mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 11:24:35 +00:00
682986a08e
* winogrande: simple implementation It doesn't look like it is working - why? For Mistral-7B it is barely better than random chance (score ~60% for 1267 tasks), while I see Mistral-7B scoring 78.4% on the HF leader board. 1-sigma statistical uncertainty for 1267 tasks is ~1.4, so no way the difference is due to statistics. * winogrande: somewhat better Score for Mistrali7-B is now 68.9 on the validation set of winogrande_debiased. Still far from the reported 78.4, but better than what I had before. * winogrande: improving Mistral-7B score is now 73.56. Still not quite 78.4 but getting there. We are also getting a lower score on HellaSwag compared to HF leader board, so I'm not expecting we will get up to 78.4 anyway. It looks like it is better to skip the choice word(s) when evaluating the average log-likelihood. This kind of makes sense because a more common word (in Winogrande this is often a name) will have a higher probability without knowing about the follow up context, and this will skew the log-likelihood towards the more common word. We can only do this if the choice words are not last in the sentence. It also looks like it is better to skip the punctuation at the end of the sentence, provided the choice words are not last. * winogrande: add dataset instructions --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
1696 lines
71 KiB
C++
1696 lines
71 KiB
C++
#include "common.h"
|
|
#include "llama.h"
|
|
|
|
#include <algorithm>
|
|
#include <cassert>
|
|
#include <cmath>
|
|
#include <cstring>
|
|
#include <ctime>
|
|
#include <fstream>
|
|
#include <iterator>
|
|
#include <iostream>
|
|
#include <regex>
|
|
#include <sstream>
|
|
#include <string>
|
|
#include <unordered_map>
|
|
#include <unordered_set>
|
|
#include <vector>
|
|
#include <cinttypes>
|
|
|
|
#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 <codecvt>
|
|
#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(_MSC_VER)
|
|
#pragma warning(disable: 4244 4267) // possible loss of data
|
|
#endif
|
|
|
|
int32_t 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"
|
|
+ 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;
|
|
}
|
|
|
|
void 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 gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|
bool result = true;
|
|
try {
|
|
if (!gpt_params_parse_ex(argc, argv, params)) {
|
|
gpt_print_usage(argc, argv, gpt_params());
|
|
exit(0);
|
|
}
|
|
}
|
|
catch (const std::invalid_argument & ex) {
|
|
fprintf(stderr, "%s\n", ex.what());
|
|
gpt_print_usage(argc, argv, gpt_params());
|
|
exit(1);
|
|
}
|
|
return result;
|
|
}
|
|
|
|
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 (arg == "-s" || arg == "--seed") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.seed = std::stoul(argv[i]);
|
|
} else if (arg == "-t" || arg == "--threads") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_threads = std::stoi(argv[i]);
|
|
if (params.n_threads <= 0) {
|
|
params.n_threads = std::thread::hardware_concurrency();
|
|
}
|
|
} else if (arg == "-tb" || arg == "--threads-batch") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_threads_batch = std::stoi(argv[i]);
|
|
if (params.n_threads_batch <= 0) {
|
|
params.n_threads_batch = std::thread::hardware_concurrency();
|
|
}
|
|
} else if (arg == "-td" || arg == "--threads-draft") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_threads_draft = std::stoi(argv[i]);
|
|
if (params.n_threads_draft <= 0) {
|
|
params.n_threads_draft = std::thread::hardware_concurrency();
|
|
}
|
|
} else if (arg == "-tbd" || arg == "--threads-batch-draft") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
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();
|
|
}
|
|
} else if (arg == "-p" || arg == "--prompt") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.prompt = argv[i];
|
|
} else if (arg == "-e" || arg == "--escape") {
|
|
params.escape = true;
|
|
} else if (arg == "--prompt-cache") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.path_prompt_cache = argv[i];
|
|
} else if (arg == "--prompt-cache-all") {
|
|
params.prompt_cache_all = true;
|
|
} else if (arg == "--prompt-cache-ro") {
|
|
params.prompt_cache_ro = true;
|
|
} else if (arg == "-f" || arg == "--file") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
// 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();
|
|
}
|
|
