using json = nlohmann::ordered_json;
-int32_t get_num_physical_cores() {
+//
+// CPU utils
+//
+
+int32_t cpu_get_num_physical_cores() {
#ifdef __linux__
// enumerate the set of thread siblings, num entries is num cores
std::unordered_set<std::string> siblings;
return core_type == intel_atom;
}
-static int count_math_cpus(int cpu_count) {
+static int cpu_count_math_cpus(int n_cpu) {
int result = 0;
- for (int cpu = 0; cpu < cpu_count; ++cpu) {
+ for (int cpu = 0; cpu < n_cpu; ++cpu) {
if (pin_cpu(cpu)) {
return -1;
}
/**
* Returns number of CPUs on system that are useful for math.
*/
-int get_math_cpu_count() {
+int32_t cpu_get_num_math() {
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
- int cpu_count = sysconf(_SC_NPROCESSORS_ONLN);
- if (cpu_count < 1) {
- return get_num_physical_cores();
+ int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
+ if (n_cpu < 1) {
+ return cpu_get_num_physical_cores();
}
if (is_hybrid_cpu()) {
cpu_set_t affinity;
if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
- int result = count_math_cpus(cpu_count);
+ int result = cpu_count_math_cpus(n_cpu);
pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
if (result > 0) {
return result;
}
}
#endif
- return get_num_physical_cores();
+ return cpu_get_num_physical_cores();
}
-void process_escapes(std::string & input) {
- std::size_t input_len = input.length();
- std::size_t output_idx = 0;
+//
+// CLI argument parsing
+//
- 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;
+void gpt_params_handle_model_default(gpt_params & params) {
+ if (!params.hf_repo.empty()) {
+ // short-hand to avoid specifying --hf-file -> default it to --model
+ if (params.hf_file.empty()) {
+ if (params.model.empty()) {
+ throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
}
- } else {
- input[output_idx++] = input[input_idx];
+ params.hf_file = params.model;
+ } else if (params.model.empty()) {
+ std::string cache_directory = fs_get_cache_directory();
+ const bool success = fs_create_directory_with_parents(cache_directory);
+ if (!success) {
+ throw std::runtime_error("failed to create cache directory: " + cache_directory);
+ }
+ params.model = cache_directory + string_split(params.hf_file, '/').back();
+ }
+ } else if (!params.model_url.empty()) {
+ if (params.model.empty()) {
+ auto f = string_split(params.model_url, '#').front();
+ f = string_split(f, '?').front();
+ f = string_split(f, '/').back();
+ params.model = "models/" + f;
}
+ } else if (params.model.empty()) {
+ params.model = DEFAULT_MODEL_PATH;
}
+}
- input.resize(output_idx);
+bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
+ bool invalid_param = false;
+ std::string arg;
+ const std::string arg_prefix = "--";
+ llama_sampling_params & sparams = params.sparams;
+
+ for (int i = 1; i < argc; i++) {
+ arg = argv[i];
+ if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+ std::replace(arg.begin(), arg.end(), '_', '-');
+ }
+ if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
+ throw std::invalid_argument("error: unknown argument: " + arg);
+ }
+ if (invalid_param) {
+ throw std::invalid_argument("error: invalid parameter for argument: " + arg);
+ }
+ }
+
+ if (params.prompt_cache_all &&
+ (params.interactive || params.interactive_first ||
+ params.instruct)) {
+
+ throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
+ }
+
+ gpt_params_handle_model_default(params);
+
+ if (params.escape) {
+ string_process_escapes(params.prompt);
+ string_process_escapes(params.input_prefix);
+ string_process_escapes(params.input_suffix);
+ string_process_escapes(sparams.cfg_negative_prompt);
+ for (auto & antiprompt : params.antiprompt) {
+ string_process_escapes(antiprompt);
+ }
+ }
+
+ if (!params.kv_overrides.empty()) {
+ params.kv_overrides.emplace_back();
+ params.kv_overrides.back().key[0] = 0;
+ }
+
+ return true;
}
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
bool result = true;
try {
if (!gpt_params_parse_ex(argc, argv, params)) {
- gpt_print_usage(argc, argv, gpt_params());
+ gpt_params_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());
+ gpt_params_print_usage(argc, argv, gpt_params());
exit(1);
}
return result;
}
-bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
- const char * sep = strchr(data, '=');
- if (sep == nullptr || sep - data >= 128) {
- fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
- return false;
- }
- llama_model_kv_override kvo;
- std::strncpy(kvo.key, data, sep - data);
- kvo.key[sep - data] = 0;
- sep++;
- if (strncmp(sep, "int:", 4) == 0) {
- sep += 4;
- kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
- kvo.val_i64 = std::atol(sep);
