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llama : improve infill support and special token detection (#9798)

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* llama : improve infill support

ggml-ci

* llama : add more FIM token strings

ggml-ci

* server : update prompt on slot restore (#9800)

* gguf : deprecate old FIM token KVs
This commit is contained in:
Georgi Gerganov 2024-10-12 08:21:51 +03:00 committed by GitHub
parent 943d20b411
commit 11ac9800af
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GPG Key ID: B5690EEEBB952194
12 changed files with 601 additions and 427 deletions
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248
common/arg.cpp
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@ -119,32 +119,6 @@ std::string common_arg::to_string() {
// utils // 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 common_params_handle_model_default(common_params & params) { static void common_params_handle_model_default(common_params & params) {
if (!params.hf_repo.empty()) { if (!params.hf_repo.empty()) {
// short-hand to avoid specifying --hf-file -> default it to --model // short-hand to avoid specifying --hf-file -> default it to --model
@ -199,7 +173,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
continue; continue;
} }
} catch (std::exception & e) { } catch (std::exception & e) {
throw std::invalid_argument(format( throw std::invalid_argument(string_format(
"error while handling environment variable \"%s\": %s\n\n", opt.env, e.what())); "error while handling environment variable \"%s\": %s\n\n", opt.env, e.what()));
} }
} }
@ -220,7 +194,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
std::replace(arg.begin(), arg.end(), '_', '-'); std::replace(arg.begin(), arg.end(), '_', '-');
} }
if (arg_to_options.find(arg) == arg_to_options.end()) { if (arg_to_options.find(arg) == arg_to_options.end()) {
throw std::invalid_argument(format("error: invalid argument: %s", arg.c_str())); throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
} }
auto opt = *arg_to_options[arg]; auto opt = *arg_to_options[arg];
if (opt.has_value_from_env()) { if (opt.has_value_from_env()) {
@ -252,7 +226,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
continue; continue;
} }
} catch (std::exception & e) { } catch (std::exception & e) {
throw std::invalid_argument(format( throw std::invalid_argument(string_format(
"error while handling argument \"%s\": %s\n\n" "error while handling argument \"%s\": %s\n\n"
"usage:\n%s\n\nto show complete usage, run with -h", "usage:\n%s\n\nto show complete usage, run with -h",
arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str())); arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str()));
@ -391,28 +365,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
)); ));
add_opt(common_arg( add_opt(common_arg(
{"--verbose-prompt"}, {"--verbose-prompt"},
format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"), string_format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.verbose_prompt = true; params.verbose_prompt = true;
} }
)); ));
add_opt(common_arg( add_opt(common_arg(
{"--no-display-prompt"}, {"--no-display-prompt"},
format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"), string_format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.display_prompt = false; params.display_prompt = false;
} }
).set_examples({LLAMA_EXAMPLE_MAIN})); ).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(common_arg( add_opt(common_arg(
{"-co", "--color"}, {"-co", "--color"},
format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"), string_format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.use_color = true; params.use_color = true;
} }
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
add_opt(common_arg( add_opt(common_arg(
{"-t", "--threads"}, "N", {"-t", "--threads"}, "N",
format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads), string_format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
[](common_params & params, int value) { [](common_params & params, int value) {
params.cpuparams.n_threads = value; params.cpuparams.n_threads = value;
if (params.cpuparams.n_threads <= 0) { if (params.cpuparams.n_threads <= 0) {
@ -472,14 +446,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
)); ));
add_opt(common_arg( add_opt(common_arg(
{"--cpu-strict"}, "<0|1>", {"--cpu-strict"}, "<0|1>",
format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu), string_format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.cpuparams.strict_cpu = std::stoul(value); params.cpuparams.strict_cpu = std::stoul(value);
} }
)); ));
add_opt(common_arg( add_opt(common_arg(
{"--prio"}, "N", {"--prio"}, "N",
format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority), string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority),
[](common_params & params, int prio) { [](common_params & params, int prio) {
if (prio < 0 || prio > 3) { if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value"); throw std::invalid_argument("invalid value");
@ -489,7 +463,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
)); ));
add_opt(common_arg( add_opt(common_arg(
{"--poll"}, "<0...100>", {"--poll"}, "<0...100>",
format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll), string_format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.cpuparams.poll = std::stoul(value); params.cpuparams.poll = std::stoul(value);
} }
@ -523,7 +497,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
)); ));
add_opt(common_arg( add_opt(common_arg(
{"--prio-batch"}, "N", {"--prio-batch"}, "N",
format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority), string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority),
[](common_params & params, int prio) { [](common_params & params, int prio) {
if (prio < 0 || prio > 3) { if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value"); throw std::invalid_argument("invalid value");
@ -567,7 +541,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg( add_opt(common_arg(
{"--prio-draft"}, "N", {"--prio-draft"}, "N",
format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams.priority), string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams.priority),
[](common_params & params, int prio) { [](common_params & params, int prio) {
if (prio < 0 || prio > 3) { if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value"); throw std::invalid_argument("invalid value");
@ -611,7 +585,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg( add_opt(common_arg(
{"--prio-batch-draft"}, "N", {"--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), string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams_batch.priority),
[](common_params & params, int prio) { [](common_params & params, int prio) {
if (prio < 0 || prio > 3) { if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value"); throw std::invalid_argument("invalid value");
@ -628,14 +602,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg( add_opt(common_arg(
{"--draft"}, "N", {"--draft"}, "N",
format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft), string_format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_draft = value; params.n_draft = value;
} }
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
add_opt(common_arg( add_opt(common_arg(
{"-ps", "--p-split"}, "N", {"-ps", "--p-split"}, "N",
format("speculative decoding split probability (default: %.1f)", (double)params.p_split), string_format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.p_split = std::stof(value); params.p_split = std::stof(value);
} }
@ -656,56 +630,56 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_LOOKUP})); ).set_examples({LLAMA_EXAMPLE_LOOKUP}));
add_opt(common_arg( add_opt(common_arg(
{"-c", "--ctx-size"}, "N", {"-c", "--ctx-size"}, "N",
format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx), string_format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_ctx = value; params.n_ctx = value;
} }
).set_env("LLAMA_ARG_CTX_SIZE")); ).set_env("LLAMA_ARG_CTX_SIZE"));
add_opt(common_arg( add_opt(common_arg(
{"-n", "--predict", "--n-predict"}, "N", {"-n", "--predict", "--n-predict"}, "N",
format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict), string_format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_predict = value; params.n_predict = value;
} }
).set_env("LLAMA_ARG_N_PREDICT")); ).set_env("LLAMA_ARG_N_PREDICT"));
add_opt(common_arg( add_opt(common_arg(
{"-b", "--batch-size"}, "N", {"-b", "--batch-size"}, "N",
format("logical maximum batch size (default: %d)", params.n_batch), string_format("logical maximum batch size (default: %d)", params.n_batch),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_batch = value; params.n_batch = value;
} }
).set_env("LLAMA_ARG_BATCH")); ).set_env("LLAMA_ARG_BATCH"));
add_opt(common_arg( add_opt(common_arg(
{"-ub", "--ubatch-size"}, "N", {"-ub", "--ubatch-size"}, "N",
format("physical maximum batch size (default: %d)", params.n_ubatch), string_format("physical maximum batch size (default: %d)", params.n_ubatch),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_ubatch = value; params.n_ubatch = value;
} }
).set_env("LLAMA_ARG_UBATCH")); ).set_env("LLAMA_ARG_UBATCH"));
add_opt(common_arg( add_opt(common_arg(
{"--keep"}, "N", {"--keep"}, "N",
format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep), string_format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_keep = value; params.n_keep = value;
} }
)); ));
add_opt(common_arg( add_opt(common_arg(
{"--no-context-shift"}, {"--no-context-shift"},
format("disables context shift on inifinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"), string_format("disables context shift on inifinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
[](common_params & params) { [](common_params & params) {
params.ctx_shift = false; params.ctx_shift = false;
} }
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
add_opt(common_arg( add_opt(common_arg(
{"--chunks"}, "N", {"--chunks"}, "N",
format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks), string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_chunks = value; params.n_chunks = value;
} }
).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL})); ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL}));
add_opt(common_arg( add_opt(common_arg(
{"-fa", "--flash-attn"}, {"-fa", "--flash-attn"},
format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"), string_format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
[](common_params & params) { [](common_params & params) {
params.flash_attn = true; params.flash_attn = true;
} }
@ -721,7 +695,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
)); ));
add_opt(common_arg( add_opt(common_arg(
{"--no-perf"}, {"--no-perf"},
format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.no_perf = true; params.no_perf = true;
params.sparams.no_perf = true; params.sparams.no_perf = true;
@ -733,7 +707,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
std::ifstream file(value); std::ifstream file(value);
if (!file) { if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str())); throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
} }
// store the external file name in params // store the external file name in params
params.prompt_file = value; params.prompt_file = value;
@ -749,7 +723,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
std::ifstream file(value); std::ifstream file(value);
if (!file) { if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str())); throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
} }
params.in_files.push_back(value); params.in_files.push_back(value);
} }
@ -760,7 +734,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
std::ifstream file(value, std::ios::binary); std::ifstream file(value, std::ios::binary);
if (!file) { if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str())); throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
} }
// store the external file name in params // store the external file name in params
params.prompt_file = value; params.prompt_file = value;
@ -772,7 +746,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
)); ));
add_opt(common_arg( add_opt(common_arg(
{"-e", "--escape"}, {"-e", "--escape"},
format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"), string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.escape = true; params.escape = true;
} }
@ -786,7 +760,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
)); ));
add_opt(common_arg( add_opt(common_arg(
{"-ptc", "--print-token-count"}, "N", {"-ptc", "--print-token-count"}, "N",
format("print token count every N tokens (default: %d)", params.n_print), string_format("print token count every N tokens (default: %d)", params.n_print),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_print = value; params.n_print = value;
} }
@ -821,14 +795,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_MAIN})); ).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(common_arg( add_opt(common_arg(
{"-sp", "--special"}, {"-sp", "--special"},
format("special tokens output enabled (default: %s)", params.special ? "true" : "false"), string_format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.special = true; params.special = true;
} }
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER})); ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg( add_opt(common_arg(
{"-cnv", "--conversation"}, {"-cnv", "--conversation"},
format( string_format(
"run in conversation mode:\n" "run in conversation mode:\n"
"- does not print special tokens and suffix/prefix\n" "- does not print special tokens and suffix/prefix\n"
"- interactive mode is also enabled\n" "- interactive mode is also enabled\n"
@ -841,14 +815,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_MAIN})); ).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(common_arg( add_opt(common_arg(
{"-i", "--interactive"}, {"-i", "--interactive"},
format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"), string_format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.interactive = true; params.interactive = true;
} }
).set_examples({LLAMA_EXAMPLE_MAIN})); ).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(common_arg( add_opt(common_arg(
{"-if", "--interactive-first"}, {"-if", "--interactive-first"},
format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"), string_format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.interactive_first = true; params.interactive_first = true;
} }
@ -893,7 +867,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_MAIN})); ).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(common_arg( add_opt(common_arg(
{"--spm-infill"}, {"--spm-infill"},
format( string_format(
"use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)",
params.spm_infill ? "enabled" : "disabled" params.spm_infill ? "enabled" : "disabled"
), ),
@ -903,7 +877,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL})); ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL}));
add_opt(common_arg( add_opt(common_arg(
{"--samplers"}, "SAMPLERS", {"--samplers"}, "SAMPLERS",
format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()), string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
const auto sampler_names = string_split(value, ';'); const auto sampler_names = string_split(value, ';');
params.sparams.samplers = common_sampler_types_from_names(sampler_names, true); params.sparams.samplers = common_sampler_types_from_names(sampler_names, true);
@ -911,14 +885,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"-s", "--seed"}, "SEED", {"-s", "--seed"}, "SEED",
