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Commit 22cb4ffc authored by wangkx1's avatar wangkx1
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remove v0.3.5

parent cb75098c
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ollama/llm/llama.cpp/common/common.cpp deleted 100755 → 0
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#if defined(_MSC_VER)
#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
#endif
#include "common.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
#include "json-schema-to-grammar.h"
#include "llama.h"
#include <algorithm>
#include <cinttypes>
#include <cmath>
#include <codecvt>
#include <cstdarg>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <iterator>
#include <regex>
#include <sstream>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
#include <sys/sysctl.h>
#endif
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
# define NOMINMAX
#endif
#include <locale>
#include <windows.h>
#include <fcntl.h>
#include <io.h>
#else
#include <sys/ioctl.h>
#include <sys/stat.h>
#include <unistd.h>
#endif
#if defined(LLAMA_USE_CURL)
#include <curl/curl.h>
#include <curl/easy.h>
#include <thread>
#include <future>
#endif
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
#define GGML_USE_CUDA_SYCL
#endif
#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
#define GGML_USE_CUDA_SYCL_VULKAN
#endif
#if defined(LLAMA_USE_CURL)
#ifdef __linux__
#include <linux/limits.h>
#elif defined(_WIN32)
#define PATH_MAX MAX_PATH
#else
#include <sys/syslimits.h>
#endif
#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
#endif // LLAMA_USE_CURL
using json = nlohmann::ordered_json;
//
// CPU utils
//
int32_t cpu_get_num_physical_cores() {
#ifdef __linux__
// enumerate the set of thread siblings, num entries is num cores
std::unordered_set<std::string> siblings;
for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
+ std::to_string(cpu) + "/topology/thread_siblings");
if (!thread_siblings.is_open()) {
break; // no more cpus
}
std::string line;
if (std::getline(thread_siblings, line)) {
siblings.insert(line);
}
}
if (!siblings.empty()) {
return static_cast<int32_t>(siblings.size());
}
#elif defined(__APPLE__) && defined(__MACH__)
int32_t num_physical_cores;
size_t len = sizeof(num_physical_cores);
int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
if (result == 0) {
return num_physical_cores;
}
result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
if (result == 0) {
return num_physical_cores;
}
#elif defined(_WIN32)
//TODO: Implement
#endif
unsigned int n_threads = std::thread::hardware_concurrency();
return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
}
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
#include <pthread.h>
static void cpuid(unsigned leaf, unsigned subleaf,
unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) {
__asm__("movq\t%%rbx,%%rsi\n\t"
"cpuid\n\t"
"xchgq\t%%rbx,%%rsi"
: "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx)
: "0"(leaf), "2"(subleaf));
}
static int pin_cpu(int cpu) {
cpu_set_t mask;
CPU_ZERO(&mask);
CPU_SET(cpu, &mask);
return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask);
}
static bool is_hybrid_cpu(void) {
unsigned eax, ebx, ecx, edx;
cpuid(7, 0, &eax, &ebx, &ecx, &edx);
return !!(edx & (1u << 15));
}
static bool is_running_on_efficiency_core(void) {
unsigned eax, ebx, ecx, edx;
cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx);
int intel_atom = 0x20;
int core_type = (eax & 0xff000000u) >> 24;
return core_type == intel_atom;
}
static int cpu_count_math_cpus(int n_cpu) {
int result = 0;
for (int cpu = 0; cpu < n_cpu; ++cpu) {
if (pin_cpu(cpu)) {
return -1;
}
if (is_running_on_efficiency_core()) {
continue; // efficiency cores harm lockstep threading
}
++cpu; // hyperthreading isn't useful for linear algebra
++result;
}
return result;
}
#endif // __x86_64__ && __linux__
/**
* Returns number of CPUs on system that are useful for math.
*/
int32_t cpu_get_num_math() {
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
if (n_cpu < 1) {
return cpu_get_num_physical_cores();
}
if (is_hybrid_cpu()) {
cpu_set_t affinity;
if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
int result = cpu_count_math_cpus(n_cpu);
pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
if (result > 0) {
return result;
}
}
}
#endif
return cpu_get_num_physical_cores();
}
//
// CLI argument parsing
//
void gpt_params_handle_hf_token(gpt_params & params) {
if (params.hf_token.empty() && std::getenv("HF_TOKEN")) {
params.hf_token = std::getenv("HF_TOKEN");
}
}
void gpt_params_handle_model_default(gpt_params & params) {
if (!params.hf_repo.empty()) {
// short-hand to avoid specifying --hf-file -> default it to --model
if (params.hf_file.empty()) {
if (params.model.empty()) {
throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
}
params.hf_file = params.model;
} else if (params.model.empty()) {
params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
}
} else if (!params.model_url.empty()) {
if (params.model.empty()) {
auto f = string_split(params.model_url, '#').front();
f = string_split(f, '?').front();
params.model = fs_get_cache_file(string_split(f, '/').back());
}
} else if (params.model.empty()) {
params.model = DEFAULT_MODEL_PATH;
}
}
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
bool invalid_param = false;
std::string arg;
const std::string arg_prefix = "--";
llama_sampling_params & sparams = params.sparams;
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
throw std::invalid_argument("error: unknown argument: " + arg);
}
if (invalid_param) {
throw std::invalid_argument("error: invalid parameter for argument: " + arg);
}
}
if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
}
gpt_params_handle_model_default(params);
gpt_params_handle_hf_token(params);
if (params.escape) {
string_process_escapes(params.prompt);
string_process_escapes(params.input_prefix);
string_process_escapes(params.input_suffix);
string_process_escapes(sparams.cfg_negative_prompt);
for (auto & antiprompt : params.antiprompt) {
string_process_escapes(antiprompt);
}
}
if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back();
params.kv_overrides.back().key[0] = 0;
}
return true;
}
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
const auto params_org = params; // the example can modify the default params
try {
if (!gpt_params_parse_ex(argc, argv, params) || params.usage) {
params = params_org;
params.usage = true;
return false;
}
} catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
params = params_org;
return false;
}
return true;
}
#define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; }
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
const char split_delim = ',';
llama_sampling_params & sparams = params.sparams;
if (arg == "-s" || arg == "--seed") {
CHECK_ARG
// TODO: this is temporary, in the future the sampling state will be moved fully to llama_sampling_context.
params.seed = std::stoul(argv[i]);
sparams.seed = std::stoul(argv[i]);
return true;
}
if (arg == "-t" || arg == "--threads") {
CHECK_ARG
params.n_threads = std::stoi(argv[i]);
if (params.n_threads <= 0) {
params.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-tb" || arg == "--threads-batch") {
CHECK_ARG
params.n_threads_batch = std::stoi(argv[i]);
if (params.n_threads_batch <= 0) {
params.n_threads_batch = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-td" || arg == "--threads-draft") {
CHECK_ARG
params.n_threads_draft = std::stoi(argv[i]);
if (params.n_threads_draft <= 0) {
params.n_threads_draft = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-tbd" || arg == "--threads-batch-draft") {
CHECK_ARG
params.n_threads_batch_draft = std::stoi(argv[i]);
if (params.n_threads_batch_draft <= 0) {
params.n_threads_batch_draft = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-p" || arg == "--prompt") {
CHECK_ARG
params.prompt = argv[i];
return true;
}
if (arg == "-e" || arg == "--escape") {
params.escape = true;
return true;
}
if (arg == "--no-escape") {
params.escape = false;
return true;
}
if (arg == "--prompt-cache") {
CHECK_ARG
params.path_prompt_cache = argv[i];
return true;
}
if (arg == "--prompt-cache-all") {
params.prompt_cache_all = true;
return true;
}
if (arg == "--prompt-cache-ro") {
params.prompt_cache_ro = true;
return true;
}
if (arg == "-bf" || arg == "--binary-file") {
CHECK_ARG
std::ifstream file(argv[i], std::ios::binary);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
// store the external file name in params
params.prompt_file = argv[i];
std::ostringstream ss;
ss << file.rdbuf();
params.prompt = ss.str();
fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
return true;
}
if (arg == "-f" || arg == "--file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
// store the external file name in params
params.prompt_file = argv[i];
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
if (!params.prompt.empty() && params.prompt.back() == '\n') {
params.prompt.pop_back();
}
return true;
}
if (arg == "--in-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
params.in_files.push_back(argv[i]);
return true;
}
if (arg == "-n" || arg == "--predict" || arg == "--n-predict") {
CHECK_ARG
params.n_predict = std::stoi(argv[i]);
return true;
}
if (arg == "--top-k") {
CHECK_ARG
sparams.top_k = std::stoi(argv[i]);
return true;
}
if (arg == "-c" || arg == "--ctx-size") {
CHECK_ARG
params.n_ctx = std::stoi(argv[i]);
return true;
}
if (arg == "--grp-attn-n" || arg == "-gan") {
CHECK_ARG
params.grp_attn_n = std::stoi(argv[i]);
return true;
}
if (arg == "--grp-attn-w" || arg == "-gaw") {
CHECK_ARG
params.grp_attn_w = std::stoi(argv[i]);
return true;
}
if (arg == "--rope-freq-base") {
CHECK_ARG
params.rope_freq_base = std::stof(argv[i]);
return true;
}
if (arg == "--rope-freq-scale") {
CHECK_ARG
params.rope_freq_scale = std::stof(argv[i]);
return true;
}
if (arg == "--rope-scaling") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
else { invalid_param = true; }
return true;
}
if (arg == "--rope-scale") {
CHECK_ARG
params.rope_freq_scale = 1.0f / std::stof(argv[i]);
return true;
}
if (arg == "--yarn-orig-ctx") {
CHECK_ARG
params.yarn_orig_ctx = std::stoi(argv[i]);
return true;
}
if (arg == "--yarn-ext-factor") {
CHECK_ARG
params.yarn_ext_factor = std::stof(argv[i]);
return true;
}
if (arg == "--yarn-attn-factor") {
CHECK_ARG
params.yarn_attn_factor = std::stof(argv[i]);
return true;
}
if (arg == "--yarn-beta-fast") {
CHECK_ARG
params.yarn_beta_fast = std::stof(argv[i]);
return true;
}
if (arg == "--yarn-beta-slow") {
CHECK_ARG
params.yarn_beta_slow = std::stof(argv[i]);
return true;
}
if (arg == "--pooling") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
else { invalid_param = true; }
return true;
}
if (arg == "--attention") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
else { invalid_param = true; }
return true;
}
if (arg == "--defrag-thold" || arg == "-dt") {
CHECK_ARG
params.defrag_thold = std::stof(argv[i]);
return true;
}
if (arg == "--samplers") {
CHECK_ARG
const auto sampler_names = string_split(argv[i], ';');
sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true);
return true;
}
if (arg == "--sampling-seq") {
CHECK_ARG
sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]);
return true;
}
if (arg == "--top-p") {
CHECK_ARG
sparams.top_p = std::stof(argv[i]);
return true;
}
if (arg == "--min-p") {
CHECK_ARG
sparams.min_p = std::stof(argv[i]);
return true;
}
if (arg == "--temp") {
CHECK_ARG
sparams.temp = std::stof(argv[i]);
sparams.temp = std::max(sparams.temp, 0.0f);
return true;
}
if (arg == "--tfs") {
CHECK_ARG
sparams.tfs_z = std::stof(argv[i]);
return true;
}
if (arg == "--typical") {
CHECK_ARG
sparams.typical_p = std::stof(argv[i]);
return true;
}
if (arg == "--repeat-last-n") {
CHECK_ARG
sparams.penalty_last_n = std::stoi(argv[i]);
sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
return true;
}
if (arg == "--repeat-penalty") {
CHECK_ARG
sparams.penalty_repeat = std::stof(argv[i]);
return true;
}
if (arg == "--frequency-penalty") {
CHECK_ARG
sparams.penalty_freq = std::stof(argv[i]);
return true;
}
if (arg == "--presence-penalty") {
CHECK_ARG
sparams.penalty_present = std::stof(argv[i]);
return true;
}
if (arg == "--dynatemp-range") {
CHECK_ARG
sparams.dynatemp_range = std::stof(argv[i]);
return true;
}
if (arg == "--dynatemp-exp") {
CHECK_ARG
sparams.dynatemp_exponent = std::stof(argv[i]);
return true;
}
if (arg == "--mirostat") {
CHECK_ARG
sparams.mirostat = std::stoi(argv[i]);
return true;
}
if (arg == "--mirostat-lr") {
CHECK_ARG
sparams.mirostat_eta = std::stof(argv[i]);
return true;
}
if (arg == "--mirostat-ent") {
CHECK_ARG
sparams.mirostat_tau = std::stof(argv[i]);
return true;
}
if (arg == "--cfg-negative-prompt") {
CHECK_ARG
sparams.cfg_negative_prompt = argv[i];
return true;
}
if (arg == "--cfg-negative-prompt-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
sparams.cfg_negative_prompt.pop_back();
}
return true;
}
if (arg == "--cfg-scale") {
CHECK_ARG
sparams.cfg_scale = std::stof(argv[i]);
return true;
}
if (arg == "-b" || arg == "--batch-size") {
CHECK_ARG
params.n_batch = std::stoi(argv[i]);
return true;
}
if (arg == "-ub" || arg == "--ubatch-size") {
CHECK_ARG
params.n_ubatch = std::stoi(argv[i]);
return true;
}
if (arg == "--keep") {
CHECK_ARG
params.n_keep = std::stoi(argv[i]);
return true;
}
if (arg == "--draft") {
CHECK_ARG
params.n_draft = std::stoi(argv[i]);
return true;
}
if (arg == "--chunks") {
CHECK_ARG
params.n_chunks = std::stoi(argv[i]);
return true;
}
if (arg == "-np" || arg == "--parallel") {
CHECK_ARG
params.n_parallel = std::stoi(argv[i]);
return true;
}
if (arg == "-ns" || arg == "--sequences") {
CHECK_ARG
params.n_sequences = std::stoi(argv[i]);
return true;
}
if (arg == "--p-split" || arg == "-ps") {
CHECK_ARG
params.p_split = std::stof(argv[i]);
return true;
}
if (arg == "-m" || arg == "--model") {
CHECK_ARG
params.model = argv[i];
return true;
}
if (arg == "-md" || arg == "--model-draft") {
CHECK_ARG
params.model_draft = argv[i];
return true;
}
if (arg == "-a" || arg == "--alias") {
CHECK_ARG
params.model_alias = argv[i];
return true;
}
if (arg == "-mu" || arg == "--model-url") {
CHECK_ARG
params.model_url = argv[i];
return true;
}
if (arg == "-hft" || arg == "--hf-token") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.hf_token = argv[i];
return true;
}
if (arg == "-hfr" || arg == "--hf-repo") {
CHECK_ARG
params.hf_repo = argv[i];
return true;
}
if (arg == "-hff" || arg == "--hf-file") {
CHECK_ARG
params.hf_file = argv[i];
return true;
}
if (arg == "--lora") {
CHECK_ARG
params.lora_adapters.push_back({
std::string(argv[i]),
1.0,
});
return true;
}
if (arg == "--lora-scaled") {
CHECK_ARG
std::string lora_adapter = argv[i];
CHECK_ARG
params.lora_adapters.push_back({
lora_adapter,
std::stof(argv[i]),
});
return true;
}
if (arg == "--lora-init-without-apply") {
params.lora_init_without_apply = true;
return true;
}
if (arg == "--control-vector") {
CHECK_ARG
params.control_vectors.push_back({ 1.0f, argv[i], });
return true;
}
if (arg == "--control-vector-scaled") {
CHECK_ARG
const char* fname = argv[i];
CHECK_ARG
params.control_vectors.push_back({ std::stof(argv[i]), fname, });
return true;
}
if (arg == "--control-vector-layer-range") {
CHECK_ARG
params.control_vector_layer_start = std::stoi(argv[i]);
CHECK_ARG
params.control_vector_layer_end = std::stoi(argv[i]);
return true;
}
if (arg == "--mmproj") {
CHECK_ARG
params.mmproj = argv[i];
return true;
}
if (arg == "--image") {
CHECK_ARG
params.image.emplace_back(argv[i]);
return true;
}
if (arg == "-i" || arg == "--interactive") {
params.interactive = true;
return true;
}
if (arg == "-sp" || arg == "--special") {
params.special = true;
return true;
}
if (arg == "--embedding" || arg == "--embeddings") {
params.embedding = true;
return true;
}
if (arg == "--embd-normalize") {
CHECK_ARG
params.embd_normalize = std::stoi(argv[i]);
return true;
}
if (arg == "--embd-output-format") {
CHECK_ARG
params.embd_out = argv[i];
return true;
}
if (arg == "--embd-separator") {
CHECK_ARG
params.embd_sep = argv[i];
return true;
}
if (arg == "-if" || arg == "--interactive-first") {
params.interactive_first = true;
return true;
}
if (arg == "-cnv" || arg == "--conversation") {
params.conversation = true;
return true;
}
if (arg == "--infill") {
params.infill = true;
return true;
}
if (arg == "-dkvc" || arg == "--dump-kv-cache") {
params.dump_kv_cache = true;
return true;
}
if (arg == "-nkvo" || arg == "--no-kv-offload") {
params.no_kv_offload = true;
return true;
}
if (arg == "-ctk" || arg == "--cache-type-k") {
params.cache_type_k = argv[++i];
return true;
}
if (arg == "-ctv" || arg == "--cache-type-v") {
params.cache_type_v = argv[++i];
return true;
}
if (arg == "-mli" || arg == "--multiline-input") {
params.multiline_input = true;
return true;
}
if (arg == "--simple-io") {
params.simple_io = true;
return true;
}
if (arg == "-cb" || arg == "--cont-batching") {
params.cont_batching = true;
return true;
}
if (arg == "-nocb" || arg == "--no-cont-batching") {
params.cont_batching = false;
return true;
}
if (arg == "-fa" || arg == "--flash-attn") {
params.flash_attn = true;
return true;
}
if (arg == "-co" || arg == "--color") {
params.use_color = true;
return true;
}
if (arg == "--mlock") {
params.use_mlock = true;
return true;
}
if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
CHECK_ARG
params.n_gpu_layers = std::stoi(argv[i]);
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
return true;
}
if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--gpu-layers-draft") {
CHECK_ARG
params.n_gpu_layers_draft = std::stoi(argv[i]);
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
return true;
}
if (arg == "--main-gpu" || arg == "-mg") {
CHECK_ARG
params.main_gpu = std::stoi(argv[i]);
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--split-mode" || arg == "-sm") {
CHECK_ARG
std::string arg_next = argv[i];
if (arg_next == "none") {
params.split_mode = LLAMA_SPLIT_MODE_NONE;
}
else if (arg_next == "layer") {
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
}
else if (arg_next == "row") {
#ifdef GGML_USE_SYCL
fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
exit(1);
#endif // GGML_USE_SYCL
params.split_mode = LLAMA_SPLIT_MODE_ROW;
}
else {
invalid_param = true;
return true;
}
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--tensor-split" || arg == "-ts") {
CHECK_ARG
std::string arg_next = argv[i];
// split string by , and /
const std::regex regex{ R"([,/]+)" };
std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
std::vector<std::string> split_arg{ it, {} };
if (split_arg.size() >= llama_max_devices()) {
invalid_param = true;
return true;
}
for (size_t i = 0; i < llama_max_devices(); ++i) {
if (i < split_arg.size()) {
params.tensor_split[i] = std::stof(split_arg[i]);
}
else {
params.tensor_split[i] = 0.0f;
}
}
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--rpc") {
CHECK_ARG
params.rpc_servers = argv[i];
return true;
}
if (arg == "--no-mmap") {
params.use_mmap = false;
return true;
}
if (arg == "--numa") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
else { invalid_param = true; }
return true;
}
if (arg == "-v" || arg == "--verbose") {
params.verbosity = 1;
return true;
}
if (arg == "--verbosity") {
CHECK_ARG
params.verbosity = std::stoi(argv[i]);
return true;
}
if (arg == "--verbose-prompt") {
params.verbose_prompt = true;
return true;
}
if (arg == "--no-display-prompt") {
params.display_prompt = false;
return true;
}
if (arg == "-r" || arg == "--reverse-prompt") {
CHECK_ARG
params.antiprompt.emplace_back(argv[i]);
return true;
}
if (arg == "-ld" || arg == "--logdir") {
CHECK_ARG
params.logdir = argv[i];
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
params.logdir += DIRECTORY_SEPARATOR;
}
return true;
}
if (arg == "-lcs" || arg == "--lookup-cache-static") {
CHECK_ARG
params.lookup_cache_static = argv[i];
return true;
}
if (arg == "-lcd" || arg == "--lookup-cache-dynamic") {
CHECK_ARG
params.lookup_cache_dynamic = argv[i];
return true;
}
if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
CHECK_ARG
params.logits_file = argv[i];
return true;
}
if (arg == "--perplexity" || arg == "--all-logits") {
params.logits_all = true;
return true;
}
if (arg == "--ppl-stride") {
CHECK_ARG
params.ppl_stride = std::stoi(argv[i]);
return true;
}
if (arg == "--ppl-output-type") {
CHECK_ARG
params.ppl_output_type = std::stoi(argv[i]);
return true;
}
if (arg == "-ptc" || arg == "--print-token-count") {
CHECK_ARG
params.n_print = std::stoi(argv[i]);
return true;
}
if (arg == "--check-tensors") {
params.check_tensors = true;
return true;
}
if (arg == "--hellaswag") {
params.hellaswag = true;
return true;
}
if (arg == "--hellaswag-tasks") {
CHECK_ARG
params.hellaswag_tasks = std::stoi(argv[i]);
return true;
}
if (arg == "--winogrande") {
params.winogrande = true;
return true;
}
if (arg == "--winogrande-tasks") {
CHECK_ARG
params.winogrande_tasks = std::stoi(argv[i]);
return true;
}
if (arg == "--multiple-choice") {
params.multiple_choice = true;
return true;
}
if (arg == "--multiple-choice-tasks") {
CHECK_ARG
params.multiple_choice_tasks = std::stoi(argv[i]);
return true;
}
if (arg == "--kl-divergence") {
params.kl_divergence = true;
return true;
}
if (arg == "--ignore-eos") {
params.ignore_eos = true;
return true;
}
if (arg == "--penalize-nl") {
sparams.penalize_nl = true;
return true;
}
if (arg == "-l" || arg == "--logit-bias") {
CHECK_ARG
std::stringstream ss(argv[i]);
llama_token key;
char sign;
std::string value_str;
try {
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
}
else {
throw std::exception();
}
}
catch (const std::exception&) {
invalid_param = true;
return true;
}
return true;
}
if (arg == "-h" || arg == "--help" || arg == "--usage" ) {
params.usage = true;
return true;
}
if (arg == "--version") {
fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
exit(0);
}
if (arg == "--in-prefix-bos") {
params.input_prefix_bos = true;
params.enable_chat_template = false;
return true;
}
if (arg == "--in-prefix") {
CHECK_ARG
params.input_prefix = argv[i];
params.enable_chat_template = false;
return true;
}
if (arg == "--in-suffix") {
CHECK_ARG
params.input_suffix = argv[i];
params.enable_chat_template = false;
return true;
}
if (arg == "--spm-infill") {
params.spm_infill = true;
return true;
}
if (arg == "--grammar") {
CHECK_ARG
sparams.grammar = argv[i];
return true;
}
if (arg == "--grammar-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(sparams.grammar)
);
return true;
}
if (arg == "-j" || arg == "--json-schema") {
CHECK_ARG
sparams.grammar = json_schema_to_grammar(json::parse(argv[i]));
return true;
}
if (arg == "--override-kv") {
CHECK_ARG
if (!string_parse_kv_override(argv[i], params.kv_overrides)) {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
return true;
}
return true;
}
if (arg == "--host") {
CHECK_ARG
params.hostname = argv[i];
return true;
}
if (arg == "--port") {
CHECK_ARG
params.port = std::stoi(argv[i]);
return true;
}
if (arg == "--path") {
CHECK_ARG
params.public_path = argv[i];
return true;
}
if (arg == "--api-key") {
CHECK_ARG
params.api_keys.push_back(argv[i]);
return true;
}
if (arg == "--api-key-file") {
CHECK_ARG
std::ifstream key_file(argv[i]);
if (!key_file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::string key;
while (std::getline(key_file, key)) {
if (!key.empty()) {
params.api_keys.push_back(key);
}
}
key_file.close();
return true;
}
if (arg == "--ssl-key-file") {
CHECK_ARG
params.ssl_file_key = argv[i];
return true;
}
if (arg == "--ssl-cert-file") {
CHECK_ARG
params.ssl_file_cert = argv[i];
return true;
}
if (arg == "--timeout" || arg == "-to") {
CHECK_ARG
params.timeout_read = std::stoi(argv[i]);
params.timeout_write = std::stoi(argv[i]);
return true;
}
if (arg == "--threads-http") {
CHECK_ARG
params.n_threads_http = std::stoi(argv[i]);
return true;
}
if (arg == "-spf" || arg == "--system-prompt-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::string system_prompt;
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(system_prompt)
);
params.system_prompt = system_prompt;
return true;
}
if (arg == "--log-format") {
CHECK_ARG
if (std::strcmp(argv[i], "json") == 0) {
params.log_json = true;
} else if (std::strcmp(argv[i], "text") == 0) {
params.log_json = false;
} else {
invalid_param = true;
return true;
}
return true;
}
if (arg == "--no-slots") {
params.endpoint_slots = false;
return true;
}
if (arg == "--metrics") {
params.endpoint_metrics = true;
return true;
}
if (arg == "--slot-save-path") {
CHECK_ARG
params.slot_save_path = argv[i];
// if doesn't end with DIRECTORY_SEPARATOR, add it
if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
params.slot_save_path += DIRECTORY_SEPARATOR;
}
return true;
}
if (arg == "--chat-template") {
CHECK_ARG
if (!llama_chat_verify_template(argv[i])) {
fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
invalid_param = true;
return true;
}
params.chat_template = argv[i];
return true;
}
if (arg == "--slot-prompt-similarity" || arg == "-sps") {
CHECK_ARG
params.slot_prompt_similarity = std::stof(argv[i]);
return true;
}
if (arg == "-pps") {
params.is_pp_shared = true;
return true;
}
if (arg == "-npp") {
CHECK_ARG
auto p = string_split<int>(argv[i], split_delim);
params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
return true;
}
if (arg == "-ntg") {
CHECK_ARG
auto p = string_split<int>(argv[i], split_delim);
params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
return true;
}
if (arg == "-npl") {
CHECK_ARG
auto p = string_split<int>(argv[i], split_delim);
params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
return true;
}
if (arg == "--context-file") {
CHECK_ARG
std::ifstream file(argv[i], std::ios::binary);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
params.context_files.push_back(argv[i]);
return true;
}
if (arg == "--chunk-size") {
CHECK_ARG
params.chunk_size = std::stoi(argv[i]);
return true;
}
if (arg == "--chunk-separator") {
CHECK_ARG
params.chunk_separator = argv[i];
return true;
}
if (arg == "--junk") {
CHECK_ARG
params.n_junk = std::stoi(argv[i]);
return true;
}
if (arg == "--pos") {
CHECK_ARG
params.i_pos = std::stoi(argv[i]);
return true;
}
if (arg == "-o" || arg == "--output" || arg == "--output-file") {
CHECK_ARG
params.out_file = argv[i];
params.cvector_outfile = argv[i];
params.lora_outfile = argv[i];
return true;
}
if (arg == "-ofreq" || arg == "--output-frequency") {
CHECK_ARG
params.n_out_freq = std::stoi(argv[i]);
return true;
}
if (arg == "--save-frequency") {
CHECK_ARG
params.n_save_freq = std::stoi(argv[i]);
return true;
}
if (arg == "--process-output") {
params.process_output = true;
return true;
}
if (arg == "--no-ppl") {
params.compute_ppl = false;
return true;
}
if (arg == "--chunk" || arg == "--from-chunk") {
CHECK_ARG
params.i_chunk = std::stoi(argv[i]);
return true;
}
// cvector params
if (arg == "--positive-file") {
CHECK_ARG
params.cvector_positive_file = argv[i];
return true;
}
if (arg == "--negative-file") {
CHECK_ARG
params.cvector_negative_file = argv[i];
return true;
}
if (arg == "--pca-batch") {
CHECK_ARG
params.n_pca_batch = std::stoi(argv[i]);
return true;
}
if (arg == "--pca-iter") {
CHECK_ARG
params.n_pca_iterations = std::stoi(argv[i]);
return true;
}
if (arg == "--method") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
else { invalid_param = true; }
return true;
}
if (arg == "--no-warmup") {
params.warmup = false;
return true;
}
#ifndef LOG_DISABLE_LOGS
// Parse args for logging parameters
if (log_param_single_parse(argv[i])) {
// Do nothing, log_param_single_parse automatically does it's thing
// and returns if a match was found and parsed.