- } else if (strncmp(sep, "float:", 6) == 0) {
- sep += 6;
- kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
- kvo.val_f64 = std::atof(sep);
- } else if (strncmp(sep, "bool:", 5) == 0) {
- sep += 5;
- kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
- if (std::strcmp(sep, "true") == 0) {
- kvo.val_bool = true;
- } else if (std::strcmp(sep, "false") == 0) {
- kvo.val_bool = false;
- } else {
- fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
- return false;
- }
- } else if (strncmp(sep, "str:", 4) == 0) {
- sep += 4;
- kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
- if (strlen(sep) > 127) {
- fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
- return false;
- }
- strncpy(kvo.val_str, sep, 127);
- kvo.val_str[127] = '\0';
- } else {
- fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
- return false;
- }
- overrides.emplace_back(std::move(kvo));
- return true;
-}
-
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
llama_sampling_params & sparams = params.sparams;
return true;
}
const auto sampler_names = string_split(argv[i], ';');
- sparams.samplers_sequence = sampler_types_from_names(sampler_names, true);
+ sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true);
return true;
}
if (arg == "--sampling-seq") {
invalid_param = true;
return true;
}
- sparams.samplers_sequence = sampler_types_from_chars(argv[i]);
+ sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]);
return true;
}
if (arg == "--top-p") {
return true;
}
if (arg == "-h" || arg == "--help") {
- gpt_print_usage(argc, argv, gpt_params());
+ gpt_params_print_usage(argc, argv, gpt_params());
exit(0);
}
if (arg == "--version") {
invalid_param = true;
return true;
}
- if (!parse_kv_override(argv[i], params.kv_overrides)) {
+ if (!string_parse_kv_override(argv[i], params.kv_overrides)) {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
return true;
return false;
}
-void gpt_params_handle_model_default(gpt_params & params) {
- if (!params.hf_repo.empty()) {
- // short-hand to avoid specifying --hf-file -> default it to --model
- if (params.hf_file.empty()) {
- if (params.model.empty()) {
- throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
- }
- params.hf_file = params.model;
- } else if (params.model.empty()) {
- std::string cache_directory = get_cache_directory();
- const bool success = create_directory_with_parents(cache_directory);
- if (!success) {
- throw std::runtime_error("failed to create cache directory: " + cache_directory);
- }
- params.model = cache_directory + string_split(params.hf_file, '/').back();
- }
- } else if (!params.model_url.empty()) {
- if (params.model.empty()) {
- auto f = string_split(params.model_url, '#').front();
- f = string_split(f, '?').front();
- f = string_split(f, '/').back();
- params.model = "models/" + f;
- }
- } else if (params.model.empty()) {
- params.model = DEFAULT_MODEL_PATH;
- }
-}
-
-bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
- bool invalid_param = false;
- std::string arg;
- const std::string arg_prefix = "--";
- llama_sampling_params & sparams = params.sparams;
-
- for (int i = 1; i < argc; i++) {
- arg = argv[i];
- if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
- std::replace(arg.begin(), arg.end(), '_', '-');
- }
- if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
- throw std::invalid_argument("error: unknown argument: " + arg);
- }
- if (invalid_param) {
- throw std::invalid_argument("error: invalid parameter for argument: " + arg);
- }
- }
-
- if (params.prompt_cache_all &&
- (params.interactive || params.interactive_first ||
- params.instruct)) {
-
- throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
- }
-
- gpt_params_handle_model_default(params);
-
- 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();
- params.kv_overrides.back().key[0] = 0;
- }
-
- return true;
-}
-
-void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
+void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
const llama_sampling_params & sparams = params.sparams;
std::string sampler_type_chars;
std::string sampler_type_names;
for (const auto sampler_type : sparams.samplers_sequence) {
sampler_type_chars += static_cast<char>(sampler_type);
- sampler_type_names += sampler_type_to_name_string(sampler_type) + ";";
+ sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";";
}
sampler_type_names.pop_back();
#endif // LOG_DISABLE_LOGS
}
-std::string get_system_info(const gpt_params & params) {
+std::string gpt_params_get_system_info(const gpt_params & params) {
std::ostringstream os;
os << "system_info: n_threads = " << params.n_threads;