format("RNG seed (default: %d, use random seed for %d)", params.sparams.seed, LLAMA_DEFAULT_SEED), string_format("RNG seed (default: %d, use random seed for %d)", params.sparams.seed, LLAMA_DEFAULT_SEED),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.seed = std::stoul(value); params.sparams.seed = std::stoul(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--sampling-seq"}, "SEQUENCE", {"--sampling-seq"}, "SEQUENCE",
format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()), string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.samplers = common_sampler_types_from_chars(value); params.sparams.samplers = common_sampler_types_from_chars(value);
} }
@ -932,14 +906,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--penalize-nl"}, {"--penalize-nl"},
format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"), string_format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.sparams.penalize_nl = true; params.sparams.penalize_nl = true;
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--temp"}, "N", {"--temp"}, "N",
format("temperature (default: %.1f)", (double)params.sparams.temp), string_format("temperature (default: %.1f)", (double)params.sparams.temp),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.temp = std::stof(value); params.sparams.temp = std::stof(value);
params.sparams.temp = std::max(params.sparams.temp, 0.0f); params.sparams.temp = std::max(params.sparams.temp, 0.0f);
@ -947,42 +921,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--top-k"}, "N", {"--top-k"}, "N",
format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k), string_format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
[](common_params & params, int value) { [](common_params & params, int value) {
params.sparams.top_k = value; params.sparams.top_k = value;
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--top-p"}, "N", {"--top-p"}, "N",
format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p), string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.top_p = std::stof(value); params.sparams.top_p = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--min-p"}, "N", {"--min-p"}, "N",
format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p), string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.min_p = std::stof(value); params.sparams.min_p = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--tfs"}, "N", {"--tfs"}, "N",
format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z), string_format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.tfs_z = std::stof(value); params.sparams.tfs_z = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--typical"}, "N", {"--typical"}, "N",
format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p), string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.typ_p = std::stof(value); params.sparams.typ_p = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--repeat-last-n"}, "N", {"--repeat-last-n"}, "N",
format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n), string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n),
[](common_params & params, int value) { [](common_params & params, int value) {
params.sparams.penalty_last_n = value; params.sparams.penalty_last_n = value;
params.sparams.n_prev = std::max(params.sparams.n_prev, params.sparams.penalty_last_n); params.sparams.n_prev = std::max(params.sparams.n_prev, params.sparams.penalty_last_n);
@ -990,42 +964,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--repeat-penalty"}, "N", {"--repeat-penalty"}, "N",
format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat), string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.penalty_repeat = std::stof(value); params.sparams.penalty_repeat = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--presence-penalty"}, "N", {"--presence-penalty"}, "N",
format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present), string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.penalty_present = std::stof(value); params.sparams.penalty_present = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--frequency-penalty"}, "N", {"--frequency-penalty"}, "N",
format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq), string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.penalty_freq = std::stof(value); params.sparams.penalty_freq = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--dynatemp-range"}, "N", {"--dynatemp-range"}, "N",
format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range), string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.dynatemp_range = std::stof(value); params.sparams.dynatemp_range = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--dynatemp-exp"}, "N", {"--dynatemp-exp"}, "N",
format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent), string_format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.dynatemp_exponent = std::stof(value); params.sparams.dynatemp_exponent = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--mirostat"}, "N", {"--mirostat"}, "N",
format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n" string_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), "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat),
[](common_params & params, int value) { [](common_params & params, int value) {
params.sparams.mirostat = value; params.sparams.mirostat = value;
@ -1033,14 +1007,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--mirostat-lr"}, "N", {"--mirostat-lr"}, "N",
format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta), string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.mirostat_eta = std::stof(value); params.sparams.mirostat_eta = std::stof(value);
} }
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--mirostat-ent"}, "N", {"--mirostat-ent"}, "N",
format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau), string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.mirostat_tau = std::stof(value); params.sparams.mirostat_tau = std::stof(value);
} }
@ -1069,7 +1043,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_sparam()); ).set_sparam());
add_opt(common_arg( add_opt(common_arg(
{"--grammar"}, "GRAMMAR", {"--grammar"}, "GRAMMAR",
format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()), string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.sparams.grammar = value; params.sparams.grammar = value;
} }
@ -1080,7 +1054,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
std::ifstream file(value); std::ifstream file(value);
if (!file) { if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str())); throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
} }
std::copy( std::copy(
std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(file),
@ -1150,49 +1124,49 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_ROPE_FREQ_SCALE")); ).set_env("LLAMA_ARG_ROPE_FREQ_SCALE"));
add_opt(common_arg( add_opt(common_arg(
{"--yarn-orig-ctx"}, "N", {"--yarn-orig-ctx"}, "N",
format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx), string_format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx),
[](common_params & params, int value) { [](common_params & params, int value) {
params.yarn_orig_ctx = value; params.yarn_orig_ctx = value;
} }
).set_env("LLAMA_ARG_YARN_ORIG_CTX")); ).set_env("LLAMA_ARG_YARN_ORIG_CTX"));
add_opt(common_arg( add_opt(common_arg(
{"--yarn-ext-factor"}, "N", {"--yarn-ext-factor"}, "N",
format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor), string_format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.yarn_ext_factor = std::stof(value); params.yarn_ext_factor = std::stof(value);
} }
).set_env("LLAMA_ARG_YARN_EXT_FACTOR")); ).set_env("LLAMA_ARG_YARN_EXT_FACTOR"));
add_opt(common_arg( add_opt(common_arg(
{"--yarn-attn-factor"}, "N", {"--yarn-attn-factor"}, "N",
format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor), string_format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.yarn_attn_factor = std::stof(value); params.yarn_attn_factor = std::stof(value);
} }
).set_env("LLAMA_ARG_YARN_ATTN_FACTOR")); ).set_env("LLAMA_ARG_YARN_ATTN_FACTOR"));
add_opt(common_arg( add_opt(common_arg(
{"--yarn-beta-slow"}, "N", {"--yarn-beta-slow"}, "N",
format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow), string_format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.yarn_beta_slow = std::stof(value); params.yarn_beta_slow = std::stof(value);
} }
).set_env("LLAMA_ARG_YARN_BETA_SLOW")); ).set_env("LLAMA_ARG_YARN_BETA_SLOW"));
add_opt(common_arg( add_opt(common_arg(
{"--yarn-beta-fast"}, "N", {"--yarn-beta-fast"}, "N",
format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast), string_format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.yarn_beta_fast = std::stof(value); params.yarn_beta_fast = std::stof(value);
} }
).set_env("LLAMA_ARG_YARN_BETA_FAST")); ).set_env("LLAMA_ARG_YARN_BETA_FAST"));
add_opt(common_arg( add_opt(common_arg(
{"-gan", "--grp-attn-n"}, "N", {"-gan", "--grp-attn-n"}, "N",
format("group-attention factor (default: %d)", params.grp_attn_n), string_format("group-attention factor (default: %d)", params.grp_attn_n),
[](common_params & params, int value) { [](common_params & params, int value) {
params.grp_attn_n = value; params.grp_attn_n = value;
} }
).set_env("LLAMA_ARG_GRP_ATTN_N")); ).set_env("LLAMA_ARG_GRP_ATTN_N"));
add_opt(common_arg( add_opt(common_arg(
{"-gaw", "--grp-attn-w"}, "N", {"-gaw", "--grp-attn-w"}, "N",
format("group-attention width (default: %.1f)", (double)params.grp_attn_w), string_format("group-attention width (default: %.1f)", (double)params.grp_attn_w),
[](common_params & params, int value) { [](common_params & params, int value) {
params.grp_attn_w = value; params.grp_attn_w = value;
} }
@ -1213,7 +1187,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_NO_KV_OFFLOAD")); ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
add_opt(common_arg( add_opt(common_arg(
{"-ctk", "--cache-type-k"}, "TYPE", {"-ctk", "--cache-type-k"}, "TYPE",
format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()), string_format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
// TODO: get the type right here // TODO: get the type right here
params.cache_type_k = value; params.cache_type_k = value;
@ -1221,7 +1195,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_CACHE_TYPE_K")); ).set_env("LLAMA_ARG_CACHE_TYPE_K"));
add_opt(common_arg( add_opt(common_arg(
{"-ctv", "--cache-type-v"}, "TYPE", {"-ctv", "--cache-type-v"}, "TYPE",
format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()), string_format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
// TODO: get the type right here // TODO: get the type right here
params.cache_type_v = value; params.cache_type_v = value;
@ -1229,7 +1203,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_CACHE_TYPE_V")); ).set_env("LLAMA_ARG_CACHE_TYPE_V"));
add_opt(common_arg( add_opt(common_arg(
{"--perplexity", "--all-logits"}, {"--perplexity", "--all-logits"},
format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"), string_format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.logits_all = true; params.logits_all = true;
} }
@ -1243,7 +1217,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(common_arg( add_opt(common_arg(
{"--hellaswag-tasks"}, "N", {"--hellaswag-tasks"}, "N",
format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks), string_format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks),
[](common_params & params, int value) { [](common_params & params, int value) {
params.hellaswag_tasks = value; params.hellaswag_tasks = value;
} }
@ -1257,7 +1231,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(common_arg( add_opt(common_arg(
{"--winogrande-tasks"}, "N", {"--winogrande-tasks"}, "N",
format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks), string_format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks),
[](common_params & params, int value) { [](common_params & params, int value) {
params.winogrande_tasks = value; params.winogrande_tasks = value;
} }
@ -1271,7 +1245,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(common_arg( add_opt(common_arg(
{"--multiple-choice-tasks"}, "N", {"--multiple-choice-tasks"}, "N",
format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks), string_format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks),
[](common_params & params, int value) { [](common_params & params, int value) {
params.multiple_choice_tasks = value; params.multiple_choice_tasks = value;
} }
@ -1292,42 +1266,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(common_arg( add_opt(common_arg(
{"--ppl-stride"}, "N", {"--ppl-stride"}, "N",
format("stride for perplexity calculation (default: %d)", params.ppl_stride), string_format("stride for perplexity calculation (default: %d)", params.ppl_stride),
[](common_params & params, int value) { [](common_params & params, int value) {
params.ppl_stride = value; params.ppl_stride = value;
} }
).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(common_arg( add_opt(common_arg(
{"--ppl-output-type"}, "<0|1>", {"--ppl-output-type"}, "<0|1>",
format("output type for perplexity calculation (default: %d)", params.ppl_output_type), string_format("output type for perplexity calculation (default: %d)", params.ppl_output_type),
[](common_params & params, int value) { [](common_params & params, int value) {
params.ppl_output_type = value; params.ppl_output_type = value;
} }
).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(common_arg( add_opt(common_arg(
{"-dt", "--defrag-thold"}, "N", {"-dt", "--defrag-thold"}, "N",
format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold), string_format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.defrag_thold = std::stof(value); params.defrag_thold = std::stof(value);
} }
).set_env("LLAMA_ARG_DEFRAG_THOLD")); ).set_env("LLAMA_ARG_DEFRAG_THOLD"));
add_opt(common_arg( add_opt(common_arg(
{"-np", "--parallel"}, "N", {"-np", "--parallel"}, "N",
format("number of parallel sequences to decode (default: %d)", params.n_parallel), string_format("number of parallel sequences to decode (default: %d)", params.n_parallel),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_parallel = value; params.n_parallel = value;
} }
).set_env("LLAMA_ARG_N_PARALLEL")); ).set_env("LLAMA_ARG_N_PARALLEL"));
add_opt(common_arg( add_opt(common_arg(
{"-ns", "--sequences"}, "N", {"-ns", "--sequences"}, "N",
format("number of sequences to decode (default: %d)", params.n_sequences), string_format("number of sequences to decode (default: %d)", params.n_sequences),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_sequences = value; params.n_sequences = value;
} }
).set_examples({LLAMA_EXAMPLE_PARALLEL})); ).set_examples({LLAMA_EXAMPLE_PARALLEL}));
add_opt(common_arg( add_opt(common_arg(
{"-cb", "--cont-batching"}, {"-cb", "--cont-batching"},
format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"), string_format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
[](common_params & params) { [](common_params & params) {
params.cont_batching = true; params.cont_batching = true;
} }
@ -1451,7 +1425,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
std::vector<std::string> split_arg{ it, {} }; std::vector<std::string> split_arg{ it, {} };
if (split_arg.size() >= llama_max_devices()) { if (split_arg.size() >= llama_max_devices()) {
throw std::invalid_argument( throw std::invalid_argument(
format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)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) { for (size_t i = 0; i < llama_max_devices(); ++i) {
@ -1468,7 +1442,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_TENSOR_SPLIT")); ).set_env("LLAMA_ARG_TENSOR_SPLIT"));
add_opt(common_arg( add_opt(common_arg(
{"-mg", "--main-gpu"}, "INDEX", {"-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), string_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),
[](common_params & params, int value) { [](common_params & params, int value) {
params.main_gpu = value; params.main_gpu = value;
if (!llama_supports_gpu_offload()) { if (!llama_supports_gpu_offload()) {
@ -1478,7 +1452,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_MAIN_GPU")); ).set_env("LLAMA_ARG_MAIN_GPU"));
add_opt(common_arg( add_opt(common_arg(
{"--check-tensors"}, {"--check-tensors"},
format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"), string_format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.check_tensors = true; params.check_tensors = true;
} }
@ -1489,7 +1463,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false", "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false",
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) { 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())); throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", value.c_str()));
} }
} }
)); ));
@ -1543,7 +1517,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"-m", "--model"}, "FNAME", {"-m", "--model"}, "FNAME",
ex == LLAMA_EXAMPLE_EXPORT_LORA ex == LLAMA_EXAMPLE_EXPORT_LORA
? std::string("model path from which to load base model") ? std::string("model path from which to load base model")
: format( : string_format(
"model path (default: `models/$filename` with filename from `--hf-file` " "model path (default: `models/$filename` with filename from `--hf-file` "
"or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH "or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH
), ),
@ -1592,42 +1566,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
std::ifstream file(value, std::ios::binary); std::ifstream file(value, std::ios::binary);
if (!file) { if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str())); throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
} }
params.context_files.push_back(value); params.context_files.push_back(value);
} }
).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
add_opt(common_arg( add_opt(common_arg(
{"--chunk-size"}, "N", {"--chunk-size"}, "N",
format("minimum length of embedded text chunks (default: %d)", params.chunk_size), string_format("minimum length of embedded text chunks (default: %d)", params.chunk_size),
[](common_params & params, int value) { [](common_params & params, int value) {
params.chunk_size = value; params.chunk_size = value;
} }
).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
add_opt(common_arg( add_opt(common_arg(
{"--chunk-separator"}, "STRING", {"--chunk-separator"}, "STRING",
format("separator between chunks (default: '%s')", params.chunk_separator.c_str()), string_format("separator between chunks (default: '%s')", params.chunk_separator.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.chunk_separator = value; params.chunk_separator = value;
} }
).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
add_opt(common_arg( add_opt(common_arg(
{"--junk"}, "N", {"--junk"}, "N",
format("number of times to repeat the junk text (default: %d)", params.n_junk), string_format("number of times to repeat the junk text (default: %d)", params.n_junk),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_junk = value; params.n_junk = value;
} }
).set_examples({LLAMA_EXAMPLE_PASSKEY})); ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
add_opt(common_arg( add_opt(common_arg(
{"--pos"}, "N", {"--pos"}, "N",
format("position of the passkey in the junk text (default: %d)", params.i_pos), string_format("position of the passkey in the junk text (default: %d)", params.i_pos),
[](common_params & params, int value) { [](common_params & params, int value) {
params.i_pos = value; params.i_pos = value;
} }
).set_examples({LLAMA_EXAMPLE_PASSKEY})); ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
add_opt(common_arg( add_opt(common_arg(
{"-o", "--output", "--output-file"}, "FNAME", {"-o", "--output", "--output-file"}, "FNAME",
format("output file (default: '%s')", string_format("output file (default: '%s')",
ex == LLAMA_EXAMPLE_EXPORT_LORA ex == LLAMA_EXAMPLE_EXPORT_LORA
? params.lora_outfile.c_str() ? params.lora_outfile.c_str()
: ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR : ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR
@ -1641,42 +1615,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA})); ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA}));
add_opt(common_arg( add_opt(common_arg(
{"-ofreq", "--output-frequency"}, "N", {"-ofreq", "--output-frequency"}, "N",
format("output the imatrix every N iterations (default: %d)", params.n_out_freq), string_format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_out_freq = value; params.n_out_freq = value;
} }
).set_examples({LLAMA_EXAMPLE_IMATRIX})); ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(common_arg( add_opt(common_arg(
{"--save-frequency"}, "N", {"--save-frequency"}, "N",
format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq), string_format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_save_freq = value; params.n_save_freq = value;
} }
).set_examples({LLAMA_EXAMPLE_IMATRIX})); ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(common_arg( add_opt(common_arg(
{"--process-output"}, {"--process-output"},
format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"), string_format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.process_output = true; params.process_output = true;
} }
).set_examples({LLAMA_EXAMPLE_IMATRIX})); ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(common_arg( add_opt(common_arg(
{"--no-ppl"}, {"--no-ppl"},
format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"), string_format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.compute_ppl = false; params.compute_ppl = false;
} }
).set_examples({LLAMA_EXAMPLE_IMATRIX})); ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(common_arg( add_opt(common_arg(
{"--chunk", "--from-chunk"}, "N", {"--chunk", "--from-chunk"}, "N",
format("start processing the input from chunk N (default: %d)", params.i_chunk), string_format("start processing the input from chunk N (default: %d)", params.i_chunk),
[](common_params & params, int value) { [](common_params & params, int value) {
params.i_chunk = value; params.i_chunk = value;
} }
).set_examples({LLAMA_EXAMPLE_IMATRIX})); ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(common_arg( add_opt(common_arg(
{"-pps"}, {"-pps"},
format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"), string_format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"),
[](common_params & params) { [](common_params & params) {
params.is_pp_shared = true; params.is_pp_shared = true;
} }
@ -1707,7 +1681,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_BENCH})); ).set_examples({LLAMA_EXAMPLE_BENCH}));
add_opt(common_arg( add_opt(common_arg(
{"--embd-normalize"}, "N", {"--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), string_format("normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize),
[](common_params & params, int value) { [](common_params & params, int value) {
params.embd_normalize = value; params.embd_normalize = value;
} }
@ -1728,35 +1702,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_EMBEDDING})); ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
add_opt(common_arg( add_opt(common_arg(
{"--host"}, "HOST", {"--host"}, "HOST",
format("ip address to listen (default: %s)", params.hostname.c_str()), string_format("ip address to listen (default: %s)", params.hostname.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.hostname = value; params.hostname = value;
} }
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST")); ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST"));
add_opt(common_arg( add_opt(common_arg(
{"--port"}, "PORT", {"--port"}, "PORT",
format("port to listen (default: %d)", params.port), string_format("port to listen (default: %d)", params.port),
[](common_params & params, int value) { [](common_params & params, int value) {
params.port = value; params.port = value;
} }
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT")); ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT"));
add_opt(common_arg( add_opt(common_arg(
{"--path"}, "PATH", {"--path"}, "PATH",
format("path to serve static files from (default: %s)", params.public_path.c_str()), string_format("path to serve static files from (default: %s)", params.public_path.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.public_path = value; params.public_path = value;
} }
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH")); ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH"));
add_opt(common_arg( add_opt(common_arg(
{"--embedding", "--embeddings"}, {"--embedding", "--embeddings"},
format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"), string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
[](common_params & params) { [](common_params & params) {
params.embedding = true; params.embedding = true;
} }
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS")); ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
add_opt(common_arg( add_opt(common_arg(
{"--reranking", "--rerank"}, {"--reranking", "--rerank"},
format("enable reranking endpoint on server (default: %s)", params.reranking ? "enabled" : "disabled"), string_format("enable reranking endpoint on server (default: %s)", params.reranking ? "enabled" : "disabled"),
[](common_params & params) { [](common_params & params) {
params.reranking = true; params.reranking = true;
} }
@ -1774,7 +1748,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
std::ifstream key_file(value); std::ifstream key_file(value);
if (!key_file) { if (!key_file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str())); throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
} }
std::string key; std::string key;
while (std::getline(key_file, key)) { while (std::getline(key_file, key)) {
@ -1801,7 +1775,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE")); ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE"));
add_opt(common_arg( add_opt(common_arg(
{"-to", "--timeout"}, "N", {"-to", "--timeout"}, "N",
format("server read/write timeout in seconds (default: %d)", params.timeout_read), string_format("server read/write timeout in seconds (default: %d)", params.timeout_read),
[](common_params & params, int value) { [](common_params & params, int value) {
params.timeout_read = value; params.timeout_read = value;
params.timeout_write = value; params.timeout_write = value;
@ -1809,7 +1783,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TIMEOUT")); ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TIMEOUT"));
add_opt(common_arg( add_opt(common_arg(
{"--threads-http"}, "N", {"--threads-http"}, "N",
format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http), string_format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_threads_http = value; params.n_threads_http = value;
} }
@ -1820,7 +1794,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
std::ifstream file(value); std::ifstream file(value);
if (!file) { if (!file) {
throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str())); throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
} }
std::string system_prompt; std::string system_prompt;
std::copy( std::copy(
@ -1833,21 +1807,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SERVER})); ).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg( add_opt(common_arg(
{"--metrics"}, {"--metrics"},
format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"), string_format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"),
[](common_params & params) { [](common_params & params) {
params.endpoint_metrics = true; params.endpoint_metrics = true;
} }
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS")); ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS"));
add_opt(common_arg( add_opt(common_arg(
{"--slots"}, {"--slots"},
format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"), string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
[](common_params & params) { [](common_params & params) {
params.endpoint_slots = true; params.endpoint_slots = true;
} }
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS")); ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS"));
add_opt(common_arg( add_opt(common_arg(
{"--props"}, {"--props"},
format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"), string_format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"),
[](common_params & params) { [](common_params & params) {
params.endpoint_props = true; params.endpoint_props = true;
} }
@ -1877,7 +1851,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
"only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template", "only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
if (!common_chat_verify_template(value)) { if (!common_chat_verify_template(value)) {
throw std::runtime_error(format( throw std::runtime_error(string_format(
"error: the supplied chat template is not supported: %s\n" "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", "note: llama.cpp does not use jinja parser, we only support commonly used templates\n",
value.c_str() value.c_str()
@ -1888,14 +1862,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE")); ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
add_opt(common_arg( add_opt(common_arg(
{"-sps", "--slot-prompt-similarity"}, "SIMILARITY", {"-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), string_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),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.slot_prompt_similarity = std::stof(value); params.slot_prompt_similarity = std::stof(value);
} }
).set_examples({LLAMA_EXAMPLE_SERVER})); ).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg( add_opt(common_arg(
{"--lora-init-without-apply"}, {"--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"), string_format("load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"),
[](common_params & params) { [](common_params & params) {
params.lora_init_without_apply = true; params.lora_init_without_apply = true;
} }
@ -1920,28 +1894,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
)); ));
add_opt(common_arg( add_opt(common_arg(
{"--positive-file"}, "FNAME", {"--positive-file"}, "FNAME",
format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()), string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.cvector_positive_file = value; params.cvector_positive_file = value;
} }
).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
add_opt(common_arg( add_opt(common_arg(
{"--negative-file"}, "FNAME", {"--negative-file"}, "FNAME",
format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()), string_format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()),
[](common_params & params, const std::string & value) { [](common_params & params, const std::string & value) {
params.cvector_negative_file = value; params.cvector_negative_file = value;
} }
).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
add_opt(common_arg( add_opt(common_arg(
{"--pca-batch"}, "N", {"--pca-batch"}, "N",
format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch), string_format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_pca_batch = value; params.n_pca_batch = value;
} }
).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
add_opt(common_arg( add_opt(common_arg(
{"--pca-iter"}, "N", {"--pca-iter"}, "N",
format("number of iterations used for PCA (default: %d)", params.n_pca_iterations), string_format("number of iterations used for PCA (default: %d)", params.n_pca_iterations),
[](common_params & params, int value) { [](common_params & params, int value) {
params.n_pca_iterations = value; params.n_pca_iterations = value;
} }

18
common/common.cpp
View File

@ -12,6 +12,7 @@
#include <algorithm> #include <algorithm>
#include <cinttypes> #include <cinttypes>
#include <climits>
#include <cmath> #include <cmath>
#include <codecvt> #include <codecvt>
#include <cstdarg> #include <cstdarg>
@ -23,10 +24,10 @@
#include <regex> #include <regex>
#include <sstream> #include <sstream>
#include <string> #include <string>
#include <thread>
#include <unordered_map> #include <unordered_map>
#include <unordered_set> #include <unordered_set>
#include <vector> #include <vector>
#include <thread>
#if defined(__APPLE__) && defined(__MACH__) #if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h> #include <sys/types.h>
@ -400,6 +401,21 @@ std::string common_params_get_system_info(const common_params & params) {
// String utils // String utils
// //
std::string 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);
}
std::vector<std::string> string_split(std::string input, char separator) { std::vector<std::string> string_split(std::string input, char separator) {
std::vector<std::string> parts; std::vector<std::string> parts;
size_t separator_pos = input.find(separator); size_t separator_pos = input.find(separator);

19
common/common.h
View File

@ -352,15 +352,28 @@ void common_init();
std::string common_params_get_system_info(const common_params & params); std::string common_params_get_system_info(const common_params & params);
bool parse_cpu_range(const std::string& range, bool(&boolmask)[GGML_MAX_N_THREADS]); bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]);
bool parse_cpu_mask(const std::string& mask, bool(&boolmask)[GGML_MAX_N_THREADS]); bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model = nullptr); void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr);
bool set_process_priority(enum ggml_sched_priority prio); bool set_process_priority(enum ggml_sched_priority prio);
// //
// String utils // String 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)
std::string string_format(const char * fmt, ...);
std::vector<std::string> string_split(std::string input, char separator); std::vector<std::string> string_split(std::string input, char separator);
std::string string_strip(const std::string & str); std::string string_strip(const std::string & str);

14
examples/infill/infill.cpp
View File

@ -205,11 +205,11 @@ int main(int argc, char ** argv) {
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false); std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false); std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
GGML_ASSERT(llama_token_prefix(model) >= 0); GGML_ASSERT(llama_token_fim_pre(model) >= 0);
GGML_ASSERT(llama_token_suffix(model) >= 0); GGML_ASSERT(llama_token_fim_suf(model) >= 0);
inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model)); inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model));
inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model)); inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model));
embd_inp = params.spm_infill ? inp_sfx : inp_pfx; embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
embd_end = params.spm_infill ? inp_pfx : inp_sfx; embd_end = params.spm_infill ? inp_pfx : inp_sfx;
@ -218,7 +218,7 @@ int main(int argc, char ** argv) {
} }
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
const llama_token middle_token = llama_token_middle(model); const llama_token middle_token = llama_token_fim_mid(model);
if (middle_token >= 0) { if (middle_token >= 0) {
embd_inp.push_back(middle_token); embd_inp.push_back(middle_token);
} }
@ -508,8 +508,8 @@ int main(int argc, char ** argv) {
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false); std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false); std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model)); inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model));
inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model)); inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model));
embd_inp = params.spm_infill ? inp_sfx : inp_pfx; embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
embd_end = params.spm_infill ? inp_pfx : inp_sfx; embd_end = params.spm_infill ? inp_pfx : inp_sfx;

2
examples/server/README.md
View File

@ -526,7 +526,7 @@ Takes a prefix and a suffix and returns the predicted completion as stream.
- `input_prefix`: Set the prefix of the code to infill. - `input_prefix`: Set the prefix of the code to infill.
- `input_suffix`: Set the suffix of the code to infill. - `input_suffix`: Set the suffix of the code to infill.
It also accepts all the options of `/completion` except `stream` and `prompt`. It also accepts all the options of `/completion`.
### **GET** `/props`: Get server global properties. ### **GET** `/props`: Get server global properties.

150
examples/server/server.cpp
View File

@ -753,12 +753,7 @@ struct server_context {
metrics.init(); metrics.init();
} }
std::vector<llama_token> tokenize(const json & json_prompt, bool add_special) const { std::vector<llama_token> tokenize(const json & json_prompt, bool add_special, bool parse_special) const {
// TODO: currently, we tokenize using special tokens by default
// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
// but it's better compared to completely ignoring ChatML and other chat templates
const bool TMP_FORCE_SPECIAL = true;
// If `add_bos` is true, we only add BOS, when json_prompt is a string, // If `add_bos` is true, we only add BOS, when json_prompt is a string,
// or the first element of the json_prompt array is a string. // or the first element of the json_prompt array is a string.
std::vector<llama_token> prompt_tokens; std::vector<llama_token> prompt_tokens;
@ -771,10 +766,10 @@ struct server_context {
std::vector<llama_token> p; std::vector<llama_token> p;
if (first) { if (first) {
p = common_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL); p = common_tokenize(ctx, s, add_special, parse_special);
first = false; first = false;
} else { } else {
p = common_tokenize(ctx, s, false, TMP_FORCE_SPECIAL); p = common_tokenize(ctx, s, false, parse_special);
} }
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
@ -788,7 +783,7 @@ struct server_context {
} }
} else { } else {
auto s = json_prompt.template get<std::string>(); auto s = json_prompt.template get<std::string>();
prompt_tokens = common_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL); prompt_tokens = common_tokenize(ctx, s, add_special, parse_special);
} }
return prompt_tokens; return prompt_tokens;
@ -1215,7 +1210,7 @@ struct server_context {
slot.params.n_predict, n_ctx_train); slot.params.n_predict, n_ctx_train);
} }
SLT_DBG(slot, "n_decoded = %d, n_remaining = %d, next token: '%s'\n", slot.n_decoded, slot.n_remaining, token_str.c_str()); SLT_DBG(slot, "n_decoded = %d, n_remaining = %d, next token: %5d '%s'\n", slot.n_decoded, slot.n_remaining, result.tok, token_str.c_str());
return slot.has_next_token; // continue return slot.has_next_token; // continue
} }
@ -1483,9 +1478,8 @@ struct server_context {
if (prompt.is_string() || json_is_array_of_numbers(prompt)) { if (prompt.is_string() || json_is_array_of_numbers(prompt)) {
data["index"] = 0; data["index"] = 0;
create_task(data, false, nullptr); create_task(data, false, nullptr);
} } else if (prompt.is_array()) {
// otherwise, it's a multiple-prompt task, we break it into smaller tasks // otherwise, it's a multiple-prompt task, we break it into smaller tasks
else if (prompt.is_array()) {
std::vector<json> prompts = prompt; std::vector<json> prompts = prompt;
if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
// prompts[0] is the question // prompts[0] is the question
@ -1510,9 +1504,8 @@ struct server_context {
} }
} }
} }
} } else {
// invalid case // invalid case
else {
throw std::runtime_error(error_msg); throw std::runtime_error(error_msg);
} }
@ -1785,6 +1778,9 @@ struct server_context {
} }
slot->cache_tokens.resize(token_count); slot->cache_tokens.resize(token_count);
// TODO: maybe detokenize the slot->cache_tokens instead?
slot->prompt = string_format("[restored %d tokens from file]", (int) token_count);
const int64_t t_end = ggml_time_us(); const int64_t t_end = ggml_time_us();
const double t_restore_ms = (t_end - t_start) / 1000.0; const double t_restore_ms = (t_end - t_start) / 1000.0;
@ -1971,63 +1967,57 @@ struct server_context {
slot.t_start_process_prompt = ggml_time_us(); slot.t_start_process_prompt = ggml_time_us();
slot.t_start_generation = 0; slot.t_start_generation = 0;
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_INFILL) { switch (slot.cmpl_type) {
const bool add_bos = llama_add_bos_token(model); case SERVER_TASK_CMPL_TYPE_NORMAL:
bool suff_rm_leading_spc = true; case SERVER_TASK_CMPL_TYPE_EMBEDDING:
if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { {
params.input_suffix.erase(0, 1); prompt_tokens = tokenize(slot.prompt, system_prompt.empty(), true); // add BOS if there isn't system prompt
suff_rm_leading_spc = false; } break;
} case SERVER_TASK_CMPL_TYPE_RERANK:
{
// require slot.prompt to be array of 2 strings
if (!slot.prompt.is_array() || slot.prompt.size() != 2) {
SLT_ERR(slot, "%s", "invalid prompt for rerank task\n");
slot.release();
send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST);
continue;
}
auto prefix_tokens = tokenize(slot.params.input_prefix, false); // prompt: [BOS]query[EOS][SEP]doc[EOS]
auto suffix_tokens = tokenize(slot.params.input_suffix, false); prompt_tokens.clear();
prompt_tokens.push_back(llama_token_bos(model));
{
const auto part = tokenize(slot.prompt[0], false, false);
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
}
prompt_tokens.push_back(llama_token_eos(model));
prompt_tokens.push_back(llama_token_sep(model));
{
const auto part = tokenize(slot.prompt[1], false, false);
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
}
prompt_tokens.push_back(llama_token_eos(model));
} break;
case SERVER_TASK_CMPL_TYPE_INFILL:
{
auto prefix_tokens = tokenize(slot.params.input_prefix, false, false);
auto suffix_tokens = tokenize(slot.params.input_suffix, false, false);
const int space_token = 29871; // TODO: this should not be hardcoded prefix_tokens.insert(prefix_tokens.begin(), llama_token_fim_pre(model));
if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) { suffix_tokens.insert(suffix_tokens.begin(), llama_token_fim_suf(model));
suffix_tokens.erase(suffix_tokens.begin());
}
prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model)); auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens;
suffix_tokens.insert(suffix_tokens.begin(), llama_token_suffix(model)); auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens;
auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens; if (llama_add_bos_token(model)) {
auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens; embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
if (add_bos) { }
embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
}
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
const llama_token middle_token = llama_token_middle(model); embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
if (middle_token >= 0) { embd_inp.push_back(llama_token_fim_mid(model));
embd_inp.push_back(middle_token);
}
prompt_tokens = embd_inp; prompt_tokens = std::move(embd_inp);
} else if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { } break;
// require slot.prompt to be array of 2 strings
if (!slot.prompt.is_array() || slot.prompt.size() != 2) {
SLT_ERR(slot, "%s", "invalid prompt for rerank task\n");
slot.release();
send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST);
continue;
}
// prompt: [BOS]query[EOS][SEP]doc[EOS]
prompt_tokens.clear();
prompt_tokens.push_back(llama_token_bos(model));
{
const auto part = tokenize(slot.prompt[0], false);
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
}
prompt_tokens.push_back(llama_token_eos(model));
prompt_tokens.push_back(llama_token_sep(model));
{
const auto part = tokenize(slot.prompt[1], false);
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
}
prompt_tokens.push_back(llama_token_eos(model));
} else {
prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
} }
slot.n_past = 0; slot.n_past = 0;
@ -2035,6 +2025,11 @@ struct server_context {
SLT_INF(slot, "prompt tokenized, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens); SLT_INF(slot, "prompt tokenized, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens);
// print prompt tokens:
for (int i = 0; i < (int) prompt_tokens.size(); i++) {
SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
}
// empty prompt passed -> release the slot and send empty response // empty prompt passed -> release the slot and send empty response
if (prompt_tokens.empty()) { if (prompt_tokens.empty()) {
SLT_WRN(slot, "%s", "empty prompt - releasing slot\n"); SLT_WRN(slot, "%s", "empty prompt - releasing slot\n");
@ -2924,7 +2919,23 @@ int main(int argc, char ** argv) {
return handle_completions_generic(SERVER_TASK_CMPL_TYPE_NORMAL, data, res); return handle_completions_generic(SERVER_TASK_CMPL_TYPE_NORMAL, data, res);
}; };
const auto handle_infill = [&handle_completions_generic](const httplib::Request & req, httplib::Response & res) { const auto handle_infill = [&ctx_server, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) {
std::string err;
if (llama_token_fim_pre(ctx_server.model) == LLAMA_TOKEN_NULL) {
err += "prefix token is missing. ";
}
if (llama_token_fim_suf(ctx_server.model) == LLAMA_TOKEN_NULL) {
err += "suffix token is missing. ";
}
if (llama_token_fim_mid(ctx_server.model) == LLAMA_TOKEN_NULL) {
err += "middle token is missing. ";
}
if (!err.empty()) {
res_error(res, format_error_response(string_format("Infill is not supported by this model: %s", err.c_str()), ERROR_TYPE_NOT_SUPPORTED));
return;
}
json data = json::parse(req.body); json data = json::parse(req.body);
return handle_completions_generic(SERVER_TASK_CMPL_TYPE_INFILL, data, res); return handle_completions_generic(SERVER_TASK_CMPL_TYPE_INFILL, data, res);
}; };
@ -3010,7 +3021,8 @@ int main(int argc, char ** argv) {
if (body.count("content") != 0) { if (body.count("content") != 0) {
const bool add_special = json_value(body, "add_special", false); const bool add_special = json_value(body, "add_special", false);
const bool with_pieces = json_value(body, "with_pieces", false); const bool with_pieces = json_value(body, "with_pieces", false);
std::vector<llama_token> tokens = ctx_server.tokenize(body.at("content"), add_special);
std::vector<llama_token> tokens = ctx_server.tokenize(body.at("content"), add_special, true);
if (with_pieces) { if (with_pieces) {
for (const auto& token : tokens) { for (const auto& token : tokens) {

26
gguf-py/gguf/constants.py
View File

@ -152,6 +152,8 @@ class Keys:
MERGES = "tokenizer.ggml.merges" MERGES = "tokenizer.ggml.merges"
BOS_ID = "tokenizer.ggml.bos_token_id" BOS_ID = "tokenizer.ggml.bos_token_id"
EOS_ID = "tokenizer.ggml.eos_token_id" EOS_ID = "tokenizer.ggml.eos_token_id"
EOT_ID = "tokenizer.ggml.eot_token_id"
EOM_ID = "tokenizer.ggml.eom_token_id"
UNK_ID = "tokenizer.ggml.unknown_token_id" UNK_ID = "tokenizer.ggml.unknown_token_id"
SEP_ID = "tokenizer.ggml.seperator_token_id" SEP_ID = "tokenizer.ggml.seperator_token_id"
PAD_ID = "tokenizer.ggml.padding_token_id" PAD_ID = "tokenizer.ggml.padding_token_id"
@ -168,11 +170,16 @@ class Keys:
CHAT_TEMPLATE_N = "tokenizer.chat_template.{name}" CHAT_TEMPLATE_N = "tokenizer.chat_template.{name}"
CHAT_TEMPLATES = "tokenizer.chat_templates" CHAT_TEMPLATES = "tokenizer.chat_templates"
# FIM/Infill special tokens constants # FIM/Infill special tokens constants
FIM_PRE_ID = "tokenizer.ggml.fim_pre_token_id"
FIM_SUF_ID = "tokenizer.ggml.fim_suf_token_id"
FIM_MID_ID = "tokenizer.ggml.fim_mid_token_id"
FIM_PAD_ID = "tokenizer.ggml.fim_pad_token_id"
FIM_REP_ID = "tokenizer.ggml.fim_rep_token_id"
FIM_SEP_ID = "tokenizer.ggml.fim_sep_token_id"
# deprecated:
PREFIX_ID = "tokenizer.ggml.prefix_token_id" PREFIX_ID = "tokenizer.ggml.prefix_token_id"
SUFFIX_ID = "tokenizer.ggml.suffix_token_id" SUFFIX_ID = "tokenizer.ggml.suffix_token_id"
MIDDLE_ID = "tokenizer.ggml.middle_token_id" MIDDLE_ID = "tokenizer.ggml.middle_token_id"
EOT_ID = "tokenizer.ggml.eot_token_id"
EOM_ID = "tokenizer.ggml.eom_token_id"
class Adapter: class Adapter:
TYPE = "adapter.type" TYPE = "adapter.type"
@ -1579,6 +1586,8 @@ KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES
KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES
KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID
KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID
KEY_TOKENIZER_EOT_ID = Keys.Tokenizer.EOT_ID
KEY_TOKENIZER_EOM_ID = Keys.Tokenizer.EOM_ID
KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID
KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID
KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID
@ -1586,8 +1595,15 @@ KEY_TOKENIZER_CLS_ID = Keys.Tokenizer.CLS_ID
KEY_TOKENIZER_MASK_ID = Keys.Tokenizer.MASK_ID KEY_TOKENIZER_MASK_ID = Keys.Tokenizer.MASK_ID
KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON
KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV
KEY_TOKENIZER_PRIFIX_ID = Keys.Tokenizer.PREFIX_ID
KEY_TOKENIZER_FIM_PRE_ID = Keys.Tokenizer.FIM_PRE_ID
KEY_TOKENIZER_FIM_SUF_ID = Keys.Tokenizer.FIM_SUF_ID
KEY_TOKENIZER_FIM_MID_ID = Keys.Tokenizer.FIM_MID_ID
KEY_TOKENIZER_FIM_PAD_ID = Keys.Tokenizer.FIM_PAD_ID
KEY_TOKENIZER_FIM_REP_ID = Keys.Tokenizer.FIM_REP_ID
KEY_TOKENIZER_FIM_SEP_ID = Keys.Tokenizer.FIM_SEP_ID
# deprecated
KEY_TOKENIZER_PREFIX_ID = Keys.Tokenizer.PREFIX_ID
KEY_TOKENIZER_SUFFIX_ID = Keys.Tokenizer.SUFFIX_ID KEY_TOKENIZER_SUFFIX_ID = Keys.Tokenizer.SUFFIX_ID
KEY_TOKENIZER_MIDDLE_ID = Keys.Tokenizer.MIDDLE_ID KEY_TOKENIZER_MIDDLE_ID = Keys.Tokenizer.MIDDLE_ID
KEY_TOKENIZER_EOT_ID = Keys.Tokenizer.EOT_ID
KEY_TOKENIZER_EOM_ID = Keys.Tokenizer.EOM_ID

9
gguf-py/gguf/gguf_writer.py
View File

@ -843,15 +843,6 @@ class GGUFWriter:
self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value) self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)
def add_prefix_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.PREFIX_ID, id)
def add_suffix_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.SUFFIX_ID, id)
def add_middle_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.MIDDLE_ID, id)
def add_eot_token_id(self, id: int) -> None: def add_eot_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.EOT_ID, id) self.add_uint32(Keys.Tokenizer.EOT_ID, id)

17
include/llama.h
View File

@ -897,6 +897,7 @@ extern "C" {
// Special tokens // Special tokens
LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
LLAMA_API llama_token llama_token_eot(const struct llama_model * model); // end-of-turn
LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
@ -905,11 +906,17 @@ extern "C" {
LLAMA_API bool llama_add_bos_token(const struct llama_model * model); LLAMA_API bool llama_add_bos_token(const struct llama_model * model);
LLAMA_API bool llama_add_eos_token(const struct llama_model * model); LLAMA_API bool llama_add_eos_token(const struct llama_model * model);
// Codellama infill tokens // infill tokens
LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix DEPRECATED(LLAMA_API llama_token llama_token_prefix(const struct llama_model * model), "use llama_token_fim_pre instead");
LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle DEPRECATED(LLAMA_API llama_token llama_token_middle(const struct llama_model * model), "use llama_token_fim_mid instead");
LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix DEPRECATED(LLAMA_API llama_token llama_token_suffix(const struct llama_model * model), "use llama_token_fim_suf instead");
LLAMA_API llama_token llama_token_eot (const struct llama_model * model); // End of infill middle
LLAMA_API llama_token llama_token_fim_pre(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_suf(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_mid(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_pad(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_rep(const struct llama_model * model);
LLAMA_API llama_token llama_token_fim_sep(const struct llama_model * model);
// //
// Tokenization // Tokenization

