bcc0eb4591
* per-layer KV * remove unnecessary copies * less code duplication, offload k and v separately * llama : offload KV cache per-layer * llama : offload K shift tensors * llama : offload for rest of the model arches * llama : enable offload debug temporarily * llama : keep the KV related layers on the device * llama : remove mirrors, perform Device -> Host when partial offload * common : add command-line arg to disable KV cache offloading * llama : update session save/load * llama : support quantum K cache (#4312) * llama : support quantum K cache (wip) * metal : add F32 -> Q8_0 copy kernel * cuda : add F32 -> Q8_0 copy kernel ggml-ci * cuda : use mmv kernel for quantum cache ops * llama : pass KV cache type through API * llama : fix build ggml-ci * metal : add F32 -> Q4_0 copy kernel * metal : add F32 -> Q4_1 copy kernel * cuda : wip * cuda : add F32 -> Q4_0 and F32 -> Q4_1 copy kernels * llama-bench : support type_k/type_v * metal : use mm kernel only for quantum KV cache * cuda : add comment * llama : remove memory_f16 and kv_f16 flags --------- Co-authored-by: slaren <slarengh@gmail.com> * readme : add API change notice --------- Co-authored-by: slaren <slarengh@gmail.com>
1612 lines
67 KiB
C++
1612 lines
67 KiB
C++
#include "common.h"
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#include "llama.h"
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#include <algorithm>
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#include <cassert>
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#include <cmath>
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#include <cstring>
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#include <ctime>
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#include <fstream>
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#include <iterator>
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#include <iostream>
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#include <regex>
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#include <sstream>
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#include <string>
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#include <unordered_map>
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#include <unordered_set>
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#include <vector>
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#include <cinttypes>
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#if defined(__APPLE__) && defined(__MACH__)
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#include <sys/types.h>
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#include <sys/sysctl.h>
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#endif
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#if defined(_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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# define NOMINMAX
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#endif
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#include <codecvt>
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#include <locale>
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#include <windows.h>
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#include <fcntl.h>
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#include <io.h>
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#else
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#include <sys/ioctl.h>
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#include <sys/stat.h>
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#include <unistd.h>
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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int32_t get_num_physical_cores() {
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#ifdef __linux__
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// enumerate the set of thread siblings, num entries is num cores
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std::unordered_set<std::string> siblings;
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for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
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std::ifstream thread_siblings("/sys/devices/system/cpu"
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+ std::to_string(cpu) + "/topology/thread_siblings");
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if (!thread_siblings.is_open()) {
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break; // no more cpus
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}
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std::string line;
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if (std::getline(thread_siblings, line)) {
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siblings.insert(line);
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}
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}
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if (!siblings.empty()) {
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return static_cast<int32_t>(siblings.size());
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}
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#elif defined(__APPLE__) && defined(__MACH__)
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int32_t num_physical_cores;
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size_t len = sizeof(num_physical_cores);
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int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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#elif defined(_WIN32)
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//TODO: Implement
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#endif
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unsigned int n_threads = std::thread::hardware_concurrency();
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return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
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}
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void process_escapes(std::string& input) {
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std::size_t input_len = input.length();
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std::size_t output_idx = 0;
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for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
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if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
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switch (input[++input_idx]) {
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case 'n': input[output_idx++] = '\n'; break;
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case 'r': input[output_idx++] = '\r'; break;
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case 't': input[output_idx++] = '\t'; break;
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case '\'': input[output_idx++] = '\''; break;
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case '\"': input[output_idx++] = '\"'; break;
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case '\\': input[output_idx++] = '\\'; break;
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case 'x':
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// Handle \x12, etc
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if (input_idx + 2 < input_len) {
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const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
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char *err_p = nullptr;
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const long val = std::strtol(x, &err_p, 16);
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if (err_p == x + 2) {
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input_idx += 2;
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input[output_idx++] = char(val);
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break;
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}
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}
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// fall through
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default: input[output_idx++] = '\\';
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input[output_idx++] = input[input_idx]; break;
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}
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} else {
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input[output_idx++] = input[input_idx];
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}
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}
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input.resize(output_idx);
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}