return os.str();
}
-std::string gpt_random_prompt(std::mt19937 & rng) {
+//
+// String utils
+//
+
+std::vector<std::string> string_split(std::string input, char separator) {
+ std::vector<std::string> parts;
+ size_t separator_pos = input.find(separator);
+ while (separator_pos != std::string::npos) {
+ std::string part = input.substr(0, separator_pos);
+ parts.emplace_back(part);
+ input = input.substr(separator_pos + 1);
+ separator_pos = input.find(separator);
+ }
+ parts.emplace_back(input);
+ return parts;
+}
+
+std::string string_strip(const std::string & str) {
+ size_t start = 0;
+ size_t end = str.size();
+ while (start < end && std::isspace(str[start])) {
+ start++;
+ }
+ while (end > start && std::isspace(str[end - 1])) {
+ end--;
+ }
+ return str.substr(start, end - start);
+}
+
+std::string string_get_sortable_timestamp() {
+ using clock = std::chrono::system_clock;
+
+ const clock::time_point current_time = clock::now();
+ const time_t as_time_t = clock::to_time_t(current_time);
+ char timestamp_no_ns[100];
+ std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
+
+ const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
+ current_time.time_since_epoch() % 1000000000).count();
+ char timestamp_ns[11];
+ snprintf(timestamp_ns, 11, "%09" PRId64, ns);
+
+ return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
+}
+
+std::string string_random_prompt(std::mt19937 & rng) {
const int r = rng() % 10;
switch (r) {
case 0: return "So";
GGML_UNREACHABLE();
}
-// Validate if a filename is safe to use
-// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
-bool validate_file_name(const std::string & filename) {
- if (!filename.length()) {
- // Empty filename invalid
- return false;
+void string_process_escapes(std::string & input) {
+ std::size_t input_len = input.length();
+ std::size_t output_idx = 0;
+
+ for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
+ if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
+ switch (input[++input_idx]) {
+ case 'n': input[output_idx++] = '\n'; break;
+ case 'r': input[output_idx++] = '\r'; break;
+ case 't': input[output_idx++] = '\t'; break;
+ case '\'': input[output_idx++] = '\''; break;
+ case '\"': input[output_idx++] = '\"'; break;
+ case '\\': input[output_idx++] = '\\'; break;
+ case 'x':
+ // Handle \x12, etc
+ if (input_idx + 2 < input_len) {
+ const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
+ char *err_p = nullptr;
+ const long val = std::strtol(x, &err_p, 16);
+ if (err_p == x + 2) {
+ input_idx += 2;
+ input[output_idx++] = char(val);
+ break;
+ }
+ }
+ // fall through
+ default: input[output_idx++] = '\\';
+ input[output_idx++] = input[input_idx]; break;
+ }
+ } else {
+ input[output_idx++] = input[input_idx];
+ }
+ }
+
+ input.resize(output_idx);
+}
+
+bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
+ const char * sep = strchr(data, '=');
+ if (sep == nullptr || sep - data >= 128) {
+ fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
+ return false;
+ }
+ llama_model_kv_override kvo;
+ std::strncpy(kvo.key, data, sep - data);
+ kvo.key[sep - data] = 0;
+ sep++;
+ if (strncmp(sep, "int:", 4) == 0) {
+ sep += 4;
+ kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+ kvo.val_i64 = std::atol(sep);
+ } else if (strncmp(sep, "float:", 6) == 0) {
+ sep += 6;
+ kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
+ kvo.val_f64 = std::atof(sep);
+ } else if (strncmp(sep, "bool:", 5) == 0) {
+ sep += 5;
+ kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
+ if (std::strcmp(sep, "true") == 0) {
+ kvo.val_bool = true;
+ } else if (std::strcmp(sep, "false") == 0) {
+ kvo.val_bool = false;
+ } else {
+ fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
+ return false;
+ }
+ } else if (strncmp(sep, "str:", 4) == 0) {
+ sep += 4;
+ kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+ if (strlen(sep) > 127) {
+ fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
+ return false;
+ }
+ strncpy(kvo.val_str, sep, 127);
+ kvo.val_str[127] = '\0';
+ } else {
+ fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
+ return false;
+ }
+ overrides.emplace_back(std::move(kvo));
+ return true;
+}
+
+//
+// Filesystem utils
+//
+
+// Validate if a filename is safe to use
+// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
+bool fs_validate_filename(const std::string & filename) {
+ if (!filename.length()) {
+ // Empty filename invalid
+ return false;
}
if (filename.length() > 255) {
// Limit at common largest possible filename on Linux filesystems
return true;
}
-//
-// String utils
-//
+// returns true if successful, false otherwise
