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common : refactor arg parser (#9308)

Browse Source 
* (wip) argparser v3

* migrated

* add test

* handle env

* fix linux build

* add export-docs example

* fix build (2)

* skip build test-arg-parser on windows

* update server docs

* bring back missing --alias

* bring back --n-predict

* clarify test-arg-parser

* small correction

* add comments

* fix args with 2 values

* refine example-specific args

* no more lamba capture

Co-authored-by: slaren@users.noreply.github.com

* params.sparams

* optimize more

* export-docs --> gen-docs
This commit is contained in:
Xuan Son Nguyen 2024-09-07 20:43:51 +02:00 committed by GitHub
parent e32d0816ed
commit 1b9ae5189c
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GPG Key ID: B5690EEEBB952194
33 changed files with 2246 additions and 1947 deletions
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1
.gitignore vendored
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@ -61,6 +61,7 @@ llama-batched-swift
/rpc-server /rpc-server
out/ out/
tmp/ tmp/
autogen-*.md
# Deprecated # Deprecated

13
Makefile
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@ -39,10 +39,12 @@ BUILD_TARGETS = \
llama-tokenize \ llama-tokenize \
llama-vdot \ llama-vdot \
llama-cvector-generator \ llama-cvector-generator \
llama-gen-docs \
tests/test-c.o tests/test-c.o
# Binaries only useful for tests # Binaries only useful for tests
TEST_TARGETS = \ TEST_TARGETS = \
tests/test-arg-parser \
tests/test-autorelease \ tests/test-autorelease \
tests/test-backend-ops \ tests/test-backend-ops \
tests/test-chat-template \ tests/test-chat-template \
@ -1442,6 +1444,12 @@ examples/server/%.hpp: examples/server/public/% Makefile
echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \ echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \
) > $@ ) > $@
llama-gen-docs: examples/gen-docs/gen-docs.cpp \
$(OBJ_ALL)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
./llama-gen-docs
libllava.a: examples/llava/llava.cpp \ libllava.a: examples/llava/llava.cpp \
examples/llava/llava.h \ examples/llava/llava.h \
examples/llava/clip.cpp \ examples/llava/clip.cpp \
@ -1499,6 +1507,11 @@ run-benchmark-matmult: llama-benchmark-matmult
.PHONY: run-benchmark-matmult swift .PHONY: run-benchmark-matmult swift
tests/test-arg-parser: tests/test-arg-parser.cpp \
$(OBJ_ALL)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp \ tests/test-llama-grammar: tests/test-llama-grammar.cpp \
$(OBJ_ALL) $(OBJ_ALL)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)

3386
common/common.cpp
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File diff suppressed because it is too large Load Diff

114
common/common.h
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@ -14,8 +14,10 @@
#include <vector> #include <vector>
#include <random> #include <random>
#include <thread> #include <thread>
#include <set>
#include <unordered_map> #include <unordered_map>
#include <tuple> #include <tuple>
#include <functional>
#ifdef _WIN32 #ifdef _WIN32
#define DIRECTORY_SEPARATOR '\\' #define DIRECTORY_SEPARATOR '\\'
@ -61,6 +63,25 @@ int32_t cpu_get_num_math();
// CLI argument parsing // CLI argument parsing
// //
enum llama_example {
LLAMA_EXAMPLE_COMMON,
LLAMA_EXAMPLE_SPECULATIVE,
LLAMA_EXAMPLE_MAIN,
LLAMA_EXAMPLE_INFILL,
LLAMA_EXAMPLE_EMBEDDING,
LLAMA_EXAMPLE_PERPLEXITY,
LLAMA_EXAMPLE_RETRIEVAL,
LLAMA_EXAMPLE_PASSKEY,
LLAMA_EXAMPLE_IMATRIX,
LLAMA_EXAMPLE_BENCH,
LLAMA_EXAMPLE_SERVER,
LLAMA_EXAMPLE_CVECTOR_GENERATOR,
LLAMA_EXAMPLE_EXPORT_LORA,
LLAMA_EXAMPLE_LLAVA,
LLAMA_EXAMPLE_COUNT,
};
// dimensionality reduction methods, used by cvector-generator // dimensionality reduction methods, used by cvector-generator
enum dimre_method { enum dimre_method {
DIMRE_METHOD_PCA, DIMRE_METHOD_PCA,
@ -77,6 +98,8 @@ struct cpu_params {
}; };
struct gpt_params { struct gpt_params {
enum llama_example curr_ex = LLAMA_EXAMPLE_COMMON;
int32_t n_predict = -1; // new tokens to predict int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 0; // context size int32_t n_ctx = 0; // context size
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
@ -166,6 +189,7 @@ struct gpt_params {
bool kl_divergence = false; // compute KL divergence bool kl_divergence = false; // compute KL divergence
std::function<void(int, char **)> print_usage = nullptr; // print example-specific usage and example
bool usage = false; // print usage bool usage = false; // print usage
bool use_color = false; // use color to distinguish generations and inputs bool use_color = false; // use color to distinguish generations and inputs
bool special = false; // enable special token output bool special = false; // enable special token output
@ -276,13 +300,91 @@ struct gpt_params {
bool batched_bench_output_jsonl = false; bool batched_bench_output_jsonl = false;
}; };
void gpt_params_parse_from_env(gpt_params & params); struct llama_arg {
void gpt_params_handle_model_default(gpt_params & params); std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
std::vector<const char *> args;
const char * value_hint = nullptr; // help text or example for arg value
const char * value_hint_2 = nullptr; // for second arg value
const char * env = nullptr;
std::string help;
void (*handler_void) (gpt_params & params) = nullptr;
void (*handler_string) (gpt_params & params, const std::string &) = nullptr;
void (*handler_str_str)(gpt_params & params, const std::string &, const std::string &) = nullptr;
void (*handler_int) (gpt_params & params, int) = nullptr;
bool gpt_params_parse_ex (int argc, char ** argv, gpt_params & params); llama_arg(
bool gpt_params_parse (int argc, char ** argv, gpt_params & params); const std::initializer_list<const char *> & args,
bool gpt_params_find_arg (int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param); const char * value_hint,
void gpt_params_print_usage(int argc, char ** argv, const gpt_params & params); const std::string & help,
void (*handler)(gpt_params & params, const std::string &)
) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
llama_arg(
const std::initializer_list<const char *> & args,
const char * value_hint,
const std::string & help,
void (*handler)(gpt_params & params, int)
) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
llama_arg(
const std::initializer_list<const char *> & args,
const std::string & help,
void (*handler)(gpt_params & params)
) : args(args), help(help), handler_void(handler) {}
// support 2 values for arg
llama_arg(
const std::initializer_list<const char *> & args,
const char * value_hint,
const char * value_hint_2,
const std::string & help,
void (*handler)(gpt_params & params, const std::string &, const std::string &)
) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
llama_arg & set_examples(std::initializer_list<enum llama_example> examples) {
this->examples = std::move(examples);
return *this;
}
llama_arg & set_env(const char * env) {
help = help + "\n(env: " + env + ")";
this->env = env;
return *this;
}
bool in_example(enum llama_example ex) {
return examples.find(ex) != examples.end();
}
bool get_value_from_env(std::string & output) const {
if (env == nullptr) return false;
char * value = std::getenv(env);
if (value) {
output = value;
return true;
}
return false;
}
bool has_value_from_env() const {
return env != nullptr && std::getenv(env);
}
std::string to_string();
};
// initialize list of options (arguments) that can be used by the current example
std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex);
// optionally, we can provide "print_usage" to print example usage
std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex, std::function<void(int, char **)> print_usage);
// parse input arguments from CLI
// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
bool gpt_params_parse (int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options);
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options);
// print full usage message; it will be called internally by gpt_params_parse() if "-h" is set
void gpt_params_print_usage(gpt_params & params, std::vector<llama_arg> & options);
std::string gpt_params_get_system_info(const gpt_params & params); std::string gpt_params_get_system_info(const gpt_params & params);