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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bool result = true;
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try {
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if (!gpt_params_parse_ex(argc, argv, params)) {
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gpt_print_usage(argc, argv, gpt_params());
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exit(0);
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}
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}
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catch (const std::invalid_argument & ex) {
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fprintf(stderr, "%s\n", ex.what());
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gpt_print_usage(argc, argv, gpt_params());
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exit(1);
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}
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return result;
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}
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bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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bool invalid_param = false;
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std::string arg;
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const std::string arg_prefix = "--";
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llama_sampling_params & sparams = params.sparams;
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for (int i = 1; i < argc; i++) {
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arg = argv[i];
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if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
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std::replace(arg.begin(), arg.end(), '_', '-');
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}
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if (arg == "-s" || arg == "--seed") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.seed = std::stoul(argv[i]);
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} else if (arg == "-t" || arg == "--threads") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_threads = std::stoi(argv[i]);
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if (params.n_threads <= 0) {
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params.n_threads = std::thread::hardware_concurrency();
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}
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} else if (arg == "-tb" || arg == "--threads-batch") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_threads_batch = std::stoi(argv[i]);
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if (params.n_threads_batch <= 0) {
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params.n_threads_batch = std::thread::hardware_concurrency();
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}
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} else if (arg == "-p" || arg == "--prompt") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.prompt = argv[i];
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} else if (arg == "-e" || arg == "--escape") {
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params.escape = true;
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} else if (arg == "--prompt-cache") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.path_prompt_cache = argv[i];
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} else if (arg == "--prompt-cache-all") {
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params.prompt_cache_all = true;
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} else if (arg == "--prompt-cache-ro") {
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params.prompt_cache_ro = true;
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} else if (arg == "-f" || arg == "--file") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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std::ifstream file(argv[i]);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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invalid_param = true;
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break;
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}
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// store the external file name in params
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params.prompt_file = argv[i];
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std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
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if (!params.prompt.empty() && params.prompt.back() == '\n') {
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params.prompt.pop_back();
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}
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} else if (arg == "-n" || arg == "--n-predict") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_predict = std::stoi(argv[i]);
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} else if (arg == "--top-k") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.top_k = std::stoi(argv[i]);
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} else if (arg == "-c" || arg == "--ctx-size") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_ctx = std::stoi(argv[i]);
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} else if (arg == "--rope-freq-base") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.rope_freq_base = std::stof(argv[i]);
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} else if (arg == "--rope-freq-scale") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.rope_freq_scale = std::stof(argv[i]);
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} else if (arg == "--rope-scaling") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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std::string value(argv[i]);
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/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
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else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
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else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
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else { invalid_param = true; break; }
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} else if (arg == "--rope-scale") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.rope_freq_scale = 1.0f/std::stof(argv[i]);
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} else if (arg == "--yarn-orig-ctx") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.yarn_orig_ctx = std::stoi(argv[i]);
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} else if (arg == "--yarn-ext-factor") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.yarn_ext_factor = std::stof(argv[i]);
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} else if (arg == "--yarn-attn-factor") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.yarn_attn_factor = std::stof(argv[i]);
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} else if (arg == "--yarn-beta-fast") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.yarn_beta_fast = std::stof(argv[i]);
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} else if (arg == "--yarn-beta-slow") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.yarn_beta_slow = std::stof(argv[i]);
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} else if (arg == "--samplers") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.samplers_sequence = parse_samplers_input(argv[i]);
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} else if (arg == "--sampling-seq") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.samplers_sequence = argv[i];
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} else if (arg == "--top-p") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.top_p = std::stof(argv[i]);
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} else if (arg == "--min-p") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.min_p = std::stof(argv[i]);
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} else if (arg == "--temp") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.temp = std::stof(argv[i]);
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sparams.temp = std::max(sparams.temp, 0.0f);
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} else if (arg == "--tfs") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.tfs_z = std::stof(argv[i]);
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} else if (arg == "--typical") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.typical_p = std::stof(argv[i]);