+bool fs_create_directory_with_parents(const std::string & path) {
+#ifdef _WIN32
+ std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+ std::wstring wpath = converter.from_bytes(path);
-std::vector<std::string> string_split(std::string input, char separator) {
- std::vector<std::string> parts;
- size_t separator_pos = input.find(separator);
- while (separator_pos != std::string::npos) {
- std::string part = input.substr(0, separator_pos);
- parts.emplace_back(part);
- input = input.substr(separator_pos + 1);
- separator_pos = input.find(separator);
+ // 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;
}
- parts.emplace_back(input);
- return parts;
-}
-std::string string_strip(const std::string & str) {
- size_t start = 0;
- size_t end = str.size();
- while (start < end && std::isspace(str[start])) {
- start++;
- }
- while (end > start && std::isspace(str[end - 1])) {
- end--;
- }
- return str.substr(start, end - start);
-}
+ size_t pos_slash = 0;
-std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
- std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
- {"top_k", llama_sampler_type::TOP_K},
- {"top_p", llama_sampler_type::TOP_P},
- {"typical_p", llama_sampler_type::TYPICAL_P},
- {"min_p", llama_sampler_type::MIN_P},
- {"tfs_z", llama_sampler_type::TFS_Z},
- {"temperature", llama_sampler_type::TEMPERATURE}
- };
+ // 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();
- // since samplers names are written multiple ways
- // make it ready for both system names and input names
- std::unordered_map<std::string, llama_sampler_type> sampler_alt_name_map {
- {"top-k", llama_sampler_type::TOP_K},
- {"top-p", llama_sampler_type::TOP_P},
- {"nucleus", llama_sampler_type::TOP_P},
- {"typical-p", llama_sampler_type::TYPICAL_P},
- {"typical", llama_sampler_type::TYPICAL_P},
- {"min-p", llama_sampler_type::MIN_P},
- {"tfs-z", llama_sampler_type::TFS_Z},
- {"tfs", llama_sampler_type::TFS_Z},
- {"temp", llama_sampler_type::TEMPERATURE}
- };
+ const bool success = CreateDirectoryW(test, NULL);
+ if (!success) {
+ const DWORD error = GetLastError();
- std::vector<llama_sampler_type> sampler_types;
- sampler_types.reserve(names.size());
- for (const auto & name : names)
- {
- auto sampler_item = sampler_canonical_name_map.find(name);
- if (sampler_item != sampler_canonical_name_map.end())
- {
- sampler_types.push_back(sampler_item->second);
- }
- else
- {
- if (allow_alt_names)
- {
- sampler_item = sampler_alt_name_map.find(name);
- if (sampler_item != sampler_alt_name_map.end())
- {
- sampler_types.push_back(sampler_item->second);
+ // 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 sampler_types;
-}
-std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & names_string) {
- std::unordered_map<char, llama_sampler_type> sampler_name_map {
- {'k', llama_sampler_type::TOP_K},
- {'p', llama_sampler_type::TOP_P},
- {'y', llama_sampler_type::TYPICAL_P},
- {'m', llama_sampler_type::MIN_P},
- {'f', llama_sampler_type::TFS_Z},
- {'t', llama_sampler_type::TEMPERATURE}
- };
+ 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;
- std::vector<llama_sampler_type> sampler_types;
- sampler_types.reserve(names_string.size());
- for (const auto & c : names_string) {
- const auto sampler_item = sampler_name_map.find(c);
- if (sampler_item != sampler_name_map.end()) {
- sampler_types.push_back(sampler_item->second);
+ // 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 sampler_types;
+
+ return true;
+#endif // _WIN32
}
-std::string sampler_type_to_name_string(llama_sampler_type sampler_type) {
- switch (sampler_type) {
- case llama_sampler_type::TOP_K: return "top_k";
- case llama_sampler_type::TFS_Z: return "tfs_z";
- case llama_sampler_type::TYPICAL_P: return "typical_p";
- case llama_sampler_type::TOP_P: return "top_p";
- case llama_sampler_type::MIN_P: return "min_p";
- case llama_sampler_type::TEMPERATURE: return "temperature";
- default : return "";
+std::string fs_get_cache_directory() {
+ std::string cache_directory = "";
+ if (getenv("LLAMA_CACHE")) {
+ cache_directory = std::getenv("LLAMA_CACHE");
+ if (cache_directory.back() != DIRECTORY_SEPARATOR) {
+ cache_directory += DIRECTORY_SEPARATOR;
+ }
+ } else {
+#ifdef __linux__
+ if (std::getenv("XDG_CACHE_HOME")) {
+ cache_directory = std::getenv("XDG_CACHE_HOME");
+ } else {
+ cache_directory = std::getenv("HOME") + std::string("/.cache/");
+ }
+#elif defined(__APPLE__)
+ cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
+#elif defined(_WIN32)
+ cache_directory = std::getenv("APPDATA");
+#endif // __linux__
+ cache_directory += "llama.cpp";
+ cache_directory += DIRECTORY_SEPARATOR;