return true;
}
if (log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i])) {
// We have a matching known parameter requiring an argument,
// now we need to check if there is anything after this argv
// and flag invalid_param or parse it.
CHECK_ARG
if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) {
invalid_param = true;
return true;
}
return true;
}
// End of Parse args for logging parameters
#endif // LOG_DISABLE_LOGS
return false;
}
#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
void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
const llama_sampling_params & sparams = params.sparams;
std::string sampler_type_chars;
std::string sampler_type_names;
for (const auto sampler_type : sparams.samplers_sequence) {
sampler_type_chars += static_cast<char>(sampler_type);
sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";";
}
sampler_type_names.pop_back();
struct option_info {
LLAMA_COMMON_ATTRIBUTE_FORMAT(4, 5)
option_info(const std::string & tags, const char * args, const char * desc, ...) : tags(tags), args(args), desc(desc) {
va_list args_list;
va_start(args_list, desc);
char buffer[1024];
vsnprintf(buffer, sizeof(buffer), desc, args_list);
va_end(args_list);
this->desc = buffer;
}
option_info(const std::string & grp) : grp(grp) {}
std::string tags;
std::string args;
std::string desc;
std::string grp;
};
std::vector<option_info> options;
// TODO: filter by tags
options.push_back({ "general" });
options.push_back({ "*", "-h, --help, --usage", "print usage and exit" });
options.push_back({ "*", " --version", "show version and build info" });
options.push_back({ "*", "-v, --verbose", "print verbose information" });
options.push_back({ "*", " --verbosity N", "set specific verbosity level (default: %d)", params.verbosity });
options.push_back({ "*", " --verbose-prompt", "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" });
options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
options.push_back({ "*", "-s, --seed SEED", "RNG seed (default: %d, use random seed for < 0)", params.seed });
options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.n_threads });
options.push_back({ "*", "-tb, --threads-batch N", "number of threads to use during batch and prompt processing (default: same as --threads)" });
options.push_back({ "speculative", "-td, --threads-draft N", "number of threads to use during generation (default: same as --threads)" });
options.push_back({ "speculative", "-tbd, --threads-batch-draft N",
"number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
options.push_back({ "speculative", " --draft N", "number of tokens to draft for speculative decoding (default: %d)", params.n_draft });
options.push_back({ "speculative", "-ps, --p-split N", "speculative decoding split probability (default: %.1f)", (double)params.p_split });
options.push_back({ "*", "-lcs, --lookup-cache-static FNAME",
"path to static lookup cache to use for lookup decoding (not updated by generation)" });
options.push_back({ "*", "-lcd, --lookup-cache-dynamic FNAME",
"path to dynamic lookup cache to use for lookup decoding (updated by generation)" });
options.push_back({ "*", "-c, --ctx-size N", "size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx });
options.push_back({ "*", "-n, --predict N", "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict });
options.push_back({ "*", "-b, --batch-size N", "logical maximum batch size (default: %d)", params.n_batch });
options.push_back({ "*", "-ub, --ubatch-size N", "physical maximum batch size (default: %d)", params.n_ubatch });
options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with\n"
"in conversation mode, this will be used as system prompt\n"
"(default: '%s')", params.prompt.c_str() });
options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" });
options.push_back({ "*", " --in-file FNAME", "an input file (repeat to specify multiple files)" });
options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" });
options.push_back({ "*", "-e, --escape", "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" });
options.push_back({ "*", " --no-escape", "do not process escape sequences" });
options.push_back({ "main", "-ptc, --print-token-count N", "print token count every N tokens (default: %d)", params.n_print });
options.push_back({ "main", " --prompt-cache FNAME", "file to cache prompt state for faster startup (default: none)" });
options.push_back({ "main", " --prompt-cache-all", "if specified, saves user input and generations to cache as well\n"
"not supported with --interactive or other interactive options" });
options.push_back({ "main", " --prompt-cache-ro", "if specified, uses the prompt cache but does not update it" });
options.push_back({ "main", "-r, --reverse-prompt PROMPT",
"halt generation at PROMPT, return control in interactive mode\n"
"can be specified more than once for multiple prompts" });
options.push_back({ "main", "-sp, --special", "special tokens output enabled (default: %s)", params.special ? "true" : "false" });
options.push_back({ "main", "-cnv, --conversation", "run in conversation mode, does not print special tokens and suffix/prefix\n"
"if suffix/prefix are not specified, default chat template will be used\n"
"(default: %s)", params.conversation ? "true" : "false" });
options.push_back({ "main infill", "-i, --interactive", "run in interactive mode (default: %s)", params.interactive ? "true" : "false" });
options.push_back({ "main infill", "-if, --interactive-first", "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" });
options.push_back({ "main infill", "-mli, --multiline-input", "allows you to write or paste multiple lines without ending each in '\\'" });
options.push_back({ "main infill", " --in-prefix-bos", "prefix BOS to user inputs, preceding the `--in-prefix` string" });
options.push_back({ "main infill", " --in-prefix STRING", "string to prefix user inputs with (default: empty)" });
options.push_back({ "main infill", " --in-suffix STRING", "string to suffix after user inputs with (default: empty)" });
options.push_back({ "main", " --no-warmup", "skip warming up the model with an empty run" });
options.push_back({ "server infill",
" --spm-infill", "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", params.spm_infill ? "enabled" : "disabled" });
options.push_back({ "sampling" });
options.push_back({ "*", " --samplers SAMPLERS", "samplers that will be used for generation in the order, separated by \';\'\n"
"(default: %s)", sampler_type_names.c_str() });
options.push_back({ "*", " --sampling-seq SEQUENCE",
"simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str() });
options.push_back({ "*", " --ignore-eos", "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)" });
options.push_back({ "*", " --penalize-nl", "penalize newline tokens (default: %s)", sparams.penalize_nl ? "true" : "false" });
options.push_back({ "*", " --temp N", "temperature (default: %.1f)", (double)sparams.temp });
options.push_back({ "*", " --top-k N", "top-k sampling (default: %d, 0 = disabled)", sparams.top_k });
options.push_back({ "*", " --top-p N", "top-p sampling (default: %.1f, 1.0 = disabled)", (double)sparams.top_p });
options.push_back({ "*", " --min-p N", "min-p sampling (default: %.1f, 0.0 = disabled)", (double)sparams.min_p });
options.push_back({ "*", " --tfs N", "tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)sparams.tfs_z });
options.push_back({ "*", " --typical N", "locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)sparams.typical_p });
options.push_back({ "*", " --repeat-last-n N", "last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", sparams.penalty_last_n });
options.push_back({ "*", " --repeat-penalty N", "penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)sparams.penalty_repeat });
options.push_back({ "*", " --presence-penalty N", "repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_present });
options.push_back({ "*", " --frequency-penalty N", "repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_freq });
options.push_back({ "*", " --dynatemp-range N", "dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)sparams.dynatemp_range });
options.push_back({ "*", " --dynatemp-exp N", "dynamic temperature exponent (default: %.1f)", (double)sparams.dynatemp_exponent });
options.push_back({ "*", " --mirostat N", "use Mirostat sampling.\n"
"Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", sparams.mirostat });
options.push_back({ "*", " --mirostat-lr N", "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta });
options.push_back({ "*", " --mirostat-ent N", "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau });
options.push_back({ "*", " -l TOKEN_ID(+/-)BIAS", "modifies the likelihood of token appearing in the completion,\n"
"i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
"or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" });
options.push_back({ "main", " --cfg-negative-prompt PROMPT",
"negative prompt to use for guidance (default: '%s')", sparams.cfg_negative_prompt.c_str() });
options.push_back({ "main", " --cfg-negative-prompt-file FNAME",
"negative prompt file to use for guidance" });
options.push_back({ "main", " --cfg-scale N", "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale });
options.push_back({ "main", " --chat-template JINJA_TEMPLATE",
"set custom jinja chat template (default: template taken from model's metadata)\n"
"if suffix/prefix are specified, template will be disabled\n"
"only commonly used templates are accepted:\n"
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
options.push_back({ "grammar" });
options.push_back({ "*", " --grammar GRAMMAR", "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.c_str() });
options.push_back({ "*", " --grammar-file FNAME", "file to read grammar from" });
options.push_back({ "*", "-j, --json-schema SCHEMA",
"JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n"
"For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" });
options.push_back({ "embedding" });
options.push_back({ "embedding", " --pooling {none,mean,cls,last}",
"pooling type for embeddings, use model default if unspecified" });
options.push_back({ "embedding", " --attention {causal,non-causal}",
"attention type for embeddings, use model default if unspecified" });
options.push_back({ "context hacking" });
options.push_back({ "*", " --rope-scaling {none,linear,yarn}",
"RoPE frequency scaling method, defaults to linear unless specified by the model" });
options.push_back({ "*", " --rope-scale N", "RoPE context scaling factor, expands context by a factor of N" });
options.push_back({ "*", " --rope-freq-base N", "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)" });
options.push_back({ "*", " --rope-freq-scale N", "RoPE frequency scaling factor, expands context by a factor of 1/N" });
options.push_back({ "*", " --yarn-orig-ctx N", "YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx });
options.push_back({ "*", " --yarn-ext-factor N", "YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor });
options.push_back({ "*", " --yarn-attn-factor N", "YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor });
options.push_back({ "*", " --yarn-beta-slow N", "YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow });
options.push_back({ "*", " --yarn-beta-fast N", "YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast });
options.push_back({ "*", "-gan, --grp-attn-n N", "group-attention factor (default: %d)", params.grp_attn_n });
options.push_back({ "*", "-gaw, --grp-attn-w N", "group-attention width (default: %.1f)", (double)params.grp_attn_w });
options.push_back({ "*", "-dkvc, --dump-kv-cache", "verbose print of the KV cache" });
options.push_back({ "*", "-nkvo, --no-kv-offload", "disable KV offload" });
options.push_back({ "*", "-ctk, --cache-type-k TYPE", "KV cache data type for K (default: %s)", params.cache_type_k.c_str() });
options.push_back({ "*", "-ctv, --cache-type-v TYPE", "KV cache data type for V (default: %s)", params.cache_type_v.c_str() });
options.push_back({ "perplexity" });
options.push_back({ "perplexity", " --all-logits", "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" });
options.push_back({ "perplexity", " --hellaswag", "compute HellaSwag score over random tasks from datafile supplied with -f" });
options.push_back({ "perplexity", " --hellaswag-tasks N", "number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks });
options.push_back({ "perplexity", " --winogrande", "compute Winogrande score over random tasks from datafile supplied with -f" });
options.push_back({ "perplexity", " --winogrande-tasks N", "number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks });
options.push_back({ "perplexity", " --multiple-choice", "compute multiple choice score over random tasks from datafile supplied with -f" });
options.push_back({ "perplexity", " --multiple-choice-tasks N",
"number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks });
options.push_back({ "perplexity", " --kl-divergence", "computes KL-divergence to logits provided via --kl-divergence-base" });
options.push_back({ "perplexity", " --ppl-stride N", "stride for perplexity calculation (default: %d)", params.ppl_stride });
options.push_back({ "perplexity", " --ppl-output-type {0,1}",
"output type for perplexity calculation (default: %d)", params.ppl_output_type });
options.push_back({ "parallel" });
options.push_back({ "*", "-dt, --defrag-thold N", "KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold });
options.push_back({ "*", "-np, --parallel N", "number of parallel sequences to decode (default: %d)", params.n_parallel });
options.push_back({ "*", "-ns, --sequences N", "number of sequences to decode (default: %d)", params.n_sequences });
options.push_back({ "*", "-cb, --cont-batching", "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" });
options.push_back({ "*", "-nocb, --no-cont-batching", "disable continuous batching" });
options.push_back({ "multi-modality" });
options.push_back({ "*", " --mmproj FILE", "path to a multimodal projector file for LLaVA. see examples/llava/README.md" });
options.push_back({ "*", " --image FILE", "path to an image file. use with multimodal models. Specify multiple times for batching" });
options.push_back({ "backend" });
options.push_back({ "*", " --rpc SERVERS", "comma separated list of RPC servers" });
if (llama_supports_mlock()) {
options.push_back({ "*", " --mlock", "force system to keep model in RAM rather than swapping or compressing" });
}
if (llama_supports_mmap()) {
options.push_back({ "*", " --no-mmap", "do not memory-map model (slower load but may reduce pageouts if not using mlock)" });
}
options.push_back({ "*", " --numa TYPE", "attempt optimizations that help on some NUMA systems\n"
" - distribute: spread execution evenly over all nodes\n"
" - isolate: only spawn threads on CPUs on the node that execution started on\n"
" - numactl: use the CPU map provided by numactl\n"
"if run without this previously, it is recommended to drop the system page cache before using this\n"
"see https://github.com/ggerganov/llama.cpp/issues/1437" });
if (llama_supports_gpu_offload()) {
options.push_back({ "*", "-ngl, --gpu-layers N",
"number of layers to store in VRAM" });
options.push_back({ "*", "-ngld, --gpu-layers-draft N",
"number of layers to store in VRAM for the draft model" });
options.push_back({ "*", "-sm, --split-mode SPLIT_MODE",
"how to split the model across multiple GPUs, one of:\n"
" - none: use one GPU only\n"
" - layer (default): split layers and KV across GPUs\n"
" - row: split rows across GPUs" });
options.push_back({ "*", "-ts, --tensor-split SPLIT",
"fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1" });
options.push_back({ "*", "-mg, --main-gpu i", "the GPU to use for the model (with split-mode = none),\n"
"or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu });
}
options.push_back({ "model" });
options.push_back({ "*", " --check-tensors", "check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false" });
options.push_back({ "*", " --override-kv KEY=TYPE:VALUE",
"advanced option to override model metadata by key. may be specified multiple times.\n"
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false" });
options.push_back({ "*", " --lora FNAME", "apply LoRA adapter (can be repeated to use multiple adapters)" });
options.push_back({ "*", " --lora-scaled FNAME S", "apply LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
options.push_back({ "*", " --control-vector FNAME", "add a control vector\n"
"note: this argument can be repeated to add multiple control vectors" });
options.push_back({ "*", " --control-vector-scaled FNAME SCALE",
"add a control vector with user defined scaling SCALE\n"
"note: this argument can be repeated to add multiple scaled control vectors" });
options.push_back({ "*", " --control-vector-layer-range START END",
"layer range to apply the control vector(s) to, start and end inclusive" });
options.push_back({ "*", "-m, --model FNAME", "model path (default: models/$filename with filename from --hf-file\n"
"or --model-url if set, otherwise %s)", DEFAULT_MODEL_PATH });
options.push_back({ "*", "-md, --model-draft FNAME", "draft model for speculative decoding (default: unused)" });
options.push_back({ "*", "-mu, --model-url MODEL_URL", "model download url (default: unused)" });
options.push_back({ "*", "-hfr, --hf-repo REPO", "Hugging Face model repository (default: unused)" });
options.push_back({ "*", "-hff, --hf-file FILE", "Hugging Face model file (default: unused)" });
options.push_back({ "*", "-hft, --hf-token TOKEN", "Hugging Face access token (default: value from HF_TOKEN environment variable)" });
options.push_back({ "retrieval" });
options.push_back({ "retrieval", " --context-file FNAME", "file to load context from (repeat to specify multiple files)" });
options.push_back({ "retrieval", " --chunk-size N", "minimum length of embedded text chunks (default: %d)", params.chunk_size });
options.push_back({ "retrieval", " --chunk-separator STRING",
"separator between chunks (default: '%s')", params.chunk_separator.c_str() });
options.push_back({ "passkey" });
options.push_back({ "passkey", " --junk N", "number of times to repeat the junk text (default: %d)", params.n_junk });
options.push_back({ "passkey", " --pos N", "position of the passkey in the junk text (default: %d)", params.i_pos });
options.push_back({ "imatrix" });
options.push_back({ "imatrix", "-o, --output FNAME", "output file (default: '%s')", params.out_file.c_str() });
options.push_back({ "imatrix", " --output-frequency N", "output the imatrix every N iterations (default: %d)", params.n_out_freq });
options.push_back({ "imatrix", " --save-frequency N", "save an imatrix copy every N iterations (default: %d)", params.n_save_freq });
options.push_back({ "imatrix", " --process-output", "collect data for the output tensor (default: %s)", params.process_output ? "true" : "false" });
options.push_back({ "imatrix", " --no-ppl", "do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false" });
options.push_back({ "imatrix", " --chunk N", "start processing the input from chunk N (default: %d)", params.i_chunk });
options.push_back({ "bench" });
options.push_back({ "bench", "-pps", "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" });
options.push_back({ "bench", "-npp n0,n1,...", "number of prompt tokens" });
options.push_back({ "bench", "-ntg n0,n1,...", "number of text generation tokens" });
options.push_back({ "bench", "-npl n0,n1,...", "number of parallel prompts" });
options.push_back({ "embedding" });
options.push_back({ "embedding", " --embd-normalize", "normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize });
options.push_back({ "embedding", " --embd-output-format", "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix" });
options.push_back({ "embedding", " --embd-separator", "separator of embendings (default \\n) for example \"<#sep#>\"" });
options.push_back({ "server" });
options.push_back({ "server", " --host HOST", "ip address to listen (default: %s)", params.hostname.c_str() });
options.push_back({ "server", " --port PORT", "port to listen (default: %d)", params.port });
options.push_back({ "server", " --path PATH", "path to serve static files from (default: %s)", params.public_path.c_str() });
options.push_back({ "server", " --embedding(s)", "restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled" });
options.push_back({ "server", " --api-key KEY", "API key to use for authentication (default: none)" });
options.push_back({ "server", " --api-key-file FNAME", "path to file containing API keys (default: none)" });
options.push_back({ "server", " --ssl-key-file FNAME", "path to file a PEM-encoded SSL private key" });
options.push_back({ "server", " --ssl-cert-file FNAME", "path to file a PEM-encoded SSL certificate" });
options.push_back({ "server", " --timeout N", "server read/write timeout in seconds (default: %d)", params.timeout_read });
options.push_back({ "server", " --threads-http N", "number of threads used to process HTTP requests (default: %d)", params.n_threads_http });
options.push_back({ "server", " --system-prompt-file FNAME",
"set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" });
options.push_back({ "server", " --log-format {text,json}",
"log output format: json or text (default: json)" });
options.push_back({ "server", " --metrics", "enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled" });
options.push_back({ "server", " --no-slots", "disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled" });
options.push_back({ "server", " --slot-save-path PATH", "path to save slot kv cache (default: disabled)" });
options.push_back({ "server", " --chat-template JINJA_TEMPLATE",
"set custom jinja chat template (default: template taken from model's metadata)\n"
"only commonly used templates are accepted:\n"
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
options.push_back({ "server", "-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: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity });
options.push_back({ "server", " --lora-init-without-apply", "load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"});
#ifndef LOG_DISABLE_LOGS
options.push_back({ "logging" });
options.push_back({ "*", " --simple-io", "use basic IO for better compatibility in subprocesses and limited consoles" });
options.push_back({ "*", "-ld, --logdir LOGDIR", "path under which to save YAML logs (no logging if unset)" });
options.push_back({ "logging", " --log-test", "Run simple logging test" });
options.push_back({ "logging", " --log-disable", "Disable trace logs" });
options.push_back({ "logging", " --log-enable", "Enable trace logs" });
options.push_back({ "logging", " --log-file FNAME", "Specify a log filename (without extension)" });
options.push_back({ "logging", " --log-new", "Create a separate new log file on start. "
"Each log file will have unique name: \"<name>.<ID>.log\"" });
options.push_back({ "logging", " --log-append", "Don't truncate the old log file." });
#endif // LOG_DISABLE_LOGS
options.push_back({ "cvector" });
options.push_back({ "cvector", "-o, --output FNAME", "output file (default: '%s')", params.cvector_outfile.c_str() });
options.push_back({ "cvector", " --positive-file FNAME", "positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str() });
options.push_back({ "cvector", " --negative-file FNAME", "negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str() });
options.push_back({ "cvector", " --pca-batch N", "batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch });
options.push_back({ "cvector", " --pca-iter N", "number of iterations used for PCA (default: %d)", params.n_pca_iterations });
options.push_back({ "cvector", " --method {pca,mean}", "dimensionality reduction method to be used (default: pca)" });
options.push_back({ "export-lora" });
options.push_back({ "export-lora", "-m, --model", "model path from which to load base model (default '%s')", params.model.c_str() });
options.push_back({ "export-lora", " --lora FNAME", "path to LoRA adapter (can be repeated to use multiple adapters)" });
options.push_back({ "export-lora", " --lora-scaled FNAME S", "path to LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
options.push_back({ "*", "-t, --threads N", "number of threads to use during computation (default: %d)", params.n_threads });
options.push_back({ "export-lora", "-o, --output FNAME", "output file (default: '%s')", params.lora_outfile.c_str() });
printf("usage: %s [options]\n", argv[0]);
for (const auto & o : options) {
if (!o.grp.empty()) {
printf("\n%s:\n\n", o.grp.c_str());
continue;
}
printf(" %-32s", o.args.c_str());
if (o.args.length() > 30) {
printf("\n%34s", "");
}
const auto desc = o.desc;
size_t start = 0;
size_t end = desc.find('\n');
while (end != std::string::npos) {
printf("%s\n%34s", desc.substr(start, end - start).c_str(), "");
start = end + 1;
end = desc.find('\n', start);
}
printf("%s\n", desc.substr(start).c_str());
}
printf("\n");
}
std::string gpt_params_get_system_info(const gpt_params & params) {
std::ostringstream os;
os << "system_info: n_threads = " << params.n_threads;
if (params.n_threads_batch != -1) {
os << " (n_threads_batch = " << params.n_threads_batch << ")";
}
os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();
return os.str();
}
//
// String utils
//
std::vector<std::string> string_split(std::string input, char separator) {
std::vector<std::string> parts;
size_t separator_pos = input.find(separator);
while (separator_pos != std::string::npos) {
std::string part = input.substr(0, separator_pos);
parts.emplace_back(part);
input = input.substr(separator_pos + 1);
separator_pos = input.find(separator);
}
parts.emplace_back(input);
return parts;
}
std::string string_strip(const std::string & str) {
size_t start = 0;
size_t end = str.size();
while (start < end && std::isspace(str[start])) {
start++;
}
while (end > start && std::isspace(str[end - 1])) {
end--;
}
return str.substr(start, end - start);
}
std::string string_get_sortable_timestamp() {
using clock = std::chrono::system_clock;
const clock::time_point current_time = clock::now();
const time_t as_time_t = clock::to_time_t(current_time);
char timestamp_no_ns[100];
std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
current_time.time_since_epoch() % 1000000000).count();
char timestamp_ns[11];
snprintf(timestamp_ns, 11, "%09" PRId64, ns);
return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
}
void string_process_escapes(std::string & input) {
std::size_t input_len = input.length();
std::size_t output_idx = 0;
for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
switch (input[++input_idx]) {
case 'n': input[output_idx++] = '\n'; break;
case 'r': input[output_idx++] = '\r'; break;
case 't': input[output_idx++] = '\t'; break;
case '\'': input[output_idx++] = '\''; break;
case '\"': input[output_idx++] = '\"'; break;
case '\\': input[output_idx++] = '\\'; break;
case 'x':
// Handle \x12, etc
if (input_idx + 2 < input_len) {
const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
char *err_p = nullptr;
const long val = std::strtol(x, &err_p, 16);
if (err_p == x + 2) {
input_idx += 2;
input[output_idx++] = char(val);
break;
}
}
// fall through
default: input[output_idx++] = '\\';
input[output_idx++] = input[input_idx]; break;
}
} else {
input[output_idx++] = input[input_idx];
}
}
input.resize(output_idx);
}
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
const char * sep = strchr(data, '=');
if (sep == nullptr || sep - data >= 128) {
fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
return false;
}
llama_model_kv_override kvo;
std::strncpy(kvo.key, data, sep - data);
kvo.key[sep - data] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
kvo.val_f64 = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.val_bool = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.val_bool = false;
} else {
fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
return false;
}
} else if (strncmp(sep, "str:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
if (strlen(sep) > 127) {
fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
return false;
}
strncpy(kvo.val_str, sep, 127);
kvo.val_str[127] = '\0';
} else {
fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
return false;
}
overrides.emplace_back(std::move(kvo));
return true;
}
//
// Filesystem utils
//
// Validate if a filename is safe to use
// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
bool fs_validate_filename(const std::string & filename) {
if (!filename.length()) {
// Empty filename invalid
return false;
}
if (filename.length() > 255) {
// Limit at common largest possible filename on Linux filesystems
// to avoid unnecessary further validation
// (On systems with smaller limits it will be caught by the OS)
return false;
}
std::u32string filename_utf32;
try {
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
filename_utf32 = converter.from_bytes(filename);
// If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
// or invalid encodings were encountered. Reject such attempts
std::string filename_reencoded = converter.to_bytes(filename_utf32);
if (filename_reencoded != filename) {
return false;
}
} catch (const std::exception &) {
return false;
}
// Check for forbidden codepoints:
// - Control characters
// - Unicode equivalents of illegal characters
// - UTF-16 surrogate pairs
// - UTF-8 replacement character
// - Byte order mark (BOM)
// - Illegal characters: / \ : * ? " < > |
for (char32_t c : filename_utf32) {
if (c <= 0x1F // Control characters (C0)
|| c == 0x7F // Control characters (DEL)
|| (c >= 0x80 && c <= 0x9F) // Control characters (C1)
|| c == 0xFF0E // Fullwidth Full Stop (period equivalent)
|| c == 0x2215 // Division Slash (forward slash equivalent)
|| c == 0x2216 // Set Minus (backslash equivalent)
|| (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
|| c == 0xFFFD // Replacement Character (UTF-8)
|| c == 0xFEFF // Byte Order Mark (BOM)
|| c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
|| c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
return false;
}
}
// Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
// Unicode and other whitespace is not affected, only 0x20 space
if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
return false;
}
// Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
if (filename.find("..") != std::string::npos) {
return false;
}
// Reject "."
