mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 03:14:35 +00:00
1a159553f9
* Rewrite special token handling from #1931 * shorten param name, add st verification by type * use offsets instead of copy by substr * formatting, remove copying iterator on delete * llama : normalize code-style * swift fix * print pfx/sfx if verb, main: split pfx input sfx * dont add space when using special tokens * minor : comment + spacing --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
184 lines
8.9 KiB
C++
184 lines
8.9 KiB
C++
// Various helper functions and utilities
|
|
|
|
#pragma once
|
|
|
|
#include "llama.h"
|
|
|
|
#include "sampling.h"
|
|
|
|
#define LOG_NO_FILE_LINE_FUNCTION
|
|
#include "log.h"
|
|
|
|
#include <string>
|
|
#include <vector>
|
|
#include <random>
|
|
#include <thread>
|
|
#include <unordered_map>
|
|
#include <tuple>
|
|
|
|
#ifdef _WIN32
|
|
#define DIRECTORY_SEPARATOR '\\'
|
|
#else
|
|
#define DIRECTORY_SEPARATOR '/'
|
|
#endif // _WIN32
|
|
|
|
#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0)
|
|
#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)
|
|
|
|
#define print_build_info() do { \
|
|
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); \
|
|
fprintf(stderr, "%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET); \
|
|
} while(0)
|
|
|
|
//
|
|
// CLI argument parsing
|
|
//
|
|
int32_t get_num_physical_cores();
|
|
|
|
struct gpt_params {
|
|
uint32_t seed = -1; // RNG seed
|
|
int32_t n_threads = get_num_physical_cores();
|
|
int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
|
|
int32_t n_predict = -1; // new tokens to predict
|
|
int32_t n_ctx = 512; // context size
|
|
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
|
|
int32_t n_keep = 0; // number of tokens to keep from initial prompt
|
|
int32_t n_draft = 16; // number of tokens to draft during speculative decoding
|
|
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
|
|
int32_t n_parallel = 1; // number of parallel sequences to decode
|
|
int32_t n_sequences = 1; // number of sequences to decode
|
|
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
|
|
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
|
|
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
|
|
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
|
|
int32_t n_beams = 0; // if non-zero then use beam search of given width.
|
|
float rope_freq_base = 0.0f; // RoPE base frequency
|
|
float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
|
|
|
|
// // sampling parameters
|
|
struct llama_sampling_params sampling_params;
|
|
|
|
std::string model = "models/7B/ggml-model-f16.gguf"; // model path
|
|
std::string model_draft = ""; // draft model for speculative decoding
|
|
std::string model_alias = "unknown"; // model alias
|
|
std::string prompt = "";
|
|
std::string prompt_file = ""; // store the external prompt file name
|
|
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
|
|
std::string input_prefix = ""; // string to prefix user inputs with
|
|
std::string input_suffix = ""; // string to suffix user inputs with
|
|
std::string grammar = ""; // optional BNF-like grammar to constrain sampling
|
|
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
|
|
std::string logdir = ""; // directory in which to save YAML log files
|
|
|
|
std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
|
|
std::string lora_base = ""; // base model path for the lora adapter
|
|
|
|
int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
|
|
int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
|
|
// (which is more convenient to use for plotting)
|
|
//
|
|
bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
|
|
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
|
|
|
|
bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
|
|
bool memory_f16 = true; // use f16 instead of f32 for memory kv
|
|
bool random_prompt = false; // do not randomize prompt if none provided
|
|
bool use_color = false; // use color to distinguish generations and inputs
|
|
bool interactive = false; // interactive mode
|
|
bool prompt_cache_all = false; // save user input and generations to prompt cache
|
|
bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it
|
|
|
|
bool embedding = false; // get only sentence embedding
|
|
bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
|
|
bool interactive_first = false; // wait for user input immediately
|
|
bool multiline_input = false; // reverse the usage of `\`
|
|
bool simple_io = false; // improves compatibility with subprocesses and limited consoles
|
|
bool cont_batching = false; // insert new sequences for decoding on-the-fly
|
|
|
|
bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
|
|
bool ignore_eos = false; // ignore generated EOS tokens
|
|
bool instruct = false; // instruction mode (used for Alpaca models)
|
|
bool logits_all = false; // return logits for all tokens in the batch
|
|
bool use_mmap = true; // use mmap for faster loads
|
|
bool use_mlock = false; // use mlock to keep model in memory
|
|
bool numa = false; // attempt optimizations that help on some NUMA systems
|
|
bool verbose_prompt = false; // print prompt tokens before generation
|
|
bool infill = false; // use infill mode
|
|
|
|
// multimodal models (see examples/llava)
|
|
std::string mmproj = ""; // path to multimodal projector
|
|
std::string image = ""; // path to an image file
|
|
};
|
|
|
|
bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
|
|
|
|
void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
|
|
|
|
std::string get_system_info(const gpt_params & params);
|
|
|
|
std::string gpt_random_prompt(std::mt19937 & rng);
|
|
|
|
void process_escapes(std::string& input);
|
|
|
|
//
|
|
// Model utils
|
|
//
|
|
|
|
std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params);
|
|
struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params);
|
|
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
|
|
|
|
//
|
|
// Vocab utils
|
|
//
|
|
|
|
// tokenizes a string into a vector of tokens
|
|
// should work similar to Python's `tokenizer.encode`
|
|
std::vector<llama_token> llama_tokenize(
|
|
const struct llama_context * ctx,
|
|
const std::string & text,
|
|
bool add_bos,
|
|
bool special = false);
|
|
|
|
std::vector<llama_token> llama_tokenize(
|
|
const struct llama_model * model,
|
|
const std::string & text,
|
|
bool add_bos,
|
|
bool special = false);
|
|
|
|
// tokenizes a token into a piece
|
|
// should work similar to Python's `tokenizer.id_to_piece`
|
|
std::string llama_token_to_piece(
|
|
const struct llama_context * ctx,
|
|
llama_token token);
|
|
|
|
// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
|
|
// that takes into account the tokenizer type and decides how to handle the leading space
|
|
//
|
|
// detokenizes a vector of tokens into a string
|
|
// should work similar to Python's `tokenizer.decode`
|
|
// removes the leading space from the first non-BOS token
|
|
std::string llama_detokenize_spm(
|
|
llama_context * ctx,
|
|
const std::vector<llama_token> & tokens);
|
|
|
|
// detokenizes a vector of tokens into a string
|
|
// should work similar to Python's `tokenizer.decode`
|
|
std::string llama_detokenize_bpe(
|
|
llama_context * ctx,
|
|
const std::vector<llama_token> & tokens);
|
|
|
|
//
|
|
// YAML utils
|
|
//
|
|
|
|
bool create_directory_with_parents(const std::string & path);
|
|
void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
|
|
void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
|
|
void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data);
|
|
std::string get_sortable_timestamp();
|
|
|
|
void dump_non_result_info_yaml(
|
|
FILE * stream, const gpt_params & params, const llama_context * lctx,
|
|
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
|