}
+ return cache_directory;
}
+
//
// Model utils
//
-struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
- auto mparams = llama_model_default_params();
+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);
- if (params.n_gpu_layers != -1) {
- mparams.n_gpu_layers = params.n_gpu_layers;
- }
- mparams.rpc_servers = params.rpc_servers.c_str();
- mparams.main_gpu = params.main_gpu;
- mparams.split_mode = params.split_mode;
- mparams.tensor_split = params.tensor_split;
- mparams.use_mmap = params.use_mmap;
- mparams.use_mlock = params.use_mlock;
- mparams.check_tensors = params.check_tensors;
- if (params.kv_overrides.empty()) {
- mparams.kv_overrides = NULL;
+ llama_model * model = nullptr;
+
+ if (!params.hf_repo.empty() && !params.hf_file.empty()) {
+ model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams);
+ } else if (!params.model_url.empty()) {
+ model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams);
} else {
- GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
- mparams.kv_overrides = params.kv_overrides.data();
+ model = llama_load_model_from_file(params.model.c_str(), mparams);
}
- return mparams;
-}
-
-static ggml_type kv_cache_type_from_str(const std::string & s) {
- if (s == "f32") {
- return GGML_TYPE_F32;
- }
- if (s == "f16") {
- return GGML_TYPE_F16;
- }
- if (s == "q8_0") {
- return GGML_TYPE_Q8_0;
- }
- if (s == "q4_0") {
- return GGML_TYPE_Q4_0;
- }
- if (s == "q4_1") {
- return GGML_TYPE_Q4_1;
- }
- if (s == "iq4_nl") {
- return GGML_TYPE_IQ4_NL;
- }
- if (s == "q5_0") {
- return GGML_TYPE_Q5_0;
- }
- if (s == "q5_1") {
- return GGML_TYPE_Q5_1;
+ if (model == NULL) {
+ fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
+ return std::make_tuple(nullptr, nullptr);
}
- throw std::runtime_error("Invalid cache type: " + s);
-}
+ 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);
+ }
+
+ if (!params.control_vectors.empty()) {
+ if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
+ if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
+
+ const auto cvec = llama_control_vector_load(params.control_vectors);
+ if (cvec.n_embd == -1) {
+ llama_free(lctx);
+ llama_free_model(model);
+ return std::make_tuple(nullptr, nullptr);
+ }
+
+ int err = llama_control_vector_apply(lctx,
+ cvec.data.data(),
+ cvec.data.size(),
+ cvec.n_embd,
+ params.control_vector_layer_start,
+ params.control_vector_layer_end);
+ if (err) {
+ llama_free(lctx);
+ llama_free_model(model);
+ return 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;
+ }
+
+ if (params.warmup) {
+ 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_synchronize(lctx);
+ llama_reset_timings(lctx);
+ }
+
+ return std::make_tuple(model, lctx);
+}
+
+struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
+ auto mparams = llama_model_default_params();
+
+ if (params.n_gpu_layers != -1) {
+ mparams.n_gpu_layers = params.n_gpu_layers;
+ }
+ mparams.rpc_servers = params.rpc_servers.c_str();
+ mparams.main_gpu = params.main_gpu;
+ mparams.split_mode = params.split_mode;
+ mparams.tensor_split = params.tensor_split;
+ mparams.use_mmap = params.use_mmap;
+ mparams.use_mlock = params.use_mlock;
+ mparams.check_tensors = params.check_tensors;
+ if (params.kv_overrides.empty()) {
+ mparams.kv_overrides = NULL;
+ } else {
+ GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
+ mparams.kv_overrides = params.kv_overrides.data();
+ }
+
+ return mparams;
+}
+
+static ggml_type kv_cache_type_from_str(const std::string & s) {
+ if (s == "f32") {
+ return GGML_TYPE_F32;
+ }
+ if (s == "f16") {
+ return GGML_TYPE_F16;
+ }
+ if (s == "q8_0") {
+ return GGML_TYPE_Q8_0;
+ }
+ if (s == "q4_0") {
+ return GGML_TYPE_Q4_0;
+ }
+ if (s == "q4_1") {
+ return GGML_TYPE_Q4_1;
+ }
+ if (s == "iq4_nl") {
+ return GGML_TYPE_IQ4_NL;
+ }
+ if (s == "q5_0") {
+ return GGML_TYPE_Q5_0;
+ }
+ if (s == "q5_1") {
+ return GGML_TYPE_Q5_1;
+ }
+
+ throw std::runtime_error("Invalid cache type: " + s);
+}
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
auto cparams = llama_context_default_params();
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++;
-}
-
#ifdef LLAMA_USE_CURL
static bool starts_with(const std::string & str, const std::string & prefix) {
#endif // LLAMA_USE_CURL
-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 = nullptr;
-
- if (!params.hf_repo.empty() && !params.hf_file.empty()) {
- model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams);
- } else if (!params.model_url.empty()) {
- model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams);
- } else {
- model = llama_load_model_from_file(params.model.c_str(), mparams);
- }
