#if defined(_WIN32)
-#include <windows.h>
+# include <windows.h>
#else
-#include <unistd.h>
+# include <unistd.h>
#endif
-#include <climits>
+#if defined(LLAMA_USE_CURL)
+# include <curl/curl.h>
+#endif
+
+#include <cstdarg>
#include <cstdio>
#include <cstring>
+#include <filesystem>
#include <iostream>
#include <sstream>
#include <string>
-#include <unordered_map>
#include <vector>
+#include "common.h"
+#include "json.hpp"
#include "llama-cpp.h"
-typedef std::unique_ptr<char[]> char_array_ptr;
-
-struct Argument {
- std::string flag;
- std::string help_text;
-};
-
-struct Options {
- std::string model_path, prompt_non_interactive;
- int ngl = 99;
- int n_ctx = 2048;
-};
+#define printe(...) \
+ do { \
+ fprintf(stderr, __VA_ARGS__); \
+ } while (0)
+
+class Opt {
+ public:
+ int init(int argc, const char ** argv) {
+ construct_help_str_();
+ // Parse arguments
+ if (parse(argc, argv)) {
+ printe("Error: Failed to parse arguments.\n");
+ help();
+ return 1;
+ }
-class ArgumentParser {
- public:
- ArgumentParser(const char * program_name) : program_name(program_name) {}
+ // If help is requested, show help and exit
+ if (help_) {
+ help();
+ return 2;
+ }
- void add_argument(const std::string & flag, std::string & var, const std::string & help_text = "") {
- string_args[flag] = &var;
- arguments.push_back({flag, help_text});
+ return 0; // Success
}
- void add_argument(const std::string & flag, int & var, const std::string & help_text = "") {
- int_args[flag] = &var;
- arguments.push_back({flag, help_text});
+ std::string model_;
+ std::string user_;
+ int context_size_ = 2048, ngl_ = -1;
+
+ private:
+ std::string help_str_;
+ bool help_ = false;
+
+ void construct_help_str_() {
+ help_str_ =
+ "Description:\n"
+ " Runs a llm\n"
+ "\n"
+ "Usage:\n"
+ " llama-run [options] model [prompt]\n"
+ "\n"
+ "Options:\n"
+ " -c, --context-size <value>\n"
+ " Context size (default: " +
+ std::to_string(context_size_);
+ help_str_ +=
+ ")\n"
+ " -n, --ngl <value>\n"
+ " Number of GPU layers (default: " +
+ std::to_string(ngl_);
+ help_str_ +=
+ ")\n"
+ " -h, --help\n"
+ " Show help message\n"
+ "\n"
+ "Commands:\n"
+ " model\n"
+ " Model is a string with an optional prefix of \n"
+ " huggingface:// (hf://), ollama://, https:// or file://.\n"
+ " If no protocol is specified and a file exists in the specified\n"
+ " path, file:// is assumed, otherwise if a file does not exist in\n"
+ " the specified path, ollama:// is assumed. Models that are being\n"
+ " pulled are downloaded with .partial extension while being\n"
+ " downloaded and then renamed as the file without the .partial\n"
+ " extension when complete.\n"
+ "\n"
+ "Examples:\n"
+ " llama-run llama3\n"
+ " llama-run ollama://granite-code\n"
+ " llama-run ollama://smollm:135m\n"
+ " llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf\n"
+ " llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n"
+ " llama-run https://example.com/some-file1.gguf\n"
+ " llama-run some-file2.gguf\n"
+ " llama-run file://some-file3.gguf\n"
+ " llama-run --ngl 99 some-file4.gguf\n"
+ " llama-run --ngl 99 some-file5.gguf Hello World\n";
}
int parse(int argc, const char ** argv) {
+ int positional_args_i = 0;
for (int i = 1; i < argc; ++i) {
- std::string arg = argv[i];
- if (string_args.count(arg)) {
- if (i + 1 < argc) {
- *string_args[arg] = argv[++i];
- } else {
- fprintf(stderr, "error: missing value for %s\n", arg.c_str());
- print_usage();
+ if (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0) {
+ if (i + 1 >= argc) {
return 1;
}
- } else if (int_args.count(arg)) {
- if (i + 1 < argc) {
- if (parse_int_arg(argv[++i], *int_args[arg]) != 0) {
- fprintf(stderr, "error: invalid value for %s: %s\n", arg.c_str(), argv[i]);
- print_usage();
- return 1;
- }
- } else {
- fprintf(stderr, "error: missing value for %s\n", arg.c_str());
- print_usage();
+
+ context_size_ = std::atoi(argv[++i]);
+ } else if (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0) {
+ if (i + 1 >= argc) {
return 1;
}
+
+ ngl_ = std::atoi(argv[++i]);