if (filename == ".") {
return false;
}
return true;
}
// returns true if successful, false otherwise
bool fs_create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
std::wstring wpath = converter.from_bytes(path);
// if the path already exists, check whether it's a directory
const DWORD attributes = GetFileAttributesW(wpath.c_str());
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return true;
}
size_t pos_slash = 0;
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
const std::wstring subpath = wpath.substr(0, pos_slash);
const wchar_t * test = subpath.c_str();
const bool success = CreateDirectoryW(test, NULL);
if (!success) {
const DWORD error = GetLastError();
// if the path already exists, ensure that it's a directory
if (error == ERROR_ALREADY_EXISTS) {
const DWORD attributes = GetFileAttributesW(subpath.c_str());
if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return false;
}
} else {
return false;
}
}
pos_slash += 1;
}
return true;
#else
// if the path already exists, check whether it's a directory
struct stat info;
if (stat(path.c_str(), &info) == 0) {
return S_ISDIR(info.st_mode);
}
size_t pos_slash = 1; // skip leading slashes for directory creation
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
const std::string subpath = path.substr(0, pos_slash);
struct stat info;
// if the path already exists, ensure that it's a directory
if (stat(subpath.c_str(), &info) == 0) {
if (!S_ISDIR(info.st_mode)) {
return false;
}
} else {
// create parent directories
const int ret = mkdir(subpath.c_str(), 0755);
if (ret != 0) {
return false;
}
}
pos_slash += 1;
}
return true;
#endif // _WIN32
}
std::string fs_get_cache_directory() {
std::string cache_directory = "";
auto ensure_trailing_slash = [](std::string p) {
// Make sure to add trailing slash
if (p.back() != DIRECTORY_SEPARATOR) {
p += DIRECTORY_SEPARATOR;
}
return p;
};
if (getenv("LLAMA_CACHE")) {
cache_directory = std::getenv("LLAMA_CACHE");
} else {
#ifdef __linux__
if (std::getenv("XDG_CACHE_HOME")) {
cache_directory = std::getenv("XDG_CACHE_HOME");
} else {
cache_directory = std::getenv("HOME") + std::string("/.cache/");
}
#elif defined(__APPLE__)
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
#elif defined(_WIN32)
cache_directory = std::getenv("LOCALAPPDATA");
#endif // __linux__
cache_directory = ensure_trailing_slash(cache_directory);
cache_directory += "llama.cpp";
}
return ensure_trailing_slash(cache_directory);
}
std::string fs_get_cache_file(const std::string & filename) {
GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
std::string cache_directory = fs_get_cache_directory();
const bool success = fs_create_directory_with_parents(cache_directory);
if (!success) {
throw std::runtime_error("failed to create cache directory: " + cache_directory);
}
return cache_directory + filename;
}
//
// Model utils
//
struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
llama_init_result iparams;
auto mparams = llama_model_params_from_gpt_params(params);
llama_model * model = nullptr;
if (!params.hf_repo.empty() && !params.hf_file.empty()) {
model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else if (!params.model_url.empty()) {
model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else {
model = llama_load_model_from_file(params.model.c_str(), mparams);
}
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return iparams;
}
auto cparams = llama_context_params_from_gpt_params(params);
llama_context * lctx = llama_new_context_with_model(model, cparams);
if (lctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
return iparams;
}
if (!params.control_vectors.empty()) {
if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
const auto cvec = llama_control_vector_load(params.control_vectors);
if (cvec.n_embd == -1) {
llama_free(lctx);
llama_free_model(model);
return iparams;
}
int err = llama_control_vector_apply(lctx,
cvec.data.data(),
cvec.data.size(),
cvec.n_embd,
params.control_vector_layer_start,
params.control_vector_layer_end);
if (err) {
llama_free(lctx);
llama_free_model(model);
return iparams;
}
}
// load and optionally apply lora adapters
for (auto & la : params.lora_adapters) {
llama_lora_adapter_container loaded_la;
loaded_la.path = la.path;
loaded_la.scale = la.scale;
loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
if (loaded_la.adapter == nullptr) {
fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
llama_free(lctx);
llama_free_model(model);
return iparams;
}
iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
}
if (!params.lora_init_without_apply) {
llama_lora_adapters_apply(lctx, iparams.lora_adapters);
}
if (params.ignore_eos) {
params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
}
if (params.warmup) {
LOG("warming up the model with an empty run\n");
std::vector<llama_token> tmp;
llama_token bos = llama_token_bos(model);
llama_token eos = llama_token_eos(model);
// some models (e.g. T5) don't have a BOS token
if (bos != -1) {
tmp.push_back(bos);
}
tmp.push_back(eos);
if (llama_model_has_encoder(model)) {
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
decoder_start_token_id = bos;
}
tmp.clear();
tmp.push_back(decoder_start_token_id);
}
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
llama_kv_cache_clear(lctx);
llama_synchronize(lctx);
llama_reset_timings(lctx);
}
iparams.model = model;
iparams.context = lctx;
return iparams;
}
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
llama_lora_adapter_clear(ctx);
for (auto & la : lora_adapters) {
if (la.scale != 0.0f) {
llama_lora_adapter_set(ctx, la.adapter, la.scale);
}
}
}
struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
auto mparams = llama_model_default_params();
if (params.n_gpu_layers != -1) {
mparams.n_gpu_layers = params.n_gpu_layers;
}
mparams.rpc_servers = params.rpc_servers.c_str();
mparams.main_gpu = params.main_gpu;
mparams.split_mode = params.split_mode;
mparams.tensor_split = params.tensor_split;
mparams.use_mmap = params.use_mmap;
mparams.use_mlock = params.use_mlock;
mparams.check_tensors = params.check_tensors;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;
} else {
GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
mparams.kv_overrides = params.kv_overrides.data();
}
return mparams;
}
static ggml_type kv_cache_type_from_str(const std::string & s) {
if (s == "f32") {
return GGML_TYPE_F32;
}
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
if (s == "q4_0") {
return GGML_TYPE_Q4_0;
}
if (s == "q4_1") {
return GGML_TYPE_Q4_1;
}
if (s == "iq4_nl") {
return GGML_TYPE_IQ4_NL;
}
if (s == "q5_0") {
return GGML_TYPE_Q5_0;
}
if (s == "q5_1") {
return GGML_TYPE_Q5_1;
}
throw std::runtime_error("Invalid cache type: " + s);
}
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
auto cparams = llama_context_default_params();
cparams.n_ctx = params.n_ctx;
cparams.n_seq_max = params.n_parallel;
cparams.n_batch = params.n_batch;
cparams.n_ubatch = params.n_ubatch;
cparams.n_threads = params.n_threads;
cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
cparams.seed = params.seed;
cparams.logits_all = params.logits_all;
cparams.embeddings = params.embedding;
cparams.rope_scaling_type = params.rope_scaling_type;
cparams.rope_freq_base = params.rope_freq_base;
cparams.rope_freq_scale = params.rope_freq_scale;
cparams.yarn_ext_factor = params.yarn_ext_factor;
cparams.yarn_attn_factor = params.yarn_attn_factor;
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
cparams.pooling_type = params.pooling_type;
cparams.attention_type = params.attention_type;
cparams.defrag_thold = params.defrag_thold;
cparams.cb_eval = params.cb_eval;
cparams.cb_eval_user_data = params.cb_eval_user_data;
cparams.offload_kqv = !params.no_kv_offload;
cparams.flash_attn = params.flash_attn;
cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
return cparams;
}
#ifdef LLAMA_USE_CURL
static bool starts_with(const std::string & str, const std::string & prefix) {
// While we wait for C++20's std::string::starts_with...
return str.rfind(prefix, 0) == 0;
}
static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
// Initialize libcurl
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
if (!curl) {
fprintf(stderr, "%s: error initializing libcurl\n", __func__);
return false;
}
bool force_download = false;
// Set the URL, allow to follow http redirection
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
// Check if hf-token or bearer-token was specified
if (!hf_token.empty()) {
std::string auth_header = "Authorization: Bearer ";
auth_header += hf_token.c_str();
struct curl_slist *http_headers = NULL;
http_headers = curl_slist_append(http_headers, auth_header.c_str());
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
}
#if defined(_WIN32)
// CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
// operating system. Currently implemented under MS-Windows.
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
#endif
// Check if the file already exists locally
struct stat model_file_info;
auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
// If the file exists, check its JSON metadata companion file.
std::string metadata_path = path + ".json";
nlohmann::json metadata;
std::string etag;
std::string last_modified;
if (file_exists) {
// Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
std::ifstream metadata_in(metadata_path);
if (metadata_in.good()) {
try {
metadata_in >> metadata;
fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
if (metadata.contains("url") && metadata.at("url").is_string()) {
auto previous_url = metadata.at("url").get<std::string>();
if (previous_url != url) {
fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
return false;
}
}
if (metadata.contains("etag") && metadata.at("etag").is_string()) {
etag = metadata.at("etag");
}
if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
last_modified = metadata.at("lastModified");
}
} catch (const nlohmann::json::exception & e) {
fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
return false;
}
}
} else {
fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
}
// Send a HEAD request to retrieve the etag and last-modified headers
struct llama_load_model_from_url_headers {
std::string etag;
std::string last_modified;
};
llama_load_model_from_url_headers headers;
{
typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
static std::regex header_regex("([^:]+): (.*)\r\n");
static std::regex etag_regex("ETag", std::regex_constants::icase);
static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
std::string header(buffer, n_items);
std::smatch match;
if (std::regex_match(header, match, header_regex)) {
const std::string & key = match[1];
const std::string & value = match[2];
if (std::regex_match(key, match, etag_regex)) {
headers->etag = value;
} else if (std::regex_match(key, match, last_modified_regex)) {
headers->last_modified = value;
}
}
return n_items;
};
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
CURLcode res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
return false;
}
long http_code = 0;
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
if (http_code != 200) {
// HEAD not supported, we don't know if the file has changed
// force trigger downloading
force_download = true;
fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
}
}
bool should_download = !file_exists || force_download;
if (!should_download) {
if (!etag.empty() && etag != headers.etag) {
fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
should_download = true;
} else if (!last_modified.empty() && last_modified != headers.last_modified) {
fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
should_download = true;
}
}
if (should_download) {
std::string path_temporary = path + ".downloadInProgress";
if (file_exists) {
fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
if (remove(path.c_str()) != 0) {
fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
return false;
}
}
// Set the output file
struct FILE_deleter {
void operator()(FILE * f) const {
fclose(f);
}
};
std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
if (!outfile) {
fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
return false;
}
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
return fwrite(data, size, nmemb, (FILE *)fd);
};
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
// display download progress
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
// helper function to hide password in URL
auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
std::size_t protocol_pos = url.find("://");
if (protocol_pos == std::string::npos) {
return url; // Malformed URL
}
std::size_t at_pos = url.find('@', protocol_pos + 3);
if (at_pos == std::string::npos) {
return url; // No password in URL
}
return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
};
// start the download
fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
auto res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
return false;
}
long http_code = 0;
curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
if (http_code < 200 || http_code >= 400) {
fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
return false;
}
// Causes file to be closed explicitly here before we rename it.
outfile.reset();
// Write the updated JSON metadata file.
metadata.update({
{"url", url},
{"etag", headers.etag},
{"lastModified", headers.last_modified}
});
std::ofstream(metadata_path) << metadata.dump(4);
fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
return false;
}
}
return true;
}
struct llama_model * llama_load_model_from_url(
const char * model_url,
const char * path_model,
const char * hf_token,
const struct llama_model_params & params) {
// Basic validation of the model_url
if (!model_url || strlen(model_url) == 0) {
fprintf(stderr, "%s: invalid model_url\n", __func__);
return NULL;
}
if (!llama_download_file(model_url, path_model, hf_token)) {
return NULL;
}
// check for additional GGUFs split to download
int n_split = 0;
{
struct gguf_init_params gguf_params = {
/*.no_alloc = */ true,
/*.ctx = */ NULL,
};
auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
if (!ctx_gguf) {
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, path_model);
return NULL;
}
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
if (key_n_split >= 0) {
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
}
gguf_free(ctx_gguf);
}
if (n_split > 1) {
char split_prefix[PATH_MAX] = {0};
char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
// Verify the first split file format
// and extract split URL and PATH prefixes
{
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
fprintf(stderr, "\n%s: unexpected model file name: %s"
" n_split=%d\n", __func__, path_model, n_split);
return NULL;
}
if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
fprintf(stderr, "\n%s: unexpected model url: %s"
" n_split=%d\n", __func__, model_url, n_split);
return NULL;
}
}
// Prepare download in parallel
std::vector<std::future<bool>> futures_download;
for (int idx = 1; idx < n_split; idx++) {
futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split, hf_token](int download_idx) -> bool {
char split_path[PATH_MAX] = {0};
llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
return llama_download_file(split_url, split_path, hf_token);
}, idx));
}
// Wait for all downloads to complete
for (auto & f : futures_download) {
if (!f.get()) {
return NULL;
}
}
}
return llama_load_model_from_file(path_model, params);
}
struct llama_model * llama_load_model_from_hf(
const char * repo,
const char * model,
const char * path_model,
const char * hf_token,
const struct llama_model_params & params) {
// construct hugging face model url:
//
// --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf
// https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf
//
// --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf
// https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf
//
std::string model_url = "https://huggingface.co/";
model_url += repo;
model_url += "/resolve/main/";
model_url += model;
return llama_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
}
#else
struct llama_model * llama_load_model_from_url(
const char * /*model_url*/,
const char * /*path_model*/,
const char * /*hf_token*/,
const struct llama_model_params & /*params*/) {
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
return nullptr;
}
struct llama_model * llama_load_model_from_hf(
const char * /*repo*/,
const char * /*model*/,
const char * /*path_model*/,
const char * /*hf_token*/,
const struct llama_model_params & /*params*/) {
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
return nullptr;
}
#endif // LLAMA_USE_CURL
//
// Batch utils
//
void llama_batch_clear(struct llama_batch & batch) {
batch.n_tokens = 0;
}
void llama_batch_add(
struct llama_batch & batch,
llama_token id,
llama_pos pos,
const std::vector<llama_seq_id> & seq_ids,
bool logits) {
batch.token [batch.n_tokens] = id;
batch.pos [batch.n_tokens] = pos;
batch.n_seq_id[batch.n_tokens] = seq_ids.size();
for (size_t i = 0; i < seq_ids.size(); ++i) {
batch.seq_id[batch.n_tokens][i] = seq_ids[i];
}
batch.logits [batch.n_tokens] = logits;
batch.n_tokens++;
}
//
// Vocab utils
//
std::vector<llama_token> llama_tokenize(
const struct llama_context * ctx,
const std::string & text,
bool add_special,
bool parse_special) {
return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
}
std::vector<llama_token> llama_tokenize(
const struct llama_model * model,
const std::string & text,
bool add_special,
bool parse_special) {
// upper limit for the number of tokens
int n_tokens = text.length() + 2 * add_special;
std::vector<llama_token> result(n_tokens);
n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
}
return result;
}
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
if (n_chars < 0) {
piece.resize(-n_chars);
int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
GGML_ASSERT(check == -n_chars);
}
else {
piece.resize(n_chars);
}
return piece;
}
std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
std::string text;
text.resize(std::max(text.capacity(), tokens.size()));
int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
if (n_chars < 0) {
text.resize(-n_chars);
n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization
}
text.resize(n_chars);
// NOTE: the original tokenizer decodes bytes after collecting the pieces.
return text;
}
bool llama_should_add_bos_token(const llama_model * model) {
const int add_bos = llama_add_bos_token(model);
return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
}
//
// Chat template utils
//
bool llama_chat_verify_template(const std::string & tmpl) {
llama_chat_message chat[] = {{"user", "test"}};
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
return res >= 0;
}
std::string llama_chat_apply_template(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & msgs,
bool add_ass) {
int alloc_size = 0;
bool fallback = false; // indicate if we must fallback to default chatml
std::vector<llama_chat_message> chat;
for (auto & msg : msgs) {
chat.push_back({msg.role.c_str(), msg.content.c_str()});
alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
}
const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
std::vector<char> buf(alloc_size);
// run the first time to get the total output length
int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
// error: chat template is not supported
if (res < 0) {
if (ptr_tmpl != nullptr) {
// if the custom "tmpl" is not supported, we throw an error
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
throw std::runtime_error("this custom template is not supported");
} else {
// If the built-in template is not supported, we default to chatml
res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
fallback = true;
}
}
// if it turns out that our buffer is too small, we resize it
if ((size_t) res > buf.size()) {
buf.resize(res);
res = llama_chat_apply_template(
fallback ? nullptr : model,
fallback ? "chatml" : ptr_tmpl,
chat.data(), chat.size(), add_ass, buf.data(), buf.size());
}
std::string formatted_chat(buf.data(), res);
return formatted_chat;
}
std::string llama_chat_format_single(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & past_msg,
const llama_chat_msg & new_msg,
bool add_ass) {
std::ostringstream ss;
auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
std::vector<llama_chat_msg> chat_new(past_msg);
// if the past_msg ends with a newline, we must preserve it in the formatted version
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
ss << "\n";
};
// format chat with new_msg
chat_new.push_back(new_msg);
auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
// get the diff part
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
return ss.str();
}
std::string llama_chat_format_example(const struct llama_model * model,
const std::string & tmpl) {
std::vector<llama_chat_msg> msgs = {
{"system", "You are a helpful assistant"},
{"user", "Hello"},
{"assistant", "Hi there"},
{"user", "How are you?"},
};
return llama_chat_apply_template(model, tmpl, msgs, true);
}
//
// KV cache utils
//
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
llama_kv_cache_view_cell * c_curr = view.cells;
llama_seq_id * cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
if (i % row_size == 0) {
printf("\n%5d: ", i);
}
int seq_count = 0;
for (int j = 0; j < view.n_seq_max; j++) {
if (cs_curr[j] >= 0) { seq_count++; }
}
putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
}
printf("\n=== Done dumping\n");
}
void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
std::unordered_map<llama_seq_id, size_t> seqs;
llama_kv_cache_view_cell * c_curr = view.cells;
llama_seq_id * cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
for (int j = 0; j < view.n_seq_max; j++) {
if (cs_curr[j] < 0) { continue; }
if (seqs.find(cs_curr[j]) == seqs.end()) {
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
const size_t sz = seqs.size();
seqs[cs_curr[j]] = sz;
}
}
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
}
printf("=== Sequence legend: ");
for (const auto & it : seqs) {
printf("%zu=%d, ", it.second, it.first);
}
printf("'+'=other sequence ids");
c_curr = view.cells;
cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
if (i % row_size == 0) {
printf("\n%5d: ", i);
}
for (int j = 0; j < view.n_seq_max; j++) {
if (cs_curr[j] >= 0) {
const auto & it = seqs.find(cs_curr[j]);
putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
} else {
putchar('.');
}
}
putchar(' ');
}
printf("\n=== Done dumping\n");
}
//
// Embedding utils
//
void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
double sum = 0.0;
switch (embd_norm) {
case -1: // no normalisation
sum = 1.0;
break;
case 0: // max absolute
for (int i = 0; i < n; i++) {
if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
}
sum /= 32760.0; // make an int16 range
break;
case 2: // euclidean
for (int i = 0; i < n; i++) {
sum += inp[i] * inp[i];
}
sum = std::sqrt(sum);
break;
default: // p-norm (euclidean is p-norm p=2)
for (int i = 0; i < n; i++) {
sum += std::pow(std::abs(inp[i]), embd_norm);
}
sum = std::pow(sum, 1.0 / embd_norm);
break;
}
const float norm = sum > 0.0 ? 1.0 / sum : 0.0f;
for (int i = 0; i < n; i++) {
out[i] = inp[i] * norm;
}
}
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
double sum = 0.0;
double sum1 = 0.0;
double sum2 = 0.0;
for (int i = 0; i < n; i++) {
sum += embd1[i] * embd2[i];
sum1 += embd1[i] * embd1[i];
sum2 += embd2[i] * embd2[i];
}
// Handle the case where one or both vectors are zero vectors
if (sum1 == 0.0 || sum2 == 0.0) {
if (sum1 == 0.0 && sum2 == 0.0) {
return 1.0f; // two zero vectors are similar
}
return 0.0f;
}
return sum / (sqrt(sum1) * sqrt(sum2));
}
//
// Control vector utils
//
static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
llama_control_vector_data result = { -1, {} };
ggml_context * ctx = nullptr;
struct gguf_init_params meta_gguf_params = {
/* .no_alloc = */ false,
/* .ctx = */ &ctx,
};
struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
if (!ctx_gguf) {
fprintf(stderr, "%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
return result;
}
int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
if (n_tensors == 0) {
fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
}
for (int i = 0; i < n_tensors; i++) {
std::string name = gguf_get_tensor_name(ctx_gguf, i);
int layer_idx = -1;
// split on '.'