-
- if (model == NULL) {
- fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
- return 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);
- }
-
- if (!params.control_vectors.empty()) {
- if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
- if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
-
- const auto cvec = llama_control_vector_load(params.control_vectors);
- if (cvec.n_embd == -1) {
- llama_free(lctx);
- llama_free_model(model);
- return std::make_tuple(nullptr, nullptr);
- }
-
- int err = llama_control_vector_apply(lctx,
- cvec.data.data(),
- cvec.data.size(),
- cvec.n_embd,
- params.control_vector_layer_start,
- params.control_vector_layer_end);
- if (err) {
- llama_free(lctx);
- llama_free_model(model);
- return 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;
- }
+//
+// Batch utils
+//
- if (params.warmup) {
- LOG("warming up the model with an empty run\n");
+void llama_batch_clear(struct llama_batch & batch) {
+ batch.n_tokens = 0;
+}
- 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_synchronize(lctx);
- llama_reset_timings(lctx);
+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;
- return std::make_tuple(model, lctx);
+ batch.n_tokens++;
}
//
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
-}
-
-std::string get_cache_directory() {
- std::string cache_directory = "";
- if (getenv("LLAMA_CACHE")) {
- cache_directory = std::getenv("LLAMA_CACHE");
- if (cache_directory.back() != DIRECTORY_SEPARATOR) {
- cache_directory += DIRECTORY_SEPARATOR;
- }
- } else {
-#ifdef __linux__
- if (std::getenv("XDG_CACHE_HOME")) {
- cache_directory = std::getenv("XDG_CACHE_HOME");
- } else {
- cache_directory = std::getenv("HOME") + std::string("/.cache/");
- }
-#elif defined(__APPLE__)
- cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
-#elif defined(_WIN32)
- cache_directory = std::getenv("APPDATA");
-#endif // __linux__
- cache_directory += "llama.cpp";
- cache_directory += DIRECTORY_SEPARATOR;
- }
- return cache_directory;
-}
-
-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 (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_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false");
- fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false");
- fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false");
- fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false");
- fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
- fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
- fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
- fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
- fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
- fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
- fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
- fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
- fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
- fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");
-
-#ifdef NDEBUG
- fprintf(stream, "debug: false\n");
-#else
- fprintf(stream, "debug: true\n");
-#endif // NDEBUG
-
- fprintf(stream, "model_desc: %s\n", model_desc);
- fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx)));
-
-#ifdef __OPTIMIZE__
- fprintf(stream, "optimize: true\n");
-#else
- fprintf(stream, "optimize: false\n");
-#endif // __OPTIMIZE__
-
- fprintf(stream, "time: %s\n", timestamp.c_str());
-
- fprintf(stream, "\n");
- fprintf(stream, "###############\n");
- fprintf(stream, "# User Inputs #\n");
- fprintf(stream, "###############\n");
- fprintf(stream, "\n");
-
- fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
- fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
- 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_specials: %s # default: false\n", params.interactive_specials ? "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, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
- fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
- fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
- fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
- fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
- fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
- fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
- fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
- fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
- fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
- fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
- fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
- fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
- fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
- fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
- fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
- 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;
+ 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);
}
- fprintf(stream, " - %s\n", ap.c_str());
+ result += piece;
}
- fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
- fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
- fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
- fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
- fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
- fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
- fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
+ return result;
+}
- 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);
+std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
+ std::string piece;
+ std::string result;
- fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
- fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
- fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
- fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
- fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
- fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
- fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
- fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
+ 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);
}
//
// KV cache utils
//
-void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) {
+void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
printf("\n=== Done dumping\n");
}
-void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
+void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
printf("\n=== Done dumping\n");
}
+//
+// Embedding utils
+//
+
void llama_embd_normalize(const float * inp, float * out, int n) {
double sum = 0.0;
for (int i = 0; i < n; i++) {
return result;
}
+
+//
+// YAML utils
+//
+
+void yaml_dump_vector_float(FILE * stream, const char * prop_name, const std::vector<float> & data) {
+ if (data.empty()) {
+ fprintf(stream, "%s:\n", prop_name);
+ return;
+ }
+
+ fprintf(stream, "%s: [", prop_name);
+ for (size_t i = 0; i < data.size() - 1; ++i) {
+ fprintf(stream, "%e, ", data[i]);
+ }
+ fprintf(stream, "%e]\n", data.back());
+}
+
+void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector<int> & data) {
+ if (data.empty()) {
+ fprintf(stream, "%s:\n", prop_name);
+ return;
+ }
+
+ fprintf(stream, "%s: [", prop_name);
+ for (size_t i = 0; i < data.size() - 1; ++i) {
+ fprintf(stream, "%d, ", data[i]);
+ }
+ fprintf(stream, "%d]\n", data.back());
+}
+
+void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) {
+ std::string data_str(data == NULL ? "" : data);
+
+ if (data_str.empty()) {
+ fprintf(stream, "%s:\n", prop_name);
+ return;
+ }
+
+ size_t pos_start = 0;
+ size_t pos_found = 0;
+
+ if (std::isspace(data_str[0]) || std::isspace(data_str.back())) {
+ data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
+ data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
+ data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)");
+ data_str = "\"" + data_str + "\"";
+ fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
+ return;
+ }
+
+ if (data_str.find('\n') == std::string::npos) {
+ fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
+ return;
+ }
+
+ fprintf(stream, "%s: |\n", prop_name);
+ while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
+ fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
+ pos_start = pos_found + 1;
+ }
+}
+
+void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
+ const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
+ const llama_sampling_params & sparams = params.sparams;
+
+ fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT);
+ fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER);
+ fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
+ fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
+ fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false");
+ fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
+ fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
+ fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
+ fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
+ fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false");
+ fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false");
+ fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false");
+ fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false");
+ fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
+ fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
+ fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
+ fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
+ fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
+ fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
+ fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
+ fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
+ fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
+ fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");
+
+#ifdef NDEBUG
+ fprintf(stream, "debug: false\n");
+#else
+ fprintf(stream, "debug: true\n");
+#endif // NDEBUG
+
+ fprintf(stream, "model_desc: %s\n", model_desc);
+ fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx)));
+
+#ifdef __OPTIMIZE__
+ fprintf(stream, "optimize: true\n");
+#else
+ fprintf(stream, "optimize: false\n");
+#endif // __OPTIMIZE__
+
+ fprintf(stream, "time: %s\n", timestamp.c_str());
+
+ fprintf(stream, "\n");
+ fprintf(stream, "###############\n");
+ fprintf(stream, "# User Inputs #\n");
+ fprintf(stream, "###############\n");
+ fprintf(stream, "\n");
+
+ fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
+ fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
+ yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
+ fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
+ fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
+ fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
+ fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
+ fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
+ fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
+ fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
+ yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str());
+ fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
+ fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
+ fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
+
+ const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));
+ const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
+ fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
+
+ yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str());
+ fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
+ yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str());
+ fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false");
+ fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
+ fprintf(stream, "interactive_specials: %s # default: false\n", params.interactive_specials ? "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, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
+ fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
+ fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
+ fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
+ fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
+ fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
+ fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
+ fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
+ fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
+ fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
+ fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
+ fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
+ fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
+ fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
+ fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
+ fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
+ yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str());
+ fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
+ fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
+ fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
+ yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens);
+ fprintf(stream, "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: %u # default: -1 (random seed)\n", params.seed);
+ fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
+ fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
+ fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
+ fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
+
+ const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
+ yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
+
+ fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
+ fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
+ fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
+ fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
+ fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
+ fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
+ fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
+ fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff