EngineX-Mthreads/enginex-mthreads-llama.cpp
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xiezhongtao
2026-01-16 11:16:14 +08:00
parent f4ae4cc7da
commit 6ee41dd9e3
380 changed files with 18435 additions and 38806 deletions
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456
common/arg.cpp
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@@ -2,11 +2,10 @@
#include "chat.h"
#include "common.h"
#include "download.h"
#include "json-schema-to-grammar.h"
#include "log.h"
#include "sampling.h"
#include "preset.h"
#include "download.h"
// fix problem with std::min and std::max
#if defined(_WIN32)
@@ -48,8 +47,6 @@
#define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
extern const char * LICENSES[];
using json = nlohmann::ordered_json;
using namespace common_arg_utils;
@@ -271,55 +268,6 @@ static void parse_tensor_buffer_overrides(const std::string & value, std::vector
}
}
static std::string clean_file_name(const std::string & fname) {
std::string clean_fname = fname;
string_replace_all(clean_fname, "\\", "_");
string_replace_all(clean_fname, "/", "_");
return clean_fname;
}
static bool common_params_handle_remote_preset(common_params & params, llama_example ex) {
GGML_ASSERT(!params.model.hf_repo.empty());
// the returned hf_repo is without tag
auto [hf_repo, hf_tag] = common_download_split_repo_tag(params.model.hf_repo);
// "latest" tag (default if not specified) is translated to "default" preset
if (hf_tag == "latest") {
hf_tag = "default";
}
const bool offline = params.offline;
std::string model_endpoint = get_model_endpoint();
auto preset_url = model_endpoint + hf_repo + "/resolve/main/preset.ini";
// prepare local path for caching
auto preset_fname = clean_file_name(hf_repo + "_preset.ini");
auto preset_path = fs_get_cache_file(preset_fname);
const int status = common_download_file_single(preset_url, preset_path, params.hf_token, offline);
const bool has_preset = status >= 200 && status < 400;
// remote preset is optional, so we don't error out if not found
if (has_preset) {
LOG_INF("applying remote preset from %s\n", preset_url.c_str());
common_preset_context ctx(ex, /* only_remote_allowed */ true);
common_preset global;
auto remote_presets = ctx.load_from_ini(preset_path, global);
remote_presets = ctx.cascade(global, remote_presets);
if (remote_presets.find(hf_tag) != remote_presets.end()) {
common_preset preset = remote_presets.at(hf_tag);
LOG_INF("\n%s", preset.to_ini().c_str()); // to_ini already added trailing newline
preset.apply_to_params(params);
} else {
throw std::runtime_error("Remote preset.ini does not contain [" + std::string(hf_tag) + "] section");
}
} else {
LOG_INF("%s", "no remote preset found, skipping\n");
}
return has_preset;
}
struct handle_model_result {
bool found_mmproj = false;
common_params_model mmproj;
@@ -341,7 +289,7 @@ static handle_model_result common_params_handle_model(
if (model.path.empty()) {
auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token, offline);
if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) {
exit(1); // error message already printed
exit(1); // built without CURL, error message already printed
}
model.name = model.hf_repo; // repo name with tag
model.hf_repo = auto_detected.repo; // repo name without tag
@@ -361,7 +309,9 @@ static handle_model_result common_params_handle_model(
// make sure model path is present (for caching purposes)
if (model.path.empty()) {
// this is to avoid different repo having same file name, or same file name in different subdirs
std::string filename = clean_file_name(model.hf_repo + "_" + model.hf_file);
std::string filename = model.hf_repo + "_" + model.hf_file;
// to make sure we don't have any slashes in the filename
string_replace_all(filename, "/", "_");
model.path = fs_get_cache_file(filename);
}
@@ -475,87 +425,61 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
}
};
auto parse_cli_args = [&]() {
std::set<std::string> seen_args;
std::set<std::string> seen_args;
for (int i = 1; i < argc; i++) {
const std::string arg_prefix = "--";
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(string_format("error: invalid argument: %s", arg.c_str()));
}
if (!seen_args.insert(arg).second) {
LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
}
auto & tmp = arg_to_options[arg];
auto opt = *tmp.first;
bool is_positive = tmp.second;
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;
}
if (opt.handler_bool) {
opt.handler_bool(params, is_positive);
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(string_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(), opt.to_string().c_str()));
}
std::string arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
};
// parse the first time to get -hf option (used for remote preset)
parse_cli_args();
// maybe handle remote preset
if (!params.model.hf_repo.empty()) {
std::string cli_hf_repo = params.model.hf_repo;
bool has_preset = common_params_handle_remote_preset(params, ctx_arg.ex);
// special case: if hf_repo explicitly set by preset, we need to preserve it (ignore CLI value)
// this is useful when we have one HF repo pointing to other HF repos (one model - multiple GGUFs)
std::string preset_hf_repo = params.model.hf_repo;
bool preset_has_hf_repo = preset_hf_repo != cli_hf_repo;
if (has_preset) {
// re-parse CLI args to override preset values
parse_cli_args();
if (arg_to_options.find(arg) == arg_to_options.end()) {
throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
}
if (!seen_args.insert(arg).second) {
LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
}
auto & tmp = arg_to_options[arg];
auto opt = *tmp.first;
bool is_positive = tmp.second;
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;
}
if (opt.handler_bool) {
opt.handler_bool(params, is_positive);
continue;
}
// preserve hf_repo from preset if needed
if (preset_has_hf_repo) {
params.model.hf_repo = preset_hf_repo;
// 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(string_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(), opt.to_string().c_str()));
}
}
@@ -755,6 +679,7 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
"llama-quantize",
"llama-qwen2vl-cli",
"llama-retrieval",
"llama-run",
"llama-save-load-state",
"llama-server",
"llama-simple",
@@ -929,54 +854,6 @@ bool common_arg_utils::is_autoy(const std::string & value) {
return value == "auto" || value == "-1";
}
// Simple CSV parser that handles quoted fields and escaped quotes
// example:
// input: value1,"value, with, commas","value with ""escaped"" quotes",value4
// output: [value1] [value, with, commas] [value with "escaped" quotes] [value4]
static std::vector<std::string> parse_csv_row(const std::string& input) {
std::vector<std::string> fields;
std::string field;
bool in_quotes = false;
for (size_t i = 0; i < input.length(); ++i) {
char ch = input[i];
if (ch == '"') {
if (!in_quotes) {
// start of quoted field (only valid if at beginning of field)
if (!field.empty()) {
// quote appeared in middle of unquoted field, treat as literal
field += '"';
} else {
in_quotes = true; // start
}
} else {
if (i + 1 < input.length() && input[i + 1] == '"') {
// escaped quote: ""
field += '"';
++i; // skip the next quote
} else {
in_quotes = false; // end
}
}
} else if (ch == ',') {
if (in_quotes) {
field += ',';
} else {
fields.push_back(std::move(field));
field.clear();
}
} else {
field += ch;
}
}
// Add the last field
fields.push_back(std::move(field));
return fields;
}
common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
// per-example default params
// we define here to make sure it's included in llama-gen-docs
@@ -1041,16 +918,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
exit(0);
}
));
add_opt(common_arg(
{"--license"},
"show source code license and dependencies",
[](common_params &) {
for (int i = 0; LICENSES[i]; ++i) {
printf("%s\n", LICENSES[i]);
}
exit(0);
}
));
add_opt(common_arg(
{"-cl", "--cache-list"},
"show list of models in cache",
@@ -1295,7 +1162,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params) {
params.kv_unified = true;
}
).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED}));
).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(common_arg(
{"--context-shift"},
{"--no-context-shift"},
@@ -1383,7 +1250,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--in-file"}, "FNAME",
"an input file (use comma-separated values to specify multiple files)",
[](common_params & params, const std::string & value) {
for (const auto & item : parse_csv_row(value)) {
for (const auto & item : string_split<std::string>(value, ',')) {
std::ifstream file(item);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
@@ -1530,7 +1397,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, bool value) {
params.warmup = value;
}
).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_DEBUG}));
).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(common_arg(
{"--spm-infill"},
string_format(
@@ -1729,26 +1596,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
).set_sparam());
add_opt(common_arg(
{"--adaptive-target"}, "N",
string_format("adaptive-p: select tokens near this probability (valid range 0.0 "
"to 1.0; negative = disabled) (default: %.2f)\n"
"[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927)",
(double)params.sampling.adaptive_target),
[](common_params & params, const std::string & value) {
params.sampling.adaptive_target = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--adaptive-decay"}, "N",
string_format("adaptive-p: decay rate for target adaptation over time. lower values "
"are more reactive, higher values are more stable.\n"
"(valid range 0.0 to 0.99) (default: %.2f)",
(double)params.sampling.adaptive_decay),
[](common_params & params, const std::string & value) {
params.sampling.adaptive_decay = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--dynatemp-range"}, "N",
string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sampling.dynatemp_range),
@@ -1848,13 +1695,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.sampling.grammar = json_schema_to_grammar(json::parse(schema));
}
).set_sparam());
add_opt(common_arg(
{"-bs", "--backend-sampling"},
"enable backend sampling (experimental) (default: disabled)",
[](common_params & params) {
params.sampling.backend_sampling = true;
}
).set_sparam().set_env("LLAMA_ARG_BACKEND_SAMPLING"));
add_opt(common_arg(
{"--pooling"}, "{none,mean,cls,last,rank}",
"pooling type for embeddings, use model default if unspecified",
@@ -1866,7 +1706,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; }
else { throw std::invalid_argument("invalid value"); }
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_DEBUG}).set_env("LLAMA_ARG_POOLING"));
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_POOLING"));
add_opt(common_arg(
{"--attention"}, "{causal,non-causal}",
"attention type for embeddings, use model default if unspecified",
@@ -2155,7 +1995,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--image", "--audio"}, "FILE",
"path to an image or audio file. use with multimodal models, use comma-separated values for multiple files\n",
[](common_params & params, const std::string & value) {
for (const auto & item : parse_csv_row(value)) {
for (const auto & item : string_split<std::string>(value, ',')) {
params.image.emplace_back(item);
}
}
@@ -2177,7 +2017,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
if (llama_supports_rpc()) {
add_opt(common_arg(
{"--rpc"}, "SERVERS",
"comma separated list of RPC servers (host:port)",
"comma separated list of RPC servers",
[](common_params & params, const std::string & value) {
add_rpc_devices(value);
GGML_UNUSED(params);
@@ -2194,22 +2034,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
add_opt(common_arg(
{"--mmap"},
{"--no-mmap"},
string_format("whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"),
string_format("whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"),
[](common_params & params, bool value) {
params.use_mmap = value;
if (value) {
params.use_direct_io = false; // disable direct io when mmap is explicitly enabled
}
}
).set_env("LLAMA_ARG_MMAP"));
add_opt(common_arg(
{"-dio", "--direct-io"},
{"-ndio", "--no-direct-io"},
string_format("use DirectIO if available. Takes precedence over --mmap (default: %s)", params.use_direct_io ? "enabled" : "disabled"),
[](common_params & params, bool value) {
params.use_direct_io = value;
}
).set_env("LLAMA_ARG_DIO"));
add_opt(common_arg(
{"--numa"}, "TYPE",
"attempt optimizations that help on some NUMA systems\n"
@@ -2258,7 +2087,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
"override tensor buffer type", [](common_params & params, const std::string & value) {
parse_tensor_buffer_overrides(value, params.tensor_buft_overrides);
}
).set_env("LLAMA_ARG_OVERRIDE_TENSOR"));
));
add_opt(common_arg(
{"-otd", "--override-tensor-draft"}, "<tensor name pattern>=<buffer type>,...",
"override tensor buffer type for draft model", [](common_params & params, const std::string & value) {
@@ -2308,18 +2137,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
GGML_ASSERT(params.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
add_opt(common_arg(
{"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
string_format("max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: %s)", params.n_gpu_layers == -1 ? "auto" : "all"),
[](common_params & params, const std::string & value) {
if (value == "auto") {
params.n_gpu_layers = -1;
} else if (value == "all") {
params.n_gpu_layers = -2;
} else {
params.n_gpu_layers = std::stoi(value);
}
string_format("max. number of layers to store in VRAM (default: %d)", params.n_gpu_layers),
[](common_params & params, int value) {
params.n_gpu_layers = value;
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: no usable GPU found, --gpu-layers option will be ignored\n");
fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n");
@@ -2361,7 +2183,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
std::vector<std::string> split_arg{ it, {} };
if (split_arg.size() >= llama_max_devices()) {
throw std::invalid_argument(
string_format("got %zu input configs, but system only has %zu devices", split_arg.size(), llama_max_devices())
string_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) {
@@ -2401,28 +2223,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_env("LLAMA_ARG_FIT"));
add_opt(common_arg(
{ "-fitt", "--fit-target" }, "MiB0,MiB1,MiB2,...",
string_format("target margin per device for --fit, comma-separated list of values, "
"single value is broadcast across all devices, default: %zu", params.fit_params_target[0]/(1024*1024)),
[](common_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(
string_format("got %zu input configs, but system only has %zu devices", split_arg.size(), llama_max_devices())
);
}
if (split_arg.size() == 1) {
std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoul(split_arg[0]) * 1024*1024);
return;
}
for (size_t i = 0; i < split_arg.size(); i++) {
params.fit_params_target[i] = std::stoul(split_arg[i]) * 1024*1024;
}
{ "-fitt", "--fit-target" }, "MiB",
string_format("target margin per device for --fit option, default: %zu", params.fit_params_target/(1024*1024)),
[](common_params & params, int value) {
params.fit_params_target = value * size_t(1024*1024);
}
).set_env("LLAMA_ARG_FIT_TARGET"));
add_opt(common_arg(
@@ -2441,12 +2245,37 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
));
add_opt(common_arg(
{"--override-kv"}, "KEY=TYPE:VALUE,...",
"advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated values.\n"
"advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.\n"
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false",
[](common_params & params, const std::string & value) {
for (const auto & item : parse_csv_row(value)) {
if (!string_parse_kv_override(item.c_str(), params.kv_overrides)) {
throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", item.c_str()));
std::vector<std::string> kv_overrides;
std::string current;
bool escaping = false;
for (const char c : value) {
if (escaping) {
current.push_back(c);
escaping = false;
} else if (c == '\\') {
escaping = true;
} else if (c == ',') {
kv_overrides.push_back(current);
current.clear();
} else {
current.push_back(c);
}
}
if (escaping) {
current.push_back('\\');
}
kv_overrides.push_back(current);
for (const auto & kv_override : kv_overrides) {
if (!string_parse_kv_override(kv_override.c_str(), params.kv_overrides)) {
throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", kv_override.c_str()));
}
}
}
@@ -2463,7 +2292,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--lora"}, "FNAME",
"path to LoRA adapter (use comma-separated values to load multiple adapters)",
[](common_params & params, const std::string & value) {
for (const auto & item : parse_csv_row(value)) {
for (const auto & item : string_split<std::string>(value, ',')) {
params.lora_adapters.push_back({ item, 1.0, "", "", nullptr });
}
}
@@ -2474,7 +2303,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
"path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)\n"
"note: use comma-separated values",
[](common_params & params, const std::string & value) {
for (const auto & item : parse_csv_row(value)) {
for (const auto & item : string_split<std::string>(value, ',')) {
auto parts = string_split<std::string>(item, ':');
if (parts.size() != 2) {
throw std::invalid_argument("lora-scaled format: FNAME:SCALE");
@@ -2488,7 +2317,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--control-vector"}, "FNAME",
"add a control vector\nnote: use comma-separated values to add multiple control vectors",
[](common_params & params, const std::string & value) {
for (const auto & item : parse_csv_row(value)) {
for (const auto & item : string_split<std::string>(value, ',')) {
params.control_vectors.push_back({ 1.0f, item, });
}
}
@@ -2498,7 +2327,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
"add a control vector with user defined scaling SCALE\n"
"note: use comma-separated values (format: FNAME:SCALE,...)",
[](common_params & params, const std::string & value) {
for (const auto & item : parse_csv_row(value)) {
for (const auto & item : string_split<std::string>(value, ',')) {
auto parts = string_split<std::string>(item, ':');
if (parts.size() != 2) {
throw std::invalid_argument("control-vector-scaled format: FNAME:SCALE");
@@ -2596,7 +2425,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--context-file"}, "FNAME",
"file to load context from (use comma-separated values to specify multiple files)",
[](common_params & params, const std::string & value) {
for (const auto & item : parse_csv_row(value)) {
for (const auto & item : string_split<std::string>(value, ',')) {
std::ifstream file(item, std::ios::binary);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
@@ -2743,7 +2572,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, int value) {
params.embd_normalize = value;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_DEBUG}));
).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
add_opt(common_arg(
{"--embd-output-format"}, "FORMAT",
"empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix, \"raw\" = plain whitespace-delimited output (one embedding per line)",
@@ -2821,7 +2650,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params) {
params.embedding = true;
}
).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_DEBUG}).set_env("LLAMA_ARG_EMBEDDINGS"));
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
add_opt(common_arg(
{"--rerank", "--reranking"},
string_format("enable reranking endpoint on server (default: %s)", "disabled"),
@@ -2832,13 +2661,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING"));
add_opt(common_arg(
{"--api-key"}, "KEY",
"API key to use for authentication, multiple keys can be provided as a comma-separated list (default: none)",
"API key to use for authentication (default: none)",
[](common_params & params, const std::string & value) {
for (const auto & key : parse_csv_row(value)) {
if (!key.empty()) {
params.api_keys.push_back(key);
}
}
params.api_keys.push_back(value);
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
add_opt(common_arg(
@@ -2852,7 +2677,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
std::string key;
while (std::getline(key_file, key)) {
if (!key.empty()) {
params.api_keys.push_back(key);
params.api_keys.push_back(key);
}
}
key_file.close();
@@ -2874,7 +2699,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE"));
add_opt(common_arg(
{"--chat-template-kwargs"}, "STRING",
"sets additional params for the json template parser, must be a valid json object string, e.g. '{\"key1\":\"value1\",\"key2\":\"value2\"}'",
string_format("sets additional params for the json template parser"),
[](common_params & params, const std::string & value) {
auto parsed = json::parse(value);
for (const auto & item : parsed.items()) {
@@ -2897,18 +2722,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.n_threads_http = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
add_opt(common_arg(
{"--cache-prompt"},
{"--no-cache-prompt"},
string_format("whether to enable prompt caching (default: %s)", params.cache_prompt ? "enabled" : "disabled"),
[](common_params & params, bool value) {
params.cache_prompt = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_PROMPT"));
add_opt(common_arg(
{"--cache-reuse"}, "N",
string_format(
"min chunk size to attempt reusing from the cache via KV shifting, requires prompt caching to be enabled (default: %d)\n"
"min chunk size to attempt reusing from the cache via KV shifting (default: %d)\n"
"[(card)](https://ggml.ai/f0.png)", params.n_cache_reuse
),
[](common_params & params, int value) {
@@ -3358,19 +3175,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.speculative.devices = parse_device_list(value);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
GGML_ASSERT(params.speculative.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
add_opt(common_arg(
{"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
string_format("max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: %s)",
params.speculative.n_gpu_layers == -1 ? "auto" : "all"),
[](common_params & params, const std::string & value) {
if (value == "auto") {
params.speculative.n_gpu_layers = -1;
} else if (value == "all") {
params.speculative.n_gpu_layers = -2;
} else {
params.speculative.n_gpu_layers = std::stoi(value);
}
"number of layers to store in VRAM for the draft model",
[](common_params & params, int value) {
params.speculative.n_gpu_layers = value;
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n");
@@ -3520,27 +3329,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
).set_examples({ LLAMA_EXAMPLE_FINETUNE }));
add_opt(common_arg(
{"--save-logits"},
string_format("save final logits to files for verification (default: %s)", params.save_logits ? "true" : "false"),
[](common_params & params) {
params.save_logits = true;
}
).set_examples({LLAMA_EXAMPLE_DEBUG}));
add_opt(common_arg(
{"--logits-output-dir"}, "PATH",
string_format("directory for saving logits output files (default: %s)", params.logits_output_dir.c_str()),
[](common_params & params, const std::string & value) {
params.logits_output_dir = value;
}
).set_examples({LLAMA_EXAMPLE_DEBUG}));
add_opt(common_arg(
{"--tensor-filter"}, "REGEX",
"filter tensor names for debug output (regex pattern, can be specified multiple times)",
[](common_params & params, const std::string & value) {
params.tensor_filter.push_back(value);
}
).set_examples({LLAMA_EXAMPLE_DEBUG}));
// presets
add_opt(common_arg(
@@ -3730,15 +3518,15 @@ void common_params_add_preset_options(std::vector<common_arg> & args) {
[](common_params &, const std::string &) { /* unused */ }
).set_env(COMMON_ARG_PRESET_LOAD_ON_STARTUP).set_preset_only());
args.push_back(common_arg(
{"stop-timeout"}, "SECONDS",
"in server router mode, force-kill model instance after this many seconds of graceful shutdown",
[](common_params &, int) { /* unused */ }
).set_env(COMMON_ARG_PRESET_STOP_TIMEOUT).set_preset_only());
// args.push_back(common_arg(
// {"pin"},
// "in server router mode, do not unload this model if models_max is exceeded",
// [](common_params &) { /* unused */ }
// ).set_preset_only());
// args.push_back(common_arg(
// {"unload-idle-seconds"}, "SECONDS",
// "in server router mode, unload models idle for more than this many seconds",
// [](common_params &, int) { /* unused */ }
// ).set_preset_only());
}