
+ "Use polling to wait for draft model work (default: --poll-draft)",
+ [](gpt_params & params, int value) {
+ params.draft_cpuparams_batch.poll = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"--draft"}, "N",
+ format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
+ [](gpt_params & params, int value) {
+ params.n_draft = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
+ add_opt(llama_arg(
+ {"-ps", "--p-split"}, "N",
+ format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
+ [](gpt_params & params, const std::string & value) {
+ params.p_split = std::stof(value);
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"-lcs", "--lookup-cache-static"}, "FNAME",
+ "path to static lookup cache to use for lookup decoding (not updated by generation)",
+ [](gpt_params & params, const std::string & value) {
+ params.lookup_cache_static = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_LOOKUP}));
+ add_opt(llama_arg(
+ {"-lcd", "--lookup-cache-dynamic"}, "FNAME",
+ "path to dynamic lookup cache to use for lookup decoding (updated by generation)",
+ [](gpt_params & params, const std::string & value) {
+ params.lookup_cache_dynamic = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_LOOKUP}));
+ add_opt(llama_arg(
+ {"-c", "--ctx-size"}, "N",
+ format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
+ [](gpt_params & params, int value) {
+ params.n_ctx = value;
+ }
+ ).set_env("LLAMA_ARG_CTX_SIZE"));
+ add_opt(llama_arg(
+ {"-n", "--predict", "--n-predict"}, "N",
+ format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict),
+ [](gpt_params & params, int value) {
+ params.n_predict = value;
+ }
+ ).set_env("LLAMA_ARG_N_PREDICT"));
+ add_opt(llama_arg(
+ {"-b", "--batch-size"}, "N",
+ format("logical maximum batch size (default: %d)", params.n_batch),
+ [](gpt_params & params, int value) {
+ params.n_batch = value;
+ }
+ ).set_env("LLAMA_ARG_BATCH"));
+ add_opt(llama_arg(
+ {"-ub", "--ubatch-size"}, "N",
+ format("physical maximum batch size (default: %d)", params.n_ubatch),
+ [](gpt_params & params, int value) {
+ params.n_ubatch = value;
+ }
+ ).set_env("LLAMA_ARG_UBATCH"));
+ add_opt(llama_arg(
+ {"--keep"}, "N",
+ format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep),
+ [](gpt_params & params, int value) {
+ params.n_keep = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--chunks"}, "N",
+ format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
+ [](gpt_params & params, int value) {
+ params.n_chunks = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL}));
+ add_opt(llama_arg(
+ {"-fa", "--flash-attn"},
+ format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
+ [](gpt_params & params) {
+ params.flash_attn = true;
+ }
+ ).set_env("LLAMA_ARG_FLASH_ATTN"));
+ add_opt(llama_arg(
+ {"-p", "--prompt"}, "PROMPT",
+ ex == LLAMA_EXAMPLE_MAIN
+ ? "prompt to start generation with\nif -cnv is set, this will be used as system prompt"
+ : "prompt to start generation with",
+ [](gpt_params & params, const std::string & value) {
+ params.prompt = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"-f", "--file"}, "FNAME",
+ "a file containing the prompt (default: none)",
+ [](gpt_params & params, const std::string & value) {
+ std::ifstream file(value);
+ if (!file) {
+ throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+ }
+ // store the external file name in params
+ params.prompt_file = value;
+ std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+ if (!params.prompt.empty() && params.prompt.back() == '\n') {
+ params.prompt.pop_back();
+ }
+ }
+ ));
+ add_opt(llama_arg(
+ {"--in-file"}, "FNAME",
+ "an input file (repeat to specify multiple files)",
+ [](gpt_params & params, const std::string & value) {
+ std::ifstream file(value);
+ if (!file) {
+ throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+ }
+ params.in_files.push_back(value);
+ }
+ ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+ add_opt(llama_arg(
+ {"-bf", "--binary-file"}, "FNAME",
+ "binary file containing the prompt (default: none)",
+ [](gpt_params & params, const std::string & value) {
+ std::ifstream file(value, std::ios::binary);
+ if (!file) {
+ throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+ }
+ // store the external file name in params
+ params.prompt_file = value;
+ std::ostringstream ss;
+ ss << file.rdbuf();
+ params.prompt = ss.str();
+ fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str());
+ }
+ ));
+ add_opt(llama_arg(
+ {"-e", "--escape"},
+ format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
+ [](gpt_params & params) {
+ params.escape = true;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--no-escape"},
+ "do not process escape sequences",
+ [](gpt_params & params) {
+ params.escape = false;
+ }
+ ));
+ add_opt(llama_arg(
+ {"-ptc", "--print-token-count"}, "N",
+ format("print token count every N tokens (default: %d)", params.n_print),
+ [](gpt_params & params, int value) {
+ params.n_print = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"--prompt-cache"}, "FNAME",
+ "file to cache prompt state for faster startup (default: none)",
+ [](gpt_params & params, const std::string & value) {
+ params.path_prompt_cache = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"--prompt-cache-all"},
+ "if specified, saves user input and generations to cache as well\n",
+ [](gpt_params & params) {
+ params.prompt_cache_all = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"--prompt-cache-ro"},
+ "if specified, uses the prompt cache but does not update it",
+ [](gpt_params & params) {
+ params.prompt_cache_ro = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"-r", "--reverse-prompt"}, "PROMPT",
+ "halt generation at PROMPT, return control in interactive mode\n",
+ [](gpt_params & params, const std::string & value) {
+ params.antiprompt.emplace_back(value);
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"-sp", "--special"},
+ format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
+ [](gpt_params & params) {
+ params.special = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"-cnv", "--conversation"},
+ format(
+ "run in conversation mode:\n"
+ "- does not print special tokens and suffix/prefix\n"
+ "- interactive mode is also enabled\n"
+ "(default: %s)",
+ params.conversation ? "true" : "false"
+ ),
+ [](gpt_params & params) {
+ params.conversation = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"-i", "--interactive"},
+ format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"),
+ [](gpt_params & params) {
+ params.interactive = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"-if", "--interactive-first"},
+ format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"),
+ [](gpt_params & params) {
+ params.interactive_first = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"-mli", "--multiline-input"},
+ "allows you to write or paste multiple lines without ending each in '\\'",
+ [](gpt_params & params) {
+ params.multiline_input = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"--in-prefix-bos"},
+ "prefix BOS to user inputs, preceding the `--in-prefix` string",
+ [](gpt_params & params) {
+ params.input_prefix_bos = true;
+ params.enable_chat_template = false;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"--in-prefix"}, "STRING",
+ "string to prefix user inputs with (default: empty)",
+ [](gpt_params & params, const std::string & value) {
+ params.input_prefix = value;
+ params.enable_chat_template = false;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"--in-suffix"}, "STRING",
+ "string to suffix after user inputs with (default: empty)",
+ [](gpt_params & params, const std::string & value) {
+ params.input_suffix = value;
+ params.enable_chat_template = false;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"--no-warmup"},
+ "skip warming up the model with an empty run",
+ [](gpt_params & params) {
+ params.warmup = false;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN}));
+ add_opt(llama_arg(
+ {"--spm-infill"},
+ format(
+ "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)",
+ params.spm_infill ? "enabled" : "disabled"
+ ),
+ [](gpt_params & params) {
+ params.spm_infill = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL}));
+ add_opt(llama_arg(
+ {"--samplers"}, "SAMPLERS",
+ format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ const auto sampler_names = string_split(value, ';');
+ params.sparams.samplers = gpt_sampler_types_from_names(sampler_names, true);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"-s", "--seed"}, "SEED",
+ format("RNG seed (default: %d, use random seed for < 0)", params.sparams.seed),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.seed = std::stoul(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--sampling-seq"}, "SEQUENCE",
+ format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.samplers = gpt_sampler_types_from_chars(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--ignore-eos"},
+ "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)",
+ [](gpt_params & params) {
+ params.sparams.ignore_eos = true;
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--penalize-nl"},
+ format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
+ [](gpt_params & params) {
+ params.sparams.penalize_nl = true;
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--temp"}, "N",
+ format("temperature (default: %.1f)", (double)params.sparams.temp),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.temp = std::stof(value);
+ params.sparams.temp = std::max(params.sparams.temp, 0.0f);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--top-k"}, "N",
+ format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
+ [](gpt_params & params, int value) {
+ params.sparams.top_k = value;
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--top-p"}, "N",
+ format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.top_p = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--min-p"}, "N",
+ format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.min_p = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--tfs"}, "N",
+ format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.tfs_z = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--typical"}, "N",
+ format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.typ_p = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--repeat-last-n"}, "N",
+ format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n),
+ [](gpt_params & params, int value) {
+ params.sparams.penalty_last_n = value;
+ params.sparams.n_prev = std::max(params.sparams.n_prev, params.sparams.penalty_last_n);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--repeat-penalty"}, "N",
+ format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.penalty_repeat = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--presence-penalty"}, "N",
+ format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.penalty_present = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--frequency-penalty"}, "N",
+ format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.penalty_freq = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--dynatemp-range"}, "N",
+ format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.dynatemp_range = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--dynatemp-exp"}, "N",
+ format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.dynatemp_exponent = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--mirostat"}, "N",
+ format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
+ "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat),
+ [](gpt_params & params, int value) {
+ params.sparams.mirostat = value;
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--mirostat-lr"}, "N",
+ format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.mirostat_eta = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--mirostat-ent"}, "N",
+ format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.mirostat_tau = std::stof(value);
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"-l", "--logit-bias"}, "TOKEN_ID(+/-)BIAS",
+ "modifies the likelihood of token appearing in the completion,\n"
+ "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
+ "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'",
+ [](gpt_params & params, const std::string & value) {
+ std::stringstream ss(value);
+ llama_token key;
+ char sign;
+ std::string value_str;
+ try {
+ if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
+ const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
+ params.sparams.logit_bias.push_back({key, bias});
+ } else {
+ throw std::invalid_argument("invalid input format");
+ }
+ } catch (const std::exception&) {
+ throw std::invalid_argument("invalid input format");
+ }
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--grammar"}, "GRAMMAR",
+ format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.grammar = value;
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--grammar-file"}, "FNAME",
+ "file to read grammar from",
+ [](gpt_params & params, const std::string & value) {
+ std::ifstream file(value);
+ if (!file) {
+ throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+ }
+ std::copy(
+ std::istreambuf_iterator<char>(file),
+ std::istreambuf_iterator<char>(),
+ std::back_inserter(params.sparams.grammar)
+ );
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"-j", "--json-schema"}, "SCHEMA",
+ "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
+ [](gpt_params & params, const std::string & value) {
+ params.sparams.grammar = json_schema_to_grammar(json::parse(value));
+ }
+ ).set_sparam());
+ add_opt(llama_arg(
+ {"--pooling"}, "{none,mean,cls,last}",
+ "pooling type for embeddings, use model default if unspecified",
+ [](gpt_params & params, const std::string & value) {
+ /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
+ else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
+ else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
+ else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
+ else { throw std::invalid_argument("invalid value"); }
+ }
+ ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+ add_opt(llama_arg(
+ {"--attention"}, "{causal,non,causal}",
+ "attention type for embeddings, use model default if unspecified",
+ [](gpt_params & params, const std::string & value) {
+ /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
+ else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
+ else { throw std::invalid_argument("invalid value"); }
+ }
+ ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+ add_opt(llama_arg(
+ {"--rope-scaling"}, "{none,linear,yarn}",
+ "RoPE frequency scaling method, defaults to linear unless specified by the model",
+ [](gpt_params & params, const std::string & value) {
+ /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
+ else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+ else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
+ else { throw std::invalid_argument("invalid value"); }
+ }
+ ));
+ add_opt(llama_arg(
+ {"--rope-scale"}, "N",
+ "RoPE context scaling factor, expands context by a factor of N",
+ [](gpt_params & params, const std::string & value) {
+ params.rope_freq_scale = 1.0f / std::stof(value);
+ }
+ ));
+ add_opt(llama_arg(
+ {"--rope-freq-base"}, "N",
+ "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)",
+ [](gpt_params & params, const std::string & value) {
+ params.rope_freq_base = std::stof(value);
+ }
+ ));
+ add_opt(llama_arg(
+ {"--rope-freq-scale"}, "N",
+ "RoPE frequency scaling factor, expands context by a factor of 1/N",
+ [](gpt_params & params, const std::string & value) {
+ params.rope_freq_scale = std::stof(value);
+ }
+ ));
+ add_opt(llama_arg(
+ {"--yarn-orig-ctx"}, "N",
+ format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx),
+ [](gpt_params & params, int value) {
+ params.yarn_orig_ctx = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--yarn-ext-factor"}, "N",
+ format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
+ [](gpt_params & params, const std::string & value) {
+ params.yarn_ext_factor = std::stof(value);
+ }
+ ));
+ add_opt(llama_arg(
+ {"--yarn-attn-factor"}, "N",
+ format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
+ [](gpt_params & params, const std::string & value) {
+ params.yarn_attn_factor = std::stof(value);
+ }
+ ));
+ add_opt(llama_arg(
+ {"--yarn-beta-slow"}, "N",
+ format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
+ [](gpt_params & params, const std::string & value) {
+ params.yarn_beta_slow = std::stof(value);
+ }
+ ));
+ add_opt(llama_arg(
+ {"--yarn-beta-fast"}, "N",
+ format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
+ [](gpt_params & params, const std::string & value) {
+ params.yarn_beta_fast = std::stof(value);
+ }
+ ));
+ add_opt(llama_arg(
+ {"-gan", "--grp-attn-n"}, "N",
+ format("group-attention factor (default: %d)", params.grp_attn_n),
+ [](gpt_params & params, int value) {
+ params.grp_attn_n = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"-gaw", "--grp-attn-w"}, "N",