38
src/llama-vocab.cpp
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@ -1663,6 +1663,14 @@ llama_token llama_token_eos_impl(const struct llama_vocab & vocab) {
return vocab.special_eos_id; return vocab.special_eos_id;
} }
llama_token llama_token_eot_impl(const struct llama_vocab & vocab) {
return vocab.special_eot_id;
}
llama_token llama_token_eom_impl(const struct llama_vocab & vocab) {
return vocab.special_eom_id;
}
llama_token llama_token_cls_impl(const struct llama_vocab & vocab) { llama_token llama_token_cls_impl(const struct llama_vocab & vocab) {
return vocab.special_cls_id; return vocab.special_cls_id;
} }
@ -1688,23 +1696,39 @@ bool llama_add_eos_token_impl(const struct llama_vocab & vocab) {
} }
llama_token llama_token_prefix_impl(const struct llama_vocab & vocab) { llama_token llama_token_prefix_impl(const struct llama_vocab & vocab) {
return vocab.special_prefix_id; return vocab.special_fim_pre_id;
} }
llama_token llama_token_middle_impl(const struct llama_vocab & vocab) { llama_token llama_token_middle_impl(const struct llama_vocab & vocab) {
return vocab.special_middle_id; return vocab.special_fim_mid_id;
} }
llama_token llama_token_suffix_impl(const struct llama_vocab & vocab) { llama_token llama_token_suffix_impl(const struct llama_vocab & vocab) {
return vocab.special_suffix_id; return vocab.special_fim_suf_id;
} }
llama_token llama_token_eot_impl(const struct llama_vocab & vocab) { llama_token llama_token_fim_pre_impl(const struct llama_vocab & vocab) {
return vocab.special_eot_id; return vocab.special_fim_pre_id;
} }
llama_token llama_token_eom_impl(const struct llama_vocab & vocab) { llama_token llama_token_fim_suf_impl(const struct llama_vocab & vocab) {
return vocab.special_eom_id; return vocab.special_fim_suf_id;
}
llama_token llama_token_fim_mid_impl(const struct llama_vocab & vocab) {
return vocab.special_fim_mid_id;
}
llama_token llama_token_fim_pad_impl(const struct llama_vocab & vocab) {
return vocab.special_fim_pad_id;
}
llama_token llama_token_fim_rep_impl(const struct llama_vocab & vocab) {
return vocab.special_fim_rep_id;
}
llama_token llama_token_fim_sep_impl(const struct llama_vocab & vocab) {
return vocab.special_fim_sep_id;
} }
int32_t llama_tokenize_impl( int32_t llama_tokenize_impl(

35
src/llama-vocab.h
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@ -37,20 +37,26 @@ struct llama_vocab {
std::map<std::pair<std::string, std::string>, int> bpe_ranks; std::map<std::pair<std::string, std::string>, int> bpe_ranks;
// default LLaMA special tokens // default LLaMA special tokens
// TODO: should we set all of these to LLAMA_TOKEN_NULL?
id special_bos_id = 1; id special_bos_id = 1;
id special_eos_id = 2; id special_eos_id = 2;
id special_eot_id = LLAMA_TOKEN_NULL;
id special_eom_id = LLAMA_TOKEN_NULL;
id special_unk_id = 0; id special_unk_id = 0;
id special_sep_id = LLAMA_TOKEN_NULL; id special_sep_id = LLAMA_TOKEN_NULL;
id special_pad_id = LLAMA_TOKEN_NULL; id special_pad_id = LLAMA_TOKEN_NULL;
id special_cls_id = LLAMA_TOKEN_NULL; id special_cls_id = LLAMA_TOKEN_NULL;
id special_mask_id = LLAMA_TOKEN_NULL; id special_mask_id = LLAMA_TOKEN_NULL;
id linefeed_id = 13; id linefeed_id = 13;
id special_prefix_id = LLAMA_TOKEN_NULL;
id special_suffix_id = LLAMA_TOKEN_NULL; // fim tokens
id special_middle_id = LLAMA_TOKEN_NULL; id special_fim_pre_id = LLAMA_TOKEN_NULL;
id special_eot_id = LLAMA_TOKEN_NULL; // TODO: move above after "eos_id", and here add "file separator" token id special_fim_suf_id = LLAMA_TOKEN_NULL;
id special_eom_id = LLAMA_TOKEN_NULL; id special_fim_mid_id = LLAMA_TOKEN_NULL;
id special_fim_pad_id = LLAMA_TOKEN_NULL;
id special_fim_rep_id = LLAMA_TOKEN_NULL; // repo
id special_fim_sep_id = LLAMA_TOKEN_NULL; // file separator
// set of all tokens that cause "end of generation" // set of all tokens that cause "end of generation"
std::set<id> special_eog_ids; std::set<id> special_eog_ids;
@ -104,19 +110,26 @@ bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token t
llama_token llama_token_bos_impl(const struct llama_vocab & vocab); llama_token llama_token_bos_impl(const struct llama_vocab & vocab);
llama_token llama_token_eos_impl(const struct llama_vocab & vocab); llama_token llama_token_eos_impl(const struct llama_vocab & vocab);
llama_token llama_token_eot_impl(const struct llama_vocab & vocab);
llama_token llama_token_eom_impl(const struct llama_vocab & vocab);
llama_token llama_token_cls_impl(const struct llama_vocab & vocab); llama_token llama_token_cls_impl(const struct llama_vocab & vocab);
llama_token llama_token_sep_impl(const struct llama_vocab & vocab); llama_token llama_token_sep_impl(const struct llama_vocab & vocab);
llama_token llama_token_nl_impl (const struct llama_vocab & vocab); llama_token llama_token_nl_impl (const struct llama_vocab & vocab);
llama_token llama_token_pad_impl(const struct llama_vocab & vocab); llama_token llama_token_pad_impl(const struct llama_vocab & vocab);
bool llama_add_bos_token_impl(const struct llama_vocab & vocab);
bool llama_add_eos_token_impl(const struct llama_vocab & vocab);
llama_token llama_token_prefix_impl(const struct llama_vocab & vocab); llama_token llama_token_prefix_impl(const struct llama_vocab & vocab);
llama_token llama_token_middle_impl(const struct llama_vocab & vocab); llama_token llama_token_middle_impl(const struct llama_vocab & vocab);
llama_token llama_token_suffix_impl(const struct llama_vocab & vocab); llama_token llama_token_suffix_impl(const struct llama_vocab & vocab);
llama_token llama_token_eot_impl (const struct llama_vocab & vocab);
llama_token llama_token_eom_impl (const struct llama_vocab & vocab); llama_token llama_token_fim_pre_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_suf_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_mid_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_pad_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_rep_impl(const struct llama_vocab & vocab);
llama_token llama_token_fim_sep_impl(const struct llama_vocab & vocab);
bool llama_add_bos_token_impl(const struct llama_vocab & vocab);
bool llama_add_eos_token_impl(const struct llama_vocab & vocab);
int32_t llama_tokenize_impl( int32_t llama_tokenize_impl(
const struct llama_vocab & vocab, const struct llama_vocab & vocab,