size_t dotpos = name.find('.');
if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
try {
layer_idx = std::stoi(name.substr(dotpos + 1));
} catch (...) {
layer_idx = -1;
}
}
if (layer_idx < 0) {
fprintf(stderr, "%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
} else if (layer_idx == 0) {
fprintf(stderr, "%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
if (tensor->type != GGML_TYPE_F32) {
fprintf(stderr, "%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
if (ggml_n_dims(tensor) != 1) {
fprintf(stderr, "%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
if (result.n_embd == -1) {
result.n_embd = ggml_nelements(tensor);
} else if (ggml_nelements(tensor) != result.n_embd) {
fprintf(stderr, "%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
// extend if necessary - do not store data for layer 0 (it's not used)
result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f);
const float * src = (const float *) tensor->data;
float * dst = result.data.data() + result.n_embd * (layer_idx - 1); // layer 1 at [0]
for (int j = 0; j < result.n_embd; j++) {
dst[j] += src[j] * load_info.strength; // allows multiple directions for same layer in same file
}
}
if (result.n_embd == -1) {
fprintf(stderr, "%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
result.data.clear();
}
gguf_free(ctx_gguf);
ggml_free(ctx);
return result;
}
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
llama_control_vector_data result = { -1, {} };
for (const auto & info : load_infos) {
auto cur = llama_control_vector_load_one(info);
if (cur.n_embd == -1) {
result.n_embd = -1;
break;
}
if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
fprintf(stderr, "%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
result.n_embd = -1;
break;
}
if (result.n_embd == -1) {
result = std::move(cur);
} else {
result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f); // extend if necessary
for (size_t i = 0; i < cur.data.size(); i++) {
result.data[i] += cur.data[i];
}
}
}
if (result.n_embd == -1) {
fprintf(stderr, "%s: no valid control vector files passed\n", __func__);
result.data.clear();
}
return result;
}
//
// YAML utils
//
void yaml_dump_vector_float(FILE * stream, const char * prop_name, const std::vector<float> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%e, ", data[i]);
}
fprintf(stream, "%e]\n", data.back());
}
void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector<int> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%d, ", data[i]);
}
fprintf(stream, "%d]\n", data.back());
}
void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) {
std::string data_str(data == NULL ? "" : data);
if (data_str.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
size_t pos_start = 0;
size_t pos_found = 0;
if (std::isspace(data_str[0]) || std::isspace(data_str.back())) {
data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)");
data_str = "\"" + data_str + "\"";
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
if (data_str.find('\n') == std::string::npos) {
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
fprintf(stream, "%s: |\n", prop_name);
while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
pos_start = pos_found + 1;
}
}
void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
const llama_sampling_params & sparams = params.sparams;
fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT);
fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER);
fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false");
fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false");
fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false");
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false");
fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");
#ifdef NDEBUG
fprintf(stream, "debug: false\n");
#else
fprintf(stream, "debug: true\n");
#endif // NDEBUG
fprintf(stream, "model_desc: %s\n", model_desc);
fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx)));
#ifdef __OPTIMIZE__
fprintf(stream, "optimize: true\n");
#else
fprintf(stream, "optimize: false\n");
#endif // __OPTIMIZE__
fprintf(stream, "time: %s\n", timestamp.c_str());
fprintf(stream, "\n");
fprintf(stream, "###############\n");
fprintf(stream, "# User Inputs #\n");
fprintf(stream, "###############\n");
fprintf(stream, "\n");
fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str());
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));
const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str());
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str());
fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
fprintf(stream, "logit_bias:\n");
for (std::pair<llama_token, float> lb : sparams.logit_bias) {
if (ignore_eos && lb.first == logit_bias_eos->first) {
continue;
}
fprintf(stream, " %d: %f", lb.first, lb.second);
}
fprintf(stream, "lora:\n");
for (auto & la : params.lora_adapters) {
if (la.scale == 1.0f) {
fprintf(stream, " - %s\n", la.path.c_str());
}
}
fprintf(stream, "lora_scaled:\n");
for (auto & la : params.lora_adapters) {
if (la.scale != 1.0f) {
fprintf(stream, " - %s: %f\n", la.path.c_str(), la.scale);
}
}
fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false");
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str());
fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens);
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat);
fprintf(stream, "reverse_prompt:\n");
for (std::string ap : params.antiprompt) {
size_t pos = 0;
while ((pos = ap.find('\n', pos)) != std::string::npos) {
ap.replace(pos, 1, "\\n");
pos += 1;
}
fprintf(stream, " - %s\n", ap.c_str());
}
fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
}
ollama/llm/llama.cpp/common/common.h deleted 100755 → 0
View file @ cb75098c
// Various helper functions and utilities
#pragma once
#include "llama.h"
#include "sampling.h"
#define LOG_NO_FILE_LINE_FUNCTION
#include "log.h"
#include <cmath>
#include <string>
#include <vector>
#include <random>
#include <thread>
#include <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__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \
fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \
} while(0)
#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
struct llama_lora_adapter_info {
std::string path;
float scale;
};
struct llama_lora_adapter_container : llama_lora_adapter_info {
struct llama_lora_adapter * adapter;
};
// build info
extern int LLAMA_BUILD_NUMBER;
extern char const * LLAMA_COMMIT;
extern char const * LLAMA_COMPILER;
extern char const * LLAMA_BUILD_TARGET;
struct llama_control_vector_load_info;
//
// CPU utils
//
int32_t cpu_get_num_physical_cores();
int32_t cpu_get_num_math();
//
// CLI argument parsing
//
// dimensionality reduction methods, used by cvector-generator
enum dimre_method {
DIMRE_METHOD_PCA,
DIMRE_METHOD_MEAN,
};
struct gpt_params {
uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed
int32_t n_threads = cpu_get_num_math();
int32_t n_threads_draft = -1;
int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
int32_t n_threads_batch_draft = -1;
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 0; // context size
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_ubatch = 512; // physical 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 = 5; // 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
float p_split = 0.1f; // speculative decoding split probability
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[128] = {0}; // how split tensors should be distributed across GPUs
int32_t grp_attn_n = 1; // group-attention factor
int32_t grp_attn_w = 512; // group-attention width
int32_t n_print = -1; // print token count every n tokens (-1 = disabled)
float rope_freq_base = 0.0f; // RoPE base frequency
float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor
float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor
float yarn_beta_fast = 32.0f; // YaRN low correction dim
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
float defrag_thold = -1.0f; // KV cache defragmentation threshold
ggml_backend_sched_eval_callback cb_eval = nullptr;
void * cb_eval_user_data = nullptr;
ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
// // sampling parameters
struct llama_sampling_params sparams;
std::string model = ""; // model path
std::string model_draft = ""; // draft model for speculative decoding
std::string model_alias = "unknown"; // model alias
std::string model_url = ""; // model url to download
std::string hf_token = ""; // HF token
std::string hf_repo = ""; // HF repo
std::string hf_file = ""; // HF file
std::string prompt = "";
std::string prompt_file = ""; // store the external prompt file name
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
std::string input_prefix = ""; // string to prefix user inputs with
std::string input_suffix = ""; // string to suffix user inputs with
std::string logdir = ""; // directory in which to save YAML log files
std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding
std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
std::string logits_file = ""; // file for saving *all* logits
std::string rpc_servers = ""; // comma separated list of RPC servers
std::vector<std::string> in_files; // all input files
std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
std::vector<llama_model_kv_override> kv_overrides;
bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_lora_adapter_apply)
std::vector<llama_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
int32_t verbosity = 0;
int32_t control_vector_layer_start = -1; // layer range for control vector
int32_t control_vector_layer_end = -1; // layer range for control vector
int32_t ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
int32_t 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 winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt
size_t winogrande_tasks = 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
bool kl_divergence = false; // compute KL divergence
bool usage = false; // print usage
bool use_color = false; // use color to distinguish generations and inputs
bool special = false; // enable special token output
bool interactive = false; // interactive mode
bool interactive_first = false; // wait for user input immediately
bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix)
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 escape = true; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
bool multiline_input = false; // reverse the usage of `\`
bool simple_io = false; // improves compatibility with subprocesses and limited consoles
bool cont_batching = true; // insert new sequences for decoding on-the-fly
bool flash_attn = false; // flash attention
bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
bool ignore_eos = false; // ignore generated EOS tokens
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 verbose_prompt = false; // print prompt tokens before generation
bool display_prompt = true; // print prompt before generation
bool infill = false; // use infill mode
bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes
bool no_kv_offload = false; // disable KV offloading
bool warmup = true; // warmup run
bool check_tensors = false; // validate tensor data
std::string cache_type_k = "f16"; // KV cache data type for the K
std::string cache_type_v = "f16"; // KV cache data type for the V
// multimodal models (see examples/llava)
std::string mmproj = ""; // path to multimodal projector
std::vector<std::string> image; // path to image file(s)
// embedding
bool embedding = false; // get only sentence embedding
int32_t embd_normalize = 2; // normalisation for embendings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
std::string embd_sep = "\n"; // separator of embendings
// server params
int32_t port = 8080; // server listens on this network port
int32_t timeout_read = 600; // http read timeout in seconds
int32_t timeout_write = timeout_read; // http write timeout in seconds
int32_t n_threads_http = -1; // number of threads to process HTTP requests
std::string hostname = "127.0.0.1";
std::string public_path = "";
std::string chat_template = "";
std::string system_prompt = "";
bool enable_chat_template = true;
std::vector<std::string> api_keys;
std::string ssl_file_key = "";
std::string ssl_file_cert = "";
bool endpoint_slots = true;
bool endpoint_metrics = false;
bool log_json = false;
std::string slot_save_path;
float slot_prompt_similarity = 0.5f;
// batched-bench params
bool is_pp_shared = false;
std::vector<int32_t> n_pp;
std::vector<int32_t> n_tg;
std::vector<int32_t> n_pl;
// retrieval params
std::vector<std::string> context_files; // context files to embed
int32_t chunk_size = 64; // chunk size for context embedding
std::string chunk_separator = "\n"; // chunk separator for context embedding
// passkey params
int32_t n_junk = 250; // number of times to repeat the junk text
int32_t i_pos = -1; // position of the passkey in the junk text
// imatrix params
std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file
int32_t n_out_freq = 10; // output the imatrix every n_out_freq iterations
int32_t n_save_freq = 0; // save the imatrix every n_save_freq iterations
int32_t i_chunk = 0; // start processing from this chunk
bool process_output = false; // collect data for the output tensor
bool compute_ppl = true; // whether to compute perplexity
// cvector-generator params
int n_pca_batch = 100;
int n_pca_iterations = 1000;
dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
std::string cvector_outfile = "control_vector.gguf";
std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
bool spm_infill = false; // suffix/prefix/middle pattern for infill
std::string lora_outfile = "ggml-lora-merged-f16.gguf";
};
void gpt_params_handle_hf_token(gpt_params & params);
void gpt_params_handle_model_default(gpt_params & params);
bool gpt_params_parse_ex (int argc, char ** argv, gpt_params & params);
bool gpt_params_parse (int argc, char ** argv, gpt_params & params);
bool gpt_params_find_arg (int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param);
void gpt_params_print_usage(int argc, char ** argv, const gpt_params & params);
std::string gpt_params_get_system_info(const gpt_params & params);
//
// String utils
//
std::vector<std::string> string_split(std::string input, char separator);
std::string string_strip(const std::string & str);
std::string string_get_sortable_timestamp();
template<class T>
static std::vector<T> string_split(const std::string & str, char delim) {
std::vector<T> values;
std::istringstream str_stream(str);
std::string token;
while (std::getline(str_stream, token, delim)) {
T value;
std::istringstream token_stream(token);
token_stream >> value;
values.push_back(value);
}
return values;
}
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
void string_process_escapes(std::string & input);
//
// Filesystem utils
//
bool fs_validate_filename(const std::string & filename);
bool fs_create_directory_with_parents(const std::string & path);
std::string fs_get_cache_directory();
std::string fs_get_cache_file(const std::string & filename);
//
// Model utils
//
struct llama_init_result {
struct llama_model * model = nullptr;
struct llama_context * context = nullptr;
std::vector<llama_lora_adapter_container> lora_adapters;
};
struct llama_init_result 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);
struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params);
struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params);
// clear LoRA adapters from context, then apply new list of adapters
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters);
// Batch utils
void llama_batch_clear(struct llama_batch & batch);
void llama_batch_add(
struct llama_batch & batch,
llama_token id,
llama_pos pos,
const std::vector<llama_seq_id> & seq_ids,
bool logits);
//
// 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_special,
bool parse_special = false);
std::vector<llama_token> llama_tokenize(
const struct llama_model * model,
const std::string & text,
bool add_special,
bool parse_special = false);
// tokenizes a token into a piece, optionally renders special/control tokens
// should work similar to Python's `tokenizer.id_to_piece`
std::string llama_token_to_piece(
const struct llama_context * ctx,
llama_token token,
bool special = true);
// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
// optionally renders special/control tokens
std::string llama_detokenize(
llama_context * ctx,
const std::vector<llama_token> & tokens,
bool special = true);
// Uses the value from the model metadata if possible, otherwise
// defaults to true when model type is SPM, otherwise false.
bool llama_should_add_bos_token(const llama_model * model);
//
// Chat template utils
//
// same with llama_chat_message, but uses std::string
struct llama_chat_msg {
std::string role;
std::string content;
};
// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
bool llama_chat_verify_template(const std::string & tmpl);
// CPP wrapper for llama_chat_apply_template
// If the built-in template is not supported, we default to chatml
// If the custom "tmpl" is not supported, we throw an error
std::string llama_chat_apply_template(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & chat,
bool add_ass);
// Format single message, while taking into account the position of that message in chat history
std::string llama_chat_format_single(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & past_msg,
const llama_chat_msg & new_msg,
bool add_ass);
// Returns an example of formatted chat
std::string llama_chat_format_example(const struct llama_model * model,
const std::string & tmpl);
//
// KV cache utils
//
// Dump the KV cache view with the number of sequences per cell.
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80);
// Dump the KV cache view showing individual sequences in each cell (long output).
void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
//
// Embedding utils
//
void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);
//
// Control vector utils
//
struct llama_control_vector_data {
int n_embd;
// stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
std::vector<float> data;
};
struct llama_control_vector_load_info {
float strength;
std::string fname;
};
// Load control vectors, scale each by strength, and add them together.
// On error, returns {-1, empty}
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos);
//
// Split utils
//
static const char * const LLM_KV_SPLIT_NO = "split.no";
static const char * const LLM_KV_SPLIT_COUNT = "split.count";
static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
//
// YAML utils
//
void yaml_dump_vector_float (FILE * stream, const char * prop_name, const std::vector<float> & data);
void yaml_dump_vector_int (FILE * stream, const char * prop_name, const std::vector<int> & data);
void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data);
void yaml_dump_non_result_info(
FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
ollama/llm/llama.cpp/common/console.cpp deleted 100755 → 0
View file @ cb75098c
#include "console.h"
#include <vector>
#include <iostream>
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <fcntl.h>
#include <io.h>
#ifndef ENABLE_VIRTUAL_TERMINAL_PROCESSING
#define ENABLE_VIRTUAL_TERMINAL_PROCESSING 0x0004
#endif
#else
#include <climits>
#include <sys/ioctl.h>
#include <unistd.h>
#include <wchar.h>
#include <stdio.h>
#include <stdlib.h>
#include <signal.h>
#include <termios.h>
#endif
#define ANSI_COLOR_RED "\x1b[31m"
#define ANSI_COLOR_GREEN "\x1b[32m"
#define ANSI_COLOR_YELLOW "\x1b[33m"
#define ANSI_COLOR_BLUE "\x1b[34m"
#define ANSI_COLOR_MAGENTA "\x1b[35m"
#define ANSI_COLOR_CYAN "\x1b[36m"
#define ANSI_COLOR_RESET "\x1b[0m"
#define ANSI_BOLD "\x1b[1m"
namespace console {
//
// Console state
//
static bool advanced_display = false;
static bool simple_io = true;
static display_t current_display = reset;
static FILE* out = stdout;
#if defined (_WIN32)
static void* hConsole;
#else
static FILE* tty = nullptr;
static termios initial_state;
#endif
//
// Init and cleanup
//
void init(bool use_simple_io, bool use_advanced_display) {
advanced_display = use_advanced_display;
simple_io = use_simple_io;
#if defined(_WIN32)
// Windows-specific console initialization
DWORD dwMode = 0;
hConsole = GetStdHandle(STD_OUTPUT_HANDLE);
if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) {
hConsole = GetStdHandle(STD_ERROR_HANDLE);
if (hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(hConsole, &dwMode))) {
hConsole = nullptr;
simple_io = true;
}
}
if (hConsole) {
// Check conditions combined to reduce nesting
if (advanced_display && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING) &&
!SetConsoleMode(hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING)) {
advanced_display = false;
}
// Set console output codepage to UTF8
SetConsoleOutputCP(CP_UTF8);
}
HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE);
if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) {
// Set console input codepage to UTF16
_setmode(_fileno(stdin), _O_WTEXT);
// Set ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT)
if (simple_io) {
dwMode |= ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT;
} else {
dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT);
}
if (!SetConsoleMode(hConIn, dwMode)) {
simple_io = true;
}
}
#else
// POSIX-specific console initialization
if (!simple_io) {
struct termios new_termios;
tcgetattr(STDIN_FILENO, &initial_state);
new_termios = initial_state;
new_termios.c_lflag &= ~(ICANON | ECHO);
new_termios.c_cc[VMIN] = 1;
new_termios.c_cc[VTIME] = 0;
tcsetattr(STDIN_FILENO, TCSANOW, &new_termios);
tty = fopen("/dev/tty", "w+");
if (tty != nullptr) {
out = tty;
}
}
setlocale(LC_ALL, "");
#endif
}
void cleanup() {
// Reset console display
set_display(reset);
#if !defined(_WIN32)
// Restore settings on POSIX systems
if (!simple_io) {
if (tty != nullptr) {
out = stdout;
fclose(tty);
tty = nullptr;
}
tcsetattr(STDIN_FILENO, TCSANOW, &initial_state);
}
#endif
}
//
// Display and IO
//
// Keep track of current display and only emit ANSI code if it changes
void set_display(display_t display) {
if (advanced_display && current_display != display) {
fflush(stdout);
switch(display) {
case reset:
fprintf(out, ANSI_COLOR_RESET);
break;
case prompt:
fprintf(out, ANSI_COLOR_YELLOW);
break;
case user_input:
fprintf(out, ANSI_BOLD ANSI_COLOR_GREEN);
break;
case error:
fprintf(out, ANSI_BOLD ANSI_COLOR_RED);
}
current_display = display;
fflush(out);
}
}
static char32_t getchar32() {
#if defined(_WIN32)
HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE);
wchar_t high_surrogate = 0;
while (true) {
INPUT_RECORD record;
DWORD count;
if (!ReadConsoleInputW(hConsole, &record, 1, &count) || count == 0) {
return WEOF;
}
if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) {
wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar;
if (wc == 0) {
continue;
}
if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
high_surrogate = wc;
continue;
}
if ((wc >= 0xDC00) && (wc <= 0xDFFF)) { // Check if wc is a low surrogate
if (high_surrogate != 0) { // Check if we have a high surrogate
return ((high_surrogate - 0xD800) << 10) + (wc - 0xDC00) + 0x10000;
}
}
high_surrogate = 0; // Reset the high surrogate
return static_cast<char32_t>(wc);
}
}
#else
wchar_t wc = getwchar();
if (static_cast<wint_t>(wc) == WEOF) {
return WEOF;
}
#if WCHAR_MAX == 0xFFFF
if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
wchar_t low_surrogate = getwchar();
if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate
return (static_cast<char32_t>(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000;
}
}
if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair
return 0xFFFD; // Return the replacement character U+FFFD
}
#endif
return static_cast<char32_t>(wc);
#endif
}
static void pop_cursor() {
#if defined(_WIN32)
if (hConsole != NULL) {
CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
GetConsoleScreenBufferInfo(hConsole, &bufferInfo);
COORD newCursorPosition = bufferInfo.dwCursorPosition;
if (newCursorPosition.X == 0) {
newCursorPosition.X = bufferInfo.dwSize.X - 1;
newCursorPosition.Y -= 1;
} else {
newCursorPosition.X -= 1;
}
SetConsoleCursorPosition(hConsole, newCursorPosition);
return;
}
#endif
putc('\b', out);
}
static int estimateWidth(char32_t codepoint) {
#if defined(_WIN32)
(void)codepoint;
return 1;
#else
return wcwidth(codepoint);
#endif
}
static int put_codepoint(const char* utf8_codepoint, size_t length, int expectedWidth) {
#if defined(_WIN32)
CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
if (!GetConsoleScreenBufferInfo(hConsole, &bufferInfo)) {
// go with the default
return expectedWidth;
}
COORD initialPosition = bufferInfo.dwCursorPosition;
DWORD nNumberOfChars = length;
WriteConsole(hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL);
CONSOLE_SCREEN_BUFFER_INFO newBufferInfo;
GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);
// Figure out our real position if we're in the last column
if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) {
DWORD nNumberOfChars;
WriteConsole(hConsole, &" \b", 2, &nNumberOfChars, NULL);
GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);
}
int width = newBufferInfo.dwCursorPosition.X - initialPosition.X;
if (width < 0) {
width += newBufferInfo.dwSize.X;
}
return width;
#else
// We can trust expectedWidth if we've got one
if (expectedWidth >= 0 || tty == nullptr) {
fwrite(utf8_codepoint, length, 1, out);
return expectedWidth;
}
fputs("\033[6n", tty); // Query cursor position
int x1;
int y1;
int x2;
int y2;
int results = 0;
results = fscanf(tty, "\033[%d;%dR", &y1, &x1);
fwrite(utf8_codepoint, length, 1, tty);
fputs("\033[6n", tty); // Query cursor position
results += fscanf(tty, "\033[%d;%dR", &y2, &x2);
if (results != 4) {
return expectedWidth;
}
int width = x2 - x1;
if (width < 0) {
// Calculate the width considering text wrapping
struct winsize w;
ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
width += w.ws_col;
}
return width;
#endif
}
static void replace_last(char ch) {
#if defined(_WIN32)
pop_cursor();
put_codepoint(&ch, 1, 1);
#else
fprintf(out, "\b%c", ch);
#endif
}
static void append_utf8(char32_t ch, std::string & out) {
if (ch <= 0x7F) {
out.push_back(static_cast<unsigned char>(ch));
} else if (ch <= 0x7FF) {
out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
} else if (ch <= 0xFFFF) {
out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
} else if (ch <= 0x10FFFF) {
out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
} else {
// Invalid Unicode code point
}
}
// Helper function to remove the last UTF-8 character from a string
static void pop_back_utf8_char(std::string & line) {
if (line.empty()) {
return;
}
size_t pos = line.length() - 1;
// Find the start of the last UTF-8 character (checking up to 4 bytes back)
for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) {
if ((line[pos] & 0xC0) != 0x80) {
break; // Found the start of the character
}
}
line.erase(pos);
}
static bool readline_advanced(std::string & line, bool multiline_input) {
if (out != stdout) {
fflush(stdout);
}
line.clear();
std::vector<int> widths;
bool is_special_char = false;
bool end_of_stream = false;
char32_t input_char;
while (true) {
fflush(out); // Ensure all output is displayed before waiting for input
input_char = getchar32();
if (input_char == '\r' || input_char == '\n') {
break;
}
if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) {
end_of_stream = true;
break;
}
if (is_special_char) {
set_display(user_input);
replace_last(line.back());
is_special_char = false;
}
if (input_char == '\033') { // Escape sequence
char32_t code = getchar32();
if (code == '[' || code == 0x1B) {
// Discard the rest of the escape sequence
while ((code = getchar32()) != (char32_t) WEOF) {
if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') {
break;
}
}
}
} else if (input_char == 0x08 || input_char == 0x7F) { // Backspace
if (!widths.empty()) {
int count;
do {
count = widths.back();
widths.pop_back();
// Move cursor back, print space, and move cursor back again
for (int i = 0; i < count; i++) {
replace_last(' ');
pop_cursor();
}
pop_back_utf8_char(line);
} while (count == 0 && !widths.empty());
}
} else {
int offset = line.length();
append_utf8(input_char, line);
int width = put_codepoint(line.c_str() + offset, line.length() - offset, estimateWidth(input_char));
if (width < 0) {
width = 0;
}
widths.push_back(width);
}
if (!line.empty() && (line.back() == '\\' || line.back() == '/')) {
set_display(prompt);
replace_last(line.back());
is_special_char = true;
}
}
bool has_more = multiline_input;
if (is_special_char) {
replace_last(' ');
pop_cursor();
char last = line.back();
line.pop_back();
if (last == '\\') {
line += '\n';
fputc('\n', out);
has_more = !has_more;
} else {
// llama will just eat the single space, it won't act as a space
if (line.length() == 1 && line.back() == ' ') {
line.clear();
pop_cursor();
}
has_more = false;
}
} else {
if (end_of_stream) {
has_more = false;
} else {
line += '\n';
fputc('\n', out);
}
}
fflush(out);
return has_more;
}
static bool readline_simple(std::string & line, bool multiline_input) {
#if defined(_WIN32)
std::wstring wline;
if (!std::getline(std::wcin, wline)) {
// Input stream is bad or EOF received
line.clear();
GenerateConsoleCtrlEvent(CTRL_C_EVENT, 0);
return false;
}
int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), NULL, 0, NULL, NULL);
line.resize(size_needed);
WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), &line[0], size_needed, NULL, NULL);
#else
if (!std::getline(std::cin, line)) {
// Input stream is bad or EOF received
line.clear();
return false;
}
#endif
if (!line.empty()) {
char last = line.back();
if (last == '/') { // Always return control on '/' symbol
line.pop_back();
return false;
}
if (last == '\\') { // '\\' changes the default action
line.pop_back();
multiline_input = !multiline_input;
}
}
line += '\n';
// By default, continue input if multiline_input is set
return multiline_input;
}
bool readline(std::string & line, bool multiline_input) {
set_display(user_input);
if (simple_io) {
return readline_simple(line, multiline_input);
}
return readline_advanced(line, multiline_input);
}
}
ollama/llm/llama.cpp/common/console.h deleted 100755 → 0
View file @ cb75098c
// Console functions
#pragma once
#include <string>
namespace console {
enum display_t {
reset = 0,
prompt,
user_input,
error
};
void init(bool use_simple_io, bool use_advanced_display);
void cleanup();
void set_display(display_t display);
bool readline(std::string & line, bool multiline_input);
}
ollama/llm/llama.cpp/common/grammar-parser.cpp deleted 100755 → 0
View file @ cb75098c
#include "grammar-parser.h"
#include <cstdint>
#include <cwchar>
#include <string>
#include <utility>
#include <stdexcept>
#include <exception>
namespace grammar_parser {
// NOTE: assumes valid utf8 (but checks for overrun)
// copied from llama.cpp
static std::pair<uint32_t, const char *> decode_utf8(const char * src) {
static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
uint8_t first_byte = static_cast<uint8_t>(*src);
uint8_t highbits = first_byte >> 4;
int len = lookup[highbits];
uint8_t mask = (1 << (8 - len)) - 1;
uint32_t value = first_byte & mask;
const char * end = src + len; // may overrun!