+ format("group-attention width (default: %.1f)", (double)params.grp_attn_w),
+ [](gpt_params & params, int value) {
+ params.grp_attn_w = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"-dkvc", "--dump-kv-cache"},
+ "verbose print of the KV cache",
+ [](gpt_params & params) {
+ params.dump_kv_cache = true;
+ }
+ ));
+ add_opt(llama_arg(
+ {"-nkvo", "--no-kv-offload"},
+ "disable KV offload",
+ [](gpt_params & params) {
+ params.no_kv_offload = true;
+ }
+ ));
+ add_opt(llama_arg(
+ {"-ctk", "--cache-type-k"}, "TYPE",
+ format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ // TODO: get the type right here
+ params.cache_type_k = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"-ctv", "--cache-type-v"}, "TYPE",
+ format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ // TODO: get the type right here
+ params.cache_type_v = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--perplexity", "--all-logits"},
+ format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
+ [](gpt_params & params) {
+ params.logits_all = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--hellaswag"},
+ "compute HellaSwag score over random tasks from datafile supplied with -f",
+ [](gpt_params & params) {
+ params.hellaswag = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--hellaswag-tasks"}, "N",
+ format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks),
+ [](gpt_params & params, int value) {
+ params.hellaswag_tasks = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--winogrande"},
+ "compute Winogrande score over random tasks from datafile supplied with -f",
+ [](gpt_params & params) {
+ params.winogrande = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--winogrande-tasks"}, "N",
+ format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks),
+ [](gpt_params & params, int value) {
+ params.winogrande_tasks = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--multiple-choice"},
+ "compute multiple choice score over random tasks from datafile supplied with -f",
+ [](gpt_params & params) {
+ params.multiple_choice = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--multiple-choice-tasks"}, "N",
+ format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks),
+ [](gpt_params & params, int value) {
+ params.multiple_choice_tasks = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--kl-divergence"},
+ "computes KL-divergence to logits provided via --kl-divergence-base",
+ [](gpt_params & params) {
+ params.kl_divergence = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--save-all-logits", "--kl-divergence-base"}, "FNAME",
+ "set logits file",
+ [](gpt_params & params, const std::string & value) {
+ params.logits_file = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--ppl-stride"}, "N",
+ format("stride for perplexity calculation (default: %d)", params.ppl_stride),
+ [](gpt_params & params, int value) {
+ params.ppl_stride = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"--ppl-output-type"}, "<0|1>",
+ format("output type for perplexity calculation (default: %d)", params.ppl_output_type),
+ [](gpt_params & params, int value) {
+ params.ppl_output_type = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+ add_opt(llama_arg(
+ {"-dt", "--defrag-thold"}, "N",
+ format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
+ [](gpt_params & params, const std::string & value) {
+ params.defrag_thold = std::stof(value);
+ }
+ ).set_env("LLAMA_ARG_DEFRAG_THOLD"));
+ add_opt(llama_arg(
+ {"-np", "--parallel"}, "N",
+ format("number of parallel sequences to decode (default: %d)", params.n_parallel),
+ [](gpt_params & params, int value) {
+ params.n_parallel = value;
+ }
+ ));
+ add_opt(llama_arg(
+ {"-ns", "--sequences"}, "N",
+ format("number of sequences to decode (default: %d)", params.n_sequences),
+ [](gpt_params & params, int value) {
+ params.n_sequences = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PARALLEL}));
+ add_opt(llama_arg(
+ {"-cb", "--cont-batching"},
+ format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
+ [](gpt_params & params) {
+ params.cont_batching = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING"));
+ add_opt(llama_arg(
+ {"-nocb", "--no-cont-batching"},
+ "disable continuous batching",
+ [](gpt_params & params) {
+ params.cont_batching = false;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
+ add_opt(llama_arg(
+ {"--mmproj"}, "FILE",
+ "path to a multimodal projector file for LLaVA. see examples/llava/README.md",
+ [](gpt_params & params, const std::string & value) {
+ params.mmproj = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_LLAVA}));
+ add_opt(llama_arg(
+ {"--image"}, "FILE",
+ "path to an image file. use with multimodal models. Specify multiple times for batching",
+ [](gpt_params & params, const std::string & value) {
+ params.image.emplace_back(value);
+ }
+ ).set_examples({LLAMA_EXAMPLE_LLAVA}));
+#ifdef GGML_USE_RPC
+ add_opt(llama_arg(
+ {"--rpc"}, "SERVERS",
+ "comma separated list of RPC servers",
+ [](gpt_params & params, const std::string & value) {
+ params.rpc_servers = value;
+ }
+ ));
+#endif
+ add_opt(llama_arg(
+ {"--mlock"},
+ "force system to keep model in RAM rather than swapping or compressing",
+ [](gpt_params & params) {
+ params.use_mlock = true;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--no-mmap"},
+ "do not memory-map model (slower load but may reduce pageouts if not using mlock)",
+ [](gpt_params & params) {
+ params.use_mmap = false;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--numa"}, "TYPE",
+ "attempt optimizations that help on some NUMA systems\n"
+ "- distribute: spread execution evenly over all nodes\n"
+ "- isolate: only spawn threads on CPUs on the node that execution started on\n"
+ "- numactl: use the CPU map provided by numactl\n"
+ "if run without this previously, it is recommended to drop the system page cache before using this\n"
+ "see https://github.com/ggerganov/llama.cpp/issues/1437",
+ [](gpt_params & params, const std::string & value) {
+ /**/ 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 { throw std::invalid_argument("invalid value"); }
+ }
+ ));
+ add_opt(llama_arg(
+ {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
+ "number of layers to store in VRAM",
+ [](gpt_params & params, int value) {
+ params.n_gpu_layers = value;
+ if (!llama_supports_gpu_offload()) {
+ fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
+ fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+ }
+ }
+ ).set_env("LLAMA_ARG_N_GPU_LAYERS"));
+ add_opt(llama_arg(
+ {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
+ "number of layers to store in VRAM for the draft model",
+ [](gpt_params & params, int value) {
+ params.n_gpu_layers_draft = value;
+ if (!llama_supports_gpu_offload()) {
+ fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
+ fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+ }
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"-sm", "--split-mode"}, "{none,layer,row}",
+ "how to split the model across multiple GPUs, one of:\n"
+ "- none: use one GPU only\n"
+ "- layer (default): split layers and KV across GPUs\n"
+ "- row: split rows across GPUs",
+ [](gpt_params & params, const std::string & value) {
+ std::string arg_next = value;
+ if (arg_next == "none") {
+ params.split_mode = LLAMA_SPLIT_MODE_NONE;
+ } else if (arg_next == "layer") {
+ params.split_mode = LLAMA_SPLIT_MODE_LAYER;
+ }
+ else if (arg_next == "row") {
+#ifdef GGML_USE_SYCL
+ fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
+ exit(1);
+#endif // GGML_USE_SYCL
+ params.split_mode = LLAMA_SPLIT_MODE_ROW;
+ }
+ else {
+ throw std::invalid_argument("invalid value");
+ }
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+ fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
+ }
+ ));
+ add_opt(llama_arg(
+ {"-ts", "--tensor-split"}, "N0,N1,N2,...",
+ "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1",
+ [](gpt_params & params, const std::string & value) {
+ std::string arg_next = value;
+
+ // split string by , and /
+ const std::regex regex{ R"([,/]+)" };
+ std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
+ std::vector<std::string> split_arg{ it, {} };
+ if (split_arg.size() >= llama_max_devices()) {
+ throw std::invalid_argument(
+ format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices())
+ );
+ }
+ for (size_t i = 0; i < llama_max_devices(); ++i) {
+ if (i < split_arg.size()) {
+ params.tensor_split[i] = std::stof(split_arg[i]);
+ } else {
+ params.tensor_split[i] = 0.0f;
+ }
+ }
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+ fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
+ }
+ ));
+ add_opt(llama_arg(
+ {"-mg", "--main-gpu"}, "INDEX",
+ format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu),
+ [](gpt_params & params, int value) {
+ params.main_gpu = value;
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+ fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
+ }
+ ));
+ add_opt(llama_arg(
+ {"--check-tensors"},
+ format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
+ [](gpt_params & params) {
+ params.check_tensors = true;
+ }
+ ));
+ add_opt(llama_arg(
+ {"--override-kv"}, "KEY=TYPE:VALUE",
+ "advanced option to override model metadata by key. may be specified multiple times.\n"
+ "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false",
+ [](gpt_params & params, const std::string & value) {
+ if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) {
+ throw std::runtime_error(format("error: Invalid type for KV override: %s\n", value.c_str()));
+ }
+ }
+ ));
+ add_opt(llama_arg(
+ {"--lora"}, "FNAME",
+ "path to LoRA adapter (can be repeated to use multiple adapters)",
+ [](gpt_params & params, const std::string & value) {
+ params.lora_adapters.push_back({ std::string(value), 1.0 });
+ }
+ // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
+ ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
+ add_opt(llama_arg(
+ {"--lora-scaled"}, "FNAME", "SCALE",
+ "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
+ [](gpt_params & params, const std::string & fname, const std::string & scale) {
+ params.lora_adapters.push_back({ fname, std::stof(scale) });
+ }
+ // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
+ ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
+ add_opt(llama_arg(
+ {"--control-vector"}, "FNAME",
+ "add a control vector\nnote: this argument can be repeated to add multiple control vectors",
+ [](gpt_params & params, const std::string & value) {
+ params.control_vectors.push_back({ 1.0f, value, });
+ }
+ ));
+ add_opt(llama_arg(
+ {"--control-vector-scaled"}, "FNAME", "SCALE",
+ "add a control vector with user defined scaling SCALE\n"
+ "note: this argument can be repeated to add multiple scaled control vectors",
+ [](gpt_params & params, const std::string & fname, const std::string & scale) {
+ params.control_vectors.push_back({ std::stof(scale), fname });
+ }
+ ));
+ add_opt(llama_arg(
+ {"--control-vector-layer-range"}, "START", "END",
+ "layer range to apply the control vector(s) to, start and end inclusive",
+ [](gpt_params & params, const std::string & start, const std::string & end) {
+ params.control_vector_layer_start = std::stoi(start);
+ params.control_vector_layer_end = std::stoi(end);
+ }
+ ));
+ add_opt(llama_arg(
+ {"-a", "--alias"}, "STRING",
+ "set alias for model name (to be used by REST API)",
+ [](gpt_params & params, const std::string & value) {
+ params.model_alias = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"-m", "--model"}, "FNAME",
+ ex == LLAMA_EXAMPLE_EXPORT_LORA
+ ? std::string("model path from which to load base model")
+ : format(
+ "model path (default: `models/$filename` with filename from `--hf-file` "
+ "or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH
+ ),
+ [](gpt_params & params, const std::string & value) {
+ params.model = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL"));
+ add_opt(llama_arg(
+ {"-md", "--model-draft"}, "FNAME",
+ "draft model for speculative decoding (default: unused)",
+ [](gpt_params & params, const std::string & value) {
+ params.model_draft = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+ add_opt(llama_arg(
+ {"-mu", "--model-url"}, "MODEL_URL",
+ "model download url (default: unused)",
+ [](gpt_params & params, const std::string & value) {
+ params.model_url = value;
+ }
+ ).set_env("LLAMA_ARG_MODEL_URL"));
+ add_opt(llama_arg(
+ {"-hfr", "--hf-repo"}, "REPO",
+ "Hugging Face model repository (default: unused)",
+ [](gpt_params & params, const std::string & value) {
+ params.hf_repo = value;
+ }
+ ).set_env("LLAMA_ARG_HF_REPO"));
+ add_opt(llama_arg(
+ {"-hff", "--hf-file"}, "FILE",
+ "Hugging Face model file (default: unused)",
+ [](gpt_params & params, const std::string & value) {
+ params.hf_file = value;
+ }
+ ).set_env("LLAMA_ARG_HF_FILE"));
+ add_opt(llama_arg(
+ {"-hft", "--hf-token"}, "TOKEN",
+ "Hugging Face access token (default: value from HF_TOKEN environment variable)",
+ [](gpt_params & params, const std::string & value) {
+ params.hf_token = value;
+ }
+ ).set_env("HF_TOKEN"));
+ add_opt(llama_arg(
+ {"--context-file"}, "FNAME",
+ "file to load context from (repeat to specify multiple files)",
+ [](gpt_params & params, const std::string & value) {
+ std::ifstream file(value, std::ios::binary);
+ if (!file) {
+ throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+ }
+ params.context_files.push_back(value);
+ }
+ ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
+ add_opt(llama_arg(
+ {"--chunk-size"}, "N",
+ format("minimum length of embedded text chunks (default: %d)", params.chunk_size),
+ [](gpt_params & params, int value) {
+ params.chunk_size = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
+ add_opt(llama_arg(
+ {"--chunk-separator"}, "STRING",
+ format("separator between chunks (default: '%s')", params.chunk_separator.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ params.chunk_separator = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
+ add_opt(llama_arg(
+ {"--junk"}, "N",
+ format("number of times to repeat the junk text (default: %d)", params.n_junk),
+ [](gpt_params & params, int value) {
+ params.n_junk = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
+ add_opt(llama_arg(
+ {"--pos"}, "N",
+ format("position of the passkey in the junk text (default: %d)", params.i_pos),
+ [](gpt_params & params, int value) {
+ params.i_pos = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
+ add_opt(llama_arg(
+ {"-o", "--output", "--output-file"}, "FNAME",
+ format("output file (default: '%s')",
+ ex == LLAMA_EXAMPLE_EXPORT_LORA
+ ? params.lora_outfile.c_str()
+ : ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR
+ ? params.cvector_outfile.c_str()
+ : params.out_file.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ params.out_file = value;
+ params.cvector_outfile = value;
+ params.lora_outfile = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA}));
+ add_opt(llama_arg(
+ {"-ofreq", "--output-frequency"}, "N",
+ format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
+ [](gpt_params & params, int value) {
+ params.n_out_freq = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+ add_opt(llama_arg(
+ {"--save-frequency"}, "N",
+ format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
+ [](gpt_params & params, int value) {
+ params.n_save_freq = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+ add_opt(llama_arg(
+ {"--process-output"},
+ format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"),
+ [](gpt_params & params) {
+ params.process_output = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+ add_opt(llama_arg(
+ {"--no-ppl"},
+ format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
+ [](gpt_params & params) {
+ params.compute_ppl = false;
+ }
+ ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+ add_opt(llama_arg(
+ {"--chunk", "--from-chunk"}, "N",
+ format("start processing the input from chunk N (default: %d)", params.i_chunk),
+ [](gpt_params & params, int value) {
+ params.i_chunk = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+ add_opt(llama_arg(
+ {"-pps"},
+ format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"),
+ [](gpt_params & params) {
+ params.is_pp_shared = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_BENCH}));
+ add_opt(llama_arg(
+ {"-npp"}, "n0,n1,...",
+ "number of prompt tokens",
+ [](gpt_params & params, const std::string & value) {
+ auto p = string_split<int>(value, ',');
+ params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
+ }
+ ).set_examples({LLAMA_EXAMPLE_BENCH}));
+ add_opt(llama_arg(
+ {"-ntg"}, "n0,n1,...",
+ "number of text generation tokens",
+ [](gpt_params & params, const std::string & value) {
+ auto p = string_split<int>(value, ',');
+ params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
+ }
+ ).set_examples({LLAMA_EXAMPLE_BENCH}));
+ add_opt(llama_arg(
+ {"-npl"}, "n0,n1,...",
+ "number of parallel prompts",
+ [](gpt_params & params, const std::string & value) {
+ auto p = string_split<int>(value, ',');
+ params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
+ }
+ ).set_examples({LLAMA_EXAMPLE_BENCH}));
+ add_opt(llama_arg(
+ {"--embd-normalize"}, "N",
+ format("normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize),
+ [](gpt_params & params, int value) {
+ params.embd_normalize = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+ add_opt(llama_arg(
+ {"--embd-output-format"}, "FORMAT",
+ "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix",
+ [](gpt_params & params, const std::string & value) {
+ params.embd_out = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+ add_opt(llama_arg(
+ {"--embd-separator"}, "STRING",
+ "separator of embendings (default \\n) for example \"<#sep#>\"",
+ [](gpt_params & params, const std::string & value) {
+ params.embd_sep = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+ add_opt(llama_arg(
+ {"--host"}, "HOST",
+ format("ip address to listen (default: %s)", params.hostname.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ params.hostname = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST"));
+ add_opt(llama_arg(
+ {"--port"}, "PORT",
+ format("port to listen (default: %d)", params.port),
+ [](gpt_params & params, int value) {
+ params.port = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT"));
+ add_opt(llama_arg(
+ {"--path"}, "PATH",
+ format("path to serve static files from (default: %s)", params.public_path.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ params.public_path = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"--embedding", "--embeddings"},
+ format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
+ [](gpt_params & params) {
+ params.embedding = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
+ add_opt(llama_arg(
+ {"--api-key"}, "KEY",
+ "API key to use for authentication (default: none)",
+ [](gpt_params & params, const std::string & value) {
+ params.api_keys.push_back(value);
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
+ add_opt(llama_arg(
+ {"--api-key-file"}, "FNAME",
+ "path to file containing API keys (default: none)",
+ [](gpt_params & params, const std::string & value) {
+ std::ifstream key_file(value);
+ if (!key_file) {
+ throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+ }
+ std::string key;
+ while (std::getline(key_file, key)) {
+ if (!key.empty()) {
+ params.api_keys.push_back(key);
+ }
+ }
+ key_file.close();
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"--ssl-key-file"}, "FNAME",
+ "path to file a PEM-encoded SSL private key",
+ [](gpt_params & params, const std::string & value) {
+ params.ssl_file_key = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"--ssl-cert-file"}, "FNAME",
+ "path to file a PEM-encoded SSL certificate",
+ [](gpt_params & params, const std::string & value) {
+ params.ssl_file_cert = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"-to", "--timeout"}, "N",
+ format("server read/write timeout in seconds (default: %d)", params.timeout_read),
+ [](gpt_params & params, int value) {
+ params.timeout_read = value;
+ params.timeout_write = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"--threads-http"}, "N",
+ format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http),
+ [](gpt_params & params, int value) {
+ params.n_threads_http = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
+ add_opt(llama_arg(
+ {"-spf", "--system-prompt-file"}, "FNAME",
+ "set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications",
+ [](gpt_params & params, const std::string & value) {
+ std::ifstream file(value);
+ if (!file) {
+ throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+ }
+ std::string system_prompt;
+ std::copy(
+ std::istreambuf_iterator<char>(file),
+ std::istreambuf_iterator<char>(),
+ std::back_inserter(system_prompt)
+ );
+ params.system_prompt = system_prompt;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"--log-format"}, "{text, json}",
+ "log output format: json or text (default: json)",
+ [](gpt_params & params, const std::string & value) {
+ if (value == "json") {
+ params.log_json = true;
+ } else if (value == "text") {
+ params.log_json = false;
+ } else {
+ throw std::invalid_argument("invalid value");
+ }
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"--metrics"},
+ format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"),
+ [](gpt_params & params) {
+ params.endpoint_metrics = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS"));
+ add_opt(llama_arg(
+ {"--no-slots"},
+ format("disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
+ [](gpt_params & params) {
+ params.endpoint_slots = false;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS"));
+ add_opt(llama_arg(
+ {"--slot-save-path"}, "PATH",
+ "path to save slot kv cache (default: disabled)",
+ [](gpt_params & params, const std::string & value) {
+ params.slot_save_path = value;
+ // if doesn't end with DIRECTORY_SEPARATOR, add it
+ if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
+ params.slot_save_path += DIRECTORY_SEPARATOR;
+ }
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"--chat-template"}, "JINJA_TEMPLATE",
+ "set custom jinja chat template (default: template taken from model's metadata)\n"
+ "if suffix/prefix are specified, template will be disabled\n"
+ "only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
+ [](gpt_params & params, const std::string & value) {
+ if (!llama_chat_verify_template(value)) {
+ throw std::runtime_error(format(
+ "error: the supplied chat template is not supported: %s\n"
+ "note: llama.cpp does not use jinja parser, we only support commonly used templates\n",
+ value.c_str()
+ ));
+ }
+ params.chat_template = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
+ add_opt(llama_arg(
+ {"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
+ format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
+ [](gpt_params & params, const std::string & value) {
+ params.slot_prompt_similarity = std::stof(value);
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"--lora-init-without-apply"},
+ format("load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"),
+ [](gpt_params & params) {
+ params.lora_init_without_apply = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_SERVER}));
+ add_opt(llama_arg(
+ {"--simple-io"},
+ "use basic IO for better compatibility in subprocesses and limited consoles",
+ [](gpt_params & params) {
+ params.simple_io = true;
+ }
+ ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
+ add_opt(llama_arg(
+ {"-ld", "--logdir"}, "LOGDIR",
+ "path under which to save YAML logs (no logging if unset)",
+ [](gpt_params & params, const std::string & value) {
+ params.logdir = value;
+
+ if (params.logdir.back() != DIRECTORY_SEPARATOR) {
+ params.logdir += DIRECTORY_SEPARATOR;
+ }
+ }
+ ));
+ add_opt(llama_arg(
+ {"--positive-file"}, "FNAME",
+ format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ params.cvector_positive_file = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+ add_opt(llama_arg(
+ {"--negative-file"}, "FNAME",
+ format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()),
+ [](gpt_params & params, const std::string & value) {
+ params.cvector_negative_file = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+ add_opt(llama_arg(
+ {"--pca-batch"}, "N",
+ format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch),
+ [](gpt_params & params, int value) {
+ params.n_pca_batch = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+ add_opt(llama_arg(
+ {"--pca-iter"}, "N",
+ format("number of iterations used for PCA (default: %d)", params.n_pca_iterations),
+ [](gpt_params & params, int value) {
+ params.n_pca_iterations = value;
+ }
+ ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+ add_opt(llama_arg(
+ {"--method"}, "{pca, mean}",
+ "dimensionality reduction method to be used (default: pca)",
+ [](gpt_params & params, const std::string & value) {
+ /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
+ else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
+ else { throw std::invalid_argument("invalid value"); }
+ }
+ ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+ add_opt(llama_arg(
+ {"--output-format"}, "{md,jsonl}",
+ "output format for batched-bench results (default: md)",
+ [](gpt_params & params, const std::string & value) {
+ /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; }
+ else if (value == "md") { params.batched_bench_output_jsonl = false; }
+ else { std::invalid_argument("invalid value"); }
+ }
+ ).set_examples({LLAMA_EXAMPLE_BENCH}));
+#ifndef LOG_DISABLE_LOGS
+ // TODO: make this looks less weird
+ add_opt(llama_arg(
+ {"--log-test"},
+ "Log test",
+ [](gpt_params &) { log_param_single_parse("--log-test"); }
+ ));
+ add_opt(llama_arg(
+ {"--log-disable"},
+ "Log disable",
+ [](gpt_params &) { log_param_single_parse("--log-disable"); }
+ ));
+ add_opt(llama_arg(
+ {"--log-enable"},
+ "Log enable",
+ [](gpt_params &) { log_param_single_parse("--log-enable"); }
+ ));
+ add_opt(llama_arg(
+ {"--log-new"},
+ "Log new",
+ [](gpt_params &) { log_param_single_parse("--log-new"); }
+ ));
+ add_opt(llama_arg(
+ {"--log-append"},
+ "Log append",
+ [](gpt_params &) { log_param_single_parse("--log-append"); }
+ ));
+ add_opt(llama_arg(
+ {"--log-file"}, "FNAME",
+ "Log file",
+ [](gpt_params &, const std::string & value) { log_param_pair_parse(false, "--log-file", value); }
+ ));
+#endif // LOG_DISABLE_LOGS
+
+ return ctx_arg;
+}
+
--- /dev/null
+#pragma once
+
+#include "common.h"
+
+#include <set>
+#include <string>
+#include <vector>
+
+//
+// CLI argument parsing
+//
+
+struct llama_arg {
+ std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
+ std::vector<const char *> args;
+ const char * value_hint = nullptr; // help text or example for arg value
+ const char * value_hint_2 = nullptr; // for second arg value
+ const char * env = nullptr;
+ std::string help;
+ bool is_sparam = false; // is current arg a sampling param?
+ void (*handler_void) (gpt_params & params) = nullptr;
+ void (*handler_string) (gpt_params & params, const std::string &) = nullptr;
+ void (*handler_str_str)(gpt_params & params, const std::string &, const std::string &) = nullptr;
+ void (*handler_int) (gpt_params & params, int) = nullptr;
+
+ llama_arg(
+ const std::initializer_list<const char *> & args,
+ const char * value_hint,
+ const std::string & help,
+ void (*handler)(gpt_params & params, const std::string &)
+ ) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
+
+ llama_arg(
+ const std::initializer_list<const char *> & args,
+ const char * value_hint,
+ const std::string & help,
+ void (*handler)(gpt_params & params, int)
+ ) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
+
+ llama_arg(
+ const std::initializer_list<const char *> & args,
+ const std::string & help,
+ void (*handler)(gpt_params & params)
+ ) : args(args), help(help), handler_void(handler) {}
+
+ // support 2 values for arg
+ llama_arg(
+ const std::initializer_list<const char *> & args,
+ const char * value_hint,
+ const char * value_hint_2,
+ const std::string & help,
+ void (*handler)(gpt_params & params, const std::string &, const std::string &)
+ ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
+
+ llama_arg & set_examples(std::initializer_list<enum llama_example> examples);
+ llama_arg & set_env(const char * env);
+ llama_arg & set_sparam();
+ bool in_example(enum llama_example ex);
+ bool get_value_from_env(std::string & output);
+ bool has_value_from_env();
+ std::string to_string();
+};
+
+struct gpt_params_context {
+ enum llama_example ex = LLAMA_EXAMPLE_COMMON;
+ gpt_params & params;
+ std::vector<llama_arg> options;
+ void(*print_usage)(int, char **) = nullptr;
+ gpt_params_context(gpt_params & params) : params(params) {}
+};
+
+// parse input arguments from CLI
+// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
+
+// function to be used by test-arg-parser
+gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
#include <unordered_map>
#include <unordered_set>
#include <vector>
-#include <climits>
#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
// CLI argument parsing
//
-#ifdef __GNUC__
-#ifdef __MINGW32__
-#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
-#else
-#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
-#endif
-#else
-#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
-#endif
-
-LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
-static std::string format(const char * fmt, ...) {
- va_list ap;
- va_list ap2;
- va_start(ap, fmt);
- va_copy(ap2, ap);
- int size = vsnprintf(NULL, 0, fmt, ap);
- GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
- std::vector<char> buf(size + 1);
- int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
- GGML_ASSERT(size2 == size);
- va_end(ap2);
- va_end(ap);
- return std::string(buf.data(), size);
-}
-
-static void gpt_params_handle_model_default(gpt_params & params) {
- if (!params.hf_repo.empty()) {
- // short-hand to avoid specifying --hf-file -> default it to --model
- if (params.hf_file.empty()) {
- if (params.model.empty()) {
- throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
- }
- params.hf_file = params.model;
- } else if (params.model.empty()) {
- params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
- }
- } else if (!params.model_url.empty()) {
- if (params.model.empty()) {
- auto f = string_split(params.model_url, '#').front();
- f = string_split(f, '?').front();
- params.model = fs_get_cache_file(string_split(f, '/').back());
- }
- } else if (params.model.empty()) {
- params.model = DEFAULT_MODEL_PATH;
- }
-}
void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
int32_t n_set = 0;
}
}
-bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options) {
- std::string arg;
- const std::string arg_prefix = "--";
- gpt_sampler_params & sparams = params.sparams;
-
- std::unordered_map<std::string, llama_arg *> arg_to_options;
- for (auto & opt : options) {
- for (const auto & arg : opt.args) {
- arg_to_options[arg] = &opt;
- }
- }
-
- // handle environment variables
- for (auto & opt : options) {
- std::string value;
- if (opt.get_value_from_env(value)) {
- try {
- if (opt.handler_void && (value == "1" || value == "true")) {
- opt.handler_void(params);
- }
- if (opt.handler_int) {
- opt.handler_int(params, std::stoi(value));
- }
- if (opt.handler_string) {
- opt.handler_string(params, value);
- continue;
- }
- } catch (std::exception & e) {
- throw std::invalid_argument(format(
- "error while handling environment variable \"%s\": %s\n\n", opt.env, e.what()));
- }
- }
- }
-
- // handle command line arguments
- auto check_arg = [&](int i) {
- if (i+1 >= argc) {
- throw std::invalid_argument("expected value for argument");
- }
- };
-
- for (int i = 1; i < argc; i++) {
- const std::string arg_prefix = "--";
-
- std::string arg = argv[i];
- if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
- std::replace(arg.begin(), arg.end(), '_', '-');
- }
- if (arg_to_options.find(arg) == arg_to_options.end()) {
- throw std::invalid_argument(format("error: invalid argument: %s", arg.c_str()));
- }
- auto opt = *arg_to_options[arg];
- if (opt.has_value_from_env()) {
- fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
- }
- try {
- if (opt.handler_void) {
- opt.handler_void(params);
- continue;
- }
-
- // arg with single value
- check_arg(i);
- std::string val = argv[++i];
- if (opt.handler_int) {
- opt.handler_int(params, std::stoi(val));
- continue;
- }
- if (opt.handler_string) {
- opt.handler_string(params, val);
- continue;
- }
-
- // arg with 2 values
- check_arg(i);
- std::string val2 = argv[++i];
- if (opt.handler_str_str) {
- opt.handler_str_str(params, val, val2);
- continue;
- }
- } catch (std::exception & e) {
- throw std::invalid_argument(format(
- "error while handling argument \"%s\": %s\n\n"
- "usage:\n%s\n\nto show complete usage, run with -h",
- arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str()));
- }
- }
-
- postprocess_cpu_params(params.cpuparams, nullptr);
- postprocess_cpu_params(params.cpuparams_batch, ¶ms.cpuparams);
- postprocess_cpu_params(params.draft_cpuparams, ¶ms.cpuparams);
- postprocess_cpu_params(params.draft_cpuparams_batch, ¶ms.cpuparams_batch);
-
- if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
- throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
- }
-
- gpt_params_handle_model_default(params);
-
- if (params.escape) {
- string_process_escapes(params.prompt);
- string_process_escapes(params.input_prefix);
- string_process_escapes(params.input_suffix);
- for (auto & antiprompt : params.antiprompt) {
- string_process_escapes(antiprompt);
- }
- }
-
- if (!params.kv_overrides.empty()) {
- params.kv_overrides.emplace_back();
- params.kv_overrides.back().key[0] = 0;
- }
-
- if (sparams.seed == LLAMA_DEFAULT_SEED) {
- sparams.seed = time(NULL);
- }
-
- return true;
-}
-
-bool gpt_params_parse(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options) {
- const auto params_org = params; // the example can modify the default params
-
- try {
- if (!gpt_params_parse_ex(argc, argv, params, options)) {
- params = params_org;
- return false;
- }
- if (params.usage) {
- gpt_params_print_usage(params, options);