452
src/llama.cpp
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@ -345,6 +345,8 @@ enum llm_kv {
LLM_KV_TOKENIZER_MERGES, LLM_KV_TOKENIZER_MERGES,
LLM_KV_TOKENIZER_BOS_ID, LLM_KV_TOKENIZER_BOS_ID,
LLM_KV_TOKENIZER_EOS_ID, LLM_KV_TOKENIZER_EOS_ID,
LLM_KV_TOKENIZER_EOT_ID,
LLM_KV_TOKENIZER_EOM_ID,
LLM_KV_TOKENIZER_UNK_ID, LLM_KV_TOKENIZER_UNK_ID,
LLM_KV_TOKENIZER_SEP_ID, LLM_KV_TOKENIZER_SEP_ID,
LLM_KV_TOKENIZER_PAD_ID, LLM_KV_TOKENIZER_PAD_ID,
@ -357,14 +359,20 @@ enum llm_kv {
LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
LLM_KV_TOKENIZER_HF_JSON, LLM_KV_TOKENIZER_HF_JSON,
LLM_KV_TOKENIZER_RWKV, LLM_KV_TOKENIZER_RWKV,
LLM_KV_TOKENIZER_PREFIX_ID, LLM_KV_TOKENIZER_FIM_PRE_ID,
LLM_KV_TOKENIZER_SUFFIX_ID, LLM_KV_TOKENIZER_FIM_SUF_ID,
LLM_KV_TOKENIZER_MIDDLE_ID, LLM_KV_TOKENIZER_FIM_MID_ID,
LLM_KV_TOKENIZER_EOT_ID, LLM_KV_TOKENIZER_FIM_PAD_ID,
LLM_KV_TOKENIZER_EOM_ID, LLM_KV_TOKENIZER_FIM_REP_ID,
LLM_KV_TOKENIZER_FIM_SEP_ID,
LLM_KV_ADAPTER_TYPE, LLM_KV_ADAPTER_TYPE,
LLM_KV_ADAPTER_LORA_ALPHA, LLM_KV_ADAPTER_LORA_ALPHA,
// deprecated:
LLM_KV_TOKENIZER_PREFIX_ID,
LLM_KV_TOKENIZER_SUFFIX_ID,
LLM_KV_TOKENIZER_MIDDLE_ID,
}; };
static const std::map<llm_kv, const char *> LLM_KV_NAMES = { static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
@ -422,57 +430,65 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" }, { LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
{ LLM_KV_ATTENTION_SCALE, "%s.attention.scale" }, { LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
{ LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" },
{ LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" }, { LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" },
{ LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" }, { LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" },
{ LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" }, { LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" },
{ LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" },
{ LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" },
{ LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier" }, { LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier" },
{ LLM_KV_SPLIT_NO, "split.no" }, { LLM_KV_SPLIT_NO, "split.no" },
{ LLM_KV_SPLIT_COUNT, "split.count" }, { LLM_KV_SPLIT_COUNT, "split.count" },
{ LLM_KV_SPLIT_TENSORS_COUNT, "split.tensors.count" }, { LLM_KV_SPLIT_TENSORS_COUNT, "split.tensors.count" },
{ LLM_KV_SSM_CONV_KERNEL, "%s.ssm.conv_kernel" }, { LLM_KV_SSM_CONV_KERNEL, "%s.ssm.conv_kernel" },
{ LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" }, { LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" },
{ LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" }, { LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" },
{ LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" }, { LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
{ LLM_KV_SSM_DT_B_C_RMS, "%s.ssm.dt_b_c_rms" }, { LLM_KV_SSM_DT_B_C_RMS, "%s.ssm.dt_b_c_rms" },
{ LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" }, { LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" },
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" }, { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" }, { LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" }, { LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
{ LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" }, { LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" },
{ LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" }, { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" },
{ LLM_KV_TOKENIZER_SCORES, "tokenizer.ggml.scores" }, { LLM_KV_TOKENIZER_SCORES, "tokenizer.ggml.scores" },
{ LLM_KV_TOKENIZER_MERGES, "tokenizer.ggml.merges" }, { LLM_KV_TOKENIZER_MERGES, "tokenizer.ggml.merges" },
{ LLM_KV_TOKENIZER_BOS_ID, "tokenizer.ggml.bos_token_id" }, { LLM_KV_TOKENIZER_BOS_ID, "tokenizer.ggml.bos_token_id" },
{ LLM_KV_TOKENIZER_EOS_ID, "tokenizer.ggml.eos_token_id" }, { LLM_KV_TOKENIZER_EOS_ID, "tokenizer.ggml.eos_token_id" },
{ LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" }, { LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" },
{ LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" }, { LLM_KV_TOKENIZER_EOM_ID, "tokenizer.ggml.eom_token_id" },
{ LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" }, { LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" },
{ LLM_KV_TOKENIZER_CLS_ID, "tokenizer.ggml.cls_token_id" }, { LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" },
{ LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" }, { LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
{ LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" }, { LLM_KV_TOKENIZER_CLS_ID, "tokenizer.ggml.cls_token_id" },
{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" }, { LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" },
{ LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" }, { LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
{ LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, "tokenizer.ggml.remove_extra_whitespaces" }, { LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
{ LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap" }, { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" }, { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, "tokenizer.ggml.remove_extra_whitespaces" },
{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" }, { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap" },
{ LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" }, { LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
{ LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" }, { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
{ LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" }, { LLM_KV_TOKENIZER_FIM_PRE_ID, "tokenizer.ggml.fim_pre_token_id" },
{ LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" }, { LLM_KV_TOKENIZER_FIM_SUF_ID, "tokenizer.ggml.fim_suf_token_id" },
{ LLM_KV_TOKENIZER_EOM_ID, "tokenizer.ggml.eom_token_id" }, { LLM_KV_TOKENIZER_FIM_MID_ID, "tokenizer.ggml.fim_mid_token_id" },
{ LLM_KV_TOKENIZER_FIM_PAD_ID, "tokenizer.ggml.fim_pad_token_id" },
{ LLM_KV_TOKENIZER_FIM_REP_ID, "tokenizer.ggml.fim_rep_token_id" },
{ LLM_KV_TOKENIZER_FIM_SEP_ID, "tokenizer.ggml.fim_sep_token_id" },
{ LLM_KV_ADAPTER_TYPE, "adapter.type" }, { LLM_KV_ADAPTER_TYPE, "adapter.type" },
{ LLM_KV_ADAPTER_LORA_ALPHA, "adapter.lora.alpha" }, { LLM_KV_ADAPTER_LORA_ALPHA, "adapter.lora.alpha" },
// deprecated
{ LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
{ LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
{ LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" },
}; };
struct LLM_KV { struct LLM_KV {
@ -6164,14 +6180,14 @@ static void llm_load_vocab(
vocab.type = LLAMA_VOCAB_TYPE_NONE; vocab.type = LLAMA_VOCAB_TYPE_NONE;
// default special tokens // default special tokens
vocab.special_bos_id = -1; vocab.special_bos_id = LLAMA_TOKEN_NULL;
vocab.special_eos_id = -1; vocab.special_eos_id = LLAMA_TOKEN_NULL;
vocab.special_unk_id = -1; vocab.special_unk_id = LLAMA_TOKEN_NULL;
vocab.special_sep_id = -1; vocab.special_sep_id = LLAMA_TOKEN_NULL;
vocab.special_pad_id = -1; vocab.special_pad_id = LLAMA_TOKEN_NULL;
vocab.special_cls_id = -1; vocab.special_cls_id = LLAMA_TOKEN_NULL;
vocab.special_mask_id = -1; vocab.special_mask_id = LLAMA_TOKEN_NULL;
vocab.linefeed_id = -1; vocab.linefeed_id = LLAMA_TOKEN_NULL;
// read vocab size from metadata // read vocab size from metadata
if (!ml.get_key(LLM_KV_VOCAB_SIZE, vocab.n_vocab, false)) { if (!ml.get_key(LLM_KV_VOCAB_SIZE, vocab.n_vocab, false)) {
@ -6188,16 +6204,16 @@ static void llm_load_vocab(
vocab.special_bos_id = 1; vocab.special_bos_id = 1;
vocab.special_eos_id = 2; vocab.special_eos_id = 2;
vocab.special_unk_id = 0; vocab.special_unk_id = 0;
vocab.special_sep_id = -1; vocab.special_sep_id = LLAMA_TOKEN_NULL;
vocab.special_pad_id = -1; vocab.special_pad_id = LLAMA_TOKEN_NULL;
vocab.special_cls_id = -1; vocab.special_cls_id = LLAMA_TOKEN_NULL;
vocab.special_mask_id = -1; vocab.special_mask_id = LLAMA_TOKEN_NULL;
} else if (tokenizer_model == "bert") { } else if (tokenizer_model == "bert") {
vocab.type = LLAMA_VOCAB_TYPE_WPM; vocab.type = LLAMA_VOCAB_TYPE_WPM;
// default special tokens // default special tokens
vocab.special_bos_id = -1; vocab.special_bos_id = LLAMA_TOKEN_NULL;
vocab.special_eos_id = -1; vocab.special_eos_id = LLAMA_TOKEN_NULL;
vocab.special_unk_id = 100; vocab.special_unk_id = 100;
vocab.special_sep_id = 102; vocab.special_sep_id = 102;
vocab.special_pad_id = 0; vocab.special_pad_id = 0;
@ -6233,22 +6249,22 @@ static void llm_load_vocab(
// default special tokens // default special tokens
vocab.special_bos_id = 11; vocab.special_bos_id = 11;
vocab.special_eos_id = 11; vocab.special_eos_id = 11;
vocab.special_unk_id = -1; vocab.special_unk_id = LLAMA_TOKEN_NULL;
vocab.special_sep_id = -1; vocab.special_sep_id = LLAMA_TOKEN_NULL;
vocab.special_pad_id = -1; vocab.special_pad_id = LLAMA_TOKEN_NULL;
vocab.special_cls_id = -1; vocab.special_cls_id = LLAMA_TOKEN_NULL;
vocab.special_mask_id = -1; vocab.special_mask_id = LLAMA_TOKEN_NULL;
} else if (tokenizer_model == "t5") { } else if (tokenizer_model == "t5") {
vocab.type = LLAMA_VOCAB_TYPE_UGM; vocab.type = LLAMA_VOCAB_TYPE_UGM;
// default special tokens // default special tokens
vocab.special_bos_id = -1; vocab.special_bos_id = LLAMA_TOKEN_NULL;
vocab.special_eos_id = 1; vocab.special_eos_id = 1;
vocab.special_unk_id = 2; vocab.special_unk_id = 2;
vocab.special_sep_id = -1; vocab.special_sep_id = LLAMA_TOKEN_NULL;
vocab.special_pad_id = 0; vocab.special_pad_id = 0;
vocab.special_cls_id = -1; vocab.special_cls_id = LLAMA_TOKEN_NULL;
vocab.special_mask_id = -1; vocab.special_mask_id = LLAMA_TOKEN_NULL;
const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str()); const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
if (precompiled_charsmap_keyidx != -1) { if (precompiled_charsmap_keyidx != -1) {
@ -6271,11 +6287,11 @@ static void llm_load_vocab(
vocab.type = LLAMA_VOCAB_TYPE_RWKV; vocab.type = LLAMA_VOCAB_TYPE_RWKV;
// default special tokens // default special tokens
vocab.special_bos_id = -1; vocab.special_bos_id = LLAMA_TOKEN_NULL;
vocab.special_eos_id = -1; vocab.special_eos_id = LLAMA_TOKEN_NULL;
vocab.special_unk_id = -1; vocab.special_unk_id = LLAMA_TOKEN_NULL;
vocab.special_sep_id = -1; vocab.special_sep_id = LLAMA_TOKEN_NULL;
vocab.special_pad_id = -1; vocab.special_pad_id = LLAMA_TOKEN_NULL;
} else { } else {
throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str())); throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
} }
@ -6359,7 +6375,7 @@ static void llm_load_vocab(
} else if ( } else if (
tokenizer_pre == "chatglm-bpe") { tokenizer_pre == "chatglm-bpe") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4; vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
vocab.special_bos_id = -1; vocab.special_bos_id = LLAMA_TOKEN_NULL;
} else if ( } else if (
tokenizer_pre == "viking") { tokenizer_pre == "viking") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING; vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
@ -6485,44 +6501,6 @@ static void llm_load_vocab(
// determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n' // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
// For Fill-In-the-Middle (FIM)/infill models which where converted
// prior to support of FIM special tokens in GGUF, the following
// will allow those models to continue to work. The general names
// of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and
// CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once
// new versions of these models have been published.
std::string gen_name;
ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false);
std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(),
[](unsigned char c){ return std::tolower(c); });
if (gen_name.find("code") != std::string::npos) {
if (model.arch == LLM_ARCH_LLAMA
&& 32010 < vocab.id_to_token.size()
&& vocab.id_to_token[32007].text.find("<PRE>") != std::string::npos
&& vocab.id_to_token[32008].text.find("<SUF>") != std::string::npos
&& vocab.id_to_token[32009].text.find("<MID>") != std::string::npos
&& vocab.id_to_token[32010].text.find("<EOT>") != std::string::npos) {
vocab.special_prefix_id = 32007;
vocab.special_suffix_id = 32008;
vocab.special_middle_id = 32009;
vocab.special_eot_id = 32010;
} else if (model.arch == LLM_ARCH_GEMMA
&& 107 < vocab.id_to_token.size()
&& vocab.id_to_token[67].text == "<|fim_prefix|>"
&& vocab.id_to_token[69].text == "<|fim_suffix|>"
&& vocab.id_to_token[68].text == "<|fim_middle|>"
&& vocab.id_to_token[107].text == "<end_of_turn>") {
vocab.special_prefix_id = 67;
vocab.special_suffix_id = 69;
vocab.special_middle_id = 68;
// TODO: this is not EOT, it is "file separator" token, needs fix
// https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
//vocab.special_eot_id = 70;
vocab.special_eot_id = 107;
}
}
try { try {
vocab.linefeed_id = llama_byte_to_token_impl(vocab, '\n'); vocab.linefeed_id = llama_byte_to_token_impl(vocab, '\n');