8
examples/batched-bench/batched-bench.cpp
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@ -28,9 +28,7 @@ static std::vector<int> parse_list(char * p) {
return ret; return ret;
} }
static void print_usage(int argc, char ** argv, const gpt_params & params) { static void print_usage(int, char ** argv) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\nexample usage:\n"); LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]); LOG_TEE("\n %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]);
LOG_TEE("\n"); LOG_TEE("\n");
@ -39,8 +37,8 @@ static void print_usage(int argc, char ** argv, const gpt_params & params) {
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_BENCH, print_usage);
print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

8
examples/batched/batched.cpp
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@ -6,9 +6,7 @@
#include <string> #include <string>
#include <vector> #include <vector>
static void print_usage(int argc, char ** argv, const gpt_params & params) { static void print_usage(int, char ** argv) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\nexample usage:\n"); LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]); LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]);
LOG_TEE("\n"); LOG_TEE("\n");
@ -20,8 +18,8 @@ int main(int argc, char ** argv) {
params.prompt = "Hello my name is"; params.prompt = "Hello my name is";
params.n_predict = 32; params.n_predict = 32;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, print_usage);
print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

8
examples/cvector-generator/cvector-generator.cpp
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@ -35,9 +35,7 @@ static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
return ret; return ret;
} }
static void print_usage(int argc, char ** argv, const gpt_params & params) { static void print_usage(int, char ** argv) {
gpt_params_print_usage(argc, argv, params);
printf("\nexample usage:\n"); printf("\nexample usage:\n");
printf("\n CPU only: %s -m ./llama-3.Q4_K_M.gguf\n", argv[0]); printf("\n CPU only: %s -m ./llama-3.Q4_K_M.gguf\n", argv[0]);
printf("\n with GPU: %s -m ./llama-3.Q4_K_M.gguf -ngl 99\n", argv[0]); printf("\n with GPU: %s -m ./llama-3.Q4_K_M.gguf -ngl 99\n", argv[0]);
@ -390,8 +388,8 @@ static int prepare_entries(gpt_params & params, train_context & ctx_train) {
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage);
print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

4
examples/embedding/embedding.cpp
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@ -79,8 +79,8 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_EMBEDDING);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

4
examples/eval-callback/eval-callback.cpp
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@ -144,8 +144,8 @@ int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

8
examples/export-lora/export-lora.cpp
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@ -391,9 +391,7 @@ struct lora_merge_ctx {
} }
}; };
static void print_usage(int argc, char ** argv, const gpt_params & params) { static void print_usage(int, char ** argv) {
gpt_params_print_usage(argc, argv, params);
printf("\nexample usage:\n"); printf("\nexample usage:\n");
printf("\n %s -m base-model.gguf --lora lora-file.gguf -o merged-model-f16.gguf\n", argv[0]); printf("\n %s -m base-model.gguf --lora lora-file.gguf -o merged-model-f16.gguf\n", argv[0]);
printf("\nNOTE: output model is F16\n"); printf("\nNOTE: output model is F16\n");
@ -403,8 +401,8 @@ static void print_usage(int argc, char ** argv, const gpt_params & params) {
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage);
print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