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} else if (arg == "--repeat-last-n") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.penalty_last_n = std::stoi(argv[i]);
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sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
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} else if (arg == "--repeat-penalty") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.penalty_repeat = std::stof(argv[i]);
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} else if (arg == "--frequency-penalty") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.penalty_freq = std::stof(argv[i]);
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} else if (arg == "--presence-penalty") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.penalty_present = std::stof(argv[i]);
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} else if (arg == "--mirostat") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.mirostat = std::stoi(argv[i]);
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} else if (arg == "--mirostat-lr") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.mirostat_eta = std::stof(argv[i]);
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} else if (arg == "--mirostat-ent") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.mirostat_tau = std::stof(argv[i]);
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} else if (arg == "--cfg-negative-prompt") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.cfg_negative_prompt = argv[i];
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} else if (arg == "--cfg-negative-prompt-file") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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std::ifstream file(argv[i]);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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invalid_param = true;
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break;
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}
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std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
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if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
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sparams.cfg_negative_prompt.pop_back();
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}
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} else if (arg == "--cfg-scale") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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sparams.cfg_scale = std::stof(argv[i]);
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} else if (arg == "-b" || arg == "--batch-size") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_batch = std::stoi(argv[i]);
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} else if (arg == "--keep") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_keep = std::stoi(argv[i]);
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} else if (arg == "--draft") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_draft = std::stoi(argv[i]);
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} else if (arg == "--chunks") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_chunks = std::stoi(argv[i]);
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} else if (arg == "-np" || arg == "--parallel") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_parallel = std::stoi(argv[i]);
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} else if (arg == "-ns" || arg == "--sequences") {
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|
if (++i >= argc) {
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invalid_param = true;
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break;
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|
}
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params.n_sequences = std::stoi(argv[i]);
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} else if (arg == "--p-accept" || arg == "-pa") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.p_accept = std::stof(argv[i]);
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} else if (arg == "--p-split" || arg == "-ps") {
|
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if (++i >= argc) {
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invalid_param = true;
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break;
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|
}
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params.p_split = std::stof(argv[i]);
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} else if (arg == "-m" || arg == "--model") {
|
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if (++i >= argc) {
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invalid_param = true;
|
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break;
|
|
}
|
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params.model = argv[i];
|
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} else if (arg == "-md" || arg == "--model-draft") {
|
|
if (++i >= argc) {
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invalid_param = true;
|
|
break;
|
|
}
|
|
params.model_draft = argv[i];
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|
} else if (arg == "-a" || arg == "--alias") {
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if (++i >= argc) {
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invalid_param = true;
|
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break;
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}
|
|
params.model_alias = argv[i];
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} else if (arg == "--lora") {
|
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
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params.use_mmap = false;
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} else if (arg == "--lora-scaled") {
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if (++i >= argc) {
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invalid_param = true;
|
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break;
|
|
}
|
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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;
|
|
}
|
|
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
params.n_gpu_layers = std::stoi(argv[i]);
|
|
#else
|
|
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;
|
|
}
|
|
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
|
params.n_gpu_layers_draft = std::stoi(argv[i]);
|
|
#else
|
|
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;
|
|
}
|
|
#ifdef GGML_USE_CUBLAS
|
|
params.main_gpu = std::stoi(argv[i]);
|
|
#else
|
|
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
|
|
#endif
|
|
} else if (arg == "--tensor-split" || arg == "-ts") {
|
|
if (++i >= argc) {
|
|
invalid_param = true;
|
|
break;
|
|
}
|
|
#ifdef GGML_USE_CUBLAS
|
|
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, {}};
|
|
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
|
|
|
|
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;
|
|
}
|
|
}
|
|
#else
|
|
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
|
|
#endif // GGML_USE_CUBLAS
|
|
} else if (arg == "--no-mul-mat-q" || arg == "-nommq") {
|
|
#ifdef GGML_USE_CUBLAS
|
|
params.mul_mat_q = false;
|
|
#else
|
|
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels 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 == "-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 == "--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 == "--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 == "--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(" -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(" -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(" --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(" -ts SPLIT --tensor-split SPLIT\n");
|
|
printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
|
printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
|
#ifdef GGML_USE_CUBLAS
|
|
printf(" -nommq, --no-mul-mat-q\n");
|
|
printf(" use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n");
|
|
printf(" Not recommended since this is both slower and uses more VRAM.\n");
|
|
#endif // GGML_USE_CUBLAS
|
|
#endif
|
|
printf(" --verbose-prompt print prompt before generation\n");
|
|
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 (default: %s)\n", params.model.c_str());
|
|
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("\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.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 == "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_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");
|
|
}
|
|
|
|
//
|
|
// 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");
|
|
}
|