} else if (arg == "-n" || arg == "--n-predict") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_predict = std::stoi(argv[i]);
|
|
} else if (arg == "--top-k") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.top_k = std::stoi(argv[i]);
|
|
} else if (arg == "-c" || arg == "--ctx-size") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_ctx = std::stoi(argv[i]);
|
|
} else if (arg == "--grp-attn-n" || arg == "-gan") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
|
|
params.grp_attn_n = std::stoi(argv[i]);
|
|
} else if (arg == "--grp-attn-w" || arg == "-gaw") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
|
|
params.grp_attn_w = std::stoi(argv[i]);
|
|
} else if (arg == "--rope-freq-base") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rope_freq_base = std::stof(argv[i]);
|
|
} else if (arg == "--rope-freq-scale") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rope_freq_scale = std::stof(argv[i]);
|
|
} else if (arg == "--rope-scaling") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string value(argv[i]);
|
|
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
|
|
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
|
|
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
|
|
else { invalid_param = true; break; }
|
|
} else if (arg == "--rope-scale") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.rope_freq_scale = 1.0f/std::stof(argv[i]);
|
|
} else if (arg == "--yarn-orig-ctx") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_orig_ctx = std::stoi(argv[i]);
|
|
} else if (arg == "--yarn-ext-factor") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_ext_factor = std::stof(argv[i]);
|
|
} else if (arg == "--yarn-attn-factor") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_attn_factor = std::stof(argv[i]);
|
|
} else if (arg == "--yarn-beta-fast") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_beta_fast = std::stof(argv[i]);
|
|
} else if (arg == "--yarn-beta-slow") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.yarn_beta_slow = std::stof(argv[i]);
|
|
} else if (arg == "--samplers") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.samplers_sequence = parse_samplers_input(argv[i]);
|
|
} else if (arg == "--sampling-seq") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.samplers_sequence = argv[i];
|
|
} else if (arg == "--top-p") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.top_p = std::stof(argv[i]);
|
|
} else if (arg == "--min-p") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.min_p = std::stof(argv[i]);
|
|
} else if (arg == "--temp") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.temp = std::stof(argv[i]);
|
|
sparams.temp = std::max(sparams.temp, 0.0f);
|
|
} else if (arg == "--tfs") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.tfs_z = std::stof(argv[i]);
|
|
} else if (arg == "--typical") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.typical_p = std::stof(argv[i]);
|
|
} else if (arg == "--repeat-last-n") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.penalty_last_n = std::stoi(argv[i]);
|
|
sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
|
|
} else if (arg == "--repeat-penalty") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.penalty_repeat = std::stof(argv[i]);
|
|
} else if (arg == "--frequency-penalty") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.penalty_freq = std::stof(argv[i]);
|
|
} else if (arg == "--presence-penalty") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.penalty_present = std::stof(argv[i]);
|
|
} else if (arg == "--mirostat") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.mirostat = std::stoi(argv[i]);
|
|
} else if (arg == "--mirostat-lr") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.mirostat_eta = std::stof(argv[i]);
|
|
} else if (arg == "--mirostat-ent") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.mirostat_tau = std::stof(argv[i]);
|
|
} else if (arg == "--cfg-negative-prompt") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.cfg_negative_prompt = argv[i];
|
|
} else if (arg == "--cfg-negative-prompt-file") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
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();
|
|
}
|
|
} else if (arg == "--cfg-scale") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.cfg_scale = std::stof(argv[i]);
|
|
} else if (arg == "-b" || arg == "--batch-size") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_batch = std::stoi(argv[i]);
|
|
} else if (arg == "--keep") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_keep = std::stoi(argv[i]);
|
|
} else if (arg == "--draft") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_draft = std::stoi(argv[i]);
|
|
} else if (arg == "--chunks") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_chunks = std::stoi(argv[i]);
|
|
} else if (arg == "-np" || arg == "--parallel") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_parallel = std::stoi(argv[i]);
|
|
} else if (arg == "-ns" || arg == "--sequences") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_sequences = std::stoi(argv[i]);
|
|
} else if (arg == "--p-accept" || arg == "-pa") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.p_accept = std::stof(argv[i]);
|
|
} else if (arg == "--p-split" || arg == "-ps") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.p_split = std::stof(argv[i]);
|
|
} else if (arg == "-m" || arg == "--model") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.model = argv[i];
|
|
} else if (arg == "-md" || arg == "--model-draft") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.model_draft = argv[i];
|
|
} else if (arg == "-a" || arg == "--alias") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.model_alias = argv[i];
|
|
} else if (arg == "--lora") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
|
|
params.use_mmap = false;
|
|
} else if (arg == "--lora-scaled") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
const char * lora_adapter = argv[i];
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
|
|
params.use_mmap = false;
|
|
} else if (arg == "--lora-base") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.lora_base = argv[i];
|
|
} else if (arg == "--mmproj") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.mmproj = argv[i];
|
|
} else if (arg == "--image") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.image = argv[i];
|
|
} else if (arg == "-i" || arg == "--interactive") {
|
|
params.interactive = true;
|
|
} else if (arg == "--embedding") {
|
|
params.embedding = true;
|
|
} else if (arg == "--interactive-first") {
|
|
params.interactive_first = true;
|
|
} else if (arg == "-ins" || arg == "--instruct") {
|
|
params.instruct = true;
|
|
} else if (arg == "-cml" || arg == "--chatml") {
|
|
params.chatml = true;
|
|
} else if (arg == "--infill") {
|
|
params.infill = true;
|
|
} else if (arg == "-dkvc" || arg == "--dump-kv-cache") {
|
|
params.dump_kv_cache = true;
|
|
} else if (arg == "-nkvo" || arg == "--no-kv-offload") {
|
|
params.no_kv_offload = true;
|
|
} else if (arg == "-ctk" || arg == "--cache-type-k") {
|
|
params.cache_type_k = argv[++i];
|
|
} else if (arg == "-ctv" || arg == "--cache-type-v") {
|
|
params.cache_type_v = argv[++i];
|
|
} else if (arg == "--multiline-input") {
|
|
params.multiline_input = true;
|
|
} else if (arg == "--simple-io") {
|
|
params.simple_io = true;
|
|
} else if (arg == "-cb" || arg == "--cont-batching") {
|
|
params.cont_batching = true;
|
|
} else if (arg == "--color") {
|
|
params.use_color = true;
|
|
} else if (arg == "--mlock") {
|
|
params.use_mlock = true;
|
|
} else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_gpu_layers = std::stoi(argv[i]);
|
|
#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
|
|
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
|
#endif
|
|
} else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_gpu_layers_draft = std::stoi(argv[i]);
|
|
#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n");
|
|
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
|
#endif
|
|
} else if (arg == "--main-gpu" || arg == "-mg") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.main_gpu = std::stoi(argv[i]);
|
|
#ifndef GGML_USE_CUBLAS
|
|
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the main GPU has no effect.\n");
|
|
#endif // GGML_USE_CUBLAS
|
|
} else if (arg == "--split-mode" || arg == "-sm") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::string arg_next = argv[i];
|
|
if (arg_next == "none") {
|
|
params.split_mode = LLAMA_SPLIT_NONE;
|
|
} else if (arg_next == "layer") {
|
|
params.split_mode = LLAMA_SPLIT_LAYER;
|
|
} else if (arg_next == "row") {
|
|
params.split_mode = LLAMA_SPLIT_ROW;
|
|
} else {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#ifndef GGML_USE_CUBLAS
|
|
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n");
|
|
#endif // GGML_USE_CUBLAS
|
|
} else if (arg == "--tensor-split" || arg == "-ts") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
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;
|
|
break;
|
|
}
|
|
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_CUBLAS
|
|
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting a tensor split has no effect.\n");
|
|
#endif // GGML_USE_CUBLAS
|
|
} else if (arg == "--no-mmap") {
|
|
params.use_mmap = false;
|
|
} else if (arg == "--numa") {
|
|
params.numa = true;
|
|
} else if (arg == "--verbose-prompt") {
|
|
params.verbose_prompt = true;
|
|
} else if (arg == "--no-display-prompt") {
|
|
params.display_prompt = false;
|
|
} else if (arg == "-r" || arg == "--reverse-prompt") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.antiprompt.push_back(argv[i]);
|
|
} else if (arg == "-ld" || arg == "--logdir") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.logdir = argv[i];
|
|
|
|
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
|
|
params.logdir += DIRECTORY_SEPARATOR;
|
|
}
|
|
} else if (arg == "--perplexity" || arg == "--all-logits") {
|
|
params.logits_all = true;
|
|
} else if (arg == "--ppl-stride") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.ppl_stride = std::stoi(argv[i]);
|
|
} else if (arg == "-ptc" || arg == "--print-token-count") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.n_print = std::stoi(argv[i]);
|
|
} else if (arg == "--ppl-output-type") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.ppl_output_type = std::stoi(argv[i]);
|
|
} else if (arg == "--hellaswag") {
|
|
params.hellaswag = true;
|
|
} else if (arg == "--hellaswag-tasks") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.hellaswag_tasks = std::stoi(argv[i]);
|
|
} else if (arg == "--winogrande") {
|
|
params.winogrande = true;
|
|
} else if (arg == "--winogrande-tasks") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.winogrande_tasks = std::stoi(argv[i]);
|
|
} else if (arg == "--ignore-eos") {
|
|
params.ignore_eos = true;
|
|
} else if (arg == "--no-penalize-nl") {
|
|
sparams.penalize_nl = false;
|
|
} else if (arg == "-l" || arg == "--logit-bias") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
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;
|
|
break;
|
|
}
|
|
} else if (arg == "-h" || arg == "--help") {
|
|
return false;
|
|
|
|
} else 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);
|
|
} else if (arg == "--random-prompt") {
|
|
params.random_prompt = true;
|
|
} else if (arg == "--in-prefix-bos") {
|
|
params.input_prefix_bos = true;
|
|
} else if (arg == "--in-prefix") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.input_prefix = argv[i];
|
|
} else if (arg == "--in-suffix") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.input_suffix = argv[i];
|
|
} else if (arg == "--grammar") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
sparams.grammar = argv[i];
|
|
} else if (arg == "--grammar-file") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::ifstream file(argv[i]);
|
|
if (!file) {
|
|
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
std::copy(
|
|
std::istreambuf_iterator<char>(file),
|
|
std::istreambuf_iterator<char>(),
|
|
std::back_inserter(sparams.grammar)
|
|
);
|
|
} else if (arg == "--override-kv") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
char * sep = strchr(argv[i], '=');
|
|
if (sep == nullptr || sep - argv[i] >= 128) {
|
|
fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
struct llama_model_kv_override kvo;
|
|
std::strncpy(kvo.key, argv[i], sep - argv[i]);
|
|
kvo.key[sep - argv[i]] = 0;
|
|
sep++;
|
|
if (strncmp(sep, "int:", 4) == 0) {
|
|
sep += 4;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_INT;
|
|
kvo.int_value = std::atol(sep);
|
|
} else if (strncmp(sep, "float:", 6) == 0) {
|
|
sep += 6;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
|
|
kvo.float_value = std::atof(sep);
|
|
} else if (strncmp(sep, "bool:", 5) == 0) {
|
|
sep += 5;
|
|
kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
|
|
if (std::strcmp(sep, "true") == 0) {
|
|
kvo.bool_value = true;
|
|
} else if (std::strcmp(sep, "false") == 0) {
|
|
kvo.bool_value = false;
|
|
} else {
|
|
fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
} else {
|
|
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
params.kv_overrides.push_back(kvo);
|
|
#ifndef LOG_DISABLE_LOGS
|
|
// Parse args for logging parameters
|
|
} else 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.
|
|
} else 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.
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
// End of Parse args for logging parameters
|
|
#endif // LOG_DISABLE_LOGS
|
|
} else {
|
|
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 ||
|
|
params.instruct)) {
|
|
|
|
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
|
|
}
|
|
|
|
if (params.escape) {
|
|
process_escapes(params.prompt);
|
|
process_escapes(params.input_prefix);
|
|
process_escapes(params.input_suffix);
|
|
process_escapes(sparams.cfg_negative_prompt);
|
|
for (auto & antiprompt : params.antiprompt) {
|
|
process_escapes(antiprompt);
|
|
}
|
|
}
|
|
|
|
if (!params.kv_overrides.empty()) {
|
|
params.kv_overrides.emplace_back(llama_model_kv_override());
|
|
params.kv_overrides.back().key[0] = 0;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|
const llama_sampling_params & sparams = params.sparams;
|
|
|
|
printf("\n");
|
|
printf("usage: %s [options]\n", argv[0]);
|
|
printf("\n");
|
|
printf("options:\n");
|
|
printf(" -h, --help show this help message and exit\n");
|
|
printf(" --version show version and build info\n");
|
|
printf(" -i, --interactive run in interactive mode\n");
|
|
printf(" --interactive-first run in interactive mode and wait for input right away\n");
|
|
printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n");
|
|
printf(" -cml, --chatml run in chatml mode (use with ChatML-compatible models)\n");
|
|
printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n");
|
|
printf(" -r PROMPT, --reverse-prompt PROMPT\n");
|
|
printf(" halt generation at PROMPT, return control in interactive mode\n");
|
|
printf(" (can be specified more than once for multiple prompts).\n");
|
|
printf(" --color colorise output to distinguish prompt and user input from generations\n");
|
|
printf(" -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
|
|
printf(" -t N, --threads N number of threads to use during generation (default: %d)\n", params.n_threads);
|
|
printf(" -tb N, --threads-batch N\n");
|
|
printf(" number of threads to use during batch and prompt processing (default: same as --threads)\n");
|
|
printf(" -td N, --threads-draft N");
|
|
printf(" number of threads to use during generation (default: same as --threads)");
|
|
printf(" -tbd N, --threads-batch-draft N\n");
|
|
printf(" number of threads to use during batch and prompt processing (default: same as --threads-draft)\n");
|
|
printf(" -p PROMPT, --prompt PROMPT\n");
|
|
printf(" prompt to start generation with (default: empty)\n");
|
|
printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
|
|
printf(" --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n");
|
|
printf(" --prompt-cache-all if specified, saves user input and generations to cache as well.\n");
|
|
printf(" not supported with --interactive or other interactive options\n");
|
|
printf(" --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n");
|
|
printf(" --random-prompt start with a randomized prompt.\n");
|
|
printf(" --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n");
|
|
printf(" --in-prefix STRING string to prefix user inputs with (default: empty)\n");
|
|
printf(" --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
|
|
printf(" -f FNAME, --file FNAME\n");
|
|
printf(" prompt file to start generation.\n");
|
|
printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
|
|
printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
|
|
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
|
printf(" --samplers samplers that will be used for generation in the order, separated by \';\', for example: \"top_k;tfs;typical;top_p;min_p;temp\"\n");
|
|
printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sparams.samplers_sequence.c_str());
|
|
printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
|
|
printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
|
|
printf(" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p);
|
|
printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)sparams.tfs_z);
|
|
printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)sparams.typical_p);
|
|
printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.penalty_last_n);
|
|
printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat);
|
|
printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present);
|
|
printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq);
|
|
printf(" --mirostat N use Mirostat sampling.\n");
|
|
printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
|
|
printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat);
|
|
printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)sparams.mirostat_eta);
|
|
printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)sparams.mirostat_tau);
|
|
printf(" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
|
|
printf(" modifies the likelihood of token appearing in the completion,\n");
|
|
printf(" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
|
|
printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
|
|
printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
|
|
printf(" --grammar-file FNAME file to read grammar from\n");
|
|
printf(" --cfg-negative-prompt PROMPT\n");
|
|
printf(" negative prompt to use for guidance. (default: empty)\n");
|
|
printf(" --cfg-negative-prompt-file FNAME\n");
|
|
printf(" negative prompt file to use for guidance. (default: empty)\n");
|
|
printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale);
|
|
printf(" --rope-scaling {none,linear,yarn}\n");
|
|
printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n");
|
|
printf(" --rope-scale N RoPE context scaling factor, expands context by a factor of N\n");
|
|
printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n");
|
|
printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n");
|
|
printf(" --yarn-orig-ctx N YaRN: original context size of model (default: 0 = model training context size)\n");
|
|
printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
|
|
printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
|
|
printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
|
|
printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
|
|
printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
|
|
printf(" --no-penalize-nl do not penalize newline token\n");
|
|
printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp);
|
|
printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n");
|
|
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
|
|
printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
|
|
printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n");
|
|
printf(" --winogrande-tasks N number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks);
|
|
printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
|
|
printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
|
|
printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
|
|
printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel);
|
|
printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences);
|
|
printf(" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept);
|
|
printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
|
|
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
|
|
printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
|
|
printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n");
|
|
if (llama_mlock_supported()) {
|
|
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
|
}
|
|
if (llama_mmap_supported()) {
|
|
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
|
}
|
|
printf(" --numa attempt optimizations that help on some NUMA systems\n");
|
|
printf(" if run without this previously, it is recommended to drop the system page cache before using this\n");
|
|
printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n");
|
|
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
printf(" -ngl N, --n-gpu-layers N\n");
|
|
printf(" number of layers to store in VRAM\n");
|
|
printf(" -ngld N, --n-gpu-layers-draft N\n");
|
|
printf(" number of layers to store in VRAM for the draft model\n");
|
|
printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
|
|
printf(" how to split the model across multiple GPUs, one of:\n");
|
|
printf(" - none: use one GPU only\n");
|
|
printf(" - layer (default): split layers and KV across GPUs\n");
|
|
printf(" - row: split rows across GPUs\n");
|
|
printf(" -ts SPLIT, --tensor-split SPLIT\n");
|
|
printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
|
|
printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n");
|
|
printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu);
|
|
#endif
|
|
printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false");
|
|
printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false");
|
|
printf(" -gan N, --grp-attn-n N\n");
|
|
printf(" group-attention factor (default: %d)\n", params.grp_attn_n);
|
|
printf(" -gaw N, --grp-attn-w N\n");
|
|
printf(" group-attention width (default: %.1f)\n", (double)params.grp_attn_w);
|
|
printf(" -dkvc, --dump-kv-cache\n");
|
|
printf(" verbose print of the KV cache\n");
|
|
printf(" -nkvo, --no-kv-offload\n");
|
|
printf(" disable KV offload\n");
|
|
printf(" -ctk TYPE, --cache-type-k TYPE\n");
|
|
printf(" KV cache data type for K (default: %s)\n", params.cache_type_k.c_str());
|
|
printf(" -ctv TYPE, --cache-type-v TYPE\n");
|
|
printf(" KV cache data type for V (default: %s)\n", params.cache_type_v.c_str());
|
|
printf(" --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n");
|
|
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
|
printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n");
|
|
printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
|
|
printf(" -m FNAME, --model FNAME\n");
|
|
printf(" model path (default: %s)\n", params.model.c_str());
|
|
printf(" -md FNAME, --model-draft FNAME\n");
|
|
printf(" draft model for speculative decoding\n");
|
|
printf(" -ld LOGDIR, --logdir LOGDIR\n");
|
|
printf(" path under which to save YAML logs (no logging if unset)\n");
|
|
printf(" --override-kv KEY=TYPE:VALUE\n");
|
|
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
|
|
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
|
|
printf(" -ptc N, --print-token-count N\n");
|
|
printf(" print token count every N tokens (default: %d)\n", params.n_print);
|
|
printf("\n");
|
|
#ifndef LOG_DISABLE_LOGS
|
|
log_print_usage();
|
|
#endif // LOG_DISABLE_LOGS
|
|
}
|
|
|
|
std::string 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();
|
|
}
|
|
|
|
std::string gpt_random_prompt(std::mt19937 & rng) {
|
|
const int r = rng() % 10;
|
|
switch (r) {
|
|
case 0: return "So";
|
|
case 1: return "Once upon a time";
|
|
case 2: return "When";
|
|
case 3: return "The";
|
|
case 4: return "After";
|
|
case 5: return "If";
|
|
case 6: return "import";
|
|
case 7: return "He";
|
|
case 8: return "She";
|
|
case 9: return "They";
|
|
}
|
|
|
|
GGML_UNREACHABLE();
|
|
}
|
|
|
|
//
|
|
// String parsing
|
|
//
|
|
|
|
std::string parse_samplers_input(std::string input) {
|
|
std::string output = "";
|
|
// since samplers names are written multiple ways
|
|
// make it ready for both system names and input names
|
|
std::unordered_map<std::string, char> samplers_symbols {
|
|
{"top_k", 'k'},
|
|
{"top-k", 'k'},
|
|
{"top_p", 'p'},
|
|
{"top-p", 'p'},
|
|
{"nucleus", 'p'},
|
|
{"typical_p", 'y'},
|
|
{"typical-p", 'y'},
|
|
{"typical", 'y'},
|
|
{"min_p", 'm'},
|
|
{"min-p", 'm'},
|
|
{"tfs_z", 'f'},
|
|
{"tfs-z", 'f'},
|
|
{"tfs", 'f'},
|
|
{"temp", 't'},
|
|
{"temperature",'t'}
|
|
};
|
|
// expected format example: "temp;top_k;tfs_z;typical_p;top_p;min_p"
|
|
size_t separator = input.find(';');
|
|
while (separator != input.npos) {
|
|
std::string name = input.substr(0,separator);
|
|
input = input.substr(separator+1);
|
|
separator = input.find(';');
|
|
|
|
if (samplers_symbols.find(name) != samplers_symbols.end()) {
|
|
output += samplers_symbols[name];
|
|
}
|
|
}
|
|
if (samplers_symbols.find(input) != samplers_symbols.end()) {
|
|
output += samplers_symbols[input];
|
|
}
|
|
return output;
|
|
}
|
|
|
|
//
|
|
// Model utils
|
|
//
|
|
|
|
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.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;
|
|
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 == "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_batch = params.n_batch;
|
|
cparams.n_threads = params.n_threads;
|
|
cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
|
|
cparams.mul_mat_q = params.mul_mat_q;
|
|
cparams.seed = params.seed;
|
|
cparams.logits_all = params.logits_all;
|
|
cparams.embedding = 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.offload_kqv = !params.no_kv_offload;
|
|
|
|
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;
|
|
}
|
|
|
|
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++;
|
|
}
|
|
|
|
std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
|
|
auto mparams = llama_model_params_from_gpt_params(params);
|
|
|
|
llama_model * 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 std::make_tuple(nullptr, nullptr);
|
|
}
|
|
|
|
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 std::make_tuple(nullptr, nullptr);
|
|
}
|
|
|
|
for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
|
|
const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);
|
|
float lora_scale = std::get<1>(params.lora_adapter[i]);
|
|
int err = llama_model_apply_lora_from_file(model,
|
|
lora_adapter.c_str(),
|
|
lora_scale,
|
|
((i > 0) || params.lora_base.empty())
|
|
? NULL
|
|
: params.lora_base.c_str(),
|
|
params.n_threads);
|
|
if (err != 0) {
|
|
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
|
|
llama_free(lctx);
|
|
llama_free_model(model);
|
|
return std::make_tuple(nullptr, nullptr);
|
|
}
|
|
}
|
|
|
|
if (params.ignore_eos) {
|
|
params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
|
|
}
|
|
|
|
{
|
|
LOG("warming up the model with an empty run\n");
|
|
|
|
std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
|
|
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_reset_timings(lctx);
|
|
}
|
|
|
|
return std::make_tuple(model, lctx);
|
|
}
|
|
|
|
//
|
|
// Vocab utils
|
|
//
|
|
|
|
std::vector<llama_token> llama_tokenize(
|
|
const struct llama_context * ctx,
|
|
const std::string & text,
|
|
bool add_bos,
|
|
bool special) {
|
|
return llama_tokenize(llama_get_model(ctx), text, add_bos, special);
|
|
}
|
|
|
|
std::vector<llama_token> llama_tokenize(
|
|
const struct llama_model * model,
|
|
const std::string & text,
|
|
bool add_bos,
|
|
bool special) {
|
|
// upper limit for the number of tokens
|
|
int n_tokens = text.length() + add_bos;
|
|
std::vector<llama_token> result(n_tokens);
|
|
n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, 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) {
|
|
std::vector<char> result(8, 0);
|
|
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
|
GGML_ASSERT(check == -n_tokens);
|
|
} else {
|
|
result.resize(n_tokens);
|
|
}
|
|
|
|
return std::string(result.data(), result.size());
|
|
}
|
|
|
|
std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
|
|
const llama_token bos_id = llama_token_bos(llama_get_model(ctx));
|
|
|
|
std::string piece;
|
|
std::string result;
|
|
|
|
for (size_t i = 0; i < tokens.size(); ++i) {
|
|
piece = llama_token_to_piece(ctx, tokens[i]);
|
|
|
|
// remove the leading space of the first non-BOS token
|
|
if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {
|
|
piece = piece.substr(1);
|
|
}
|
|
|
|
result += piece;
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
|
|
std::string piece;
|
|
std::string result;
|
|
|
|
for (size_t i = 0; i < tokens.size(); ++i) {
|
|
piece = llama_token_to_piece(ctx, tokens[i]);
|
|
|
|
result += piece;
|
|
}
|
|
|
|
// NOTE: the original tokenizer decodes bytes after collecting the pieces.
|
|
return result;
|
|
}
|
|
|
|
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);
|
|
}
|
|
|
|
//
|
|
// YAML utils
|
|
//
|
|
|
|
// returns true if successful, false otherwise
|
|
bool 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
|
|
}
|
|
|
|
void dump_vector_float_yaml(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 dump_vector_int_yaml(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 dump_string_yaml_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 (!data_str.empty() && (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;
|
|
}
|
|
}
|
|
|
|
std::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 dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,
|
|
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
|
|
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_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false");
|
|
fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "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_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");
|
|
|
|
#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);
|
|
dump_string_yaml_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);
|
|
dump_string_yaml_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");
|
|
|
|
dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str());
|
|
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
|
|
dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str());
|
|
fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false");
|
|
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 (std::tuple<std::string, float> la : params.lora_adapter) {
|
|
if (std::get<1>(la) != 1.0f) {
|
|
continue;
|
|
}
|
|
fprintf(stream, " - %s\n", std::get<0>(la).c_str());
|
|
}
|
|
fprintf(stream, "lora_scaled:\n");
|
|
for (std::tuple<std::string, float> la : params.lora_adapter) {
|
|
if (std::get<1>(la) == 1.0f) {
|
|
continue;
|
|
}
|
|
fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la));
|
|
}
|
|
fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
|
|
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
|
|
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: models/7B/ggml-model.bin\n", params.model.c_str());
|
|
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, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
|
|
fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
|
|
fprintf(stream, "numa: %s # default: false\n", params.numa ? "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);
|
|
dump_string_yaml_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");
|
|
dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens);
|
|
fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false");
|
|
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: %d # 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, "temp: %f # default: 0.8\n", sparams.temp);
|
|
|
|
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
|
|
dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
|
|
|
|
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
|
|
fprintf(stream, "threads: %d # default: %d\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");
|
|
}
|
|
|
|
//
|
|
// KV cache utils
|
|
//
|
|
|
|
void dump_kv_cache_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_max_seq, 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_max_seq) {
|
|
if (i % row_size == 0) {
|
|
printf("\n%5d: ", i);
|
|
}
|
|
int seq_count = 0;
|
|
for (int j = 0; j < view.n_max_seq; 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 dump_kv_cache_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_max_seq, 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_max_seq) {
|
|
for (int j = 0; j < view.n_max_seq; j++) {
|
|
if (cs_curr[j] < 0) { continue; }
|
|
if (seqs.find(cs_curr[j]) == seqs.end()) {
|
|
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
|
|
seqs[cs_curr[j]] = seqs.size();
|
|
}
|
|
}
|
|
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_max_seq) {
|
|
if (i % row_size == 0) {
|
|
printf("\n%5d: ", i);
|
|
}
|
|
for (int j = 0; j < view.n_max_seq; 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");
|
|
}
|