5
examples/gen-docs/CMakeLists.txt Normal file
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@ -0,0 +1,5 @@
set(TARGET llama-gen-docs)
add_executable(${TARGET} gen-docs.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

51
examples/gen-docs/gen-docs.cpp Normal file
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@ -0,0 +1,51 @@
#include "common.h"
#include <fstream>
#include <string>
// Export usage message (-h) to markdown format
static void export_md(std::string fname, llama_example ex) {
std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
gpt_params params;
auto options = gpt_params_parser_init(params, ex);
file << "| Argument | Explanation |\n";
file << "| -------- | ----------- |\n";
for (auto & opt : options) {
file << "| `";
// args
for (const auto & arg : opt.args) {
if (arg == opt.args.front()) {
file << arg;
if (opt.args.size() > 1) file << ", ";
} else {
file << arg << (arg != opt.args.back() ? ", " : "");
}
}
// value hint
if (opt.value_hint) {
std::string md_value_hint(opt.value_hint);
string_replace_all(md_value_hint, "|", "\\|");
file << " " << md_value_hint;
}
if (opt.value_hint_2) {
std::string md_value_hint_2(opt.value_hint_2);
string_replace_all(md_value_hint_2, "|", "\\|");
file << " " << md_value_hint_2;
}
// help text
std::string md_help(opt.help);
string_replace_all(md_help, "\n", "<br/>");
string_replace_all(md_help, "|", "\\|");
file << "` | " << md_help << " |\n";
}
}
int main(int, char **) {
export_md("autogen-main.md", LLAMA_EXAMPLE_MAIN);
export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER);
return 0;
}

4
examples/gritlm/gritlm.cpp
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@ -154,8 +154,8 @@ static std::string gritlm_instruction(const std::string & instruction) {
int main(int argc, char * argv[]) { int main(int argc, char * argv[]) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

8
examples/imatrix/imatrix.cpp
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@ -17,9 +17,7 @@
#pragma warning(disable: 4244 4267) // possible loss of data #pragma warning(disable: 4244 4267) // possible loss of data
#endif #endif
static void print_usage(int argc, char ** argv, const gpt_params & params) { static void print_usage(int, char ** argv) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\nexample usage:\n"); LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s \\\n" LOG_TEE("\n %s \\\n"
" -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n" " -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n"
@ -579,8 +577,8 @@ int main(int argc, char ** argv) {
params.logits_all = true; params.logits_all = true;
params.verbosity = 1; params.verbosity = 1;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, print_usage);
print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

4
examples/infill/infill.cpp
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@ -105,8 +105,8 @@ int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
g_params = &params; g_params = &params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_INFILL);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

10
examples/llava/llava-cli.cpp
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@ -112,9 +112,7 @@ struct llava_context {
struct llama_model * model = NULL; struct llama_model * model = NULL;
}; };
static void print_usage(int argc, char ** argv, const gpt_params & params) { static void print_usage(int, char ** argv) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\n example usage:\n"); LOG_TEE("\n example usage:\n");
LOG_TEE("\n %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); LOG_TEE("\n %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
LOG_TEE("\n note: a lower temperature value like 0.1 is recommended for better quality.\n"); LOG_TEE("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
@ -280,8 +278,8 @@ int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_LLAVA, print_usage);
print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }
@ -293,7 +291,7 @@ int main(int argc, char ** argv) {
#endif // LOG_DISABLE_LOGS #endif // LOG_DISABLE_LOGS
if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
print_usage(argc, argv, {}); print_usage(argc, argv);
return 1; return 1;
} }
auto model = llava_init(&params); auto model = llava_init(&params);

5
examples/llava/minicpmv-cli.cpp
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@ -253,8 +253,8 @@ int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, show_additional_info);
show_additional_info(argc, argv); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }
@ -266,7 +266,6 @@ int main(int argc, char ** argv) {
#endif // LOG_DISABLE_LOGS #endif // LOG_DISABLE_LOGS
if (params.mmproj.empty() || (params.image.empty())) { if (params.mmproj.empty() || (params.image.empty())) {
gpt_params_print_usage(argc, argv, params);
show_additional_info(argc, argv); show_additional_info(argc, argv);
return 1; return 1;
} }

4
examples/lookahead/lookahead.cpp
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@ -36,8 +36,8 @@ struct ngram_container {
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

4
examples/lookup/lookup-create.cpp
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@ -13,8 +13,8 @@
int main(int argc, char ** argv){ int main(int argc, char ** argv){
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

4
examples/lookup/lookup-stats.cpp
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@ -15,8 +15,8 @@
int main(int argc, char ** argv){ int main(int argc, char ** argv){
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

4
examples/lookup/lookup.cpp
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@ -12,8 +12,8 @@
int main(int argc, char ** argv){ int main(int argc, char ** argv){
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

11
examples/main/main.cpp
View File

@ -41,6 +41,13 @@ static std::vector<llama_token> * g_output_tokens;
static bool is_interacting = false; static bool is_interacting = false;
static bool need_insert_eot = false; static bool need_insert_eot = false;
static void print_usage(int, char ** argv) {
printf("\nexample usage:\n");
printf("\n text generation: %s -m your_model.gguf -p \"I believe the meaning of life is\" -n 128\n", argv[0]);
printf("\n chat (conversation): %s -m your_model.gguf -p \"You are a helpful assistant\" -cnv\n", argv[0]);
printf("\n");
}
static bool file_exists(const std::string & path) { static bool file_exists(const std::string & path) {
std::ifstream f(path.c_str()); std::ifstream f(path.c_str());
return f.good(); return f.good();
@ -131,9 +138,9 @@ static std::string chat_add_and_format(struct llama_model * model, std::vector<l
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
g_params = &params; g_params = &params;
auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_MAIN, print_usage);
if (!gpt_params_parse(argc, argv, params)) { if (!gpt_params_parse(argc, argv, params, options)) {
gpt_params_print_usage(argc, argv, params);
return 1; return 1;
} }

4
examples/parallel/parallel.cpp
View File

@ -100,8 +100,8 @@ int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

8
examples/passkey/passkey.cpp
View File

@ -6,9 +6,7 @@
#include <string> #include <string>
#include <vector> #include <vector>
static void print_usage(int argc, char ** argv, const gpt_params & params) { static void print_usage(int, char ** argv) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\nexample usage:\n"); LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]); LOG_TEE("\n %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]);
LOG_TEE("\n"); LOG_TEE("\n");
@ -21,8 +19,8 @@ int main(int argc, char ** argv) {
params.n_keep = 32; params.n_keep = 32;
params.i_pos = -1; params.i_pos = -1;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_PASSKEY, print_usage);
print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

4
examples/perplexity/perplexity.cpp
View File

@ -1967,8 +1967,8 @@ int main(int argc, char ** argv) {
params.n_ctx = 512; params.n_ctx = 512;
params.logits_all = true; params.logits_all = true;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_PERPLEXITY);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

8
examples/retrieval/retrieval.cpp
View File

@ -4,9 +4,7 @@
#include <algorithm> #include <algorithm>
#include <fstream> #include <fstream>
static void print_usage(int argc, char ** argv, const gpt_params & params) { static void print_usage(int, char ** argv) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\nexample usage:\n"); LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]); LOG_TEE("\n %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]);
LOG_TEE("\n"); LOG_TEE("\n");
@ -113,8 +111,8 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_RETRIEVAL, print_usage);
print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

4
examples/save-load-state/save-load-state.cpp
View File

@ -10,8 +10,8 @@ int main(int argc, char ** argv) {
params.prompt = "The quick brown fox"; params.prompt = "The quick brown fox";
params.sparams.seed = 1234; params.sparams.seed = 1234;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

381
examples/server/README.md
View File

@ -17,262 +17,131 @@ The project is under active development, and we are [looking for feedback and co
## Usage ## Usage
``` | Argument | Explanation |
usage: ./llama-server [options] | -------- | ----------- |
| `-h, --help, --usage` | print usage and exit |
| `--version` | show version and build info |
| `-v, --verbose` | print verbose information |
| `--verbosity N` | set specific verbosity level (default: 0) |
| `--verbose-prompt` | print a verbose prompt before generation (default: false) |
| `--no-display-prompt` | don't print prompt at generation (default: false) |
| `-s, --seed SEED` | RNG seed (default: -1, use random seed for < 0) |
| `-t, --threads N` | number of threads to use during generation (default: -1)<br/>(env: LLAMA_ARG_THREADS) |
| `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) |
| `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") |
| `-Cr, --cpu-range lo-hi` | range of CPUs for affinity. Complements --cpu-mask |
| `--cpu-strict <0\|1>` | use strict CPU placement (default: 0)<br/> |
| `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50)<br/> |
| `-Cb, --cpu-mask-batch M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) |
| `-Crb, --cpu-range-batch lo-hi` | ranges of CPUs for affinity. Complements --cpu-mask-batch |
| `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) |
| `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) |
| `-lcs, --lookup-cache-static FNAME` | path to static lookup cache to use for lookup decoding (not updated by generation) |
| `-lcd, --lookup-cache-dynamic FNAME` | path to dynamic lookup cache to use for lookup decoding (updated by generation) |
| `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE) |
| `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)<br/>(env: LLAMA_ARG_N_PREDICT) |
| `-b, --batch-size N` | logical maximum batch size (default: 2048)<br/>(env: LLAMA_ARG_BATCH) |
| `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
| `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
| `--chunks N` | max number of chunks to process (default: -1, -1 = all) |
| `-fa, --flash-attn` | enable Flash Attention (default: disabled)<br/>(env: LLAMA_ARG_FLASH_ATTN) |
| `-p, --prompt PROMPT` | prompt to start generation with |
| `-f, --file FNAME` | a file containing the prompt (default: none) |
| `--in-file FNAME` | an input file (repeat to specify multiple files) |
| `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) |
| `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
| `--no-escape` | do not process escape sequences |
| `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typical_p;top_p;min_p;temperature) |
| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) |
| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
| `--penalize-nl` | penalize newline tokens (default: false) |
| `--temp N` | temperature (default: 0.8) |
| `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
| `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
| `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) |
| `--tfs N` | tail free sampling, parameter z (default: 1.0, 1.0 = disabled) |
| `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) |
| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) |
| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) |
| `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) |
| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) |
| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) |
| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) |
| `--mirostat N` | use Mirostat sampling.<br/>Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.<br/>(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) |
| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) |
| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) |
| `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,<br/>i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',<br/>or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' |
| `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') |
| `--grammar-file FNAME` | file to read grammar from |
| `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
| `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model |
| `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N |
| `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model) |
| `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N |
| `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size) |
| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation) |
| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: 1.0) |
| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0) |
| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0) |
| `-gan, --grp-attn-n N` | group-attention factor (default: 1) |
| `-gaw, --grp-attn-w N` | group-attention width (default: 512.0) |
| `-dkvc, --dump-kv-cache` | verbose print of the KV cache |
| `-nkvo, --no-kv-offload` | disable KV offload |
| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16) |
| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16) |
| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: -1.0, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
| `-np, --parallel N` | number of parallel sequences to decode (default: 1) |
| `-ns, --sequences N` | number of sequences to decode (default: 1) |
| `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
| `-nocb, --no-cont-batching` | disable continuous batching<br/>(env: LLAMA_ARG_NO_CONT_BATCHING) |
| `--mlock` | force system to keep model in RAM rather than swapping or compressing |
| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock) |
| `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggerganov/llama.cpp/issues/1437 |
| `-ngl, --gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs |
| `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1 |
| `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0) |
| `--check-tensors` | check model tensor data for invalid values (default: false) |
| `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false |
| `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) |
| `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) |
| `--control-vector FNAME` | add a control vector<br/>note: this argument can be repeated to add multiple control vectors |
| `--control-vector-scaled FNAME SCALE` | add a control vector with user defined scaling SCALE<br/>note: this argument can be repeated to add multiple scaled control vectors |
| `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive |
| `-a, --alias STRING` | set alias for model name (to be used by REST API)<br/>(env: LLAMA_ARG_MODEL) |
| `-m, --model FNAME` | model path (default: `models/$filename` with filename from `--hf-file` or `--model-url` if set, otherwise models/7B/ggml-model-f16.gguf)<br/>(env: LLAMA_ARG_MODEL) |
| `-mu, --model-url MODEL_URL` | model download url (default: unused)<br/>(env: LLAMA_ARG_MODEL_URL) |
| `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
| `-hff, --hf-file FILE` | Hugging Face model file (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)<br/>(env: HF_TOKEN) |
| `--host HOST` | ip address to listen (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
| `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
| `--path PATH` | path to serve static files from (default: ) |
| `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
| `--api-key KEY` | API key to use for authentication (default: none)<br/>(env: LLAMA_API_KEY) |
| `--api-key-file FNAME` | path to file containing API keys (default: none) |
| `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key |
| `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate |
| `--timeout N` | server read/write timeout in seconds (default: 600) |
| `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(env: LLAMA_ARG_THREADS_HTTP) |
| `-spf, --system-prompt-file FNAME` | set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications |
| `--log-format {text, json}` | log output format: json or text (default: json) |
| `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_METRICS) |
| `--no-slots` | disables slots monitoring endpoint (default: enabled)<br/>(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) |
| `--slot-save-path PATH` | path to save slot kv cache (default: disabled) |
| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted:<br/>https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
| `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)<br/> |
| `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
| `-ld, --logdir LOGDIR` | path under which to save YAML logs (no logging if unset) |
| `--log-test` | Log test |
| `--log-disable` | Log disable |
| `--log-enable` | Log enable |
| `--log-new` | Log new |
| `--log-append` | Log append |
| `--log-file FNAME` | Log file |
general: Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.
-h, --help, --usage print usage and exit
--version show version and build info
-v, --verbose print verbose information
--verbosity N set specific verbosity level (default: 0)
--verbose-prompt print a verbose prompt before generation (default: false)
--no-display-prompt don't print prompt at generation (default: false)
-co, --color colorise output to distinguish prompt and user input from generations (default: false)
-s, --seed SEED RNG seed (default: -1, use random seed for < 0)
-t, --threads N number of threads to use during generation (default: 8)
-tb, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)
-td, --threads-draft N number of threads to use during generation (default: same as --threads)
-tbd, --threads-batch-draft N number of threads to use during batch and prompt processing (default: same as --threads-draft)
--draft N number of tokens to draft for speculative decoding (default: 5)
-ps, --p-split N speculative decoding split probability (default: 0.1)
-lcs, --lookup-cache-static FNAME
path to static lookup cache to use for lookup decoding (not updated by generation)
-lcd, --lookup-cache-dynamic FNAME
path to dynamic lookup cache to use for lookup decoding (updated by generation)
-c, --ctx-size N size of the prompt context (default: 0, 0 = loaded from model)
-n, --predict N number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)
-b, --batch-size N logical maximum batch size (default: 2048)
-ub, --ubatch-size N physical maximum batch size (default: 512)
--keep N number of tokens to keep from the initial prompt (default: 0, -1 = all)
--chunks N max number of chunks to process (default: -1, -1 = all)
-fa, --flash-attn enable Flash Attention (default: disabled)
-p, --prompt PROMPT prompt to start generation with
in conversation mode, this will be used as system prompt
(default: '')
-f, --file FNAME a file containing the prompt (default: none)
--in-file FNAME an input file (repeat to specify multiple files)
-bf, --binary-file FNAME binary file containing the prompt (default: none)
-e, --escape process escapes sequences (\n, \r, \t, \', \", \\) (default: true)
--no-escape do not process escape sequences
-ptc, --print-token-count N print token count every N tokens (default: -1)
--prompt-cache FNAME file to cache prompt state for faster startup (default: none)
--prompt-cache-all if specified, saves user input and generations to cache as well
not supported with --interactive or other interactive options
--prompt-cache-ro if specified, uses the prompt cache but does not update it
-r, --reverse-prompt PROMPT halt generation at PROMPT, return control in interactive mode
can be specified more than once for multiple prompts
-sp, --special special tokens output enabled (default: false)
-cnv, --conversation run in conversation mode, does not print special tokens and suffix/prefix
if suffix/prefix are not specified, default chat template will be used
(default: false)
-i, --interactive run in interactive mode (default: false)
-if, --interactive-first run in interactive mode and wait for input right away (default: false)
-mli, --multiline-input allows you to write or paste multiple lines without ending each in '\'
--in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string
--in-prefix STRING string to prefix user inputs with (default: empty)
--in-suffix STRING string to suffix after user inputs with (default: empty)
--spm-infill use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled)
sampling:
--samplers SAMPLERS samplers that will be used for generation in the order, separated by ';'
(default: top_k;tfs_z;typical_p;top_p;min_p;temperature)
--sampling-seq SEQUENCE simplified sequence for samplers that will be used (default: kfypmt)
--ignore-eos ignore end of stream token and continue generating (implies --logit-bias EOS-inf)
--penalize-nl penalize newline tokens (default: false)
--temp N temperature (default: 0.8)
--top-k N top-k sampling (default: 40, 0 = disabled)
--top-p N top-p sampling (default: 0.9, 1.0 = disabled)
--min-p N min-p sampling (default: 0.1, 0.0 = disabled)
--tfs N tail free sampling, parameter z (default: 1.0, 1.0 = disabled)
--typical N locally typical sampling, parameter p (default: 1.0, 1.0 = disabled)
--repeat-last-n N last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size)
--repeat-penalty N penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled)
--presence-penalty N repeat alpha presence penalty (default: 0.0, 0.0 = disabled)
--frequency-penalty N repeat alpha frequency penalty (default: 0.0, 0.0 = disabled)
--dynatemp-range N dynamic temperature range (default: 0.0, 0.0 = disabled)
--dynatemp-exp N dynamic temperature exponent (default: 1.0)
--mirostat N use Mirostat sampling.
Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)
--mirostat-lr N Mirostat learning rate, parameter eta (default: 0.1)
--mirostat-ent N Mirostat target entropy, parameter tau (default: 5.0)
-l TOKEN_ID(+/-)BIAS modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'
--cfg-negative-prompt PROMPT
negative prompt to use for guidance (default: '')
--cfg-negative-prompt-file FNAME
negative prompt file to use for guidance
--cfg-scale N strength of guidance (default: 1.0, 1.0 = disable)
--chat-template JINJA_TEMPLATE
set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted:
https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
grammar:
--grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '')
--grammar-file FNAME file to read grammar from
-j, --json-schema SCHEMA JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead
embedding:
--pooling {none,mean,cls,last}
pooling type for embeddings, use model default if unspecified
--attention {causal,non-causal}
attention type for embeddings, use model default if unspecified
context hacking:
--rope-scaling {none,linear,yarn}
RoPE frequency scaling method, defaults to linear unless specified by the model
--rope-scale N RoPE context scaling factor, expands context by a factor of N
--rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
--rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N
--yarn-orig-ctx N YaRN: original context size of model (default: 0 = model training context size)
--yarn-ext-factor N YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
--yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
--yarn-beta-slow N YaRN: high correction dim or alpha (default: 1.0)
--yarn-beta-fast N YaRN: low correction dim or beta (default: 32.0)
-gan, --grp-attn-n N group-attention factor (default: 1)
-gaw, --grp-attn-w N group-attention width (default: 512.0)
-dkvc, --dump-kv-cache verbose print of the KV cache
-nkvo, --no-kv-offload disable KV offload
-ctk, --cache-type-k TYPE KV cache data type for K (default: f16)
-ctv, --cache-type-v TYPE KV cache data type for V (default: f16)
perplexity:
--all-logits return logits for all tokens in the batch (default: false)
--hellaswag compute HellaSwag score over random tasks from datafile supplied with -f
--hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: 400)
--winogrande compute Winogrande score over random tasks from datafile supplied with -f
--winogrande-tasks N number of tasks to use when computing the Winogrande score (default: 0)
--multiple-choice compute multiple choice score over random tasks from datafile supplied with -f
--multiple-choice-tasks N
number of tasks to use when computing the multiple choice score (default: 0)
--kl-divergence computes KL-divergence to logits provided via --kl-divergence-base
--ppl-stride N stride for perplexity calculation (default: 0)
--ppl-output-type {0,1} output type for perplexity calculation (default: 0)
parallel:
-dt, --defrag-thold N KV cache defragmentation threshold (default: -1.0, < 0 - disabled)
-np, --parallel N number of parallel sequences to decode (default: 1)
-ns, --sequences N number of sequences to decode (default: 1)
-cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: enabled)
multi-modality:
--mmproj FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md
--image FILE path to an image file. use with multimodal models. Specify multiple times for batching
backend:
--rpc SERVERS comma separated list of RPC servers
--mlock force system to keep model in RAM rather than swapping or compressing
--no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)
--numa TYPE attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggerganov/llama.cpp/issues/1437
model:
--check-tensors check model tensor data for invalid values (default: false)
--override-kv KEY=TYPE:VALUE
advanced option to override model metadata by key. may be specified multiple times.
types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false
--lora FNAME apply LoRA adapter (implies --no-mmap)
--lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)
--lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter
--control-vector FNAME add a control vector
note: this argument can be repeated to add multiple control vectors
--control-vector-scaled FNAME SCALE
add a control vector with user defined scaling SCALE
note: this argument can be repeated to add multiple scaled control vectors
--control-vector-layer-range START END
layer range to apply the control vector(s) to, start and end inclusive
-m, --model FNAME model path (default: models/$filename with filename from --hf-file
or --model-url if set, otherwise models/7B/ggml-model-f16.gguf)
-md, --model-draft FNAME draft model for speculative decoding (default: unused)
-mu, --model-url MODEL_URL model download url (default: unused)
-hfr, --hf-repo REPO Hugging Face model repository (default: unused)
-hff, --hf-file FILE Hugging Face model file (default: unused)
-hft, --hf-token TOKEN Hugging Face access token (default: value from HF_TOKEN environment variable)
server:
--host HOST ip address to listen (default: 127.0.0.1)
--port PORT port to listen (default: 8080)
--path PATH path to serve static files from (default: )
--embedding(s) restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
--api-key KEY API key to use for authentication (default: none)
--api-key-file FNAME path to file containing API keys (default: none)
--ssl-key-file FNAME path to file a PEM-encoded SSL private key
--ssl-cert-file FNAME path to file a PEM-encoded SSL certificate
--timeout N server read/write timeout in seconds (default: 600)
--threads-http N number of threads used to process HTTP requests (default: -1)
--system-prompt-file FNAME
set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications
--log-format {text,json}
log output format: json or text (default: json)
--metrics enable prometheus compatible metrics endpoint (default: disabled)
--no-slots disables slots monitoring endpoint (default: enabled)
--slot-save-path PATH path to save slot kv cache (default: disabled)
--chat-template JINJA_TEMPLATE
set custom jinja chat template (default: template taken from model's metadata)
only commonly used templates are accepted:
https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
-sps, --slot-prompt-similarity SIMILARITY
how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)
--lora-init-without-apply
load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled)
logging:
--simple-io use basic IO for better compatibility in subprocesses and limited consoles
-ld, --logdir LOGDIR path under which to save YAML logs (no logging if unset)
--log-test Run simple logging test
--log-disable Disable trace logs
--log-enable Enable trace logs
--log-file FNAME Specify a log filename (without extension)
--log-new Create a separate new log file on start. Each log file will have unique name: "<name>.<ID>.log"
--log-append Don't truncate the old log file.
```
Available environment variables (if specified, these variables will override parameters specified in arguments):
- `LLAMA_CACHE`: cache directory, used by `--hf-repo`
- `HF_TOKEN`: Hugging Face access token, used when accessing a gated model with `--hf-repo`
- `LLAMA_ARG_MODEL`: equivalent to `-m`
- `LLAMA_ARG_MODEL_URL`: equivalent to `-mu`
- `LLAMA_ARG_MODEL_ALIAS`: equivalent to `-a`
- `LLAMA_ARG_HF_REPO`: equivalent to `--hf-repo`
- `LLAMA_ARG_HF_FILE`: equivalent to `--hf-file`
- `LLAMA_ARG_THREADS`: equivalent to `-t`
- `LLAMA_ARG_CTX_SIZE`: equivalent to `-c`
- `LLAMA_ARG_N_PARALLEL`: equivalent to `-np`
- `LLAMA_ARG_BATCH`: equivalent to `-b`
- `LLAMA_ARG_UBATCH`: equivalent to `-ub`
- `LLAMA_ARG_N_GPU_LAYERS`: equivalent to `-ngl`
- `LLAMA_ARG_THREADS_HTTP`: equivalent to `--threads-http`
- `LLAMA_ARG_CHAT_TEMPLATE`: equivalent to `--chat-template`
- `LLAMA_ARG_N_PREDICT`: equivalent to `-n`
- `LLAMA_ARG_ENDPOINT_METRICS`: if set to `1`, it will enable metrics endpoint (equivalent to `--metrics`)
- `LLAMA_ARG_ENDPOINT_SLOTS`: if set to `0`, it will **disable** slots endpoint (equivalent to `--no-slots`). This feature is enabled by default.
- `LLAMA_ARG_EMBEDDINGS`: if set to `1`, it will enable embeddings endpoint (equivalent to `--embeddings`)
- `LLAMA_ARG_FLASH_ATTN`: if set to `1`, it will enable flash attention (equivalent to `-fa`)
- `LLAMA_ARG_CONT_BATCHING`: if set to `0`, it will **disable** continuous batching (equivalent to `--no-cont-batching`). This feature is enabled by default.
- `LLAMA_ARG_DEFRAG_THOLD`: equivalent to `-dt`
- `LLAMA_ARG_HOST`: equivalent to `--host`
- `LLAMA_ARG_PORT`: equivalent to `--port`
Example usage of docker compose with environment variables: Example usage of docker compose with environment variables:
@ -289,7 +158,7 @@ services:
LLAMA_ARG_MODEL: /models/my_model.gguf LLAMA_ARG_MODEL: /models/my_model.gguf
LLAMA_ARG_CTX_SIZE: 4096 LLAMA_ARG_CTX_SIZE: 4096
LLAMA_ARG_N_PARALLEL: 2 LLAMA_ARG_N_PARALLEL: 2
LLAMA_ARG_ENDPOINT_METRICS: 1 # to disable, either remove or set to 0 LLAMA_ARG_ENDPOINT_METRICS: 1
LLAMA_ARG_PORT: 8080 LLAMA_ARG_PORT: 8080
``` ```

7
examples/server/server.cpp
View File

@ -2423,14 +2423,11 @@ int main(int argc, char ** argv) {
// own arguments required by this example // own arguments required by this example
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_SERVER);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }
// parse arguments from environment variables
gpt_params_parse_from_env(params);
// TODO: not great to use extern vars // TODO: not great to use extern vars
server_log_json = params.log_json; server_log_json = params.log_json;
server_verbose = params.verbosity > 0; server_verbose = params.verbosity > 0;

8
examples/simple/simple.cpp
View File

@ -6,9 +6,7 @@
#include <string> #include <string>
#include <vector> #include <vector>
static void print_usage(int argc, char ** argv, const gpt_params & params) { static void print_usage(int, char ** argv) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\nexample usage:\n"); LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32\n", argv[0]); LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32\n", argv[0]);
LOG_TEE("\n"); LOG_TEE("\n");
@ -20,8 +18,8 @@ int main(int argc, char ** argv) {
params.prompt = "Hello my name is"; params.prompt = "Hello my name is";
params.n_predict = 32; params.n_predict = 32;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, print_usage);
print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

4
examples/speculative/speculative.cpp
View File

@ -27,8 +27,8 @@ struct seq_draft {
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
gpt_params params; gpt_params params;
if (!gpt_params_parse(argc, argv, params)) { auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_SPECULATIVE);
gpt_params_print_usage(argc, argv, params); if (!gpt_params_parse(argc, argv, params, options)) {
return 1; return 1;
} }

1
tests/CMakeLists.txt
View File

@ -108,6 +108,7 @@ llama_test(test-tokenizer-1-spm NAME test-tokenizer-1-llama-spm ARGS ${CMAKE_CU
#llama_test(test-tokenizer-1-spm NAME test-tokenizer-1-baichuan ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf) #llama_test(test-tokenizer-1-spm NAME test-tokenizer-1-baichuan ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
# llama_target_and_test(test-double-float.cpp) # SLOW # llama_target_and_test(test-double-float.cpp) # SLOW
llama_target_and_test(test-arg-parser.cpp)
llama_target_and_test(test-quantize-fns.cpp) llama_target_and_test(test-quantize-fns.cpp)
llama_target_and_test(test-quantize-perf.cpp) llama_target_and_test(test-quantize-perf.cpp)
llama_target_and_test(test-sampling.cpp) llama_target_and_test(test-sampling.cpp)

96
tests/test-arg-parser.cpp Normal file
View File

@ -0,0 +1,96 @@
#include <string>
#include <vector>
#include <sstream>
#undef NDEBUG
#include <cassert>
#include "common.h"
int main(void) {
gpt_params params;
printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
for (int ex = 0; ex < LLAMA_EXAMPLE_COUNT; ex++) {
try {
gpt_params_parser_init(params, (enum llama_example)ex);
} catch (std::exception & e) {
printf("%s\n", e.what());
assert(false);
}
}
auto list_str_to_char = [](std::vector<std::string> & argv) -> std::vector<char *> {
std::vector<char *> res;
for (auto & arg : argv) {
res.push_back(const_cast<char *>(arg.data()));
}
return res;
};
std::vector<std::string> argv;
auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
printf("test-arg-parser: test invalid usage\n\n");
argv = {"binary_name", "-m"};
assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
argv = {"binary_name", "-ngl", "hello"};
assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
argv = {"binary_name", "-sm", "hello"};
assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
printf("test-arg-parser: test valid usage\n\n");
argv = {"binary_name", "-m", "model_file.gguf"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
assert(params.model == "model_file.gguf");
argv = {"binary_name", "-t", "1234"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
assert(params.cpuparams.n_threads == 1234);
argv = {"binary_name", "--verbose"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
assert(params.verbosity == 1);
argv = {"binary_name", "-m", "abc.gguf", "--predict", "6789", "--batch-size", "9090"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
assert(params.model == "abc.gguf");
assert(params.n_predict == 6789);
assert(params.n_batch == 9090);
// skip this part on windows, because setenv is not supported
#ifdef _WIN32
printf("test-arg-parser: skip on windows build\n");
#else
printf("test-arg-parser: test environment variables (valid + invalid usages)\n\n");
setenv("LLAMA_ARG_THREADS", "blah", true);
argv = {"binary_name"};
assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
setenv("LLAMA_ARG_THREADS", "1010", true);
argv = {"binary_name"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
assert(params.model == "blah.gguf");
assert(params.cpuparams.n_threads == 1010);
printf("test-arg-parser: test environment variables being overwritten\n\n");
setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
setenv("LLAMA_ARG_THREADS", "1010", true);
argv = {"binary_name", "-m", "overwritten.gguf"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
assert(params.model == "overwritten.gguf");
assert(params.cpuparams.n_threads == 1010);
#endif // _WIN32
printf("test-arg-parser: all tests OK\n\n");
}