- if (params.print_usage) {
- params.print_usage(argc, argv);
- }
- exit(0);
- }
- } catch (const std::invalid_argument & ex) {
- fprintf(stderr, "%s\n", ex.what());
- params = params_org;
- return false;
- }
-
- return true;
-}
-
bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
size_t dash_loc = range.find('-');
if (dash_loc == std::string::npos) {
return true;
}
-static std::vector<std::string> break_str_into_lines(std::string input, size_t max_char_per_line) {
- std::vector<std::string> result;
- std::istringstream iss(input);
- std::string line;
- auto add_line = [&](const std::string& l) {
- if (l.length() <= max_char_per_line) {
- result.push_back(l);
- } else {
- std::istringstream line_stream(l);
- std::string word, current_line;
- while (line_stream >> word) {
- if (current_line.length() + !current_line.empty() + word.length() > max_char_per_line) {
- if (!current_line.empty()) result.push_back(current_line);
- current_line = word;
- } else {
- current_line += (!current_line.empty() ? " " : "") + word;
- }
- }
- if (!current_line.empty()) result.push_back(current_line);
- }
- };
- while (std::getline(iss, line)) {
- add_line(line);
- }
- return result;
-}
-
-std::string llama_arg::to_string() {
- // params for printing to console
- const static int n_leading_spaces = 40;
- const static int n_char_per_line_help = 70; // TODO: detect this based on current console
- std::string leading_spaces(n_leading_spaces, ' ');
-
- std::ostringstream ss;
- for (const auto arg : args) {
- if (arg == args.front()) {
- if (args.size() == 1) {
- ss << arg;
- } else {
- // first arg is usually abbreviation, we need padding to make it more beautiful
- auto tmp = std::string(arg) + ", ";
- ss << format("%-7s", tmp.c_str());
- }
- } else {
- ss << arg << (arg != args.back() ? ", " : "");
- }
- }
- if (value_hint) ss << " " << value_hint;
- if (value_hint_2) ss << " " << value_hint_2;
- if (ss.tellp() > n_leading_spaces - 3) {
- // current line is too long, add new line
- ss << "\n" << leading_spaces;
- } else {
- // padding between arg and help, same line
- ss << std::string(leading_spaces.size() - ss.tellp(), ' ');
- }
- const auto help_lines = break_str_into_lines(help, n_char_per_line_help);
- for (const auto & line : help_lines) {
- ss << (&line == &help_lines.front() ? "" : leading_spaces) << line << "\n";
- }
- return ss.str();
-}
-
-void gpt_params_print_usage(gpt_params & params, std::vector<llama_arg> & options) {
- auto print_options = [](std::vector<llama_arg *> & options) {
- for (llama_arg * opt : options) {
- printf("%s", opt->to_string().c_str());
- }
- };
-
- std::vector<llama_arg *> common_options;
- std::vector<llama_arg *> specific_options;
- for (auto & opt : options) {
- // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
- if (opt.in_example(params.curr_ex)) {
- specific_options.push_back(&opt);
- } else {
- common_options.push_back(&opt);
- }
- }
- printf("----- common options -----\n\n");
- print_options(common_options);
- // TODO: maybe convert enum llama_example to string
- printf("\n\n----- example-specific options -----\n\n");
- print_options(specific_options);
-}
-
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex) {
- return gpt_params_parser_init(params, ex, nullptr);
-}
-
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex, std::function<void(int, char **)> print_usage) {
- std::vector<llama_arg> options;
- params.print_usage = print_usage;
- params.curr_ex = ex;
-
- std::string sampler_type_chars;
- std::string sampler_type_names;
- for (const auto & sampler : params.sparams.samplers) {
- sampler_type_chars += gpt_sampler_type_to_chr(sampler);
- sampler_type_names += gpt_sampler_type_to_str(sampler) + ";";
- }
- sampler_type_names.pop_back();
-
-
- /**
- * filter options by example
- * rules:
- * - all examples inherit options from LLAMA_EXAMPLE_COMMON
- * - if LLAMA_EXAMPLE_* is set (other than COMMON), we only show the option in the corresponding example
- * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
- */
- auto add_opt = [&](llama_arg arg) {
- if (arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) {
- options.push_back(std::move(arg));
- }
- };
-
-
- add_opt(llama_arg(
- {"-h", "--help", "--usage"},
- "print usage and exit",
- [](gpt_params & params) {
- params.usage = true;
- }
- ));
- add_opt(llama_arg(
- {"--version"},
- "show version and build info",
- [](gpt_params &) {
- fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
- fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
- exit(0);
- }
- ));
- add_opt(llama_arg(
- {"-v", "--verbose"},
- "print verbose information",
- [](gpt_params & params) {
- params.verbosity = 1;
- }
- ));
- add_opt(llama_arg(
- {"--verbosity"}, "N",
- format("set specific verbosity level (default: %d)", params.verbosity),
- [](gpt_params & params, int value) {
- params.verbosity = value;
- }
- ));
- add_opt(llama_arg(
- {"--verbose-prompt"},
- format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
- [](gpt_params & params) {
- params.verbose_prompt = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"--no-display-prompt"},
- format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
- [](gpt_params & params) {
- params.display_prompt = false;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"-co", "--color"},
- format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
- [](gpt_params & params) {
- params.use_color = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
- add_opt(llama_arg(
- {"-s", "--seed"}, "SEED",
- format("RNG seed (default: %d, use random seed for < 0)", params.sparams.seed),
- [](gpt_params & params, const std::string & value) {
- params.sparams.seed = std::stoul(value);
- }
- ));
- add_opt(llama_arg(
- {"-t", "--threads"}, "N",
- format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
- [](gpt_params & params, int value) {
- params.cpuparams.n_threads = value;
- if (params.cpuparams.n_threads <= 0) {
- params.cpuparams.n_threads = std::thread::hardware_concurrency();
- }
- }
- ).set_env("LLAMA_ARG_THREADS"));
- add_opt(llama_arg(
- {"-tb", "--threads-batch"}, "N",
- "number of threads to use during batch and prompt processing (default: same as --threads)",
- [](gpt_params & params, int value) {
- params.cpuparams_batch.n_threads = value;
- if (params.cpuparams_batch.n_threads <= 0) {
- params.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
- }
- }
- ));
- add_opt(llama_arg(
- {"-td", "--threads-draft"}, "N",
- "number of threads to use during generation (default: same as --threads)",
- [](gpt_params & params, int value) {
- params.draft_cpuparams.n_threads = value;
- if (params.draft_cpuparams.n_threads <= 0) {
- params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
- }
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"-tbd", "--threads-batch-draft"}, "N",
- "number of threads to use during batch and prompt processing (default: same as --threads-draft)",
- [](gpt_params & params, int value) {
- params.draft_cpuparams_batch.n_threads = value;
- if (params.draft_cpuparams_batch.n_threads <= 0) {
- params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
- }
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"-C", "--cpu-mask"}, "M",
- "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")",
- [](gpt_params & params, const std::string & mask) {
- params.cpuparams.mask_valid = true;
- if (!parse_cpu_mask(mask, params.cpuparams.cpumask)) {
- throw std::invalid_argument("invalid cpumask");
- }
- }
- ));
- add_opt(llama_arg(
- {"-Cr", "--cpu-range"}, "lo-hi",
- "range of CPUs for affinity. Complements --cpu-mask",
- [](gpt_params & params, const std::string & range) {
- params.cpuparams.mask_valid = true;
- if (!parse_cpu_range(range, params.cpuparams.cpumask)) {
- throw std::invalid_argument("invalid range");
- }
- }
- ));
- add_opt(llama_arg(
- {"--cpu-strict"}, "<0|1>",
- format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu),
- [](gpt_params & params, const std::string & value) {
- params.cpuparams.strict_cpu = std::stoul(value);
- }
- ));
- add_opt(llama_arg(
- {"--prio"}, "N",
- format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority),
- [](gpt_params & params, int prio) {
- if (prio < 0 || prio > 3) {
- throw std::invalid_argument("invalid value");
- }
- params.cpuparams.priority = (enum ggml_sched_priority) prio;
- }
- ));
- add_opt(llama_arg(
- {"--poll"}, "<0...100>",
- format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll),
- [](gpt_params & params, const std::string & value) {
- params.cpuparams.poll = std::stoul(value);
- }
- ));
- add_opt(llama_arg(
- {"-Cb", "--cpu-mask-batch"}, "M",
- "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)",
- [](gpt_params & params, const std::string & mask) {
- params.cpuparams_batch.mask_valid = true;
- if (!parse_cpu_mask(mask, params.cpuparams_batch.cpumask)) {
- throw std::invalid_argument("invalid cpumask");
- }
- }
- ));
- add_opt(llama_arg(
- {"-Crb", "--cpu-range-batch"}, "lo-hi",
- "ranges of CPUs for affinity. Complements --cpu-mask-batch",
- [](gpt_params & params, const std::string & range) {
- params.cpuparams_batch.mask_valid = true;
- if (!parse_cpu_range(range, params.cpuparams_batch.cpumask)) {
- throw std::invalid_argument("invalid range");
- }
- }
- ));
- add_opt(llama_arg(
- {"--cpu-strict-batch"}, "<0|1>",
- "use strict CPU placement (default: same as --cpu-strict)",
- [](gpt_params & params, int value) {
- params.cpuparams_batch.strict_cpu = value;
- }
- ));
- add_opt(llama_arg(
- {"--prio-batch"}, "N",
- format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority),
- [](gpt_params & params, int prio) {
- if (prio < 0 || prio > 3) {
- throw std::invalid_argument("invalid value");
- }
- params.cpuparams_batch.priority = (enum ggml_sched_priority) prio;
- }
- ));
- add_opt(llama_arg(
- {"--poll-batch"}, "<0|1>",
- "use polling to wait for work (default: same as --poll)",
- [](gpt_params & params, int value) {
- params.cpuparams_batch.poll = value;
- }
- ));
- add_opt(llama_arg(
- {"-Cd", "--cpu-mask-draft"}, "M",
- "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
- [](gpt_params & params, const std::string & mask) {
- params.draft_cpuparams.mask_valid = true;
- if (!parse_cpu_mask(mask, params.draft_cpuparams.cpumask)) {
- throw std::invalid_argument("invalid cpumask");
- }
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"-Crd", "--cpu-range-draft"}, "lo-hi",
- "Ranges of CPUs for affinity. Complements --cpu-mask-draft",
- [](gpt_params & params, const std::string & range) {
- params.draft_cpuparams.mask_valid = true;
- if (!parse_cpu_range(range, params.draft_cpuparams.cpumask)) {
- throw std::invalid_argument("invalid range");
- }
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"--cpu-strict-draft"}, "<0|1>",
- "Use strict CPU placement for draft model (default: same as --cpu-strict)",
- [](gpt_params & params, int value) {
- params.draft_cpuparams.strict_cpu = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"--prio-draft"}, "N",
- format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams.priority),
- [](gpt_params & params, int prio) {
- if (prio < 0 || prio > 3) {
- throw std::invalid_argument("invalid value");
- }
- params.draft_cpuparams.priority = (enum ggml_sched_priority) prio;
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"--poll-draft"}, "<0|1>",
- "Use polling to wait for draft model work (default: same as --poll])",
- [](gpt_params & params, int value) {
- params.draft_cpuparams.poll = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"-Cbd", "--cpu-mask-batch-draft"}, "M",
- "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
- [](gpt_params & params, const std::string & mask) {
- params.draft_cpuparams_batch.mask_valid = true;
- if (!parse_cpu_mask(mask, params.draft_cpuparams_batch.cpumask)) {
- throw std::invalid_argument("invalid cpumask");
- }
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
- "Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
- [](gpt_params & params, const std::string & range) {
- params.draft_cpuparams_batch.mask_valid = true;
- if (!parse_cpu_range(range, params.draft_cpuparams_batch.cpumask)) {
- throw std::invalid_argument("invalid cpumask");
- }
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"--cpu-strict-batch-draft"}, "<0|1>",
- "Use strict CPU placement for draft model (default: --cpu-strict-draft)",
- [](gpt_params & params, int value) {
- params.draft_cpuparams_batch.strict_cpu = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"--prio-batch-draft"}, "N",
- format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams_batch.priority),
- [](gpt_params & params, int prio) {
- if (prio < 0 || prio > 3) {
- throw std::invalid_argument("invalid value");
- }
- params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) prio;
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"--poll-batch-draft"}, "<0|1>",
- "Use polling to wait for draft model work (default: --poll-draft)",
- [](gpt_params & params, int value) {
- params.draft_cpuparams_batch.poll = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"--draft"}, "N",
- format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
- [](gpt_params & params, int value) {
- params.n_draft = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"-ps", "--p-split"}, "N",
- format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
- [](gpt_params & params, const std::string & value) {
- params.p_split = std::stof(value);
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"-lcs", "--lookup-cache-static"}, "FNAME",
- "path to static lookup cache to use for lookup decoding (not updated by generation)",
- [](gpt_params & params, const std::string & value) {
- params.lookup_cache_static = value;
- }
- ));
- add_opt(llama_arg(
- {"-lcd", "--lookup-cache-dynamic"}, "FNAME",
- "path to dynamic lookup cache to use for lookup decoding (updated by generation)",
- [](gpt_params & params, const std::string & value) {
- params.lookup_cache_dynamic = value;
- }
- ));
- add_opt(llama_arg(
- {"-c", "--ctx-size"}, "N",
- format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
- [](gpt_params & params, int value) {
- params.n_ctx = value;
- }
- ).set_env("LLAMA_ARG_CTX_SIZE"));
- add_opt(llama_arg(
- {"-n", "--predict", "--n-predict"}, "N",
- format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict),
- [](gpt_params & params, int value) {
- params.n_predict = value;
- }
- ).set_env("LLAMA_ARG_N_PREDICT"));
- add_opt(llama_arg(
- {"-b", "--batch-size"}, "N",
- format("logical maximum batch size (default: %d)", params.n_batch),
- [](gpt_params & params, int value) {
- params.n_batch = value;
- }
- ).set_env("LLAMA_ARG_BATCH"));
- add_opt(llama_arg(
- {"-ub", "--ubatch-size"}, "N",
- format("physical maximum batch size (default: %d)", params.n_ubatch),
- [](gpt_params & params, int value) {
- params.n_ubatch = value;
- }
- ).set_env("LLAMA_ARG_UBATCH"));
- add_opt(llama_arg(
- {"--keep"}, "N",
- format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep),
- [](gpt_params & params, int value) {
- params.n_keep = value;
- }
- ));
- add_opt(llama_arg(
- {"--chunks"}, "N",
- format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
- [](gpt_params & params, int value) {
- params.n_chunks = value;
- }
- ));
- add_opt(llama_arg(
- {"-fa", "--flash-attn"},
- format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
- [](gpt_params & params) {
- params.flash_attn = true;
- }
- ).set_env("LLAMA_ARG_FLASH_ATTN"));
- add_opt(llama_arg(
- {"-p", "--prompt"}, "PROMPT",
- ex == LLAMA_EXAMPLE_MAIN
- ? "prompt to start generation with\nif -cnv is set, this will be used as system prompt"
- : "prompt to start generation with",
- [](gpt_params & params, const std::string & value) {
- params.prompt = value;
- }
- ));
- add_opt(llama_arg(
- {"-f", "--file"}, "FNAME",
- "a file containing the prompt (default: none)",
- [](gpt_params & params, const std::string & value) {
- std::ifstream file(value);
- if (!file) {
- throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
- }
- // store the external file name in params
- params.prompt_file = value;
- std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
- if (!params.prompt.empty() && params.prompt.back() == '\n') {
- params.prompt.pop_back();
- }
- }
- ));
- add_opt(llama_arg(
- {"--in-file"}, "FNAME",
- "an input file (repeat to specify multiple files)",
- [](gpt_params & params, const std::string & value) {
- std::ifstream file(value);
- if (!file) {
- throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
- }
- params.in_files.push_back(value);
- }
- ));
- add_opt(llama_arg(
- {"-bf", "--binary-file"}, "FNAME",
- "binary file containing the prompt (default: none)",
- [](gpt_params & params, const std::string & value) {
- std::ifstream file(value, std::ios::binary);
- if (!file) {
- throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
- }
- // store the external file name in params
- params.prompt_file = value;
- std::ostringstream ss;
- ss << file.rdbuf();
- params.prompt = ss.str();
- fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str());
- }
- ));
- add_opt(llama_arg(
- {"-e", "--escape"},
- format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
- [](gpt_params & params) {
- params.escape = true;
- }
- ));
- add_opt(llama_arg(
- {"--no-escape"},
- "do not process escape sequences",
- [](gpt_params & params) {
- params.escape = false;
- }
- ));
- add_opt(llama_arg(
- {"-ptc", "--print-token-count"}, "N",
- format("print token count every N tokens (default: %d)", params.n_print),
- [](gpt_params & params, int value) {
- params.n_print = value;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"--prompt-cache"}, "FNAME",
- "file to cache prompt state for faster startup (default: none)",
- [](gpt_params & params, const std::string & value) {
- params.path_prompt_cache = value;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"--prompt-cache-all"},
- "if specified, saves user input and generations to cache as well\n",
- [](gpt_params & params) {
- params.prompt_cache_all = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"--prompt-cache-ro"},
- "if specified, uses the prompt cache but does not update it",
- [](gpt_params & params) {
- params.prompt_cache_ro = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"-r", "--reverse-prompt"}, "PROMPT",
- "halt generation at PROMPT, return control in interactive mode\n",
- [](gpt_params & params, const std::string & value) {
- params.antiprompt.emplace_back(value);
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"-sp", "--special"},
- format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
- [](gpt_params & params) {
- params.special = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"-cnv", "--conversation"},
- format(
- "run in conversation mode:\n"
- "- does not print special tokens and suffix/prefix\n"
- "- interactive mode is also enabled\n"
- "(default: %s)",
- params.conversation ? "true" : "false"
- ),
- [](gpt_params & params) {
- params.conversation = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"-i", "--interactive"},
- format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"),
- [](gpt_params & params) {
- params.interactive = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"-if", "--interactive-first"},
- format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"),
- [](gpt_params & params) {
- params.interactive_first = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"-mli", "--multiline-input"},
- "allows you to write or paste multiple lines without ending each in '\\'",
- [](gpt_params & params) {
- params.multiline_input = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"--in-prefix-bos"},
- "prefix BOS to user inputs, preceding the `--in-prefix` string",
- [](gpt_params & params) {
- params.input_prefix_bos = true;
- params.enable_chat_template = false;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"--in-prefix"}, "STRING",
- "string to prefix user inputs with (default: empty)",
- [](gpt_params & params, const std::string & value) {
- params.input_prefix = value;
- params.enable_chat_template = false;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"--in-suffix"}, "STRING",
- "string to suffix after user inputs with (default: empty)",
- [](gpt_params & params, const std::string & value) {
- params.input_suffix = value;
- params.enable_chat_template = false;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"--no-warmup"},
- "skip warming up the model with an empty run",
- [](gpt_params & params) {
- params.warmup = false;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN}));
- add_opt(llama_arg(
- {"--spm-infill"},
- format(
- "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)",
- params.spm_infill ? "enabled" : "disabled"
- ),
- [](gpt_params & params) {
- params.spm_infill = true;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL}));
- add_opt(llama_arg(
- {"--samplers"}, "SAMPLERS",
- format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
- [](gpt_params & params, const std::string & value) {
- const auto sampler_names = string_split(value, ';');
- params.sparams.samplers = gpt_sampler_types_from_names(sampler_names, true);
- }
- ));
- add_opt(llama_arg(
- {"--sampling-seq"}, "SEQUENCE",
- format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
- [](gpt_params & params, const std::string & value) {
- params.sparams.samplers = gpt_sampler_types_from_chars(value);
- }
- ));
- add_opt(llama_arg(
- {"--ignore-eos"},
- "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)",
- [](gpt_params & params) {
- params.sparams.ignore_eos = true;
- }
- ));
- add_opt(llama_arg(
- {"--penalize-nl"},
- format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
- [](gpt_params & params) {
- params.sparams.penalize_nl = true;
- }
- ));
- add_opt(llama_arg(
- {"--temp"}, "N",
- format("temperature (default: %.1f)", (double)params.sparams.temp),
- [](gpt_params & params, const std::string & value) {
- params.sparams.temp = std::stof(value);
- params.sparams.temp = std::max(params.sparams.temp, 0.0f);
- }
- ));
- add_opt(llama_arg(
- {"--top-k"}, "N",
- format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
- [](gpt_params & params, int value) {
- params.sparams.top_k = value;
- }
- ));
- add_opt(llama_arg(
- {"--top-p"}, "N",
- format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
- [](gpt_params & params, const std::string & value) {
- params.sparams.top_p = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--min-p"}, "N",
- format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
- [](gpt_params & params, const std::string & value) {
- params.sparams.min_p = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--tfs"}, "N",
- format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
- [](gpt_params & params, const std::string & value) {
- params.sparams.tfs_z = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--typical"}, "N",
- format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
- [](gpt_params & params, const std::string & value) {
- params.sparams.typ_p = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--repeat-last-n"}, "N",
- format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n),
- [](gpt_params & params, int value) {
- params.sparams.penalty_last_n = value;
- params.sparams.n_prev = std::max(params.sparams.n_prev, params.sparams.penalty_last_n);
- }
- ));
- add_opt(llama_arg(
- {"--repeat-penalty"}, "N",
- format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
- [](gpt_params & params, const std::string & value) {
- params.sparams.penalty_repeat = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--presence-penalty"}, "N",
- format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
- [](gpt_params & params, const std::string & value) {
- params.sparams.penalty_present = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--frequency-penalty"}, "N",
- format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
- [](gpt_params & params, const std::string & value) {
- params.sparams.penalty_freq = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--dynatemp-range"}, "N",
- format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
- [](gpt_params & params, const std::string & value) {
- params.sparams.dynatemp_range = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--dynatemp-exp"}, "N",
- format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
- [](gpt_params & params, const std::string & value) {
- params.sparams.dynatemp_exponent = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--mirostat"}, "N",
- format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
- "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat),
- [](gpt_params & params, int value) {
- params.sparams.mirostat = value;
- }
- ));
- add_opt(llama_arg(
- {"--mirostat-lr"}, "N",
- format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
- [](gpt_params & params, const std::string & value) {
- params.sparams.mirostat_eta = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--mirostat-ent"}, "N",
- format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
- [](gpt_params & params, const std::string & value) {
- params.sparams.mirostat_tau = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"-l", "--logit-bias"}, "TOKEN_ID(+/-)BIAS",
- "modifies the likelihood of token appearing in the completion,\n"
- "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
- "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'",
- [](gpt_params & params, const std::string & value) {
- std::stringstream ss(value);
- llama_token key;
- char sign;
- std::string value_str;
- try {
- if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
- const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
- params.sparams.logit_bias.push_back({key, bias});
- } else {
- throw std::invalid_argument("invalid input format");
- }
- } catch (const std::exception&) {
- throw std::invalid_argument("invalid input format");
- }
- }
- ));
- add_opt(llama_arg(
- {"--grammar"}, "GRAMMAR",
- format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
- [](gpt_params & params, const std::string & value) {
- params.sparams.grammar = value;
- }
- ));
- add_opt(llama_arg(
- {"--grammar-file"}, "FNAME",
- "file to read grammar from",
- [](gpt_params & params, const std::string & value) {
- std::ifstream file(value);
- if (!file) {
- throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
- }
- std::copy(
- std::istreambuf_iterator<char>(file),
- std::istreambuf_iterator<char>(),
- std::back_inserter(params.sparams.grammar)
- );
- }
- ));
- add_opt(llama_arg(
- {"-j", "--json-schema"}, "SCHEMA",
- "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
- [](gpt_params & params, const std::string & value) {
- params.sparams.grammar = json_schema_to_grammar(json::parse(value));
- }
- ));
- add_opt(llama_arg(
- {"--pooling"}, "{none,mean,cls,last}",
- "pooling type for embeddings, use model default if unspecified",
- [](gpt_params & params, const std::string & value) {
- /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
- else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
- else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
- else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
- else { throw std::invalid_argument("invalid value"); }
- }
- ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
- add_opt(llama_arg(
- {"--attention"}, "{causal,non,causal}",
- "attention type for embeddings, use model default if unspecified",
- [](gpt_params & params, const std::string & value) {
- /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
- else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
- else { throw std::invalid_argument("invalid value"); }
- }
- ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
- add_opt(llama_arg(
- {"--rope-scaling"}, "{none,linear,yarn}",
- "RoPE frequency scaling method, defaults to linear unless specified by the model",
- [](gpt_params & params, const std::string & value) {
- /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
- else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
- else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
- else { throw std::invalid_argument("invalid value"); }
- }
- ));
- add_opt(llama_arg(
- {"--rope-scale"}, "N",
- "RoPE context scaling factor, expands context by a factor of N",
- [](gpt_params & params, const std::string & value) {
- params.rope_freq_scale = 1.0f / std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--rope-freq-base"}, "N",
- "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)",
- [](gpt_params & params, const std::string & value) {
- params.rope_freq_base = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--rope-freq-scale"}, "N",
- "RoPE frequency scaling factor, expands context by a factor of 1/N",
- [](gpt_params & params, const std::string & value) {
- params.rope_freq_scale = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--yarn-orig-ctx"}, "N",
- format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx),
- [](gpt_params & params, int value) {
- params.yarn_orig_ctx = value;
- }
- ));
- add_opt(llama_arg(
- {"--yarn-ext-factor"}, "N",
- format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
- [](gpt_params & params, const std::string & value) {
- params.yarn_ext_factor = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--yarn-attn-factor"}, "N",
- format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
- [](gpt_params & params, const std::string & value) {
- params.yarn_attn_factor = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--yarn-beta-slow"}, "N",
- format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
- [](gpt_params & params, const std::string & value) {
- params.yarn_beta_slow = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"--yarn-beta-fast"}, "N",
- format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
- [](gpt_params & params, const std::string & value) {
- params.yarn_beta_fast = std::stof(value);
- }
- ));
- add_opt(llama_arg(
- {"-gan", "--grp-attn-n"}, "N",
- format("group-attention factor (default: %d)", params.grp_attn_n),
- [](gpt_params & params, int value) {
- params.grp_attn_n = value;
- }
- ));
- add_opt(llama_arg(
- {"-gaw", "--grp-attn-w"}, "N",
- format("group-attention width (default: %.1f)", (double)params.grp_attn_w),
- [](gpt_params & params, int value) {
- params.grp_attn_w = value;
- }
- ));
- add_opt(llama_arg(
- {"-dkvc", "--dump-kv-cache"},
- "verbose print of the KV cache",
- [](gpt_params & params) {
- params.dump_kv_cache = true;
- }
- ));
- add_opt(llama_arg(
- {"-nkvo", "--no-kv-offload"},
- "disable KV offload",
- [](gpt_params & params) {
- params.no_kv_offload = true;
- }
- ));
- add_opt(llama_arg(
- {"-ctk", "--cache-type-k"}, "TYPE",
- format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
- [](gpt_params & params, const std::string & value) {
- // TODO: get the type right here
- params.cache_type_k = value;
- }
- ));
- add_opt(llama_arg(
- {"-ctv", "--cache-type-v"}, "TYPE",
- format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
- [](gpt_params & params, const std::string & value) {
- // TODO: get the type right here
- params.cache_type_v = value;
- }
- ));
- add_opt(llama_arg(
- {"--perplexity", "--all-logits"},
- format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
- [](gpt_params & params) {
- params.logits_all = true;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--hellaswag"},
- "compute HellaSwag score over random tasks from datafile supplied with -f",
- [](gpt_params & params) {
- params.hellaswag = true;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--hellaswag-tasks"}, "N",
- format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks),
- [](gpt_params & params, int value) {
- params.hellaswag_tasks = value;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--winogrande"},
- "compute Winogrande score over random tasks from datafile supplied with -f",
- [](gpt_params & params) {
- params.winogrande = true;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--winogrande-tasks"}, "N",
- format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks),
- [](gpt_params & params, int value) {
- params.winogrande_tasks = value;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--multiple-choice"},
- "compute multiple choice score over random tasks from datafile supplied with -f",
- [](gpt_params & params) {
- params.multiple_choice = true;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--multiple-choice-tasks"}, "N",
- format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks),
- [](gpt_params & params, int value) {
- params.multiple_choice_tasks = value;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--kl-divergence"},
- "computes KL-divergence to logits provided via --kl-divergence-base",
- [](gpt_params & params) {
- params.kl_divergence = true;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--save-all-logits", "--kl-divergence-base"}, "FNAME",
- "set logits file",
- [](gpt_params & params, const std::string & value) {
- params.logits_file = value;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--ppl-stride"}, "N",
- format("stride for perplexity calculation (default: %d)", params.ppl_stride),
- [](gpt_params & params, int value) {
- params.ppl_stride = value;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"--ppl-output-type"}, "<0|1>",
- format("output type for perplexity calculation (default: %d)", params.ppl_output_type),
- [](gpt_params & params, int value) {
- params.ppl_output_type = value;
- }
- ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
- add_opt(llama_arg(
- {"-dt", "--defrag-thold"}, "N",
- format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
- [](gpt_params & params, const std::string & value) {
- params.defrag_thold = std::stof(value);
- }
- ).set_env("LLAMA_ARG_DEFRAG_THOLD"));
- add_opt(llama_arg(
- {"-np", "--parallel"}, "N",
- format("number of parallel sequences to decode (default: %d)", params.n_parallel),
- [](gpt_params & params, int value) {
- params.n_parallel = value;
- }
- ));
- add_opt(llama_arg(
- {"-ns", "--sequences"}, "N",
- format("number of sequences to decode (default: %d)", params.n_sequences),
- [](gpt_params & params, int value) {
- params.n_sequences = value;
- }
- ));
- add_opt(llama_arg(
- {"-cb", "--cont-batching"},
- format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
- [](gpt_params & params) {
- params.cont_batching = true;
- }
- ).set_env("LLAMA_ARG_CONT_BATCHING"));
- add_opt(llama_arg(
- {"-nocb", "--no-cont-batching"},
- "disable continuous batching",
- [](gpt_params & params) {
- params.cont_batching = false;
- }
- ).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
- add_opt(llama_arg(
- {"--mmproj"}, "FILE",
- "path to a multimodal projector file for LLaVA. see examples/llava/README.md",
- [](gpt_params & params, const std::string & value) {
- params.mmproj = value;
- }
- ).set_examples({LLAMA_EXAMPLE_LLAVA}));
- add_opt(llama_arg(
- {"--image"}, "FILE",
- "path to an image file. use with multimodal models. Specify multiple times for batching",
- [](gpt_params & params, const std::string & value) {
- params.image.emplace_back(value);
- }
- ).set_examples({LLAMA_EXAMPLE_LLAVA}));
-#ifdef GGML_USE_RPC
- add_opt(llama_arg(
- {"--rpc"}, "SERVERS",
- "comma separated list of RPC servers",
- [](gpt_params & params, const std::string & value) {
- params.rpc_servers = value;
- }
- ));
-#endif
- add_opt(llama_arg(
- {"--mlock"},
- "force system to keep model in RAM rather than swapping or compressing",
- [](gpt_params & params) {
- params.use_mlock = true;
- }
- ));
- add_opt(llama_arg(
- {"--no-mmap"},
- "do not memory-map model (slower load but may reduce pageouts if not using mlock)",
- [](gpt_params & params) {
- params.use_mmap = false;
- }
- ));
- add_opt(llama_arg(
- {"--numa"}, "TYPE",
- "attempt optimizations that help on some NUMA systems\n"
- "- distribute: spread execution evenly over all nodes\n"
- "- isolate: only spawn threads on CPUs on the node that execution started on\n"
- "- numactl: use the CPU map provided by numactl\n"
- "if run without this previously, it is recommended to drop the system page cache before using this\n"
- "see https://github.com/ggerganov/llama.cpp/issues/1437",
- [](gpt_params & params, const std::string & value) {
- /**/ 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 { throw std::invalid_argument("invalid value"); }
- }
- ));
- add_opt(llama_arg(
- {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
- "number of layers to store in VRAM",
- [](gpt_params & params, int value) {
- params.n_gpu_layers = value;
- if (!llama_supports_gpu_offload()) {
- fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
- fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
- }
- }
- ).set_env("LLAMA_ARG_N_GPU_LAYERS"));
- add_opt(llama_arg(
- {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
- "number of layers to store in VRAM for the draft model",
- [](gpt_params & params, int value) {
- params.n_gpu_layers_draft = value;
- if (!llama_supports_gpu_offload()) {
- fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
- fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
- }
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"-sm", "--split-mode"}, "{none,layer,row}",
- "how to split the model across multiple GPUs, one of:\n"
- "- none: use one GPU only\n"
- "- layer (default): split layers and KV across GPUs\n"
- "- row: split rows across GPUs",
- [](gpt_params & params, const std::string & value) {
- std::string arg_next = value;
- if (arg_next == "none") {
- params.split_mode = LLAMA_SPLIT_MODE_NONE;
- } else if (arg_next == "layer") {
- params.split_mode = LLAMA_SPLIT_MODE_LAYER;
- }
- else if (arg_next == "row") {
-#ifdef GGML_USE_SYCL
- fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
- exit(1);
-#endif // GGML_USE_SYCL
- params.split_mode = LLAMA_SPLIT_MODE_ROW;
- }
- else {
- throw std::invalid_argument("invalid value");
- }
-#ifndef GGML_USE_CUDA_SYCL_VULKAN
- fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL_VULKAN
- }
- ));
- add_opt(llama_arg(
- {"-ts", "--tensor-split"}, "N0,N1,N2,...",
- "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1",
- [](gpt_params & params, const std::string & value) {
- std::string arg_next = value;
-
- // split string by , and /
- const std::regex regex{ R"([,/]+)" };
- std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
- std::vector<std::string> split_arg{ it, {} };
- if (split_arg.size() >= llama_max_devices()) {
- throw std::invalid_argument(
- format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices())
- );
- }
- for (size_t i = 0; i < llama_max_devices(); ++i) {
- if (i < split_arg.size()) {
- params.tensor_split[i] = std::stof(split_arg[i]);
- } else {
- params.tensor_split[i] = 0.0f;
- }
- }
-#ifndef GGML_USE_CUDA_SYCL_VULKAN
- fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL_VULKAN
- }
- ));
- add_opt(llama_arg(
- {"-mg", "--main-gpu"}, "INDEX",
- format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu),
- [](gpt_params & params, int value) {
- params.main_gpu = value;
-#ifndef GGML_USE_CUDA_SYCL_VULKAN
- fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL_VULKAN
- }
- ));
- add_opt(llama_arg(
- {"--check-tensors"},
- format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
- [](gpt_params & params) {
- params.check_tensors = true;
- }
- ));
- add_opt(llama_arg(
- {"--override-kv"}, "KEY=TYPE:VALUE",
- "advanced option to override model metadata by key. may be specified multiple times.\n"
- "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false",
- [](gpt_params & params, const std::string & value) {
- if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) {
- throw std::runtime_error(format("error: Invalid type for KV override: %s\n", value.c_str()));
- }
- }
- ));
- add_opt(llama_arg(
- {"--lora"}, "FNAME",
- "path to LoRA adapter (can be repeated to use multiple adapters)",
- [](gpt_params & params, const std::string & value) {
- params.lora_adapters.push_back({ std::string(value), 1.0 });
- }
- ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
- add_opt(llama_arg(
- {"--lora-scaled"}, "FNAME", "SCALE",
- "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
- [](gpt_params & params, const std::string & fname, const std::string & scale) {
- params.lora_adapters.push_back({ fname, std::stof(scale) });
- }
- ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
- add_opt(llama_arg(
- {"--control-vector"}, "FNAME",
- "add a control vector\nnote: this argument can be repeated to add multiple control vectors",
- [](gpt_params & params, const std::string & value) {
- params.control_vectors.push_back({ 1.0f, value, });
- }
- ));
- add_opt(llama_arg(
- {"--control-vector-scaled"}, "FNAME", "SCALE",
- "add a control vector with user defined scaling SCALE\n"
- "note: this argument can be repeated to add multiple scaled control vectors",
- [](gpt_params & params, const std::string & fname, const std::string & scale) {
- params.control_vectors.push_back({ std::stof(scale), fname });
- }
- ));
- add_opt(llama_arg(
- {"--control-vector-layer-range"}, "START", "END",
- "layer range to apply the control vector(s) to, start and end inclusive",
- [](gpt_params & params, const std::string & start, const std::string & end) {
- params.control_vector_layer_start = std::stoi(start);
- params.control_vector_layer_end = std::stoi(end);
- }
- ));
- add_opt(llama_arg(
- {"-a", "--alias"}, "STRING",
- "set alias for model name (to be used by REST API)",
- [](gpt_params & params, const std::string & value) {
- params.model_alias = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"-m", "--model"}, "FNAME",
- ex == LLAMA_EXAMPLE_EXPORT_LORA
- ? std::string("model path from which to load base model")
- : format(
- "model path (default: `models/$filename` with filename from `--hf-file` "
- "or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH
- ),
- [](gpt_params & params, const std::string & value) {
- params.model = value;
- }
- ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL"));
- add_opt(llama_arg(
- {"-md", "--model-draft"}, "FNAME",
- "draft model for speculative decoding (default: unused)",
- [](gpt_params & params, const std::string & value) {
- params.model_draft = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
- add_opt(llama_arg(
- {"-mu", "--model-url"}, "MODEL_URL",
- "model download url (default: unused)",
- [](gpt_params & params, const std::string & value) {
- params.model_url = value;
- }
- ).set_env("LLAMA_ARG_MODEL_URL"));
- add_opt(llama_arg(
- {"-hfr", "--hf-repo"}, "REPO",
- "Hugging Face model repository (default: unused)",
- [](gpt_params & params, const std::string & value) {
- params.hf_repo = value;
- }
- ).set_env("LLAMA_ARG_HF_REPO"));
- add_opt(llama_arg(
- {"-hff", "--hf-file"}, "FILE",
- "Hugging Face model file (default: unused)",
- [](gpt_params & params, const std::string & value) {
- params.hf_file = value;
- }
- ).set_env("LLAMA_ARG_HF_FILE"));
- add_opt(llama_arg(
- {"-hft", "--hf-token"}, "TOKEN",
- "Hugging Face access token (default: value from HF_TOKEN environment variable)",
- [](gpt_params & params, const std::string & value) {
- params.hf_token = value;
- }
- ).set_env("HF_TOKEN"));
- add_opt(llama_arg(
- {"--context-file"}, "FNAME",
- "file to load context from (repeat to specify multiple files)",
- [](gpt_params & params, const std::string & value) {
- std::ifstream file(value, std::ios::binary);
- if (!file) {
- throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
- }
- params.context_files.push_back(value);
- }
- ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
- add_opt(llama_arg(
- {"--chunk-size"}, "N",
- format("minimum length of embedded text chunks (default: %d)", params.chunk_size),
- [](gpt_params & params, int value) {
- params.chunk_size = value;
- }
- ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
- add_opt(llama_arg(
- {"--chunk-separator"}, "STRING",
- format("separator between chunks (default: '%s')", params.chunk_separator.c_str()),
- [](gpt_params & params, const std::string & value) {
- params.chunk_separator = value;
- }
- ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
- add_opt(llama_arg(
- {"--junk"}, "N",
- format("number of times to repeat the junk text (default: %d)", params.n_junk),
- [](gpt_params & params, int value) {
- params.n_junk = value;
- }
- ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
- add_opt(llama_arg(
- {"--pos"}, "N",
- format("position of the passkey in the junk text (default: %d)", params.i_pos),
- [](gpt_params & params, int value) {
- params.i_pos = value;
- }
- ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
- add_opt(llama_arg(
- {"-o", "--output", "--output-file"}, "FNAME",
- format("output file (default: '%s')",
- ex == LLAMA_EXAMPLE_EXPORT_LORA
- ? params.lora_outfile.c_str()
- : ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR
- ? params.cvector_outfile.c_str()
- : params.out_file.c_str()),
- [](gpt_params & params, const std::string & value) {
- params.out_file = value;
- params.cvector_outfile = value;
- params.lora_outfile = value;
- }
- ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA}));
- add_opt(llama_arg(
- {"-ofreq", "--output-frequency"}, "N",
- format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
- [](gpt_params & params, int value) {
- params.n_out_freq = value;
- }
- ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
- add_opt(llama_arg(
- {"--save-frequency"}, "N",
- format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
- [](gpt_params & params, int value) {
- params.n_save_freq = value;
- }
- ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
- add_opt(llama_arg(
- {"--process-output"},
- format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"),
- [](gpt_params & params) {
- params.process_output = true;
- }
- ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
- add_opt(llama_arg(
- {"--no-ppl"},
- format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
- [](gpt_params & params) {
- params.compute_ppl = false;
- }
- ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
- add_opt(llama_arg(
- {"--chunk", "--from-chunk"}, "N",
- format("start processing the input from chunk N (default: %d)", params.i_chunk),
- [](gpt_params & params, int value) {
- params.i_chunk = value;
- }
- ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
- add_opt(llama_arg(
- {"-pps"},
- format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"),
- [](gpt_params & params) {
- params.is_pp_shared = true;
- }
- ).set_examples({LLAMA_EXAMPLE_BENCH}));
- add_opt(llama_arg(
- {"-npp"}, "n0,n1,...",
- "number of prompt tokens",
- [](gpt_params & params, const std::string & value) {
- auto p = string_split<int>(value, ',');
- params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
- }
- ).set_examples({LLAMA_EXAMPLE_BENCH}));
- add_opt(llama_arg(
- {"-ntg"}, "n0,n1,...",
- "number of text generation tokens",
- [](gpt_params & params, const std::string & value) {
- auto p = string_split<int>(value, ',');
- params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
- }
- ).set_examples({LLAMA_EXAMPLE_BENCH}));
- add_opt(llama_arg(
- {"-npl"}, "n0,n1,...",
- "number of parallel prompts",
- [](gpt_params & params, const std::string & value) {
- auto p = string_split<int>(value, ',');
- params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
- }
- ).set_examples({LLAMA_EXAMPLE_BENCH}));
- add_opt(llama_arg(
- {"--embd-normalize"}, "N",
- format("normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize),
- [](gpt_params & params, int value) {
- params.embd_normalize = value;
- }
- ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
- add_opt(llama_arg(
- {"--embd-output-format"}, "FORMAT",
- "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix",
- [](gpt_params & params, const std::string & value) {
- params.embd_out = value;
- }
- ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
- add_opt(llama_arg(
- {"--embd-separator"}, "STRING",
- "separator of embendings (default \\n) for example \"<#sep#>\"",
- [](gpt_params & params, const std::string & value) {
- params.embd_sep = value;
- }
- ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
- add_opt(llama_arg(
- {"--host"}, "HOST",
- format("ip address to listen (default: %s)", params.hostname.c_str()),
- [](gpt_params & params, const std::string & value) {
- params.hostname = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST"));
- add_opt(llama_arg(
- {"--port"}, "PORT",
- format("port to listen (default: %d)", params.port),
- [](gpt_params & params, int value) {
- params.port = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT"));
- add_opt(llama_arg(
- {"--path"}, "PATH",
- format("path to serve static files from (default: %s)", params.public_path.c_str()),
- [](gpt_params & params, const std::string & value) {
- params.public_path = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"--embedding", "--embeddings"},
- format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
- [](gpt_params & params) {
- params.embedding = true;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
- add_opt(llama_arg(
- {"--api-key"}, "KEY",
- "API key to use for authentication (default: none)",
- [](gpt_params & params, const std::string & value) {
- params.api_keys.push_back(value);
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
- add_opt(llama_arg(
- {"--api-key-file"}, "FNAME",
- "path to file containing API keys (default: none)",
- [](gpt_params & params, const std::string & value) {
- std::ifstream key_file(value);
- if (!key_file) {
- throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
- }
- std::string key;
- while (std::getline(key_file, key)) {
- if (!key.empty()) {
- params.api_keys.push_back(key);
- }
- }
- key_file.close();
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"--ssl-key-file"}, "FNAME",
- "path to file a PEM-encoded SSL private key",
- [](gpt_params & params, const std::string & value) {
- params.ssl_file_key = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"--ssl-cert-file"}, "FNAME",
- "path to file a PEM-encoded SSL certificate",
- [](gpt_params & params, const std::string & value) {
- params.ssl_file_cert = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"-to", "--timeout"}, "N",
- format("server read/write timeout in seconds (default: %d)", params.timeout_read),
- [](gpt_params & params, int value) {
- params.timeout_read = value;
- params.timeout_write = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"--threads-http"}, "N",
- format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http),
- [](gpt_params & params, int value) {
- params.n_threads_http = value;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
- add_opt(llama_arg(
- {"-spf", "--system-prompt-file"}, "FNAME",
- "set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications",
- [](gpt_params & params, const std::string & value) {
- std::ifstream file(value);
- if (!file) {
- throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
- }
- std::string system_prompt;
- std::copy(
- std::istreambuf_iterator<char>(file),
- std::istreambuf_iterator<char>(),
- std::back_inserter(system_prompt)
- );
- params.system_prompt = system_prompt;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"--log-format"}, "{text, json}",
- "log output format: json or text (default: json)",
- [](gpt_params & params, const std::string & value) {
- if (value == "json") {
- params.log_json = true;
- } else if (value == "text") {
- params.log_json = false;
- } else {
- throw std::invalid_argument("invalid value");
- }
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"--metrics"},
- format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"),
- [](gpt_params & params) {
- params.endpoint_metrics = true;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS"));
- add_opt(llama_arg(
- {"--no-slots"},
- format("disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
- [](gpt_params & params) {
- params.endpoint_slots = false;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS"));
- add_opt(llama_arg(
- {"--slot-save-path"}, "PATH",
- "path to save slot kv cache (default: disabled)",
- [](gpt_params & params, const std::string & value) {
- params.slot_save_path = value;
- // if doesn't end with DIRECTORY_SEPARATOR, add it
- if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
- params.slot_save_path += DIRECTORY_SEPARATOR;
- }
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"--chat-template"}, "JINJA_TEMPLATE",
- "set custom jinja chat template (default: template taken from model's metadata)\n"
- "if suffix/prefix are specified, template will be disabled\n"
- "only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
- [](gpt_params & params, const std::string & value) {
- if (!llama_chat_verify_template(value)) {
- throw std::runtime_error(format(
- "error: the supplied chat template is not supported: %s\n"
- "note: llama.cpp does not use jinja parser, we only support commonly used templates\n",
- value.c_str()
- ));
- }
- params.chat_template = value;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
- add_opt(llama_arg(
- {"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
- format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
- [](gpt_params & params, const std::string & value) {
- params.slot_prompt_similarity = std::stof(value);
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"--lora-init-without-apply"},
- format("load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"),
- [](gpt_params & params) {
- params.lora_init_without_apply = true;
- }
- ).set_examples({LLAMA_EXAMPLE_SERVER}));
- add_opt(llama_arg(
- {"--simple-io"},
- "use basic IO for better compatibility in subprocesses and limited consoles",
- [](gpt_params & params) {
- params.simple_io = true;
- }
- ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
- add_opt(llama_arg(
- {"-ld", "--logdir"}, "LOGDIR",
- "path under which to save YAML logs (no logging if unset)",
- [](gpt_params & params, const std::string & value) {
- params.logdir = value;
-
- if (params.logdir.back() != DIRECTORY_SEPARATOR) {
- params.logdir += DIRECTORY_SEPARATOR;
- }
- }
- ));
- add_opt(llama_arg(
- {"--positive-file"}, "FNAME",
- format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
- [](gpt_params & params, const std::string & value) {
- params.cvector_positive_file = value;
- }
- ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
- add_opt(llama_arg(
- {"--negative-file"}, "FNAME",
- format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()),
- [](gpt_params & params, const std::string & value) {
- params.cvector_negative_file = value;
- }
- ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
- add_opt(llama_arg(
- {"--pca-batch"}, "N",
- format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch),
- [](gpt_params & params, int value) {
- params.n_pca_batch = value;
- }
- ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
- add_opt(llama_arg(
- {"--pca-iter"}, "N",
- format("number of iterations used for PCA (default: %d)", params.n_pca_iterations),
- [](gpt_params & params, int value) {
- params.n_pca_iterations = value;
- }
- ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
- add_opt(llama_arg(
- {"--method"}, "{pca, mean}",
- "dimensionality reduction method to be used (default: pca)",
- [](gpt_params & params, const std::string & value) {
- /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
- else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
- else { throw std::invalid_argument("invalid value"); }
- }
- ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
- add_opt(llama_arg(
- {"--output-format"}, "{md,jsonl}",
- "output format for batched-bench results (default: md)",
- [](gpt_params & params, const std::string & value) {
- /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; }
- else if (value == "md") { params.batched_bench_output_jsonl = false; }
- else { std::invalid_argument("invalid value"); }
- }
- ).set_examples({LLAMA_EXAMPLE_BENCH}));
-#ifndef LOG_DISABLE_LOGS
- // TODO: make this looks less weird
- add_opt(llama_arg(
- {"--log-test"},
- "Log test",
- [](gpt_params &) { log_param_single_parse("--log-test"); }
- ));
- add_opt(llama_arg(
- {"--log-disable"},
- "Log disable",
- [](gpt_params &) { log_param_single_parse("--log-disable"); }
- ));
- add_opt(llama_arg(
- {"--log-enable"},
- "Log enable",
- [](gpt_params &) { log_param_single_parse("--log-enable"); }
- ));
- add_opt(llama_arg(
- {"--log-new"},
- "Log new",
- [](gpt_params &) { log_param_single_parse("--log-new"); }
- ));
- add_opt(llama_arg(
- {"--log-append"},
- "Log append",
- [](gpt_params &) { log_param_single_parse("--log-append"); }
- ));
- add_opt(llama_arg(
- {"--log-file"}, "FNAME",
- "Log file",
- [](gpt_params &, const std::string & value) { log_param_pair_parse(false, "--log-file", value); }
- ));
-#endif // LOG_DISABLE_LOGS
-
- return options;
-}
-
std::string gpt_params_get_system_info(const gpt_params & params) {
std::ostringstream os;
#include "llama.h"
-#include "sampling.h"
-
#define LOG_NO_FILE_LINE_FUNCTION
#include "log.h"
-#include <cmath>
#include <string>
#include <vector>
-#include <random>
-#include <thread>
-#include <set>
-#include <unordered_map>
-#include <tuple>
-#include <functional>
#ifdef _WIN32
#define DIRECTORY_SEPARATOR '\\'
// CPU utils
//
+struct cpu_params {
+ int n_threads = -1;
+ bool cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
+ bool mask_valid = false; // Default: any CPU
+ enum ggml_sched_priority priority = GGML_SCHED_PRIO_NORMAL; // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
+ bool strict_cpu = false; // Use strict CPU placement
+ uint32_t poll = 50; // Polling (busywait) level (0 - no polling, 100 - mostly polling)
+};
+
int32_t cpu_get_num_physical_cores();
int32_t cpu_get_num_math();
//
-// CLI argument parsing
+// Common params
//
enum llama_example {
LLAMA_EXAMPLE_CVECTOR_GENERATOR,
LLAMA_EXAMPLE_EXPORT_LORA,
LLAMA_EXAMPLE_LLAVA,
+ LLAMA_EXAMPLE_LOOKUP,
+ LLAMA_EXAMPLE_PARALLEL,
LLAMA_EXAMPLE_COUNT,
};
+enum gpt_sampler_type {
+ GPT_SAMPLER_TYPE_NONE = 0,
+ GPT_SAMPLER_TYPE_TOP_K = 1,
+ GPT_SAMPLER_TYPE_TOP_P = 2,
+ GPT_SAMPLER_TYPE_MIN_P = 3,
+ GPT_SAMPLER_TYPE_TFS_Z = 4,
+ GPT_SAMPLER_TYPE_TYPICAL_P = 5,
+ GPT_SAMPLER_TYPE_TEMPERATURE = 6,
+};
+
// dimensionality reduction methods, used by cvector-generator
enum dimre_method {
DIMRE_METHOD_PCA,
DIMRE_METHOD_MEAN,
};
-struct cpu_params {
- int n_threads = -1;
- bool cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
- bool mask_valid = false; // Default: any CPU
- enum ggml_sched_priority priority = GGML_SCHED_PRIO_NORMAL; // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
- bool strict_cpu = false; // Use strict CPU placement
- uint32_t poll = 50; // Polling (busywait) level (0 - no polling, 100 - mostly polling)
+// sampler parameters
+struct gpt_sampler_params {
+ uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
+
+ int32_t n_prev = 64; // number of previous tokens to remember
+ int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
+ int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
+ int32_t top_k = 40; // <= 0 to use vocab size
+ float top_p = 0.95f; // 1.0 = disabled
+ float min_p = 0.05f; // 0.0 = disabled
+ float tfs_z = 1.00f; // 1.0 = disabled
+ float typ_p = 1.00f; // typical_p, 1.0 = disabled
+ float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
+ float dynatemp_range = 0.00f; // 0.0 = disabled
+ float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
+ int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
+ float penalty_repeat = 1.00f; // 1.0 = disabled
+ float penalty_freq = 0.00f; // 0.0 = disabled
+ float penalty_present = 0.00f; // 0.0 = disabled
+ int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+ float mirostat_tau = 5.00f; // target entropy
+ float mirostat_eta = 0.10f; // learning rate
+ bool penalize_nl = false; // consider newlines as a repeatable token
+ bool ignore_eos = false;
+
+ std::vector<enum gpt_sampler_type> samplers = {
+ GPT_SAMPLER_TYPE_TOP_K,
+ GPT_SAMPLER_TYPE_TFS_Z,
+ GPT_SAMPLER_TYPE_TYPICAL_P,
+ GPT_SAMPLER_TYPE_TOP_P,
+ GPT_SAMPLER_TYPE_MIN_P,
+ GPT_SAMPLER_TYPE_TEMPERATURE
+ };
+
+ std::string grammar; // optional BNF-like grammar to constrain sampling
+
+ std::vector<llama_logit_bias> logit_bias; // logit biases to apply
+
+ // print the parameters into a string
+ std::string print() const;
};
struct gpt_params {
- enum llama_example curr_ex = LLAMA_EXAMPLE_COMMON;
-
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 0; // context size
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
struct gpt_sampler_params sparams;
- std::string model = ""; // model path
- std::string model_draft = ""; // draft model for speculative decoding
- std::string model_alias = "unknown"; // model alias
- std::string model_url = ""; // model url to download
- std::string hf_token = ""; // HF token
- std::string hf_repo = ""; // HF repo
- std::string hf_file = ""; // HF file
- std::string prompt = "";
- std::string prompt_file = ""; // store the external prompt file name
- std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
- std::string input_prefix = ""; // string to prefix user inputs with
- std::string input_suffix = ""; // string to suffix user inputs with
- std::string logdir = ""; // directory in which to save YAML log files
- std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding
- std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
- std::string logits_file = ""; // file for saving *all* logits
- std::string rpc_servers = ""; // comma separated list of RPC servers
+ std::string model = ""; // model path // NOLINT
+ std::string model_draft = ""; // draft model for speculative decoding // NOLINT
+ std::string model_alias = "unknown"; // model alias // NOLINT
+ std::string model_url = ""; // model url to download // NOLINT
+ std::string hf_token = ""; // HF token // NOLINT
+ std::string hf_repo = ""; // HF repo // NOLINT
+ std::string hf_file = ""; // HF file // NOLINT
+ std::string prompt = ""; // NOLINT
+ std::string prompt_file = ""; // store the external prompt file name // NOLINT
+ std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state // NOLINT
+ std::string input_prefix = ""; // string to prefix user inputs with // NOLINT
+ std::string input_suffix = ""; // string to suffix user inputs with // NOLINT
+ std::string logdir = ""; // directory in which to save YAML log files // NOLINT
+ std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding // NOLINT
+ std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding // NOLINT
+ std::string logits_file = ""; // file for saving *all* logits // NOLINT
+ std::string rpc_servers = ""; // comma separated list of RPC servers // NOLINT
std::vector<std::string> in_files; // all input files
std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
bool kl_divergence = false; // compute KL divergence
- std::function<void(int, char **)> print_usage = nullptr; // print example-specific usage and example
bool usage = false; // print usage
bool use_color = false; // use color to distinguish generations and inputs
bool special = false; // enable special token output
std::string cache_type_v = "f16"; // KV cache data type for the V
// multimodal models (see examples/llava)
- std::string mmproj = ""; // path to multimodal projector
+ std::string mmproj = ""; // path to multimodal projector // NOLINT
std::vector<std::string> image; // path to image file(s)
// embedding
int n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool)
std::string hostname = "127.0.0.1";
- std::string public_path = "";
- std::string chat_template = "";
- std::string system_prompt = "";
+ std::string public_path = ""; // NOLINT
+ std::string chat_template = ""; // NOLINT
+ std::string system_prompt = ""; // NOLINT
bool enable_chat_template = true;
std::vector<std::string> api_keys;
- std::string ssl_file_key = "";
- std::string ssl_file_cert = "";
+ std::string ssl_file_key = ""; // NOLINT
+ std::string ssl_file_cert = ""; // NOLINT
bool endpoint_slots = true;
bool endpoint_metrics = false;
bool batched_bench_output_jsonl = false;
};
-struct llama_arg {
- std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
- std::vector<const char *> args;
- const char * value_hint = nullptr; // help text or example for arg value
- const char * value_hint_2 = nullptr; // for second arg value
- const char * env = nullptr;
- std::string help;
- void (*handler_void) (gpt_params & params) = nullptr;
- void (*handler_string) (gpt_params & params, const std::string &) = nullptr;
- void (*handler_str_str)(gpt_params & params, const std::string &, const std::string &) = nullptr;
- void (*handler_int) (gpt_params & params, int) = nullptr;
-
- llama_arg(
- const std::initializer_list<const char *> & args,
- const char * value_hint,
- const std::string & help,
- void (*handler)(gpt_params & params, const std::string &)
- ) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
-
- llama_arg(
- const std::initializer_list<const char *> & args,
- const char * value_hint,
- const std::string & help,
- void (*handler)(gpt_params & params, int)
- ) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
-
- llama_arg(
- const std::initializer_list<const char *> & args,
- const std::string & help,
- void (*handler)(gpt_params & params)
- ) : args(args), help(help), handler_void(handler) {}
-
- // support 2 values for arg
- llama_arg(
- const std::initializer_list<const char *> & args,
- const char * value_hint,
- const char * value_hint_2,
- const std::string & help,
- void (*handler)(gpt_params & params, const std::string &, const std::string &)
- ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
-
- llama_arg & set_examples(std::initializer_list<enum llama_example> examples) {
- this->examples = std::move(examples);
- return *this;
- }
-
- llama_arg & set_env(const char * env) {
- help = help + "\n(env: " + env + ")";
- this->env = env;
- return *this;
- }
-
- bool in_example(enum llama_example ex) {
- return examples.find(ex) != examples.end();
- }
-
- bool get_value_from_env(std::string & output) const {
- if (env == nullptr) return false;
- char * value = std::getenv(env);
- if (value) {
- output = value;
- return true;
- }
- return false;
- }
-
- bool has_value_from_env() const {
- return env != nullptr && std::getenv(env);
- }
-
- std::string to_string();
-};
-
-// initialize list of options (arguments) that can be used by the current example
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex);
-// optionally, we can provide "print_usage" to print example usage
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex, std::function<void(int, char **)> print_usage);
-
-// parse input arguments from CLI
-// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
-bool gpt_params_parse (int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options);
-bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options);
-
-// print full usage message; it will be called internally by gpt_params_parse() if "-h" is set
-void gpt_params_print_usage(gpt_params & params, std::vector<llama_arg> & options);
-
std::string gpt_params_get_system_info(const gpt_params & params);
bool parse_cpu_range(const std::string& range, bool(&boolmask)[GGML_MAX_N_THREADS]);
#include "common.h"
+#include <cmath>
+#include <unordered_map>
+
// the ring buffer works similarly to std::deque, but with a fixed capacity
// TODO: deduplicate with llama-impl.h
template<typename T>
}
std::vector<gpt_sampler_type> gpt_sampler_types_from_chars(const std::string & chars) {
- std::unordered_map<char, gpt_sampler_type> sampler_name_map {
+ std::unordered_map<char, gpt_sampler_type> sampler_name_map = {
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TOP_K), GPT_SAMPLER_TYPE_TOP_K },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TFS_Z), GPT_SAMPLER_TYPE_TFS_Z },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TYPICAL_P), GPT_SAMPLER_TYPE_TYPICAL_P },
#include "llama.h"
+#include "common.h"
+
#include <string>
#include <vector>
-enum gpt_sampler_type {
- GPT_SAMPLER_TYPE_NONE = 0,
- GPT_SAMPLER_TYPE_TOP_K = 1,
- GPT_SAMPLER_TYPE_TOP_P = 2,
- GPT_SAMPLER_TYPE_MIN_P = 3,
- GPT_SAMPLER_TYPE_TFS_Z = 4,
- GPT_SAMPLER_TYPE_TYPICAL_P = 5,
- GPT_SAMPLER_TYPE_TEMPERATURE = 6,
-};
-
-// sampling parameters
-struct gpt_sampler_params {
- uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
-
- int32_t n_prev = 64; // number of previous tokens to remember
- int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
- int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
- int32_t top_k = 40; // <= 0 to use vocab size
- float top_p = 0.95f; // 1.0 = disabled
- float min_p = 0.05f; // 0.0 = disabled
- float tfs_z = 1.00f; // 1.0 = disabled
- float typ_p = 1.00f; // typical_p, 1.0 = disabled
- float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
- float dynatemp_range = 0.00f; // 0.0 = disabled
- float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
- int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
- float penalty_repeat = 1.00f; // 1.0 = disabled
- float penalty_freq = 0.00f; // 0.0 = disabled
- float penalty_present = 0.00f; // 0.0 = disabled
- int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
- float mirostat_tau = 5.00f; // target entropy
- float mirostat_eta = 0.10f; // learning rate
- bool penalize_nl = false; // consider newlines as a repeatable token
- bool ignore_eos = false;
-
- std::vector<enum gpt_sampler_type> samplers = {
- GPT_SAMPLER_TYPE_TOP_K,
- GPT_SAMPLER_TYPE_TFS_Z,
- GPT_SAMPLER_TYPE_TYPICAL_P,
- GPT_SAMPLER_TYPE_TOP_P,
- GPT_SAMPLER_TYPE_MIN_P,
- GPT_SAMPLER_TYPE_TEMPERATURE
- };
-
- std::string grammar; // optional BNF-like grammar to constrain sampling
-
- std::vector<llama_logit_bias> logit_bias; // logit biases to apply
-
- // print the parameters into a string
- std::string print() const;
-};
-
// gpt_sampler extends llama_sampler with additional functionality:
//
// - grammar support
+#include "arg.h"
#include "common.h"
#include "llama.h"
int main(int argc, char ** argv) {
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_BENCH, print_usage);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "llama.h"
params.prompt = "Hello my name is";
params.n_predict = 32;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, print_usage);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "llama.h"
#include "ggml.h"
int main(int argc, char ** argv) {
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
return 1;
}
#include <cstdio>
#include <ctime>
+#include <random>
#include <string>
-#include <tuple>
#include <vector>
-#include <algorithm>
-#include <iostream>
-#include <fstream>
#define DEBUG_POS 5
+#include "arg.h"
#include "common.h"
#include "llama.h"
int main(int argc, char ** argv) {
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_EMBEDDING);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "llama.h"
#include "ggml.h"
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "ggml.h"
#include "ggml-alloc.h"
int main(int argc, char ** argv) {
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include <fstream>
std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
gpt_params params;
- auto options = gpt_params_parser_init(params, ex);
+ auto ctx_arg = gpt_params_parser_init(params, ex);
file << "| Argument | Explanation |\n";
file << "| -------- | ----------- |\n";
- for (auto & opt : options) {
+ for (auto & opt : ctx_arg.options) {
file << "| `";
// args
for (const auto & arg : opt.args) {
+#include "arg.h"
#include "common.h"
#include "llama.h"
int main(int argc, char * argv[]) {
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "llama.h"
params.logits_all = true;
params.verbosity = 1;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_IMATRIX, print_usage);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
return 1;
}
+#include "arg.h"
#include "common.h"
-
#include "console.h"
+#include "sampling.h"
#include "llama.h"
#include <cassert>
gpt_params params;
g_params = ¶ms;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_INFILL);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) {
return 1;
}
-#include "ggml.h"
+#include "arg.h"
+#include "base64.hpp"
#include "log.h"
#include "common.h"
+#include "sampling.h"
#include "clip.h"
#include "llava.h"
#include "llama.h"
-
-#include "base64.hpp"
+#include "ggml.h"
#include <cstdio>
#include <cstdlib>
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_LLAVA, print_usage);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
return 1;
}
-#include "ggml.h"
+#include "arg.h"
#include "log.h"
#include "common.h"
+#include "sampling.h"
#include "clip.h"
#include "llava.h"
#include "llama.h"
+#include "ggml.h"
#include <cstdio>
#include <cstdlib>
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, show_additional_info);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, show_additional_info)) {
return 1;
}
+#include "arg.h"
#include "common.h"
+#include "sampling.h"
#include "llama.h"
#include <cstdio>
int main(int argc, char ** argv) {
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
-#include "ggml.h"
-#include "llama.h"
+#include "arg.h"
#include "common.h"
#include "ngram-cache.h"
+#include "ggml.h"
+#include "llama.h"
#include <cstdint>
#include <fstream>
int main(int argc, char ** argv){
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
llama_ngram_cache_save(ngram_cache, params.lookup_cache_static);
+
+ return 0;
}
-#include "ggml.h"
+#include "arg.h"
#include "common.h"
-#include "llama.h"
#include "log.h"
#include "ngram-cache.h"
+#include "llama.h"
+#include "ggml.h"
#include <cmath>
#include <cstdint>
int main(int argc, char ** argv){
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
+#include "arg.h"
#include "ggml.h"
-#include "llama.h"
#include "common.h"
#include "ngram-cache.h"
+#include "sampling.h"
+#include "llama.h"
#include <cstdint>
#include <cstdio>
int main(int argc, char ** argv){
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
+#include "arg.h"
#include "common.h"
-
#include "console.h"
+#include "sampling.h"
#include "llama.h"
#include <cassert>
int main(int argc, char ** argv) {
gpt_params params;
g_params = ¶ms;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_MAIN, print_usage);
-
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) {
return 1;
}
// A basic application simulating a server with multiple clients.
// The clients submit requests to the server and they are processed in parallel.
+#include "arg.h"
#include "common.h"
+#include "sampling.h"
#include "llama.h"
#include <cmath>
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "llama.h"
params.n_keep = 32;
params.i_pos = -1;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_PASSKEY, print_usage);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "llama.h"
+#include <array>
+#include <atomic>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
+#include <fstream>
+#include <mutex>
+#include <random>
#include <sstream>
#include <thread>
-#include <mutex>
-#include <atomic>
#include <vector>
-#include <array>
-#include <fstream>
-#include <sstream>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
params.n_ctx = 512;
params.logits_all = true;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_PERPLEXITY);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "llama.h"
int main(int argc, char ** argv) {
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_RETRIEVAL, print_usage);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "llama.h"
params.prompt = "The quick brown fox";
params.sparams.seed = 1234;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
| `--version` | show version and build info |
| `-v, --verbose` | print verbose information |
| `--verbosity N` | set specific verbosity level (default: 0) |
-| `--verbose-prompt` | print a verbose prompt before generation (default: false) |
-| `--no-display-prompt` | don't print prompt at generation (default: false) |
-| `-s, --seed SEED` | RNG seed (default: -1, use random seed for < 0) |
| `-t, --threads N` | number of threads to use during generation (default: -1)<br/>(env: LLAMA_ARG_THREADS) |
| `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) |
| `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") |
| `-Cr, --cpu-range lo-hi` | range of CPUs for affinity. Complements --cpu-mask |
| `--cpu-strict <0\|1>` | use strict CPU placement (default: 0)<br/> |
+| `--prio N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)<br/> |
| `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50)<br/> |
| `-Cb, --cpu-mask-batch M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) |
| `-Crb, --cpu-range-batch lo-hi` | ranges of CPUs for affinity. Complements --cpu-mask-batch |
| `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) |
+| `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)<br/> |
| `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) |
-| `-lcs, --lookup-cache-static FNAME` | path to static lookup cache to use for lookup decoding (not updated by generation) |
-| `-lcd, --lookup-cache-dynamic FNAME` | path to dynamic lookup cache to use for lookup decoding (updated by generation) |
| `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE) |
| `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)<br/>(env: LLAMA_ARG_N_PREDICT) |
| `-b, --batch-size N` | logical maximum batch size (default: 2048)<br/>(env: LLAMA_ARG_BATCH) |
| `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
| `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
-| `--chunks N` | max number of chunks to process (default: -1, -1 = all) |
| `-fa, --flash-attn` | enable Flash Attention (default: disabled)<br/>(env: LLAMA_ARG_FLASH_ATTN) |
| `-p, --prompt PROMPT` | prompt to start generation with |
| `-f, --file FNAME` | a file containing the prompt (default: none) |
-| `--in-file FNAME` | an input file (repeat to specify multiple files) |
| `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) |
| `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
| `--no-escape` | do not process escape sequences |
| `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
-| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typical_p;top_p;min_p;temperature) |
+| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) |
+| `-s, --seed SEED` | RNG seed (default: -1, use random seed for < 0) |
| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) |
| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
| `--penalize-nl` | penalize newline tokens (default: false) |
| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16) |
| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: -1.0, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
| `-np, --parallel N` | number of parallel sequences to decode (default: 1) |
-| `-ns, --sequences N` | number of sequences to decode (default: 1) |
| `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
| `-nocb, --no-cont-batching` | disable continuous batching<br/>(env: LLAMA_ARG_NO_CONT_BATCHING) |
| `--mlock` | force system to keep model in RAM rather than swapping or compressing |
| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock) |
| `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggerganov/llama.cpp/issues/1437 |
-| `-ngl, --gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
+| `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs |
| `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1 |
| `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0) |
| `--control-vector FNAME` | add a control vector<br/>note: this argument can be repeated to add multiple control vectors |
| `--control-vector-scaled FNAME SCALE` | add a control vector with user defined scaling SCALE<br/>note: this argument can be repeated to add multiple scaled control vectors |
| `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive |
-| `-a, --alias STRING` | set alias for model name (to be used by REST API)<br/>(env: LLAMA_ARG_MODEL) |
+| `-a, --alias STRING` | set alias for model name (to be used by REST API) |
| `-m, --model FNAME` | model path (default: `models/$filename` with filename from `--hf-file` or `--model-url` if set, otherwise models/7B/ggml-model-f16.gguf)<br/>(env: LLAMA_ARG_MODEL) |
| `-mu, --model-url MODEL_URL` | model download url (default: unused)<br/>(env: LLAMA_ARG_MODEL_URL) |
| `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
| `--api-key-file FNAME` | path to file containing API keys (default: none) |
| `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key |
| `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate |
-| `--timeout N` | server read/write timeout in seconds (default: 600) |
+| `-to, --timeout N` | server read/write timeout in seconds (default: 600) |
| `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(env: LLAMA_ARG_THREADS_HTTP) |
| `-spf, --system-prompt-file FNAME` | set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications |
| `--log-format {text, json}` | log output format: json or text (default: json) |
#include "utils.hpp"
+#include "arg.h"
#include "common.h"
+#include "sampling.h"
#include "json-schema-to-grammar.h"
#include "llama.h"
// own arguments required by this example
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_SERVER);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
return 1;
}
+#include "arg.h"
#include "common.h"
#include "llama.h"
params.prompt = "Hello my name is";
params.n_predict = 32;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, print_usage);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
return 1;
}
+#include "arg.h"
#include "common.h"
+#include "sampling.h"
#include "llama.h"
-#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
#include <set>
+#include <random>
#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100
#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
int main(int argc, char ** argv) {
gpt_params params;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_SPECULATIVE);
- if (!gpt_params_parse(argc, argv, params, options)) {
+ if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
return 1;
}
+#include "arg.h"
+#include "common.h"
+
#include <string>
#include <vector>
#include <sstream>
#undef NDEBUG
#include <cassert>
-#include "common.h"
-
int main(void) {
gpt_params params;
printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
for (int ex = 0; ex < LLAMA_EXAMPLE_COUNT; ex++) {
try {
- auto options = gpt_params_parser_init(params, (enum llama_example)ex);
+ auto ctx_arg = gpt_params_parser_init(params, (enum llama_example)ex);
std::unordered_set<std::string> seen_args;
std::unordered_set<std::string> seen_env_vars;
- for (const auto & opt : options) {
+ for (const auto & opt : ctx_arg.options) {
// check for args duplications
for (const auto & arg : opt.args) {
if (seen_args.find(arg) == seen_args.end()) {
};
std::vector<std::string> argv;
- auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
printf("test-arg-parser: test invalid usage\n\n");
+ // missing value
argv = {"binary_name", "-m"};
- assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+ // wrong value (int)
argv = {"binary_name", "-ngl", "hello"};
- assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+ // wrong value (enum)
argv = {"binary_name", "-sm", "hello"};
- assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+
+ // non-existence arg in specific example (--draft cannot be used outside llama-speculative)
+ argv = {"binary_name", "--draft", "123"};
+ assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SERVER));
printf("test-arg-parser: test valid usage\n\n");
argv = {"binary_name", "-m", "model_file.gguf"};
- assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model == "model_file.gguf");
argv = {"binary_name", "-t", "1234"};
- assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.cpuparams.n_threads == 1234);
argv = {"binary_name", "--verbose"};
- assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.verbosity == 1);
argv = {"binary_name", "-m", "abc.gguf", "--predict", "6789", "--batch-size", "9090"};
- assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model == "abc.gguf");
assert(params.n_predict == 6789);
assert(params.n_batch == 9090);
+ // --draft cannot be used outside llama-speculative
+ argv = {"binary_name", "--draft", "123"};
+ assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE));
+ assert(params.n_draft == 123);
+
// skip this part on windows, because setenv is not supported
#ifdef _WIN32
printf("test-arg-parser: skip on windows build\n");
setenv("LLAMA_ARG_THREADS", "blah", true);
argv = {"binary_name"};
- assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
setenv("LLAMA_ARG_THREADS", "1010", true);
argv = {"binary_name"};
- assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model == "blah.gguf");
assert(params.cpuparams.n_threads == 1010);
setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
setenv("LLAMA_ARG_THREADS", "1010", true);
argv = {"binary_name", "-m", "overwritten.gguf"};
- assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+ assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model == "overwritten.gguf");
assert(params.cpuparams.n_threads == 1010);
#endif // _WIN32
overview / pkg / ggml / sources / llama.cpp / commitdiff