} catch (const std::exception & e) { } catch (const std::exception & e) {
@ -6550,18 +6528,26 @@ static void llm_load_vocab(
// special tokens // special tokens
{ {
const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = { const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
{ LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id }, { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id },
{ LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id }, { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id },
{ LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id }, { LLM_KV_TOKENIZER_EOT_ID, vocab.special_eot_id },
{ LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id }, { LLM_KV_TOKENIZER_EOM_ID, vocab.special_eom_id },
{ LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id }, { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id },
{ LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id }, { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id },
{ LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id }, { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id },
{ LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_prefix_id }, { LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id },
{ LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_suffix_id }, { LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id },
{ LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id }, { LLM_KV_TOKENIZER_FIM_PRE_ID, vocab.special_fim_pre_id },
{ LLM_KV_TOKENIZER_EOT_ID, vocab.special_eot_id }, { LLM_KV_TOKENIZER_FIM_SUF_ID, vocab.special_fim_suf_id },
{ LLM_KV_TOKENIZER_EOM_ID, vocab.special_eom_id }, { LLM_KV_TOKENIZER_FIM_MID_ID, vocab.special_fim_mid_id },
{ LLM_KV_TOKENIZER_FIM_PAD_ID, vocab.special_fim_pad_id },
{ LLM_KV_TOKENIZER_FIM_REP_ID, vocab.special_fim_rep_id },
{ LLM_KV_TOKENIZER_FIM_SEP_ID, vocab.special_fim_sep_id },
// deprecated
{ LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_fim_pre_id },
{ LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_fim_suf_id },
{ LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_fim_mid_id },
}; };
for (const auto & it : special_token_types) { for (const auto & it : special_token_types) {
@ -6592,22 +6578,21 @@ static void llm_load_vocab(
} }
} }
// find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc. // auto-detect special tokens by text
// // TODO: convert scripts should provide these tokens through the KV metadata LLM_KV_TOKENIZER_...
// TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID // for now, we apply this workaround to find the tokens based on their text
// for now, we apply this workaround to find the EOT token based on its text
if (vocab.special_eot_id == -1) { for (const auto & t : vocab.token_to_id) {
for (const auto & t : vocab.token_to_id) { // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
if (vocab.special_eot_id == LLAMA_TOKEN_NULL) {
if (false if (false
// TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
// need to fix convert script
//vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
|| t.first == "<|eot_id|>" || t.first == "<|eot_id|>"
|| t.first == "<|im_end|>" || t.first == "<|im_end|>"
|| t.first == "<|end|>" || t.first == "<|end|>"
|| t.first == "<end_of_turn>" || t.first == "<end_of_turn>"
|| t.first == "<|endoftext|>" || t.first == "<|endoftext|>"
|| t.first == "<EOT>" || t.first == "<EOT>"
|| t.first == "<｜end▁of▁sentence｜>" // DeepSeek
) { ) {
vocab.special_eot_id = t.second; vocab.special_eot_id = t.second;
if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@ -6615,23 +6600,118 @@ static void llm_load_vocab(
__func__, t.first.c_str()); __func__, t.first.c_str());
vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
} }
break;
} }
} }
}
// find EOM token: "<|eom_id|>" // find EOM token: "<|eom_id|>"
// if (vocab.special_eom_id == LLAMA_TOKEN_NULL) {
// TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOM_ID if (false
// for now, we apply this workaround to find the EOM token based on its text || t.first == "<|eom_id|>"
if (vocab.special_eom_id == -1) { ) {
const auto & t = vocab.token_to_id.find("<|eom_id|>"); vocab.special_eom_id = t.second;
if (t != vocab.token_to_id.end()) { if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
vocab.special_eom_id = t->second; LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
if ((vocab.id_to_token[t->second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { __func__, t.first.c_str());
LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
__func__, t->first.c_str()); }
vocab.id_to_token[t->second].attr = LLAMA_TOKEN_ATTR_CONTROL; }
}
// find FIM_PRE token: "<|fim_prefix|>", "<fim-prefix>", "<PRE>", etc.
if (vocab.special_fim_pre_id == LLAMA_TOKEN_NULL) {
if (false
|| t.first == "<|fim_prefix|>" // Qwen
|| t.first == "<fim-prefix>"
|| t.first == "<｜fim▁begin｜>" // DeepSeek
|| t.first == "<PRE>"
) {
vocab.special_fim_pre_id = t.second;
if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
__func__, t.first.c_str());
vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
}
}
}
// find FIM_SUF token: "<|fim_suffix|>", "<fim-suffix>", "<SUF>", etc.
if (vocab.special_fim_suf_id == LLAMA_TOKEN_NULL) {
if (false
|| t.first == "<|fim_suffix|>" // Qwen
|| t.first == "<fim-suffix>"
|| t.first == "<｜fim▁hole｜>" // DeepSeek
|| t.first == "<SUF>"
) {
vocab.special_fim_suf_id = t.second;
if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
__func__, t.first.c_str());
vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
}
}
}
// find FIM_MID token: "<|fim_middle|>", "<fim-middle>", "<MID>", etc.
if (vocab.special_fim_mid_id == LLAMA_TOKEN_NULL) {
if (false
|| t.first == "<|fim_middle|>" // Qwen
|| t.first == "<fim-middle>"
|| t.first == "<｜fim▁end｜>" // DeepSeek
|| t.first == "<MID>"
) {
vocab.special_fim_mid_id = t.second;
if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
__func__, t.first.c_str());
vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
}
}
}
// find FIM_PAD token: "<|fim_pad|>", "<fim-pad>", "<PAD>", etc.
if (vocab.special_fim_pad_id == LLAMA_TOKEN_NULL) {
if (false
|| t.first == "<|fim_pad|>" // Qwen
|| t.first == "<fim-pad>"
|| t.first == "<PAD>"
) {
vocab.special_fim_pad_id = t.second;
if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
__func__, t.first.c_str());
vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
}
}
}
// find FIM_REP token: "<|fim_repo|>", "<fim-repo>", "<REP>", etc.
if (vocab.special_fim_rep_id == LLAMA_TOKEN_NULL) {
if (false
|| t.first == "<|fim_repo|>" // Qwen
|| t.first == "<|repo_name|>"
|| t.first == "<fim-repo>"
|| t.first == "<REPO>"
) {
vocab.special_fim_rep_id = t.second;
if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
__func__, t.first.c_str());
vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
}
}
}
// find FIM_SEP token: "<|file_sep|>"
if (vocab.special_fim_sep_id == LLAMA_TOKEN_NULL) {
if (false
|| t.first == "<|file_sep|>" // Qwen
) {
vocab.special_fim_sep_id = t.second;
if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
__func__, t.first.c_str());
vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
}
} }
} }
} }
@ -6659,17 +6739,17 @@ static void llm_load_vocab(
} }
} }
if (vocab.special_eos_id != -1 && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) { if (vocab.special_eos_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) {
vocab.special_eog_ids.insert(vocab.special_eos_id); vocab.special_eog_ids.insert(vocab.special_eos_id);
LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__); LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
} }
if (vocab.special_eot_id != -1 && vocab.special_eog_ids.count(vocab.special_eot_id) == 0) { if (vocab.special_eot_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eot_id) == 0) {
vocab.special_eog_ids.insert(vocab.special_eot_id); vocab.special_eog_ids.insert(vocab.special_eot_id);
LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__); LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
} }
if (vocab.special_eom_id != -1 && vocab.special_eog_ids.count(vocab.special_eom_id) == 0) { if (vocab.special_eom_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eom_id) == 0) {
vocab.special_eog_ids.insert(vocab.special_eom_id); vocab.special_eog_ids.insert(vocab.special_eom_id);
LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__); LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
} }
@ -6863,20 +6943,24 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str()); LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str());
// special tokens // special tokens
if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); } if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); } if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); } if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); } if (vocab.special_eom_id != -1) { LLAMA_LOG_INFO( "%s: EOM token = %d '%s'\n", __func__, vocab.special_eom_id, vocab.id_to_token[vocab.special_eom_id].text.c_str() ); }
if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); } if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
if (vocab.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); } if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); } if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
if (vocab.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); }
if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); } if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
if (vocab.special_prefix_id != -1) { LLAMA_LOG_INFO( "%s: PRE token = %d '%s'\n", __func__, vocab.special_prefix_id, vocab.id_to_token[vocab.special_prefix_id].text.c_str() ); }
if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); } if (vocab.special_fim_pre_id != -1) { LLAMA_LOG_INFO( "%s: FIM PRE token = %d '%s'\n", __func__, vocab.special_fim_pre_id, vocab.id_to_token[vocab.special_fim_pre_id].text.c_str() ); }
if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); } if (vocab.special_fim_suf_id != -1) { LLAMA_LOG_INFO( "%s: FIM SUF token = %d '%s'\n", __func__, vocab.special_fim_suf_id, vocab.id_to_token[vocab.special_fim_suf_id].text.c_str() ); }
if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); } if (vocab.special_fim_mid_id != -1) { LLAMA_LOG_INFO( "%s: FIM MID token = %d '%s'\n", __func__, vocab.special_fim_mid_id, vocab.id_to_token[vocab.special_fim_mid_id].text.c_str() ); }
if (vocab.special_eom_id != -1) { LLAMA_LOG_INFO( "%s: EOM token = %d '%s'\n", __func__, vocab.special_eom_id, vocab.id_to_token[vocab.special_eom_id].text.c_str() ); } if (vocab.special_fim_pad_id != -1) { LLAMA_LOG_INFO( "%s: FIM PAD token = %d '%s'\n", __func__, vocab.special_fim_pad_id, vocab.id_to_token[vocab.special_fim_pad_id].text.c_str() ); }
if (vocab.special_fim_rep_id != -1) { LLAMA_LOG_INFO( "%s: FIM REP token = %d '%s'\n", __func__, vocab.special_fim_rep_id, vocab.id_to_token[vocab.special_fim_rep_id].text.c_str() ); }
if (vocab.special_fim_sep_id != -1) { LLAMA_LOG_INFO( "%s: FIM SEP token = %d '%s'\n", __func__, vocab.special_fim_sep_id, vocab.id_to_token[vocab.special_fim_sep_id].text.c_str() ); }
for (const auto & id : vocab.special_eog_ids) { for (const auto & id : vocab.special_eog_ids) {
LLAMA_LOG_INFO( "%s: EOG token = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() ); LLAMA_LOG_INFO( "%s: EOG token = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() );
@ -19453,7 +19537,7 @@ struct llama_context * llama_new_context_with_model(
} }
LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__, LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
(float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f), ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
} }
@ -21307,6 +21391,10 @@ llama_token llama_token_eos(const struct llama_model * model) {
return llama_token_eos_impl(model->vocab); return llama_token_eos_impl(model->vocab);
} }
llama_token llama_token_eot(const struct llama_model * model) {
return llama_token_eot_impl(model->vocab);
}
llama_token llama_token_cls(const struct llama_model * model) { llama_token llama_token_cls(const struct llama_model * model) {
return llama_token_cls_impl(model->vocab); return llama_token_cls_impl(model->vocab);
} }
@ -21343,8 +21431,28 @@ llama_token llama_token_suffix(const struct llama_model * model) {
return llama_token_suffix_impl(model->vocab); return llama_token_suffix_impl(model->vocab);
} }
llama_token llama_token_eot(const struct llama_model * model) { llama_token llama_token_fim_pre(const struct llama_model * model) {
return llama_token_eot_impl(model->vocab); return llama_token_fim_pre_impl(model->vocab);
}
llama_token llama_token_fim_suf(const struct llama_model * model) {
return llama_token_fim_suf_impl(model->vocab);
}
llama_token llama_token_fim_mid(const struct llama_model * model) {
return llama_token_fim_mid_impl(model->vocab);
}
llama_token llama_token_fim_pad(const struct llama_model * model) {
return llama_token_fim_pad_impl(model->vocab);
}
llama_token llama_token_fim_rep(const struct llama_model * model) {
return llama_token_fim_rep_impl(model->vocab);
}
llama_token llama_token_fim_sep(const struct llama_model * model) {
return llama_token_fim_sep_impl(model->vocab);
} }
// //