const char * pos = src + 1;
for ( ; pos < end && *pos; pos++) {
value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
}
return std::make_pair(value, pos);
}
static uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) {
uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size());
auto result = state.symbol_ids.emplace(std::string(src, len), next_id);
return result.first->second;
}
static uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) {
uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size());
state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id;
return next_id;
}
static void add_rule(
parse_state & state,
uint32_t rule_id,
const std::vector<llama_grammar_element> & rule) {
if (state.rules.size() <= rule_id) {
state.rules.resize(rule_id + 1);
}
state.rules[rule_id] = rule;
}
static bool is_digit_char(char c) {
return '0' <= c && c <= '9';
}
static bool is_word_char(char c) {
return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || is_digit_char(c);
}
static std::pair<uint32_t, const char *> parse_hex(const char * src, int size) {
const char * pos = src;
const char * end = src + size;
uint32_t value = 0;
for ( ; pos < end && *pos; pos++) {
value <<= 4;
char c = *pos;
if ('a' <= c && c <= 'f') {
value += c - 'a' + 10;
} else if ('A' <= c && c <= 'F') {
value += c - 'A' + 10;
} else if ('0' <= c && c <= '9') {
value += c - '0';
} else {
break;
}
}
if (pos != end) {
throw std::runtime_error("expecting " + std::to_string(size) + " hex chars at " + src);
}
return std::make_pair(value, pos);
}
static const char * parse_space(const char * src, bool newline_ok) {
const char * pos = src;
while (*pos == ' ' || *pos == '\t' || *pos == '#' ||
(newline_ok && (*pos == '\r' || *pos == '\n'))) {
if (*pos == '#') {
while (*pos && *pos != '\r' && *pos != '\n') {
pos++;
}
} else {
pos++;
}
}
return pos;
}
static const char * parse_name(const char * src) {
const char * pos = src;
while (is_word_char(*pos)) {
pos++;
}
if (pos == src) {
throw std::runtime_error(std::string("expecting name at ") + src);
}
return pos;
}
static const char * parse_int(const char * src) {
const char * pos = src;
while (is_digit_char(*pos)) {
pos++;
}
if (pos == src) {
throw std::runtime_error(std::string("expecting integer at ") + src);
}
return pos;
}
static std::pair<uint32_t, const char *> parse_char(const char * src) {
if (*src == '\\') {
switch (src[1]) {
case 'x': return parse_hex(src + 2, 2);
case 'u': return parse_hex(src + 2, 4);
case 'U': return parse_hex(src + 2, 8);
case 't': return std::make_pair('\t', src + 2);
case 'r': return std::make_pair('\r', src + 2);
case 'n': return std::make_pair('\n', src + 2);
case '\\':
case '"':
case '[':
case ']':
return std::make_pair(src[1], src + 2);
default:
throw std::runtime_error(std::string("unknown escape at ") + src);
}
} else if (*src) {
return decode_utf8(src);
}
throw std::runtime_error("unexpected end of input");
}
const char * parse_alternates(
parse_state & state,
const char * src,
const std::string & rule_name,
uint32_t rule_id,
bool is_nested);
static const char * parse_sequence(
parse_state & state,
const char * src,
const std::string & rule_name,
std::vector<llama_grammar_element> & out_elements,
bool is_nested) {
size_t last_sym_start = out_elements.size();
const char * pos = src;
auto handle_repetitions = [&](int min_times, int max_times) {
if (last_sym_start == out_elements.size()) {
throw std::runtime_error(std::string("expecting preceding item to */+/?/{ at ") + pos);
}
// apply transformation to previous symbol (last_sym_start to end) according to
// the following rewrite rules:
// S{m,n} --> S S S (m times) S'(n-m)
// S'(x) ::= S S'(x-1) |
// (... n-m definitions of these S' rules ...)
// S'(1) ::= S |
// S{m,} --> S S S (m times) S'
// S' ::= S S' |
// S* --> S{0,}
// --> S' ::= S S' |
// S+ --> S{1,}
// --> S S'
// S' ::= S S' |
// S? --> S{0,1}
// --> S'
// S' ::= S |
std::vector<llama_grammar_element> previous_elements(out_elements.begin() + last_sym_start, out_elements.end());
if (min_times == 0) {
out_elements.resize(last_sym_start);
} else {
// Repeat the previous elements (min_times - 1) times
for (int i = 1; i < min_times; i++) {
out_elements.insert(out_elements.end(), previous_elements.begin(), previous_elements.end());
}
}
uint32_t last_rec_rule_id = 0;
auto n_opt = max_times < 0 ? 1 : max_times - min_times;
std::vector<llama_grammar_element> rec_rule(previous_elements);
for (int i = 0; i < n_opt; i++) {
rec_rule.resize(previous_elements.size());
uint32_t rec_rule_id = generate_symbol_id(state, rule_name);
if (i > 0 || max_times < 0) {
rec_rule.push_back({LLAMA_GRETYPE_RULE_REF, max_times < 0 ? rec_rule_id : last_rec_rule_id});
}
rec_rule.push_back({LLAMA_GRETYPE_ALT, 0});
rec_rule.push_back({LLAMA_GRETYPE_END, 0});
add_rule(state, rec_rule_id, rec_rule);
last_rec_rule_id = rec_rule_id;
}
if (n_opt > 0) {
out_elements.push_back({LLAMA_GRETYPE_RULE_REF, last_rec_rule_id});
}
};
while (*pos) {
if (*pos == '"') { // literal string
pos++;
last_sym_start = out_elements.size();
while (*pos != '"') {
if (!*pos) {
throw std::runtime_error("unexpected end of input");
}
auto char_pair = parse_char(pos);
pos = char_pair.second;
out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first});
}
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '[') { // char range(s)
pos++;
enum llama_gretype start_type = LLAMA_GRETYPE_CHAR;
if (*pos == '^') {
pos++;
start_type = LLAMA_GRETYPE_CHAR_NOT;
}
last_sym_start = out_elements.size();
while (*pos != ']') {
if (!*pos) {
throw std::runtime_error("unexpected end of input");
}
auto char_pair = parse_char(pos);
pos = char_pair.second;
enum llama_gretype type = last_sym_start < out_elements.size()
? LLAMA_GRETYPE_CHAR_ALT
: start_type;
out_elements.push_back({type, char_pair.first});
if (pos[0] == '-' && pos[1] != ']') {
if (!pos[1]) {
throw std::runtime_error("unexpected end of input");
}
auto endchar_pair = parse_char(pos + 1);
pos = endchar_pair.second;
out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first});
}
}
pos = parse_space(pos + 1, is_nested);
} else if (is_word_char(*pos)) { // rule reference
const char * name_end = parse_name(pos);
uint32_t ref_rule_id = get_symbol_id(state, pos, name_end - pos);
pos = parse_space(name_end, is_nested);
last_sym_start = out_elements.size();
out_elements.push_back({LLAMA_GRETYPE_RULE_REF, ref_rule_id});
} else if (*pos == '(') { // grouping
// parse nested alternates into synthesized rule
pos = parse_space(pos + 1, true);
uint32_t sub_rule_id = generate_symbol_id(state, rule_name);
pos = parse_alternates(state, pos, rule_name, sub_rule_id, true);
last_sym_start = out_elements.size();
// output reference to synthesized rule
out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
if (*pos != ')') {
throw std::runtime_error(std::string("expecting ')' at ") + pos);
}
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '.') { // any char
last_sym_start = out_elements.size();
out_elements.push_back({LLAMA_GRETYPE_CHAR_ANY, 0});
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '*') {
pos = parse_space(pos + 1, is_nested);
handle_repetitions(0, -1);
} else if (*pos == '+') {
pos = parse_space(pos + 1, is_nested);
handle_repetitions(1, -1);
} else if (*pos == '?') {
pos = parse_space(pos + 1, is_nested);
handle_repetitions(0, 1);
} else if (*pos == '{') {
pos = parse_space(pos + 1, is_nested);
if (!is_digit_char(*pos)) {
throw std::runtime_error(std::string("expecting an int at ") + pos);
}
const char * int_end = parse_int(pos);
int min_times = std::stoul(std::string(pos, int_end - pos));
pos = parse_space(int_end, is_nested);
int max_times = -1;
if (*pos == '}') {
max_times = min_times;
pos = parse_space(pos + 1, is_nested);
} else if (*pos == ',') {
pos = parse_space(pos + 1, is_nested);
if (is_digit_char(*pos)) {
const char * int_end = parse_int(pos);
max_times = std::stoul(std::string(pos, int_end - pos));
pos = parse_space(int_end, is_nested);
}
if (*pos != '}') {
throw std::runtime_error(std::string("expecting '}' at ") + pos);
}
pos = parse_space(pos + 1, is_nested);
} else {
throw std::runtime_error(std::string("expecting ',' at ") + pos);
}
handle_repetitions(min_times, max_times);
} else {
break;
}
}
return pos;
}
const char * parse_alternates(
parse_state & state,
const char * src,
const std::string & rule_name,
uint32_t rule_id,
bool is_nested) {
std::vector<llama_grammar_element> rule;
const char * pos = parse_sequence(state, src, rule_name, rule, is_nested);
while (*pos == '|') {
rule.push_back({LLAMA_GRETYPE_ALT, 0});
pos = parse_space(pos + 1, true);
pos = parse_sequence(state, pos, rule_name, rule, is_nested);
}
rule.push_back({LLAMA_GRETYPE_END, 0});
add_rule(state, rule_id, rule);
return pos;
}
static const char * parse_rule(parse_state & state, const char * src) {
const char * name_end = parse_name(src);
const char * pos = parse_space(name_end, false);
size_t name_len = name_end - src;
uint32_t rule_id = get_symbol_id(state, src, name_len);
const std::string name(src, name_len);
if (!(pos[0] == ':' && pos[1] == ':' && pos[2] == '=')) {
throw std::runtime_error(std::string("expecting ::= at ") + pos);
}
pos = parse_space(pos + 3, true);
pos = parse_alternates(state, pos, name, rule_id, false);
if (*pos == '\r') {
pos += pos[1] == '\n' ? 2 : 1;
} else if (*pos == '\n') {
pos++;
} else if (*pos) {
throw std::runtime_error(std::string("expecting newline or end at ") + pos);
}
return parse_space(pos, true);
}
parse_state parse(const char * src) {
try {
parse_state state;
const char * pos = parse_space(src, true);
while (*pos) {
pos = parse_rule(state, pos);
}
// Validate the state to ensure that all rules are defined
for (const auto & rule : state.rules) {
for (const auto & elem : rule) {
if (elem.type == LLAMA_GRETYPE_RULE_REF) {
// Ensure that the rule at that location exists
if (elem.value >= state.rules.size() || state.rules[elem.value].empty()) {
// Get the name of the rule that is missing
for (const auto & kv : state.symbol_ids) {
if (kv.second == elem.value) {
throw std::runtime_error("Undefined rule identifier '" + kv.first + "'");
}
}
}
}
}
}
return state;
} catch (const std::exception & err) {
fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what());
return parse_state();
}
}
static void print_grammar_char(FILE * file, uint32_t c) {
if (0x20 <= c && c <= 0x7f) {
fprintf(file, "%c", static_cast<char>(c));
} else {
// cop out of encoding UTF-8
fprintf(file, "<U+%04X>", c);
}
}
static bool is_char_element(llama_grammar_element elem) {
switch (elem.type) {
case LLAMA_GRETYPE_CHAR: return true;
case LLAMA_GRETYPE_CHAR_NOT: return true;
case LLAMA_GRETYPE_CHAR_ALT: return true;
case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true;
case LLAMA_GRETYPE_CHAR_ANY: return true;
default: return false;
}
}
static void print_rule_binary(FILE * file, const std::vector<llama_grammar_element> & rule) {
for (auto elem : rule) {
switch (elem.type) {
case LLAMA_GRETYPE_END: fprintf(file, "END"); break;
case LLAMA_GRETYPE_ALT: fprintf(file, "ALT"); break;
case LLAMA_GRETYPE_RULE_REF: fprintf(file, "RULE_REF"); break;
case LLAMA_GRETYPE_CHAR: fprintf(file, "CHAR"); break;
case LLAMA_GRETYPE_CHAR_NOT: fprintf(file, "CHAR_NOT"); break;
case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break;
case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, "CHAR_ALT"); break;
case LLAMA_GRETYPE_CHAR_ANY: fprintf(file, "CHAR_ANY"); break;
}
switch (elem.type) {
case LLAMA_GRETYPE_END:
case LLAMA_GRETYPE_ALT:
case LLAMA_GRETYPE_RULE_REF:
fprintf(file, "(%u) ", elem.value);
break;
case LLAMA_GRETYPE_CHAR:
case LLAMA_GRETYPE_CHAR_NOT:
case LLAMA_GRETYPE_CHAR_RNG_UPPER:
case LLAMA_GRETYPE_CHAR_ALT:
case LLAMA_GRETYPE_CHAR_ANY:
fprintf(file, "(\"");
print_grammar_char(file, elem.value);
fprintf(file, "\") ");
break;
}
}
fprintf(file, "\n");
}
static void print_rule(
FILE * file,
uint32_t rule_id,
const std::vector<llama_grammar_element> & rule,
const std::map<uint32_t, std::string> & symbol_id_names) {
if (rule.empty() || rule.back().type != LLAMA_GRETYPE_END) {
throw std::runtime_error(
"malformed rule, does not end with LLAMA_GRETYPE_END: " + std::to_string(rule_id));
}
fprintf(file, "%s ::= ", symbol_id_names.at(rule_id).c_str());
for (size_t i = 0, end = rule.size() - 1; i < end; i++) {
llama_grammar_element elem = rule[i];
switch (elem.type) {
case LLAMA_GRETYPE_END:
throw std::runtime_error(
"unexpected end of rule: " + std::to_string(rule_id) + "," +
std::to_string(i));
case LLAMA_GRETYPE_ALT:
fprintf(file, "| ");
break;
case LLAMA_GRETYPE_RULE_REF:
fprintf(file, "%s ", symbol_id_names.at(elem.value).c_str());
break;
case LLAMA_GRETYPE_CHAR:
fprintf(file, "[");
print_grammar_char(file, elem.value);
break;
case LLAMA_GRETYPE_CHAR_NOT:
fprintf(file, "[^");
print_grammar_char(file, elem.value);
break;
case LLAMA_GRETYPE_CHAR_RNG_UPPER:
if (i == 0 || !is_char_element(rule[i - 1])) {
throw std::runtime_error(
"LLAMA_GRETYPE_CHAR_RNG_UPPER without preceding char: " +
std::to_string(rule_id) + "," + std::to_string(i));
}
fprintf(file, "-");
print_grammar_char(file, elem.value);
break;
case LLAMA_GRETYPE_CHAR_ALT:
if (i == 0 || !is_char_element(rule[i - 1])) {
throw std::runtime_error(
"LLAMA_GRETYPE_CHAR_ALT without preceding char: " +
std::to_string(rule_id) + "," + std::to_string(i));
}
print_grammar_char(file, elem.value);
break;
case LLAMA_GRETYPE_CHAR_ANY:
fprintf(file, ".");
break;
}
if (is_char_element(elem)) {
switch (rule[i + 1].type) {
case LLAMA_GRETYPE_CHAR_ALT:
case LLAMA_GRETYPE_CHAR_RNG_UPPER:
case LLAMA_GRETYPE_CHAR_ANY:
break;
default:
fprintf(file, "] ");
}
}
}
fprintf(file, "\n");
}
void print_grammar(FILE * file, const parse_state & state) {
try {
std::map<uint32_t, std::string> symbol_id_names;
for (const auto & kv : state.symbol_ids) {
symbol_id_names[kv.second] = kv.first;
}
for (size_t i = 0, end = state.rules.size(); i < end; i++) {
// fprintf(file, "%zu: ", i);
// print_rule_binary(file, state.rules[i]);
print_rule(file, uint32_t(i), state.rules[i], symbol_id_names);
// fprintf(file, "\n");
}
} catch (const std::exception & err) {
fprintf(stderr, "\n%s: error printing grammar: %s\n", __func__, err.what());
}
}
std::vector<const llama_grammar_element *> parse_state::c_rules() {
std::vector<const llama_grammar_element *> ret;
ret.reserve(rules.size());
for (const auto & rule : rules) {
ret.push_back(rule.data());
}
return ret;
}
}
ollama/llm/llama.cpp/common/grammar-parser.h deleted 100755 → 0
View file @ cb75098c
// Implements a parser for an extended Backus-Naur form (BNF), producing the
// binary context-free grammar format specified by llama.h. Supports character
// ranges, grouping, and repetition operators. As an example, a grammar for
// arithmetic might look like:
//
// root ::= expr
// expr ::= term ([-+*/] term)*
// term ::= num | "(" space expr ")" space
// num ::= [0-9]+ space
// space ::= [ \t\n]*
#pragma once
#include "llama.h"
#include <vector>
#include <map>
#include <cstdint>
#include <string>
namespace grammar_parser {
struct parse_state {
std::map<std::string, uint32_t> symbol_ids;
std::vector<std::vector<llama_grammar_element>> rules;
std::vector<const llama_grammar_element *> c_rules();
};
parse_state parse(const char * src);
void print_grammar(FILE * file, const parse_state & state);
}
ollama/llm/llama.cpp/common/json-schema-to-grammar.cpp deleted 100755 → 0
View file @ cb75098c
#include "json-schema-to-grammar.h"
#include <algorithm>
#include <fstream>
#include <map>
#include <regex>
#include <sstream>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
using json = nlohmann::ordered_json;
template <typename Iterator>
static std::string join(Iterator begin, Iterator end, const std::string & separator);
static std::string repeat(const std::string & str, size_t n);
static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
auto has_max = max_items != std::numeric_limits<int>::max();
if (min_items == 0 && max_items == 1) {
return item_rule + "?";
}
if (separator_rule.empty()) {
if (min_items == 1 && !has_max) {
return item_rule + "+";
} else if (min_items == 0 && !has_max) {
return item_rule + "*";
} else {
return item_rule + "{" + std::to_string(min_items) + "," + (has_max ? std::to_string(max_items) : "") + "}";
}
}
auto result = item_rule + " " + build_repetition("(" + separator_rule + " " + item_rule + ")", min_items == 0 ? 0 : min_items - 1, has_max ? max_items - 1 : max_items);
if (min_items == 0) {
result = "(" + result + ")?";
}
return result;
}
/* Minimalistic replacement for std::string_view, which is only available from C++17 onwards */
class string_view {
const std::string & _str;
const size_t _start;
const size_t _end;
public:
string_view(const std::string & str, size_t start = 0, size_t end = std::string::npos) : _str(str), _start(start), _end(end == std::string::npos ? str.length() : end) {}
size_t size() const {
return _end - _start;
}
size_t length() const {
return size();
}
operator std::string() const {
return str();
}
std::string str() const {
return _str.substr(_start, _end - _start);
}
string_view substr(size_t pos, size_t len = std::string::npos) const {
return string_view(_str, _start + pos, len == std::string::npos ? _end : _start + pos + len);
}
char operator[](size_t pos) const {
auto index = _start + pos;
if (index >= _end) {
throw std::out_of_range("string_view index out of range");
}
return _str[_start + pos];
}
bool operator==(const string_view & other) const {
std::string this_str = *this;
std::string other_str = other;
return this_str == other_str;
}
};
static void _build_min_max_int(int min_value, int max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) {
auto has_min = min_value != std::numeric_limits<int>::min();
auto has_max = max_value != std::numeric_limits<int>::max();
auto digit_range = [&](char from, char to) {
out << "[";
if (from == to) {
out << from;
} else {
out << from << "-" << to;
}
out << "]";
};
auto more_digits = [&](int min_digits, int max_digits) {
out << "[0-9]";
if (min_digits == max_digits && min_digits == 1) {
return;
}
out << "{";
out << min_digits;
if (max_digits != min_digits) {
out << ",";
if (max_digits != std::numeric_limits<int>::max()) {
out << max_digits;
}
}
out << "}";
};
std::function<void(const string_view &, const string_view &)> uniform_range =
[&](const string_view & from, const string_view & to) {
size_t i = 0;
while (i < from.length() && i < to.length() && from[i] == to[i]) {
i++;
}
if (i > 0) {
out << "\"" << from.substr(0, i).str() << "\"";
}
if (i < from.length() && i < to.length()) {
if (i > 0) {
out << " ";
}
auto sub_len = from.length() - i - 1;
if (sub_len > 0) {
auto from_sub = from.substr(i + 1);
auto to_sub = to.substr(i + 1);
auto sub_zeros = repeat("0", sub_len);
auto sub_nines = repeat("9", sub_len);
auto to_reached = false;
out << "(";
if (from_sub == sub_zeros) {
digit_range(from[i], to[i] - 1);
out << " ";
more_digits(sub_len, sub_len);
} else {
out << "[" << from[i] << "] ";
out << "(";
uniform_range(from_sub, sub_nines);
out << ")";
if (from[i] < to[i] - 1) {
out << " | ";
if (to_sub == sub_nines) {
digit_range(from[i] + 1, to[i]);
to_reached = true;
} else {
digit_range(from[i] + 1, to[i] - 1);
}
out << " ";
more_digits(sub_len, sub_len);
}
}
if (!to_reached) {
out << " | ";
digit_range(to[i], to[i]);
out << " ";
uniform_range(sub_zeros, to_sub);
}
out << ")";
} else {
out << "[" << from[i] << "-" << to[i] << "]";
}
}
};
if (has_min && has_max) {
if (min_value < 0 && max_value < 0) {
out << "\"-\" (";
_build_min_max_int(-max_value, -min_value, out, decimals_left, /* top_level= */ true);
out << ")";
return;
}
if (min_value < 0) {
out << "\"-\" (";
_build_min_max_int(0, -min_value, out, decimals_left, /* top_level= */ true);
out << ") | ";
min_value = 0;
}
auto min_s = std::to_string(min_value);
auto max_s = std::to_string(max_value);
auto min_digits = min_s.length();
auto max_digits = max_s.length();
for (auto digits = min_digits; digits < max_digits; digits++) {
uniform_range(min_s, repeat("9", digits));
min_s = "1" + repeat("0", digits);
out << " | ";
}
uniform_range(min_s, max_s);
return;
}
auto less_decimals = std::max(decimals_left - 1, 1);
if (has_min) {
if (min_value < 0) {
out << "\"-\" (";
_build_min_max_int(std::numeric_limits<int>::min(), -min_value, out, decimals_left, /* top_level= */ false);
out << ") | [0] | [1-9] ";
more_digits(0, decimals_left - 1);
} else if (min_value == 0) {
if (top_level) {
out << "[0] | [1-9] ";
more_digits(0, less_decimals);
} else {
more_digits(1, decimals_left);
}
} else if (min_value <= 9) {
char c = '0' + min_value;
auto range_start = top_level ? '1' : '0';
if (c > range_start) {
digit_range(range_start, c - 1);
out << " ";
more_digits(1, less_decimals);
out << " | ";
}
digit_range(c, '9');
out << " ";
more_digits(0, less_decimals);
} else {
auto min_s = std::to_string(min_value);
auto len = min_s.length();
auto c = min_s[0];
if (c > '1') {
digit_range(top_level ? '1' : '0', c - 1);
out << " ";
more_digits(len, less_decimals);
out << " | ";
}
digit_range(c, c);
out << " (";
_build_min_max_int(std::stoi(min_s.substr(1)), std::numeric_limits<int>::max(), out, less_decimals, /* top_level= */ false);
out << ")";
if (c < '9') {
out << " | ";
digit_range(c + 1, '9');
out << " ";
more_digits(len - 1, less_decimals);
}
}
return;
}
if (has_max) {
if (max_value >= 0) {
if (top_level) {
out << "\"-\" [1-9] ";
more_digits(0, less_decimals);
out << " | ";
}
_build_min_max_int(0, max_value, out, decimals_left, /* top_level= */ true);
} else {
out << "\"-\" (";
_build_min_max_int(-max_value, std::numeric_limits<int>::max(), out, decimals_left, /* top_level= */ false);
out << ")";
}
return;
}
throw std::runtime_error("At least one of min_value or max_value must be set");
}
const std::string SPACE_RULE = "| \" \" | \"\\n\" [ \\t]{0,20}";
struct BuiltinRule {
std::string content;
std::vector<std::string> deps;
};
std::unordered_map<std::string, BuiltinRule> PRIMITIVE_RULES = {
{"boolean", {"(\"true\" | \"false\") space", {}}},
{"decimal-part", {"[0-9]{1,16}", {}}},
{"integral-part", {"[0] | [1-9] [0-9]{0,15}", {}}},
{"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}},
{"integer", {"(\"-\"? integral-part) space", {"integral-part"}}},
{"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}},
{"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}},
{"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}},
{"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}},
{"char", {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}},
{"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}},
{"null", {"\"null\" space", {}}},
};
std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
{"date", {"[0-9]{4} \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
{"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9]{3} )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
{"date-time", {"date \"T\" time", {"date", "time"}}},
{"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}},
{"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}},
{"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}}
};
static bool is_reserved_name(const std::string & name) {
static std::unordered_set<std::string> RESERVED_NAMES;
if (RESERVED_NAMES.empty()) {
RESERVED_NAMES.insert("root");
for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first);
for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first);
}
return RESERVED_NAMES.find(name) != RESERVED_NAMES.end();
}
std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+");
std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"]");
std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]");
std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
{'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"}
};
std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
template <typename Iterator>
std::string join(Iterator begin, Iterator end, const std::string & separator) {
std::ostringstream result;
if (begin != end) {
result << *begin;
for (Iterator it = begin + 1; it != end; ++it) {
result << separator << *it;
}
}
return result.str();
}
static std::vector<std::string> split(const std::string & str, const std::string & delimiter) {
std::vector<std::string> tokens;
size_t start = 0;
size_t end = str.find(delimiter);
while (end != std::string::npos) {
tokens.push_back(str.substr(start, end - start));
start = end + delimiter.length();
end = str.find(delimiter, start);
}
tokens.push_back(str.substr(start));
return tokens;
}
static std::string repeat(const std::string & str, size_t n) {
if (n == 0) {
return "";
}
std::string result;
result.reserve(str.length() * n);
for (size_t i = 0; i < n; ++i) {
result += str;
}
return result;
}
static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function<std::string(const std::smatch &)> & replacement) {
std::smatch match;
std::string result;
std::string::const_iterator searchStart(input.cbegin());
std::string::const_iterator searchEnd(input.cend());
while (std::regex_search(searchStart, searchEnd, match, regex)) {
result.append(searchStart, searchStart + match.position());
result.append(replacement(match));
searchStart = match.suffix().first;
}
result.append(searchStart, searchEnd);
return result;
}
static std::string format_literal(const std::string & literal) {
std::string escaped = replacePattern(literal, GRAMMAR_LITERAL_ESCAPE_RE, [&](const std::smatch & match) {
char c = match.str()[0];
return GRAMMAR_LITERAL_ESCAPES.at(c);
});
return "\"" + escaped + "\"";
}
class SchemaConverter {
private:
std::function<json(const std::string &)> _fetch_json;
bool _dotall;
std::map<std::string, std::string> _rules;
std::unordered_map<std::string, json> _refs;
std::unordered_set<std::string> _refs_being_resolved;
std::vector<std::string> _errors;
std::vector<std::string> _warnings;
std::string _add_rule(const std::string & name, const std::string & rule) {
std::string esc_name = regex_replace(name, INVALID_RULE_CHARS_RE, "-");
if (_rules.find(esc_name) == _rules.end() || _rules[esc_name] == rule) {
_rules[esc_name] = rule;
return esc_name;
} else {
int i = 0;
while (_rules.find(esc_name + std::to_string(i)) != _rules.end() && _rules[esc_name + std::to_string(i)] != rule) {
i++;
}
std::string key = esc_name + std::to_string(i);
_rules[key] = rule;
return key;
}
}
std::string _generate_union_rule(const std::string & name, const std::vector<json> & alt_schemas) {
std::vector<std::string> rules;
for (size_t i = 0; i < alt_schemas.size(); i++) {
rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i)));
}
return join(rules.begin(), rules.end(), " | ");
}
std::string _visit_pattern(const std::string & pattern, const std::string & name) {
if (!(pattern.front() == '^' && pattern.back() == '$')) {
_errors.push_back("Pattern must start with '^' and end with '$'");
return "";
}
std::string sub_pattern = pattern.substr(1, pattern.length() - 2);
std::unordered_map<std::string, std::string> sub_rule_ids;
size_t i = 0;
size_t length = sub_pattern.length();
using literal_or_rule = std::pair<std::string, bool>;
auto to_rule = [&](const literal_or_rule & ls) {
auto is_literal = ls.second;
auto s = ls.first;
return is_literal ? "\"" + s + "\"" : s;
};
std::function<literal_or_rule()> transform = [&]() -> literal_or_rule {
size_t start = i;
std::vector<literal_or_rule> seq;
auto get_dot = [&]() {
std::string rule;
if (_dotall) {
rule = "[\\U00000000-\\U0010FFFF]";
} else {
rule = "[^\\x0A\\x0D]";
}
return _add_rule("dot", rule);
};
// Joins the sequence, merging consecutive literals together.
auto join_seq = [&]() {
std::vector<literal_or_rule> ret;
std::string literal;
auto flush_literal = [&]() {
if (literal.empty()) {
return false;
}
ret.emplace_back(literal, true);
literal.clear();
return true;
};
for (const auto & item : seq) {
auto is_literal = item.second;
if (is_literal) {
literal += item.first;
} else {
flush_literal();
ret.push_back(item);
}
}
flush_literal();
std::vector<std::string> results;
for (const auto & item : ret) {
results.push_back(to_rule(item));
}
return std::make_pair(join(results.begin(), results.end(), " "), false);
};
while (i < length) {
char c = sub_pattern[i];
if (c == '.') {
seq.emplace_back(get_dot(), false);
i++;
} else if (c == '(') {
i++;
if (i < length) {
if (sub_pattern[i] == '?') {
_warnings.push_back("Unsupported pattern syntax");
}
}
seq.emplace_back("(" + to_rule(transform()) + ")", false);
} else if (c == ')') {
i++;
if (start > 0 && sub_pattern[start - 1] != '(') {
_errors.push_back("Unbalanced parentheses");
}
return join_seq();
} else if (c == '[') {
std::string square_brackets = std::string(1, c);
i++;
while (i < length && sub_pattern[i] != ']') {
if (sub_pattern[i] == '\\') {
square_brackets += sub_pattern.substr(i, 2);
i += 2;
} else {
square_brackets += sub_pattern[i];
i++;
}
}
if (i >= length) {
_errors.push_back("Unbalanced square brackets");
}
square_brackets += ']';
i++;
seq.emplace_back(square_brackets, false);
} else if (c == '|') {
seq.emplace_back("|", false);
i++;
} else if (c == '*' || c == '+' || c == '?') {
seq.back() = std::make_pair(to_rule(seq.back()) + c, false);
i++;
} else if (c == '{') {
std::string curly_brackets = std::string(1, c);
i++;
while (i < length && sub_pattern[i] != '}') {
curly_brackets += sub_pattern[i];
i++;
}
if (i >= length) {
_errors.push_back("Unbalanced curly brackets");
}
curly_brackets += '}';
i++;
auto nums = split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
int min_times = 0;
int max_times = std::numeric_limits<int>::max();
try {
if (nums.size() == 1) {
min_times = max_times = std::stoi(nums[0]);
} else if (nums.size() != 2) {
_errors.push_back("Wrong number of values in curly brackets");
} else {
if (!nums[0].empty()) {
min_times = std::stoi(nums[0]);
}
if (!nums[1].empty()) {
max_times = std::stoi(nums[1]);
}
}
} catch (const std::invalid_argument & e) {
_errors.push_back("Invalid number in curly brackets");
return std::make_pair("", false);
}
auto &last = seq.back();
auto &sub = last.first;
auto sub_is_literal = last.second;
if (!sub_is_literal) {
std::string & sub_id = sub_rule_ids[sub];
if (sub_id.empty()) {
sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub);
}
sub = sub_id;
}
seq.back().first = build_repetition(
sub_is_literal ? "\"" + sub + "\"" : sub,
min_times,
max_times,
""
);
seq.back().second = false;
} else {
std::string literal;
auto is_non_literal = [&](char c) {
return NON_LITERAL_SET.find(c) != NON_LITERAL_SET.end();
};
while (i < length) {
if (sub_pattern[i] == '\\' && i < length - 1) {
char next = sub_pattern[i + 1];
if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.find(next) != ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.end()) {
i++;
literal += sub_pattern[i];
i++;
} else {
literal += sub_pattern.substr(i, 2);
i += 2;
}
} else if (sub_pattern[i] == '"') {
literal += "\\\"";
i++;
} else if (!is_non_literal(sub_pattern[i]) &&
(i == length - 1 || literal.empty() || sub_pattern[i + 1] == '.' || !is_non_literal(sub_pattern[i + 1]))) {
literal += sub_pattern[i];
i++;
} else {
break;
}
}
if (!literal.empty()) {
seq.emplace_back(literal, true);
}
}
}
return join_seq();
};
return _add_rule(name, "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space");
}
/*
Returns a rule that matches a JSON string that is none of the provided strings
not_strings({"a"})
-> ["] ( [a] char+ | [^"a] char* )? ["] space
not_strings({"and", "also"})
-> ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["] space
*/
std::string _not_strings(const std::vector<std::string> & strings) {
struct TrieNode {
std::map<char, TrieNode> children;
bool is_end_of_string;
TrieNode() : is_end_of_string(false) {}
void insert(const std::string & string) {
auto node = this;
for (char c : string) {
node = &node->children[c];
}
node->is_end_of_string = true;
}
};
TrieNode trie;
for (const auto & s : strings) {
trie.insert(s);
}
std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char"));
std::ostringstream out;
out << "[\"] ( ";
std::function<void(const TrieNode &)> visit = [&](const TrieNode & node) {
std::ostringstream rejects;
auto first = true;
for (const auto & kv : node.children) {
rejects << kv.first;
if (first) {
first = false;
} else {
out << " | ";
}
out << "[" << kv.first << "]";
if (!kv.second.children.empty()) {
out << " (";
visit(kv.second);
out << ")";
} else if (kv.second.is_end_of_string) {
out << " " << char_rule << "+";
}
}
if (!node.children.empty()) {
if (!first) {
out << " | ";
}
out << "[^\"" << rejects.str() << "] " << char_rule << "*";
}
};
visit(trie);
out << " )";
if (!trie.is_end_of_string) {
out << "?";
}
out << " [\"] space";
return out.str();
}
std::string _resolve_ref(const std::string & ref) {
std::string ref_name = ref.substr(ref.find_last_of('/') + 1);
if (_rules.find(ref_name) == _rules.end() && _refs_being_resolved.find(ref) == _refs_being_resolved.end()) {
_refs_being_resolved.insert(ref);
json resolved = _refs[ref];
ref_name = visit(resolved, ref_name);
_refs_being_resolved.erase(ref);
}
return ref_name;
}
std::string _build_object_rule(
const std::vector<std::pair<std::string, json>> & properties,
const std::unordered_set<std::string> & required,
const std::string & name,
const json & additional_properties)
{
std::vector<std::string> required_props;
std::vector<std::string> optional_props;
std::unordered_map<std::string, std::string> prop_kv_rule_names;
std::vector<std::string> prop_names;
for (const auto & kv : properties) {
const auto &prop_name = kv.first;
const auto &prop_schema = kv.second;
std::string prop_rule_name = visit(prop_schema, name + (name.empty() ? "" : "-") + prop_name);
prop_kv_rule_names[prop_name] = _add_rule(
name + (name.empty() ? "" : "-") + prop_name + "-kv",
format_literal(json(prop_name).dump()) + " space \":\" space " + prop_rule_name
);
if (required.find(prop_name) != required.end()) {
required_props.push_back(prop_name);
} else {
optional_props.push_back(prop_name);
}
prop_names.push_back(prop_name);
}
if ((additional_properties.is_boolean() && additional_properties.get<bool>()) || additional_properties.is_object()) {
std::string sub_name = name + (name.empty() ? "" : "-") + "additional";
std::string value_rule =
additional_properties.is_object() ? visit(additional_properties, sub_name + "-value")
: _add_primitive("value", PRIMITIVE_RULES.at("value"));
auto key_rule =
prop_names.empty() ? _add_primitive("string", PRIMITIVE_RULES.at("string"))
: _add_rule(sub_name + "-k", _not_strings(prop_names));
std::string kv_rule = _add_rule(sub_name + "-kv", key_rule + " \":\" space " + value_rule);
prop_kv_rule_names["*"] = kv_rule;
optional_props.push_back("*");
}
std::string rule = "\"{\" space ";
for (size_t i = 0; i < required_props.size(); i++) {
if (i > 0) {
rule += " \",\" space ";
}
rule += prop_kv_rule_names[required_props[i]];
}
if (!optional_props.empty()) {
rule += " (";
if (!required_props.empty()) {
rule += " \",\" space ( ";
}
std::function<std::string(const std::vector<std::string> &, bool)> get_recursive_refs = [&](const std::vector<std::string> & ks, bool first_is_optional) {
std::string res;
if (ks.empty()) {
return res;
}
std::string k = ks[0];
std::string kv_rule_name = prop_kv_rule_names[k];
std::string comma_ref = "( \",\" space " + kv_rule_name + " )";
if (first_is_optional) {
res = comma_ref + (k == "*" ? "*" : "?");
} else {
res = kv_rule_name + (k == "*" ? " " + comma_ref + "*" : "");
}
if (ks.size() > 1) {
res += " " + _add_rule(
name + (name.empty() ? "" : "-") + k + "-rest",
get_recursive_refs(std::vector<std::string>(ks.begin() + 1, ks.end()), true)
);
}
return res;
};
for (size_t i = 0; i < optional_props.size(); i++) {
if (i > 0) {
rule += " | ";
}
rule += get_recursive_refs(std::vector<std::string>(optional_props.begin() + i, optional_props.end()), false);
}
if (!required_props.empty()) {
rule += " )";
}
rule += " )?";
}
rule += " \"}\" space";
return rule;
}
std::string _add_primitive(const std::string & name, const BuiltinRule & rule) {
auto n = _add_rule(name, rule.content);
for (const auto & dep : rule.deps) {
BuiltinRule dep_rule;
auto it = PRIMITIVE_RULES.find(dep);
if (it == PRIMITIVE_RULES.end()) {
it = STRING_FORMAT_RULES.find(dep);
if (it == STRING_FORMAT_RULES.end()) {
_errors.push_back("Rule " + dep + " not known");
continue;
}
}
if (_rules.find(dep) == _rules.end()) {
_add_primitive(dep, it->second);
}
}
return n;
}
public:
SchemaConverter(
const std::function<json(const std::string &)> & fetch_json,
bool dotall)
: _fetch_json(fetch_json), _dotall(dotall)
{
_rules["space"] = SPACE_RULE;
}
void resolve_refs(json & schema, const std::string & url) {
/*
* Resolves all $ref fields in the given schema, fetching any remote schemas,
* replacing each $ref with absolute reference URL and populates _refs with the
* respective referenced (sub)schema dictionaries.
*/
std::function<void(json &)> visit_refs = [&](json & n) {
if (n.is_array()) {
for (auto & x : n) {
visit_refs(x);
}
} else if (n.is_object()) {
if (n.contains("$ref")) {
std::string ref = n["$ref"];
if (_refs.find(ref) == _refs.end()) {
json target;
if (ref.find("https://") == 0) {
std::string base_url = ref.substr(0, ref.find('#'));
auto it = _refs.find(base_url);
if (it != _refs.end()) {
target = it->second;
} else {
// Fetch the referenced schema and resolve its refs
auto referenced = _fetch_json(ref);
resolve_refs(referenced, base_url);
_refs[base_url] = referenced;
}
if (ref.find('#') == std::string::npos || ref.substr(ref.find('#') + 1).empty()) {
return;
}
} else if (ref.find("#/") == 0) {
target = schema;
n["$ref"] = url + ref;
ref = url + ref;
} else {
_errors.push_back("Unsupported ref: " + ref);
return;
}
std::string pointer = ref.substr(ref.find('#') + 1);
std::vector<std::string> tokens = split(pointer, "/");
for (size_t i = 1; i < tokens.size(); ++i) {
std::string sel = tokens[i];
if (target.is_null() || !target.contains(sel)) {
_errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump());
return;
}
target = target[sel];
}
_refs[ref] = target;
}
} else {
for (auto & kv : n.items()) {
visit_refs(kv.value());
}
}
}
};
visit_refs(schema);
}
std::string _generate_constant_rule(const json & value) {
return format_literal(value.dump());
}
std::string visit(const json & schema, const std::string & name) {
json schema_type = schema.contains("type") ? schema["type"] : json();
std::string schema_format = schema.contains("format") ? schema["format"].get<std::string>() : "";
std::string rule_name = is_reserved_name(name) ? name + "-" : name.empty() ? "root" : name;
if (schema.contains("$ref")) {
return _add_rule(rule_name, _resolve_ref(schema["$ref"]));
} else if (schema.contains("oneOf") || schema.contains("anyOf")) {
std::vector<json> alt_schemas = schema.contains("oneOf") ? schema["oneOf"].get<std::vector<json>>() : schema["anyOf"].get<std::vector<json>>();
return _add_rule(rule_name, _generate_union_rule(name, alt_schemas));
} else if (schema_type.is_array()) {
std::vector<json> schema_types;
for (const auto & t : schema_type) {
json schema_copy(schema);
schema_copy["type"] = t;
schema_types.push_back(schema_copy);
}
return _add_rule(rule_name, _generate_union_rule(name, schema_types));
} else if (schema.contains("const")) {
return _add_rule(rule_name, _generate_constant_rule(schema["const"]) + " space");
} else if (schema.contains("enum")) {
std::vector<std::string> enum_values;
for (const auto & v : schema["enum"]) {
enum_values.push_back(_generate_constant_rule(v));
}
return _add_rule(rule_name, "(" + join(enum_values.begin(), enum_values.end(), " | ") + ") space");
} else if ((schema_type.is_null() || schema_type == "object")
&& (schema.contains("properties") ||
(schema.contains("additionalProperties") && schema["additionalProperties"] != true))) {
std::unordered_set<std::string> required;
if (schema.contains("required") && schema["required"].is_array()) {
for (const auto & item : schema["required"]) {
if (item.is_string()) {
required.insert(item.get<std::string>());
}
}
}
std::vector<std::pair<std::string, json>> properties;
if (schema.contains("properties")) {
for (const auto & prop : schema["properties"].items()) {
properties.emplace_back(prop.key(), prop.value());
}
}
return _add_rule(rule_name,
_build_object_rule(
properties, required, name,
schema.contains("additionalProperties") ? schema["additionalProperties"] : json()));
} else if ((schema_type.is_null() || schema_type == "object") && schema.contains("allOf")) {
std::unordered_set<std::string> required;
std::vector<std::pair<std::string, json>> properties;
std::string hybrid_name = name;
std::function<void(const json &, bool)> add_component = [&](const json & comp_schema, bool is_required) {
if (comp_schema.contains("$ref")) {
add_component(_refs[comp_schema["$ref"]], is_required);
} else if (comp_schema.contains("properties")) {
for (const auto & prop : comp_schema["properties"].items()) {
properties.emplace_back(prop.key(), prop.value());
if (is_required) {
required.insert(prop.key());
}
}
} else {
// todo warning
}
};
for (auto & t : schema["allOf"]) {
if (t.contains("anyOf")) {
for (auto & tt : t["anyOf"]) {
add_component(tt, false);
}
} else {
add_component(t, true);
}
}
return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json()));
} else if ((schema_type.is_null() || schema_type == "array") && (schema.contains("items") || schema.contains("prefixItems"))) {
json items = schema.contains("items") ? schema["items"] : schema["prefixItems"];
if (items.is_array()) {
std::string rule = "\"[\" space ";
for (size_t i = 0; i < items.size(); i++) {
if (i > 0) {
rule += " \",\" space ";
}
rule += visit(items[i], name + (name.empty() ? "" : "-") + "tuple-" + std::to_string(i));
}
rule += " \"]\" space";
return _add_rule(rule_name, rule);
} else {
std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item");
int min_items = schema.contains("minItems") ? schema["minItems"].get<int>() : 0;
json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json();
int max_items = max_items_json.is_number_integer() ? max_items_json.get<int>() : std::numeric_limits<int>::max();
return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space");
}
} else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) {
return _visit_pattern(schema["pattern"], rule_name);
} else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) {
return _add_primitive(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid"));
} else if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) {
auto prim_name = schema_format + "-string";
return _add_rule(rule_name, _add_primitive(prim_name, STRING_FORMAT_RULES.at(prim_name)));
} else if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) {
std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char"));
int min_len = schema.contains("minLength") ? schema["minLength"].get<int>() : 0;
int max_len = schema.contains("maxLength") ? schema["maxLength"].get<int>() : std::numeric_limits<int>::max();
return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space");
} else if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) {
int min_value = std::numeric_limits<int>::min();
int max_value = std::numeric_limits<int>::max();
if (schema.contains("minimum")) {
min_value = schema["minimum"].get<int>();
} else if (schema.contains("exclusiveMinimum")) {
min_value = schema["exclusiveMinimum"].get<int>() + 1;
}
if (schema.contains("maximum")) {
max_value = schema["maximum"].get<int>();
} else if (schema.contains("exclusiveMaximum")) {
max_value = schema["exclusiveMaximum"].get<int>() - 1;
}
std::stringstream out;
out << "(";
_build_min_max_int(min_value, max_value, out);
out << ") space";
return _add_rule(rule_name, out.str());
} else if (schema.empty() || schema_type == "object") {
return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object")));
} else {
if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get<std::string>()) == PRIMITIVE_RULES.end()) {
_errors.push_back("Unrecognized schema: " + schema.dump());
return "";
}
// TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
return _add_primitive(rule_name == "root" ? "root" : schema_type.get<std::string>(), PRIMITIVE_RULES.at(schema_type.get<std::string>()));
}
}
void check_errors() {
if (!_errors.empty()) {
throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n"));
}
if (!_warnings.empty()) {
fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", join(_warnings.begin(), _warnings.end(), "; ").c_str());
}
}
std::string format_grammar() {
std::stringstream ss;
for (const auto & kv : _rules) {
ss << kv.first << " ::= " << kv.second << std::endl;
}
return ss.str();
}
};
std::string json_schema_to_grammar(const json & schema) {
SchemaConverter converter([](const std::string &) { return json::object(); }, /* dotall= */ false);
auto copy = schema;
converter.resolve_refs(copy, "input");
converter.visit(copy, "");
converter.check_errors();
return converter.format_grammar();
}
ollama/llm/llama.cpp/common/json-schema-to-grammar.h deleted 100755 → 0
View file @ cb75098c
#pragma once
#include "ggml.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
std::string json_schema_to_grammar(const nlohmann::ordered_json& schema);
ollama/llm/llama.cpp/common/json.hpp deleted 100755 → 0
View file @ cb75098c
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ollama/llm/llama.cpp/common/log.h deleted 100755 → 0
View file @ cb75098c
#pragma once
#include <chrono>
#include <cstring>
#include <sstream>
#include <iostream>
#include <thread>
#include <vector>
#include <algorithm>
#include <cinttypes>
// --------------------------------
//
// Basic usage:
//
// --------
//
// The LOG() and LOG_TEE() macros are ready to go by default
// they do not require any initialization.
//
// LOGLN() and LOG_TEELN() are variants which automatically
// include \n character at the end of the log string.
//
// LOG() behaves exactly like printf, by default writing to a logfile.
// LOG_TEE() additionally, prints to the screen too ( mimics Unix tee command ).
//
// Default logfile is named
// "llama.<threadID>.log"
// Default LOG_TEE() secondary output target is
// stderr
//
// Logs can be dynamically disabled or enabled using functions:
// log_disable()
// and
// log_enable()
//
// A log target can be changed with:
// log_set_target( string )
// creating and opening, or re-opening a file by string filename
// or
// log_set_target( FILE* )
// allowing to point at stderr, stdout, or any valid FILE* file handler.
//
// --------
//
// End of Basic usage.
//
// --------------------------------
// Specifies a log target.
// default uses log_handler() with "llama.log" log file
// this can be changed, by defining LOG_TARGET
// like so:
//
// #define LOG_TARGET (a valid FILE*)
// #include "log.h"
//
// or it can be simply redirected to stdout or stderr
// like so:
//
// #define LOG_TARGET stderr
// #include "log.h"
//
// The log target can also be redirected to a different function
// like so:
//
// #define LOG_TARGET log_handler_different()
// #include "log.h"
//
// FILE* log_handler_different()
// {
// return stderr;
// }
//
// or:
//
// #define LOG_TARGET log_handler_another_one("somelog.log")
// #include "log.h"
//
// FILE* log_handler_another_one(char*filename)
// {
// static FILE* logfile = nullptr;
// (...)
// if( !logfile )
// {
// fopen(...)
// }
// (...)
// return logfile
// }
//
#ifndef LOG_TARGET
#define LOG_TARGET log_handler()
#endif
#ifndef LOG_TEE_TARGET
#define LOG_TEE_TARGET stderr
#endif
// Utility for synchronizing log configuration state
// since std::optional was introduced only in c++17
enum LogTriState
{
LogTriStateSame,
LogTriStateFalse,
LogTriStateTrue
};
// Utility to obtain "pid" like unique process id and use it when creating log files.
inline std::string log_get_pid()
{
static std::string pid;
if (pid.empty())
{
// std::this_thread::get_id() is the most portable way of obtaining a "process id"
// it's not the same as "pid" but is unique enough to solve multiple instances
// trying to write to the same log.
std::stringstream ss;
ss << std::this_thread::get_id();
pid = ss.str();
}
return pid;
}
// Utility function for generating log file names with unique id based on thread id.
// invocation with log_filename_generator( "llama", "log" ) creates a string "llama.<number>.log"
// where the number is a runtime id of the current thread.
#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(LogTriStateSame, log_file_basename, log_file_extension)
// INTERNAL, DO NOT USE
inline std::string log_filename_generator_impl(LogTriState multilog, const std::string & log_file_basename, const std::string & log_file_extension)
{
static bool _multilog = false;
if (multilog != LogTriStateSame)
{
_multilog = multilog == LogTriStateTrue;
}
std::stringstream buf;
buf << log_file_basename;
if (_multilog)
{
buf << ".";
buf << log_get_pid();
}
buf << ".";
buf << log_file_extension;
return buf.str();
}
#ifndef LOG_DEFAULT_FILE_NAME
#define LOG_DEFAULT_FILE_NAME log_filename_generator("llama", "log")
#endif
// Utility for turning #define values into string literals
// so we can have a define for stderr and
// we can print "stderr" instead of literal stderr, etc.
#define LOG_STRINGIZE1(s) #s
#define LOG_STRINGIZE(s) LOG_STRINGIZE1(s)
#define LOG_TEE_TARGET_STRING LOG_STRINGIZE(LOG_TEE_TARGET)
// Allows disabling timestamps.
// in order to disable, define LOG_NO_TIMESTAMPS
// like so:
//
// #define LOG_NO_TIMESTAMPS
// #include "log.h"
//
#ifndef LOG_NO_TIMESTAMPS
#ifndef _MSC_VER
#define LOG_TIMESTAMP_FMT "[%" PRIu64 "] "
#define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
#else
#define LOG_TIMESTAMP_FMT "[%" PRIu64 "] "
#define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
#endif
#else
#define LOG_TIMESTAMP_FMT "%s"
#define LOG_TIMESTAMP_VAL ,""
#endif
#ifdef LOG_TEE_TIMESTAMPS
#ifndef _MSC_VER
#define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] "
#define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
#else
#define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] "
#define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
#endif
#else
#define LOG_TEE_TIMESTAMP_FMT "%s"
#define LOG_TEE_TIMESTAMP_VAL ,""
#endif
// Allows disabling file/line/function prefix
// in order to disable, define LOG_NO_FILE_LINE_FUNCTION
// like so:
//
// #define LOG_NO_FILE_LINE_FUNCTION
// #include "log.h"
//
#ifndef LOG_NO_FILE_LINE_FUNCTION
#ifndef _MSC_VER
#define LOG_FLF_FMT "[%24s:%5d][%24s] "
#define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
#else
#define LOG_FLF_FMT "[%24s:%5ld][%24s] "
#define LOG_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__
#endif
#else
#define LOG_FLF_FMT "%s"
#define LOG_FLF_VAL ,""
#endif
#ifdef LOG_TEE_FILE_LINE_FUNCTION
#ifndef _MSC_VER
#define LOG_TEE_FLF_FMT "[%24s:%5d][%24s] "
#define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
#else
#define LOG_TEE_FLF_FMT "[%24s:%5ld][%24s] "
#define LOG_TEE_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__
#endif
#else
#define LOG_TEE_FLF_FMT "%s"
#define LOG_TEE_FLF_VAL ,""
#endif
// INTERNAL, DO NOT USE
// USE LOG() INSTEAD
//
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
#define LOG_IMPL(str, ...) \
do { \
if (LOG_TARGET != nullptr) \
{ \
fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \
fflush(LOG_TARGET); \
} \
} while (0)
#else
#define LOG_IMPL(str, ...) \
do { \
if (LOG_TARGET != nullptr) \
{ \
fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__); \
fflush(LOG_TARGET); \
} \
} while (0)
#endif
// INTERNAL, DO NOT USE
// USE LOG_TEE() INSTEAD
//
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
#define LOG_TEE_IMPL(str, ...) \
do { \
if (LOG_TARGET != nullptr) \
{ \
fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \
fflush(LOG_TARGET); \
} \
if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \
{ \
fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL, __VA_ARGS__); \
fflush(LOG_TEE_TARGET); \
} \
} while (0)
#else
#define LOG_TEE_IMPL(str, ...) \
do { \
if (LOG_TARGET != nullptr) \
{ \
fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__); \
fflush(LOG_TARGET); \
} \
if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \
{ \
fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL "", ##__VA_ARGS__); \
fflush(LOG_TEE_TARGET); \
} \
} while (0)
#endif
// The '\0' as a last argument, is a trick to bypass the silly
// "warning: ISO C++11 requires at least one argument for the "..." in a variadic macro"
// so we can have a single macro which can be called just like printf.
// Main LOG macro.
// behaves like printf, and supports arguments the exact same way.
//
#if !defined(_MSC_VER) || defined(__clang__)
#define LOG(...) LOG_IMPL(__VA_ARGS__, "")
#else
#define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "")
#endif
// Main TEE macro.
// does the same as LOG
// and
// simultaneously writes stderr.
//
// Secondary target can be changed just like LOG_TARGET
// by defining LOG_TEE_TARGET
//
#if !defined(_MSC_VER) || defined(__clang__)
#define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "")
#else
#define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "")
#endif
// LOG macro variants with auto endline.
#if !defined(_MSC_VER) || defined(__clang__)
#define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n")
#define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n")
#else
#define LOGLN(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "\n")
#define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "\n")
#endif
// INTERNAL, DO NOT USE
inline FILE *log_handler1_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr)
{
static bool _initialized = false;
static bool _append = false;
static bool _disabled = filename.empty() && target == nullptr;
static std::string log_current_filename{filename};
static FILE *log_current_target{target};
static FILE *logfile = nullptr;
if (change)
{
if (append != LogTriStateSame)
{
_append = append == LogTriStateTrue;
return logfile;
}
if (disable == LogTriStateTrue)
{
// Disable primary target
_disabled = true;
}
// If previously disabled, only enable, and keep previous target
else if (disable == LogTriStateFalse)
{
_disabled = false;
}
// Otherwise, process the arguments
else if (log_current_filename != filename || log_current_target != target)
{
_initialized = false;
}
}
if (_disabled)
{
// Log is disabled
return nullptr;
}
if (_initialized)
{
// with fallback in case something went wrong
return logfile ? logfile : stderr;
}
// do the (re)initialization
if (target != nullptr)
{
if (logfile != nullptr && logfile != stdout && logfile != stderr)
{
fclose(logfile);
}
log_current_filename = LOG_DEFAULT_FILE_NAME;
log_current_target = target;
logfile = target;
}
else
{
if (log_current_filename != filename)
{
if (logfile != nullptr && logfile != stdout && logfile != stderr)
{
fclose(logfile);
}
}
logfile = fopen(filename.c_str(), _append ? "a" : "w");
}
if (!logfile)
{
// Verify whether the file was opened, otherwise fallback to stderr
logfile = stderr;
fprintf(stderr, "Failed to open logfile '%s' with error '%s'\n", filename.c_str(), std::strerror(errno));
fflush(stderr);
// At this point we let the init flag be to true below, and let the target fallback to stderr
// otherwise we would repeatedly fopen() which was already unsuccessful
}
_initialized = true;
return logfile ? logfile : stderr;
}
// INTERNAL, DO NOT USE
inline FILE *log_handler2_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME)
{
return log_handler1_impl(change, append, disable, filename, target);
}
// Disables logs entirely at runtime.
// Makes LOG() and LOG_TEE() produce no output,
// until enabled back.
#define log_disable() log_disable_impl()
// INTERNAL, DO NOT USE
inline FILE *log_disable_impl()
{
return log_handler1_impl(true, LogTriStateSame, LogTriStateTrue);
}
// Enables logs at runtime.
#define log_enable() log_enable_impl()
// INTERNAL, DO NOT USE
inline FILE *log_enable_impl()
{
return log_handler1_impl(true, LogTriStateSame, LogTriStateFalse);
}
// Sets target fir logs, either by a file name or FILE* pointer (stdout, stderr, or any valid FILE*)
#define log_set_target(target) log_set_target_impl(target)
// INTERNAL, DO NOT USE
inline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, LogTriStateSame, filename); }
inline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, LogTriStateSame, target); }
// INTERNAL, DO NOT USE
inline FILE *log_handler() { return log_handler1_impl(); }
// Enable or disable creating separate log files for each run.
// can ONLY be invoked BEFORE first log use.
#define log_multilog(enable) log_filename_generator_impl((enable) ? LogTriStateTrue : LogTriStateFalse, "", "")
// Enable or disable append mode for log file.
// can ONLY be invoked BEFORE first log use.
#define log_append(enable) log_append_impl(enable)
// INTERNAL, DO NOT USE
inline FILE *log_append_impl(bool enable)
{
return log_handler1_impl(true, enable ? LogTriStateTrue : LogTriStateFalse, LogTriStateSame);
}
inline void log_test()
{
log_disable();
LOG("01 Hello World to nobody, because logs are disabled!\n");
log_enable();
LOG("02 Hello World to default output, which is \"%s\" ( Yaaay, arguments! )!\n", LOG_STRINGIZE(LOG_TARGET));
LOG_TEE("03 Hello World to **both** default output and " LOG_TEE_TARGET_STRING "!\n");
log_set_target(stderr);
LOG("04 Hello World to stderr!\n");
LOG_TEE("05 Hello World TEE with double printing to stderr prevented!\n");
log_set_target(LOG_DEFAULT_FILE_NAME);
LOG("06 Hello World to default log file!\n");
log_set_target(stdout);
LOG("07 Hello World to stdout!\n");
log_set_target(LOG_DEFAULT_FILE_NAME);
LOG("08 Hello World to default log file again!\n");
log_disable();
LOG("09 Hello World _1_ into the void!\n");
log_enable();
LOG("10 Hello World back from the void ( you should not see _1_ in the log or the output )!\n");
log_disable();
log_set_target("llama.anotherlog.log");
LOG("11 Hello World _2_ to nobody, new target was selected but logs are still disabled!\n");
log_enable();
LOG("12 Hello World this time in a new file ( you should not see _2_ in the log or the output )?\n");
log_set_target("llama.yetanotherlog.log");
LOG("13 Hello World this time in yet new file?\n");
log_set_target(log_filename_generator("llama_autonamed", "log"));
LOG("14 Hello World in log with generated filename!\n");
#ifdef _MSC_VER
LOG_TEE("15 Hello msvc TEE without arguments\n");
LOG_TEE("16 Hello msvc TEE with (%d)(%s) arguments\n", 1, "test");
LOG_TEELN("17 Hello msvc TEELN without arguments\n");
LOG_TEELN("18 Hello msvc TEELN with (%d)(%s) arguments\n", 1, "test");
LOG("19 Hello msvc LOG without arguments\n");
LOG("20 Hello msvc LOG with (%d)(%s) arguments\n", 1, "test");
LOGLN("21 Hello msvc LOGLN without arguments\n");
LOGLN("22 Hello msvc LOGLN with (%d)(%s) arguments\n", 1, "test");
#endif
}
inline bool log_param_single_parse(const std::string & param)
{
if ( param == "--log-test")
{
log_test();
return true;
}
if ( param == "--log-disable")
{
log_disable();
return true;
}
if ( param == "--log-enable")
{
log_enable();
return true;
}
if (param == "--log-new")
{
log_multilog(true);
return true;
}
if (param == "--log-append")
{
log_append(true);
return true;
}
return false;
}
inline bool log_param_pair_parse(bool check_but_dont_parse, const std::string & param, const std::string & next = std::string())
{
if ( param == "--log-file")
{
if (!check_but_dont_parse)
{
log_set_target(log_filename_generator(next.empty() ? "unnamed" : next, "log"));
}
return true;
}
return false;
}
inline void log_print_usage()
{
printf("log options:\n");
/* format
printf(" -h, --help show this help message and exit\n");*/
/* spacing
printf("__-param----------------Description\n");*/
printf(" --log-test Run simple logging test\n");
printf(" --log-disable Disable trace logs\n");
printf(" --log-enable Enable trace logs\n");
printf(" --log-file Specify a log filename (without extension)\n");
printf(" --log-new Create a separate new log file on start. "
"Each log file will have unique name: \"<name>.<ID>.log\"\n");
printf(" --log-append Don't truncate the old log file.\n");
printf("\n");
}
#define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)
// INTERNAL, DO NOT USE
inline void log_dump_cmdline_impl(int argc, char **argv)
{
std::stringstream buf;
for (int i = 0; i < argc; ++i)
{
if (std::string(argv[i]).find(' ') != std::string::npos)
{
buf << " \"" << argv[i] <<"\"";
}
else
{
buf << " " << argv[i];
}
}
LOGLN("Cmd:%s", buf.str().c_str());
}
#define log_tostr(var) log_var_to_string_impl(var).c_str()
inline std::string log_var_to_string_impl(bool var)
{
return var ? "true" : "false";
}
inline std::string log_var_to_string_impl(std::string var)
{
return var;
}
inline std::string log_var_to_string_impl(const std::vector<int> & var)
{
std::stringstream buf;
buf << "[ ";
bool first = true;
for (auto e : var)
{
if (first)
{
first = false;
}
else
{
buf << ", ";
}
buf << std::to_string(e);
}
buf << " ]";
return buf.str();
}
template <typename C, typename T>
inline std::string LOG_TOKENS_TOSTR_PRETTY(const C & ctx, const T & tokens)
{
std::stringstream buf;
buf << "[ ";
bool first = true;
for (const auto & token : tokens)
{
if (!first) {
buf << ", ";
} else {
first = false;
}
auto detokenized = llama_token_to_piece(ctx, token);
detokenized.erase(
std::remove_if(
detokenized.begin(),
detokenized.end(),
[](const unsigned char c) { return !std::isprint(c); }),
detokenized.end());
buf
<< "'" << detokenized << "'"
<< ":" << std::to_string(token);
}
buf << " ]";
return buf.str();
}
template <typename C, typename B>
inline std::string LOG_BATCH_TOSTR_PRETTY(const C & ctx, const B & batch)
{
std::stringstream buf;
buf << "[ ";
bool first = true;
for (int i = 0; i < batch.n_tokens; ++i)
{
if (!first) {
buf << ", ";
} else {
first = false;
}
auto detokenized = llama_token_to_piece(ctx, batch.token[i]);
detokenized.erase(
std::remove_if(
detokenized.begin(),
detokenized.end(),
[](const unsigned char c) { return !std::isprint(c); }),
detokenized.end());
buf
<< "\n" << std::to_string(i)
<< ":token '" << detokenized << "'"
<< ":pos " << std::to_string(batch.pos[i])
<< ":n_seq_id " << std::to_string(batch.n_seq_id[i])
<< ":seq_id " << std::to_string(batch.seq_id[i][0])
<< ":logits " << std::to_string(batch.logits[i]);
}
buf << " ]";
return buf.str();
}
#ifdef LOG_DISABLE_LOGS
#undef LOG
#define LOG(...) // dummy stub
#undef LOGLN
#define LOGLN(...) // dummy stub
#undef LOG_TEE
#define LOG_TEE(...) fprintf(stderr, __VA_ARGS__) // convert to normal fprintf
#undef LOG_TEELN
#define LOG_TEELN(...) fprintf(stderr, __VA_ARGS__) // convert to normal fprintf
#undef LOG_DISABLE
#define LOG_DISABLE() // dummy stub
#undef LOG_ENABLE
#define LOG_ENABLE() // dummy stub
#undef LOG_ENABLE
#define LOG_ENABLE() // dummy stub
#undef LOG_SET_TARGET
#define LOG_SET_TARGET(...) // dummy stub
#undef LOG_DUMP_CMDLINE
#define LOG_DUMP_CMDLINE(...) // dummy stub
#endif // LOG_DISABLE_LOGS
ollama/llm/llama.cpp/common/ngram-cache.cpp deleted 100755 → 0
View file @ cb75098c
#include "ngram-cache.h"
#include "common.h"
#include "log.h"
#include <cstdint>
#include <fstream>
void llama_ngram_cache_update(llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max,
std::vector<llama_token> & inp, int nnew, bool print_progress) {
const int64_t t_start_ms = ggml_time_ms();
const int64_t inp_size = inp.size();
const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1);
int64_t n_done = 0;
for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) {
const int64_t i_start = std::max(inp_size - nnew, ngram_size);
for (int64_t i = i_start; i < inp_size; ++i) {
const int64_t ngram_start = i - ngram_size;
llama_ngram ngram(&inp[ngram_start], ngram_size);
const llama_token token = inp[i];
llama_ngram_cache::iterator part_it = ngram_cache.find(ngram);
if (part_it == ngram_cache.end()) {
llama_ngram_cache_part part;
part.emplace(token, 1);
ngram_cache.emplace(ngram, part);
} else {
llama_ngram_cache_part::iterator token_count_it = part_it->second.find(token);
if (token_count_it == part_it->second.end()) {
part_it->second.emplace(token, 1);
} else {
token_count_it->second++;
}
}
++n_done;
if (print_progress && n_done % 10000000 == 0) {
const int64_t t_now_ms = ggml_time_ms();
const int64_t eta_ms = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done;
const int64_t eta_min = eta_ms / (60*1000);
const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000;
fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s);
}
}
}
}
// Helper function to get a token from the combined, speculative sequence of inp and draft.
static llama_token get_token(const std::vector<llama_token> & inp, const std::vector<llama_token> & draft, const size_t i) {
return i < inp.size() ? inp[i] : draft[1 + i - inp.size()];
}
// If sample size or percentage are below these thresholds the draft is aborted early:
constexpr int draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2, 2, 1, 1};
constexpr int draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50};
constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4, 3, 2, 2};
constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
// Helper function that tries to draft a token from only the static ngram cache:
static llama_token try_draft(llama_ngram_cache & nc_static, const llama_ngram ngram_static) {
llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
if (part_static_it == nc_static.end()) {
return -1;
}
const llama_ngram_cache_part part_static = part_static_it->second;
int max_count_static = 0;
int sum_count_static = 0;
llama_token max_token = -1;
for (std::pair<llama_token, int> token_count_static : part_static) {
const llama_token token = token_count_static.first;
const int32_t count_static = token_count_static.second;
if (count_static > max_count_static) {
max_token = token;
max_count_static = count_static;
}
sum_count_static += count_static;
}
if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
return -1;
}
if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
return -1;
}
return max_token;
}
// Try to draft a token from primary cache (context/dynamic), validate with static cache:
static llama_token try_draft(
llama_ngram_cache & nc_primary, const std::vector<llama_ngram> & ngrams_primary, llama_ngram_cache_part & part_static,
const int * min_sample_size, const int * min_percent) {
llama_token drafted_token = -1;
for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) {
const llama_ngram ngram_primary = ngrams_primary[i];
llama_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
if (part_primary_it == nc_primary.end()) {
continue;
}
const llama_ngram_cache_part part_primary = part_primary_it->second;
int max_count_primary = 0;
int max_count_static = 0;
int sum_count_primary = 0;
llama_token max_token = -1;
for (std::pair<llama_token, int> token_count_primary : part_primary) {
const llama_token token = token_count_primary.first;
llama_ngram_cache_part::iterator token_count_static_it = part_static.find(token);
const int32_t count_primary = token_count_primary.second;
const int32_t count_static = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1;
if (count_primary*count_static > max_count_primary*max_count_static) {
max_token = token;
max_count_primary = count_primary;
max_count_static = count_static;
}
sum_count_primary += count_primary;
}
if (sum_count_primary < min_sample_size[i]) {
continue;
}
if (100*max_count_primary < min_percent[i]*sum_count_primary) {
continue;;
}
drafted_token = max_token;
}
return drafted_token;
}
void llama_ngram_cache_draft(
std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static
) {
GGML_ASSERT(draft.size() == 1);
const int inp_size = inp.size();
if (inp_size < LLAMA_NGRAM_STATIC) {
return;
}
while ((int) draft.size()-1 < n_draft) {
llama_token drafted_token = -1;
const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
llama_ngram ngram_static;
for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) {
ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j);
}
llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
llama_ngram_cache_part part_static;
if (part_static_it != nc_static.end()) {
part_static = part_static_it->second;
}
// cd = context + dynamic
std::vector<llama_ngram> ngrams_cd;
for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) {
const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1;
llama_ngram ngram_cd;
for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) {
ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j);
}
ngrams_cd.push_back(ngram_cd);
}
if (drafted_token == -1) {
drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
}
if (drafted_token == -1) {
drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
}
if (drafted_token == -1) {
drafted_token = try_draft(nc_static, ngram_static);
}
if (drafted_token == -1) {
break;
}
LOG(" - draft candidate: token=%d\n", drafted_token);
draft.push_back(drafted_token);
}
}
void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename) {
std::ofstream file_out(filename, std::ios::binary);
for (std::pair<llama_ngram, llama_ngram_cache_part> item : ngram_cache) {
const llama_ngram ngram = item.first;
llama_ngram_cache_part token_counts = item.second;
GGML_ASSERT(!token_counts.empty());
const int32_t ntokens = token_counts.size();
GGML_ASSERT(ntokens > 0);
file_out.write(reinterpret_cast<const char *>(&ngram), sizeof(llama_ngram));
file_out.write(reinterpret_cast<const char *>(&ntokens), sizeof(int32_t));
for (std::pair<llama_token, int32_t> item2 : token_counts) {
const llama_token token = item2.first;
const int32_t count = item2.second;
GGML_ASSERT(count > 0);
file_out.write(reinterpret_cast<const char *>(&token), sizeof(llama_token));
file_out.write(reinterpret_cast<const char *>(&count), sizeof(int32_t));
}
}
}
llama_ngram_cache llama_ngram_cache_load(std::string & filename) {
std::ifstream hashmap_file(filename, std::ios::binary);
if (!hashmap_file) {
throw std::ifstream::failure("Unable to open file " + filename);
}
llama_ngram_cache ngram_cache;
llama_ngram ngram;
int32_t ntokens;
llama_token token;
int32_t count;
char * ngramc = reinterpret_cast<char*>(&ngram);
char * ntokensc = reinterpret_cast<char*>(&ntokens);
char * tokenc = reinterpret_cast<char*>(&token);
char * countc = reinterpret_cast<char*>(&count);
while(hashmap_file.read(ngramc, sizeof(llama_ngram))) {
GGML_ASSERT(!hashmap_file.eof());
GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t)));
GGML_ASSERT(ntokens > 0);
llama_ngram_cache_part token_counts;
for (int i = 0; i < ntokens; ++i) {
GGML_ASSERT(!hashmap_file.eof());
GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token)));
GGML_ASSERT(!hashmap_file.eof());
GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t)));
GGML_ASSERT(count > 0);
token_counts.emplace(token, count);
}
ngram_cache.emplace(ngram, token_counts);
}
GGML_ASSERT(hashmap_file.eof());
return ngram_cache;
}
void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add) {
for (std::pair<llama_ngram, llama_ngram_cache_part> ngram_part : ngram_cache_add) {
const llama_ngram ngram = ngram_part.first;
llama_ngram_cache_part part = ngram_part.second;
llama_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram);
if (part_merged_it == ngram_cache_target.end()) {
ngram_cache_target.emplace(ngram, part);
continue;
}
for (std::pair<llama_token, int32_t> token_count : part) {
const llama_token token = token_count.first;
const int32_t count = token_count.second;
GGML_ASSERT(count > 0);
llama_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token);
if (token_count_merged_it == part_merged_it->second.end()) {
part_merged_it->second.emplace(token, count);
continue;
}
token_count_merged_it->second += count;
}
}
}
ollama/llm/llama.cpp/common/ngram-cache.h deleted 100755 → 0
View file @ cb75098c
#pragma once
#include "llama.h"
#include <unordered_map>
#include <string>
#include <vector>
#define LLAMA_NGRAM_MIN 1
#define LLAMA_NGRAM_MAX 4
#define LLAMA_NGRAM_STATIC 2
// Data structures to map n-grams to empirical token probabilities:
struct llama_ngram {
llama_token tokens[LLAMA_NGRAM_MAX];
llama_ngram() {
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
tokens[i] = -1;
}
}
llama_ngram(const llama_token * input, const int ngram_size) {
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
tokens[i] = i < ngram_size ? input[i] : -1;
}
}
bool operator==(const llama_ngram & other) const {
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
if (tokens[i] != other.tokens[i]) {
return false;
}
}
return true;
}
};
struct llama_token_hash_function {
size_t operator()(const llama_token token) const {
// see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/
return token * 11400714819323198485llu;
}
};
struct llama_ngram_hash_function {
size_t operator()(const llama_ngram & ngram) const {
size_t hash = llama_token_hash_function{}(ngram.tokens[0]);
for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) {
hash ^= llama_token_hash_function{}(ngram.tokens[i]);
}
return hash;
}
};
// token -> number of times token has been seen
typedef std::unordered_map<llama_token, int32_t> llama_ngram_cache_part;
// n-gram -> empirical distribution of following tokens
typedef std::unordered_map<llama_ngram, llama_ngram_cache_part, llama_ngram_hash_function> llama_ngram_cache;
// Update an ngram cache with tokens.
// ngram_cache: the cache to modify.
// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data.
// inp_data: the token sequence with which to update ngram_cache.
// nnew: how many new tokens have been appended to inp_data since the last call to this function.
// print_progress: whether to print progress to stderr.
//
// In order to get correct results inp_data can ONLY BE APPENDED TO.
// Changes in the middle need a complete rebuild.
void llama_ngram_cache_update(
llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector<llama_token> & inp_data, int nnew, bool print_progress);
// Try to draft tokens from ngram caches.
// inp: the tokens generated so far.
// draft: the token sequence to draft. Expected to initially contain the previously sampled token.
// n_draft: maximum number of tokens to add to draft.
// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic.
// nc_context: ngram cache based on current context.
// nc_dynamic: ngram cache based on previous user generations.
// nc_static: ngram cache generated from a large text corpus, used for validation.
void llama_ngram_cache_draft(
std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static);
// Save an ngram cache to a file.
// ngram_cache: the ngram cache to save.
// filename: the path under which to save the ngram cache.
void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename);
// Load an ngram cache saved with llama_ngram_cache_save.
// filename: the path from which to load the ngram cache.
// returns: an ngram cache containing the information saved to filename.
llama_ngram_cache llama_ngram_cache_load(std::string & filename);
// Merge two ngram caches.
// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add.
// ngram_cache_add: the ngram cache to add to ngram_cache_target.
void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add);
ollama/llm/llama.cpp/common/sampling.cpp deleted 100755 → 0
View file @ cb75098c
#define LLAMA_API_INTERNAL
#include "sampling.h"
#include <random>
struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
struct llama_sampling_context * result = new llama_sampling_context();
result->params = params;
result->grammar = nullptr;
// if there is a grammar, parse it
if (!params.grammar.empty()) {
result->parsed_grammar = grammar_parser::parse(params.grammar.c_str());
// will be empty (default) if there are parse errors
if (result->parsed_grammar.rules.empty()) {
fprintf(stderr, "%s: failed to parse grammar\n", __func__);
delete result;
return nullptr;
}
// Ensure that there is a "root" node.
if (result->parsed_grammar.symbol_ids.find("root") == result->parsed_grammar.symbol_ids.end()) {
fprintf(stderr, "%s: grammar does not contain a 'root' symbol\n", __func__);
delete result;
return nullptr;
}
std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
struct llama_grammar * grammar = llama_grammar_init(
grammar_rules.data(),
grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root"));
if (grammar == nullptr) {
throw std::runtime_error("Failed to initialize llama_grammar");
}
result->grammar = grammar;
}
result->prev.resize(params.n_prev);
result->n_valid = 0;
llama_sampling_set_rng_seed(result, params.seed);
return result;
}
void llama_sampling_free(struct llama_sampling_context * ctx) {
if (ctx->grammar != NULL) {
llama_grammar_free(ctx->grammar);
}
delete ctx;
}
void llama_sampling_reset(llama_sampling_context * ctx) {
if (ctx->grammar != NULL) {
llama_grammar_free(ctx->grammar);
ctx->grammar = NULL;
}
if (!ctx->parsed_grammar.rules.empty()) {
std::vector<const llama_grammar_element *> grammar_rules(ctx->parsed_grammar.c_rules());
struct llama_grammar * grammar = llama_grammar_init(
grammar_rules.data(),
grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root"));
if (grammar == nullptr) {
throw std::runtime_error("Failed to initialize llama_grammar");
}
ctx->grammar = grammar;
}
std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
ctx->cur.clear();
ctx->n_valid = 0;
}
void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) {
if (seed == LLAMA_DEFAULT_SEED) {
seed = std::random_device{}();
}
ctx->rng.seed(seed);
}
void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
if (dst->grammar) {
llama_grammar_free(dst->grammar);
dst->grammar = nullptr;
}
if (src->grammar) {
dst->grammar = llama_grammar_copy(src->grammar);
}
dst->prev = src->prev;
}
llama_token llama_sampling_last(llama_sampling_context * ctx) {
return ctx->prev.back();
}
std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n) {
const int size = ctx_sampling->prev.size();
n = std::min(n, size);
std::string result;
for (int i = size - n; i < size; i++) {
result += llama_token_to_piece(ctx_main, ctx_sampling->prev[i]);
}
return result;
}
std::string llama_sampling_print(const llama_sampling_params & params) {
char result[1024];
snprintf(result, sizeof(result),
"\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
"\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present,
params.top_k, params.tfs_z, params.top_p, params.min_p, params.typical_p, params.temp,
params.mirostat, params.mirostat_eta, params.mirostat_tau);
return std::string(result);
}
std::string llama_sampling_order_print(const llama_sampling_params & params) {
std::string result = "CFG -> Penalties ";
if (params.mirostat == 0) {
for (auto sampler_type : params.samplers_sequence) {
const auto sampler_type_name = llama_sampling_type_to_str(sampler_type);
if (!sampler_type_name.empty()) {
result += "-> " + sampler_type_name + " ";
}
}
} else {
result += "-> mirostat ";
}
return result;
}
std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) {
switch (sampler_type) {
case llama_sampler_type::TOP_K: return "top_k";
case llama_sampler_type::TFS_Z: return "tfs_z";
case llama_sampler_type::TYPICAL_P: return "typical_p";
case llama_sampler_type::TOP_P: return "top_p";
case llama_sampler_type::MIN_P: return "min_p";
case llama_sampler_type::TEMPERATURE: return "temperature";
default : return "";
}
}
std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
{"top_k", llama_sampler_type::TOP_K},
{"top_p", llama_sampler_type::TOP_P},
{"typical_p", llama_sampler_type::TYPICAL_P},
{"min_p", llama_sampler_type::MIN_P},
{"tfs_z", llama_sampler_type::TFS_Z},
{"temperature", llama_sampler_type::TEMPERATURE}
};
// since samplers names are written multiple ways
// make it ready for both system names and input names
std::unordered_map<std::string, llama_sampler_type> sampler_alt_name_map {
{"top-k", llama_sampler_type::TOP_K},
{"top-p", llama_sampler_type::TOP_P},
{"nucleus", llama_sampler_type::TOP_P},
{"typical-p", llama_sampler_type::TYPICAL_P},
{"typical", llama_sampler_type::TYPICAL_P},
{"min-p", llama_sampler_type::MIN_P},
{"tfs-z", llama_sampler_type::TFS_Z},
{"tfs", llama_sampler_type::TFS_Z},
{"temp", llama_sampler_type::TEMPERATURE}
};
std::vector<llama_sampler_type> sampler_types;
sampler_types.reserve(names.size());
for (const auto & name : names)
{
auto sampler_item = sampler_canonical_name_map.find(name);
if (sampler_item != sampler_canonical_name_map.end())
{
sampler_types.push_back(sampler_item->second);
}
else
{
if (allow_alt_names)
{
sampler_item = sampler_alt_name_map.find(name);
if (sampler_item != sampler_alt_name_map.end())
{
sampler_types.push_back(sampler_item->second);
}
}
}
}
return sampler_types;
}
std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::string & names_string) {
std::unordered_map<char, llama_sampler_type> sampler_name_map {
{'k', llama_sampler_type::TOP_K},
{'p', llama_sampler_type::TOP_P},
{'y', llama_sampler_type::TYPICAL_P},
{'m', llama_sampler_type::MIN_P},
{'f', llama_sampler_type::TFS_Z},
{'t', llama_sampler_type::TEMPERATURE}
};
std::vector<llama_sampler_type> sampler_types;
sampler_types.reserve(names_string.size());
for (const auto & c : names_string) {
const auto sampler_item = sampler_name_map.find(c);
if (sampler_item != sampler_name_map.end()) {
sampler_types.push_back(sampler_item->second);
}
}
return sampler_types;
}
// no reasons to expose this function in header
static void sampler_queue(
struct llama_context * ctx_main,
const llama_sampling_params & params,
llama_token_data_array & cur_p,
size_t min_keep) {
const float temp = params.temp;
const float dynatemp_range = params.dynatemp_range;
const float dynatemp_exponent = params.dynatemp_exponent;
const int32_t top_k = params.top_k;
const float top_p = params.top_p;
const float min_p = params.min_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const std::vector<llama_sampler_type> & samplers_sequence = params.samplers_sequence;
for (auto sampler_type : samplers_sequence) {
switch (sampler_type) {
case llama_sampler_type::TOP_K : llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break;
case llama_sampler_type::TFS_Z : llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break;
case llama_sampler_type::TYPICAL_P: llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break;
case llama_sampler_type::TOP_P : llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break;
case llama_sampler_type::MIN_P : llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break;
case llama_sampler_type::TEMPERATURE:
if (dynatemp_range > 0) {
float dynatemp_min = std::max(0.0f, temp - dynatemp_range);
float dynatemp_max = std::max(0.0f, temp + dynatemp_range);
llama_sample_entropy(ctx_main, &cur_p, dynatemp_min, dynatemp_max, dynatemp_exponent);
} else {
llama_sample_temp(ctx_main, &cur_p, temp);
}
break;
default : break;
}
}
}
static llama_token llama_sampling_sample_impl(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
const int idx,
bool is_resampling) {
const llama_sampling_params & params = ctx_sampling->params;
const float temp = params.temp;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
std::vector<float> original_logits;
auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, /* apply_grammar= */ is_resampling, &original_logits);
if (ctx_sampling->grammar != NULL && !is_resampling) {
GGML_ASSERT(!original_logits.empty());
}
llama_token id = 0;
if (temp < 0.0) {
// greedy sampling, with probs
llama_sample_softmax(ctx_main, &cur_p);
id = cur_p.data[0].id;
} else if (temp == 0.0) {
// greedy sampling, no probs
id = llama_sample_token_greedy(ctx_main, &cur_p);
} else {
if (mirostat == 1) {
const int mirostat_m = 100;
llama_sample_temp(ctx_main, &cur_p, temp);
id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);
} else if (mirostat == 2) {
llama_sample_temp(ctx_main, &cur_p, temp);
id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
} else {
// temperature sampling
size_t min_keep = std::max(1, params.min_keep);
sampler_queue(ctx_main, params, cur_p, min_keep);
id = llama_sample_token_with_rng(ctx_main, &cur_p, ctx_sampling->rng);
//{
// const int n_top = 10;
// LOG("top %d candidates:\n", n_top);
// for (int i = 0; i < n_top; i++) {
// const llama_token id = cur_p.data[i].id;
// (void)id; // To avoid a warning that id is unused when logging is disabled.
// LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);
// }
//}
//LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
}
}
if (ctx_sampling->grammar != NULL && !is_resampling) {
// Get a pointer to the logits
float * logits = llama_get_logits_ith(ctx_main, idx);
// Create an array with a single token data element for the sampled id
llama_token_data single_token_data = {id, logits[id], 0.0f};
llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
// Apply grammar constraints to the single token
llama_grammar_sample(ctx_sampling->grammar, ctx_main, &single_token_data_array);
// Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
// If the token is not valid according to the grammar, perform resampling
if (!is_valid) {
LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str());
// Restore logits from the copy
std::copy(original_logits.begin(), original_logits.end(), logits);
return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ true);
}
}
ctx_sampling->n_valid = temp == 0.0f ? 0 : cur_p.size;
return id;
}
static llama_token_data_array llama_sampling_prepare_impl(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
const int idx,
bool apply_grammar,
std::vector<float> * original_logits) {
const llama_sampling_params & params = ctx_sampling->params;
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
const float penalty_repeat = params.penalty_repeat;
const float penalty_freq = params.penalty_freq;
const float penalty_present = params.penalty_present;
const bool penalize_nl = params.penalize_nl;
auto & prev = ctx_sampling->prev;
auto & cur = ctx_sampling->cur;
// Get a pointer to the logits
float * logits = llama_get_logits_ith(ctx_main, idx);
if (ctx_sampling->grammar != NULL && !apply_grammar) {
GGML_ASSERT(original_logits != NULL);
// Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this.
*original_logits = {logits, logits + n_vocab};
}
// apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
if (ctx_cfg) {
float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx);
llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
}
cur.resize(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
}
llama_token_data_array cur_p = { cur.data(), cur.size(), false };
// apply penalties
const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);
if (penalty_tokens_used_size) {
const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
llama_sample_repetition_penalties(ctx_main, &cur_p,
penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size,
penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present);
if (!penalize_nl) {
for (size_t idx = 0; idx < cur_p.size; idx++) {
if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
cur_p.data[idx].logit = nl_logit;
break;
}
}
}
}
// apply grammar checks before sampling logic
if (apply_grammar && ctx_sampling->grammar != NULL) {
llama_grammar_sample(ctx_sampling->grammar, ctx_main, &cur_p);
}
return cur_p;
}
llama_token llama_sampling_sample(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
const int idx) {
// Call the implementation function with is_resampling set to false by default
return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ false);
}
llama_token_data_array llama_sampling_prepare(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
const int idx,
bool apply_grammar,
std::vector<float> * original_logits) {
return llama_sampling_prepare_impl(ctx_sampling,ctx_main, ctx_cfg, idx, apply_grammar, original_logits);
}
void llama_sampling_accept(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
llama_token id,
bool apply_grammar) {
ctx_sampling->prev.erase(ctx_sampling->prev.begin());
ctx_sampling->prev.push_back(id);
if (ctx_sampling->grammar != NULL && apply_grammar) {
llama_grammar_accept_token(ctx_sampling->grammar, ctx_main, id);
}
}
ollama/llm/llama.cpp/common/sampling.h deleted 100755 → 0
View file @ cb75098c
#pragma once
#include "llama.h"
#include "grammar-parser.h"
#include <random>
#include <string>
#include <unordered_map>
#include <vector>
// sampler types
enum class llama_sampler_type : char {
TOP_K = 'k',
TOP_P = 'p',
MIN_P = 'm',
TFS_Z = 'f',
TYPICAL_P = 'y',
TEMPERATURE = 't'
};
// sampling parameters
typedef struct llama_sampling_params {
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
float dynatemp_range = 0.00f; // 0.0 = disabled
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.00f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = false; // consider newlines as a repeatable token
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampling_context
std::vector<llama_sampler_type> samplers_sequence = {
llama_sampler_type::TOP_K,
llama_sampler_type::TFS_Z,
llama_sampler_type::TYPICAL_P,
llama_sampler_type::TOP_P,
llama_sampler_type::MIN_P,
llama_sampler_type::TEMPERATURE
};
std::string grammar; // optional BNF-like grammar to constrain sampling
// Classifier-Free Guidance
// https://arxiv.org/abs/2306.17806
std::string cfg_negative_prompt; // string to help guidance
float cfg_scale = 1.f; // how strong is guidance
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
std::vector<llama_token> penalty_prompt_tokens;
bool use_penalty_prompt_tokens = false;
} llama_sampling_params;
// general sampler context
// TODO: move to llama.h
struct llama_sampling_context {
// parameters that will be used for sampling
llama_sampling_params params;
// mirostat sampler state
float mirostat_mu;
llama_grammar * grammar;
// internal
grammar_parser::parse_state parsed_grammar;
// TODO: replace with ring-buffer
std::vector<llama_token> prev;
std::vector<llama_token_data> cur;
size_t n_valid; // Number of correct top tokens with correct probabilities.
std::mt19937 rng;
};
#include "common.h"
// Create a new sampling context instance.
struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params);
void llama_sampling_free(struct llama_sampling_context * ctx);
// Reset the sampler context
// - clear prev tokens
// - reset grammar
void llama_sampling_reset(llama_sampling_context * ctx);
// Set the sampler seed
void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed);
// Copy the sampler context
void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);
// Get the last sampled token
llama_token llama_sampling_last(llama_sampling_context * ctx);
// Get a string representation of the last sampled tokens
std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n);
// Print sampling parameters into a string
std::string llama_sampling_print(const llama_sampling_params & params);
// Print sampling order into a string
std::string llama_sampling_order_print(const llama_sampling_params & params);
std::string llama_sampling_type_to_str(llama_sampler_type sampler_type);
std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::string & names_string);
// this is a common sampling function used across the examples for convenience
// it can serve as a starting point for implementing your own sampling function
// Note: When using multiple sequences, it is the caller's responsibility to call
// llama_sampling_reset when a sequence ends
//
// required:
// - ctx_main: context to use for sampling
// - ctx_sampling: sampling-specific context
//
// optional:
// - ctx_cfg: context to use for classifier-free guidance
// - idx: sample from llama_get_logits_ith(ctx, idx)
//
// returns:
// - token: sampled token
// - candidates: vector of candidate tokens
//
llama_token llama_sampling_sample(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
int idx = -1);
// Prepares and adjusts the set of token candidates for sampling based on penalties, biases, and sampling parameters.
llama_token_data_array llama_sampling_prepare(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
int idx = 0,
bool apply_grammar = true,
std::vector<float> * original_logits = nullptr);
void llama_sampling_accept(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
llama_token id,
bool apply_grammar);
ollama/llm/llama.cpp/common/stb_image.h deleted 100755 → 0
View file @ cb75098c
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