+ } else if (strcmp(argv[i], "-h") == 0 || strcmp(argv[i], "--help") == 0) {
+ help_ = true;
+ return 0;
+ } else if (!positional_args_i) {
+ ++positional_args_i;
+ model_ = argv[i];
+ } else if (positional_args_i == 1) {
+ ++positional_args_i;
+ user_ = argv[i];
} else {
- fprintf(stderr, "error: unrecognized argument %s\n", arg.c_str());
- print_usage();
- return 1;
+ user_ += " " + std::string(argv[i]);
}
}
- if (string_args["-m"]->empty()) {
- fprintf(stderr, "error: -m is required\n");
- print_usage();
+ return model_.empty(); // model_ is the only required value
+ }
+
+ void help() const { printf("%s", help_str_.c_str()); }
+};
+
+struct progress_data {
+ size_t file_size = 0;
+ std::chrono::steady_clock::time_point start_time = std::chrono::steady_clock::now();
+ bool printed = false;
+};
+
+struct FileDeleter {
+ void operator()(FILE * file) const {
+ if (file) {
+ fclose(file);
+ }
+ }
+};
+
+typedef std::unique_ptr<FILE, FileDeleter> FILE_ptr;
+
+#ifdef LLAMA_USE_CURL
+class CurlWrapper {
+ public:
+ int init(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
+ const bool progress, std::string * response_str = nullptr) {
+ std::string output_file_partial;
+ curl = curl_easy_init();
+ if (!curl) {
return 1;
}
+ progress_data data;
+ FILE_ptr out;
+ if (!output_file.empty()) {
+ output_file_partial = output_file + ".partial";
+ out.reset(fopen(output_file_partial.c_str(), "ab"));
+ }
+
+ set_write_options(response_str, out);
+ data.file_size = set_resume_point(output_file_partial);
+ set_progress_options(progress, data);
+ set_headers(headers);
+ perform(url);
+ if (!output_file.empty()) {
+ std::filesystem::rename(output_file_partial, output_file);
+ }
+
return 0;
}
- private:
- const char * program_name;
- std::unordered_map<std::string, std::string *> string_args;
- std::unordered_map<std::string, int *> int_args;
- std::vector<Argument> arguments;
+ ~CurlWrapper() {
+ if (chunk) {
+ curl_slist_free_all(chunk);
+ }
- int parse_int_arg(const char * arg, int & value) {
- char * end;
- const long val = std::strtol(arg, &end, 10);
- if (*end == '\0' && val >= INT_MIN && val <= INT_MAX) {
- value = static_cast<int>(val);
- return 0;
+ if (curl) {
+ curl_easy_cleanup(curl);
}
- return 1;
}
- void print_usage() const {
- printf("\nUsage:\n");
- printf(" %s [OPTIONS]\n\n", program_name);
- printf("Options:\n");
- for (const auto & arg : arguments) {
- printf(" %-10s %s\n", arg.flag.c_str(), arg.help_text.c_str());
+ private:
+ CURL * curl = nullptr;
+ struct curl_slist * chunk = nullptr;
+
+ void set_write_options(std::string * response_str, const FILE_ptr & out) {
+ if (response_str) {
+ curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, capture_data);
+ curl_easy_setopt(curl, CURLOPT_WRITEDATA, response_str);
+ } else {
+ curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_data);
+ curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.get());
+ }
+ }
+
+ size_t set_resume_point(const std::string & output_file) {
+ size_t file_size = 0;
+ if (std::filesystem::exists(output_file)) {
+ file_size = std::filesystem::file_size(output_file);
+ curl_easy_setopt(curl, CURLOPT_RESUME_FROM_LARGE, static_cast<curl_off_t>(file_size));
+ }
+
+ return file_size;
+ }
+
+ void set_progress_options(bool progress, progress_data & data) {
+ if (progress) {
+ curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
+ curl_easy_setopt(curl, CURLOPT_XFERINFODATA, &data);
+ curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, progress_callback);
}
+ }
- printf("\n");
+ void set_headers(const std::vector<std::string> & headers) {
+ if (!headers.empty()) {
+ if (chunk) {
+ curl_slist_free_all(chunk);
+ chunk = 0;
+ }
+
+ for (const auto & header : headers) {
+ chunk = curl_slist_append(chunk, header.c_str());
+ }
+
+ curl_easy_setopt(curl, CURLOPT_HTTPHEADER, chunk);
+ }
+ }
+
+ void perform(const std::string & url) {
+ CURLcode res;
+ curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
+ curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
+ curl_easy_setopt(curl, CURLOPT_DEFAULT_PROTOCOL, "https");
+ curl_easy_setopt(curl, CURLOPT_FAILONERROR, 1L);
+ res = curl_easy_perform(curl);
+ if (res != CURLE_OK) {
+ printe("curl_easy_perform() failed: %s\n", curl_easy_strerror(res));
+ }
+ }
+
+ static std::string human_readable_time(double seconds) {
+ int hrs = static_cast<int>(seconds) / 3600;
+ int mins = (static_cast<int>(seconds) % 3600) / 60;
+ int secs = static_cast<int>(seconds) % 60;
+
+ std::ostringstream out;
+ if (hrs > 0) {
+ out << hrs << "h " << std::setw(2) << std::setfill('0') << mins << "m " << std::setw(2) << std::setfill('0')
+ << secs << "s";
+ } else if (mins > 0) {
+ out << mins << "m " << std::setw(2) << std::setfill('0') << secs << "s";
+ } else {
+ out << secs << "s";
+ }
+
+ return out.str();
+ }
+
+ static std::string human_readable_size(curl_off_t size) {
+ static const char * suffix[] = { "B", "KB", "MB", "GB", "TB" };
+ char length = sizeof(suffix) / sizeof(suffix[0]);
+ int i = 0;
+ double dbl_size = size;
+ if (size > 1024) {
+ for (i = 0; (size / 1024) > 0 && i < length - 1; i++, size /= 1024) {
+ dbl_size = size / 1024.0;
+ }
+ }
+
+ std::ostringstream out;
+ out << std::fixed << std::setprecision(2) << dbl_size << " " << suffix[i];
+ return out.str();
+ }
+
+ static int progress_callback(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t,
+ curl_off_t) {
+ progress_data * data = static_cast<progress_data *>(ptr);
+ if (total_to_download <= 0) {
+ return 0;
+ }
+
+ total_to_download += data->file_size;
+ const curl_off_t now_downloaded_plus_file_size = now_downloaded + data->file_size;
+ const curl_off_t percentage = (now_downloaded_plus_file_size * 100) / total_to_download;
+ const curl_off_t pos = (percentage / 5);
+ std::string progress_bar;
+ for (int i = 0; i < 20; ++i) {
+ progress_bar.append((i < pos) ? "█" : " ");
+ }
+
+ // Calculate download speed and estimated time to completion
+ const auto now = std::chrono::steady_clock::now();
+ const std::chrono::duration<double> elapsed_seconds = now - data->start_time;
+ const double speed = now_downloaded / elapsed_seconds.count();
+ const double estimated_time = (total_to_download - now_downloaded) / speed;
+ printe("\r%ld%% |%s| %s/%s %.2f MB/s %s ", percentage, progress_bar.c_str(),
+ human_readable_size(now_downloaded).c_str(), human_readable_size(total_to_download).c_str(),
+ speed / (1024 * 1024), human_readable_time(estimated_time).c_str());
+ fflush(stderr);
+ data->printed = true;
+
+ return 0;
+ }
+
+ // Function to write data to a file
+ static size_t write_data(void * ptr, size_t size, size_t nmemb, void * stream) {
+ FILE * out = static_cast<FILE *>(stream);
+ return fwrite(ptr, size, nmemb, out);
+ }
+
+ // Function to capture data into a string
+ static size_t capture_data(void * ptr, size_t size, size_t nmemb, void * stream) {
+ std::string * str = static_cast<std::string *>(stream);
+ str->append(static_cast<char *>(ptr), size * nmemb);
+ return size * nmemb;
}
};
+#endif
class LlamaData {
- public:
- llama_model_ptr model;
- llama_sampler_ptr sampler;
- llama_context_ptr context;
+ public:
+ llama_model_ptr model;
+ llama_sampler_ptr sampler;
+ llama_context_ptr context;
std::vector<llama_chat_message> messages;
+ std::vector<std::string> msg_strs;
+ std::vector<char> fmtted;
- int init(const Options & opt) {
- model = initialize_model(opt.model_path, opt.ngl);
+ int init(Opt & opt) {
+ model = initialize_model(opt);
if (!model) {
return 1;
}
- context = initialize_context(model, opt.n_ctx);
+ context = initialize_context(model, opt.context_size_);
if (!context) {
return 1;
}
return 0;
}
- private:
+ private:
+#ifdef LLAMA_USE_CURL
+ int download(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
+ const bool progress, std::string * response_str = nullptr) {
+ CurlWrapper curl;
+ if (curl.init(url, headers, output_file, progress, response_str)) {
+ return 1;
+ }
+
+ return 0;
+ }
+#else
+ int download(const std::string &, const std::vector<std::string> &, const std::string &, const bool,
+ std::string * = nullptr) {
+ printe("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
+ return 1;
+ }
+#endif
+
+ int huggingface_dl(const std::string & model, const std::vector<std::string> headers, const std::string & bn) {
+ // Find the second occurrence of '/' after protocol string
+ size_t pos = model.find('/');
+ pos = model.find('/', pos + 1);
+ if (pos == std::string::npos) {
+ return 1;
+ }
+
+ const std::string hfr = model.substr(0, pos);
+ const std::string hff = model.substr(pos + 1);
+ const std::string url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff;
+ return download(url, headers, bn, true);
+ }
+
+ int ollama_dl(std::string & model, const std::vector<std::string> headers, const std::string & bn) {
+ if (model.find('/') == std::string::npos) {
+ model = "library/" + model;
+ }
+
+ std::string model_tag = "latest";
+ size_t colon_pos = model.find(':');
+ if (colon_pos != std::string::npos) {
+ model_tag = model.substr(colon_pos + 1);
+ model = model.substr(0, colon_pos);
+ }
+
+ std::string manifest_url = "https://registry.ollama.ai/v2/" + model + "/manifests/" + model_tag;
+ std::string manifest_str;
+ const int ret = download(manifest_url, headers, "", false, &manifest_str);
+ if (ret) {
+ return ret;
+ }
+
+ nlohmann::json manifest = nlohmann::json::parse(manifest_str);
+ std::string layer;
+ for (const auto & l : manifest["layers"]) {
+ if (l["mediaType"] == "application/vnd.ollama.image.model") {
+ layer = l["digest"];
+ break;
+ }
+ }
+
+ std::string blob_url = "https://registry.ollama.ai/v2/" + model + "/blobs/" + layer;
+ return download(blob_url, headers, bn, true);
+ }
+
+ std::string basename(const std::string & path) {
+ const size_t pos = path.find_last_of("/\\");
+ if (pos == std::string::npos) {
+ return path;
+ }
+
+ return path.substr(pos + 1);
+ }
+
+ int remove_proto(std::string & model_) {
+ const std::string::size_type pos = model_.find("://");
+ if (pos == std::string::npos) {
+ return 1;
+ }
+
+ model_ = model_.substr(pos + 3); // Skip past "://"
+ return 0;
+ }
+
+ int resolve_model(std::string & model_) {
+ const std::string bn = basename(model_);
+ const std::vector<std::string> headers = { "--header",
+ "Accept: application/vnd.docker.distribution.manifest.v2+json" };
+ int ret = 0;
+ if (string_starts_with(model_, "file://") || std::filesystem::exists(bn)) {
+ remove_proto(model_);
+ } else if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) {
+ remove_proto(model_);
+ ret = huggingface_dl(model_, headers, bn);
+ } else if (string_starts_with(model_, "ollama://")) {
+ remove_proto(model_);
+ ret = ollama_dl(model_, headers, bn);
+ } else if (string_starts_with(model_, "https://")) {
+ download(model_, headers, bn, true);
+ } else {
+ ret = ollama_dl(model_, headers, bn);
+ }
+
+ model_ = bn;
+
+ return ret;
+ }
+
// Initializes the model and returns a unique pointer to it
- llama_model_ptr initialize_model(const std::string & model_path, const int ngl) {
+ llama_model_ptr initialize_model(Opt & opt) {
+ ggml_backend_load_all();
llama_model_params model_params = llama_model_default_params();
- model_params.n_gpu_layers = ngl;
-
- llama_model_ptr model(llama_load_model_from_file(model_path.c_str(), model_params));
+ model_params.n_gpu_layers = opt.ngl_ >= 0 ? opt.ngl_ : model_params.n_gpu_layers;
+ resolve_model(opt.model_);
+ llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), model_params));
if (!model) {
- fprintf(stderr, "%s: error: unable to load model\n", __func__);
+ printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str());
}
return model;
// Initializes the context with the specified parameters
llama_context_ptr initialize_context(const llama_model_ptr & model, const int n_ctx) {
llama_context_params ctx_params = llama_context_default_params();
- ctx_params.n_ctx = n_ctx;
- ctx_params.n_batch = n_ctx;
-
+ ctx_params.n_ctx = n_ctx;
+ ctx_params.n_batch = n_ctx;
llama_context_ptr context(llama_new_context_with_model(model.get(), ctx_params));
if (!context) {
- fprintf(stderr, "%s: error: failed to create the llama_context\n", __func__);
+ printe("%s: error: failed to create the llama_context\n", __func__);
}
return context;
}
};
-// Add a message to `messages` and store its content in `owned_content`
-static void add_message(const char * role, const std::string & text, LlamaData & llama_data,
- std::vector<char_array_ptr> & owned_content) {
- char_array_ptr content(new char[text.size() + 1]);
- std::strcpy(content.get(), text.c_str());
- llama_data.messages.push_back({role, content.get()});
- owned_content.push_back(std::move(content));
+// Add a message to `messages` and store its content in `msg_strs`
+static void add_message(const char * role, const std::string & text, LlamaData & llama_data) {
+ llama_data.msg_strs.push_back(std::move(text));
+ llama_data.messages.push_back({ role, llama_data.msg_strs.back().c_str() });
}
// Function to apply the chat template and resize `formatted` if needed
-static int apply_chat_template(const LlamaData & llama_data, std::vector<char> & formatted, const bool append) {
- int result = llama_chat_apply_template(llama_data.model.get(), nullptr, llama_data.messages.data(),
- llama_data.messages.size(), append, formatted.data(), formatted.size());
- if (result > static_cast<int>(formatted.size())) {
- formatted.resize(result);
+static int apply_chat_template(LlamaData & llama_data, const bool append) {
+ int result = llama_chat_apply_template(
+ llama_data.model.get(), nullptr, llama_data.messages.data(), llama_data.messages.size(), append,
+ append ? llama_data.fmtted.data() : nullptr, append ? llama_data.fmtted.size() : 0);
+ if (append && result > static_cast<int>(llama_data.fmtted.size())) {
+ llama_data.fmtted.resize(result);
result = llama_chat_apply_template(llama_data.model.get(), nullptr, llama_data.messages.data(),
- llama_data.messages.size(), append, formatted.data(), formatted.size());
+ llama_data.messages.size(), append, llama_data.fmtted.data(),
+ llama_data.fmtted.size());
}
return result;
prompt_tokens.resize(n_prompt_tokens);
if (llama_tokenize(model.get(), prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true,
true) < 0) {
- GGML_ABORT("failed to tokenize the prompt\n");
+ printe("failed to tokenize the prompt\n");
+ return -1;
}
return n_prompt_tokens;
// Check if we have enough space in the context to evaluate this batch
static int check_context_size(const llama_context_ptr & ctx, const llama_batch & batch) {
- const int n_ctx = llama_n_ctx(ctx.get());
+ const int n_ctx = llama_n_ctx(ctx.get());
const int n_ctx_used = llama_get_kv_cache_used_cells(ctx.get());
if (n_ctx_used + batch.n_tokens > n_ctx) {
printf("\033[0m\n");
- fprintf(stderr, "context size exceeded\n");
+ printe("context size exceeded\n");
return 1;
}
// convert the token to a string
static int convert_token_to_string(const llama_model_ptr & model, const llama_token token_id, std::string & piece) {
char buf[256];
- int n = llama_token_to_piece(model.get(), token_id, buf, sizeof(buf), 0, true);
+ int n = llama_token_to_piece(model.get(), token_id, buf, sizeof(buf), 0, true);
if (n < 0) {
- GGML_ABORT("failed to convert token to piece\n");
+ printe("failed to convert token to piece\n");
+ return 1;
}
piece = std::string(buf, n);
// helper function to evaluate a prompt and generate a response
static int generate(LlamaData & llama_data, const std::string & prompt, std::string & response) {
- std::vector<llama_token> prompt_tokens;
- const int n_prompt_tokens = tokenize_prompt(llama_data.model, prompt, prompt_tokens);
- if (n_prompt_tokens < 0) {
+ std::vector<llama_token> tokens;
+ if (tokenize_prompt(llama_data.model, prompt, tokens) < 0) {
return 1;
}
// prepare a batch for the prompt
- llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
+ llama_batch batch = llama_batch_get_one(tokens.data(), tokens.size());
llama_token new_token_id;
while (true) {
check_context_size(llama_data.context, batch);
if (llama_decode(llama_data.context.get(), batch)) {
- GGML_ABORT("failed to decode\n");
+ printe("failed to decode\n");
+ return 1;
}
// sample the next token, check is it an end of generation?
return 0;
}
-static int parse_arguments(const int argc, const char ** argv, Options & opt) {
- ArgumentParser parser(argv[0]);
- parser.add_argument("-m", opt.model_path, "model");
- parser.add_argument("-p", opt.prompt_non_interactive, "prompt");
- parser.add_argument("-c", opt.n_ctx, "context_size");
- parser.add_argument("-ngl", opt.ngl, "n_gpu_layers");
- if (parser.parse(argc, argv)) {
- return 1;
- }
-
- return 0;
-}
-
static int read_user_input(std::string & user) {
std::getline(std::cin, user);
- return user.empty(); // Indicate an error or empty input
+ return user.empty(); // Should have data in happy path
}
// Function to generate a response based on the prompt
// Set response color
printf("\033[33m");
if (generate(llama_data, prompt, response)) {
- fprintf(stderr, "failed to generate response\n");
+ printe("failed to generate response\n");
return 1;
}
}
// Helper function to apply the chat template and handle errors
-static int apply_chat_template_with_error_handling(const LlamaData & llama_data, std::vector<char> & formatted,
- const bool is_user_input, int & output_length) {
- const int new_len = apply_chat_template(llama_data, formatted, is_user_input);
+static int apply_chat_template_with_error_handling(LlamaData & llama_data, const bool append, int & output_length) {
+ const int new_len = apply_chat_template(llama_data, append);
if (new_len < 0) {
- fprintf(stderr, "failed to apply the chat template\n");
+ printe("failed to apply the chat template\n");
return -1;
}
}
// Helper function to handle user input
-static bool handle_user_input(std::string & user_input, const std::string & prompt_non_interactive) {
- if (!prompt_non_interactive.empty()) {
- user_input = prompt_non_interactive;
- return true; // No need for interactive input
+static int handle_user_input(std::string & user_input, const std::string & user_) {
+ if (!user_.empty()) {
+ user_input = user_;
+ return 0; // No need for interactive input
}
- printf("\033[32m> \033[0m");
- return !read_user_input(user_input); // Returns false if input ends the loop
+ printf(
+ "\r "
+ "\r\033[32m> \033[0m");
+ return read_user_input(user_input); // Returns true if input ends the loop
}
// Function to tokenize the prompt
-static int chat_loop(LlamaData & llama_data, std::string & prompt_non_interactive) {
- std::vector<char_array_ptr> owned_content;
- std::vector<char> fmtted(llama_n_ctx(llama_data.context.get()));
+static int chat_loop(LlamaData & llama_data, const std::string & user_) {
int prev_len = 0;
-
+ llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
while (true) {
// Get user input
std::string user_input;
- if (!handle_user_input(user_input, prompt_non_interactive)) {
- break;
+ while (handle_user_input(user_input, user_)) {
}
- add_message("user", prompt_non_interactive.empty() ? user_input : prompt_non_interactive, llama_data,
- owned_content);
-
+ add_message("user", user_.empty() ? user_input : user_, llama_data);
int new_len;
- if (apply_chat_template_with_error_handling(llama_data, fmtted, true, new_len) < 0) {
+ if (apply_chat_template_with_error_handling(llama_data, true, new_len) < 0) {
return 1;
}
- std::string prompt(fmtted.begin() + prev_len, fmtted.begin() + new_len);
+ std::string prompt(llama_data.fmtted.begin() + prev_len, llama_data.fmtted.begin() + new_len);
std::string response;
if (generate_response(llama_data, prompt, response)) {
return 1;
}
+
+ if (!user_.empty()) {
+ break;
+ }
+
+ add_message("assistant", response, llama_data);
+ if (apply_chat_template_with_error_handling(llama_data, false, prev_len) < 0) {
+ return 1;
+ }
}
+
return 0;
}
static void log_callback(const enum ggml_log_level level, const char * text, void *) {
if (level == GGML_LOG_LEVEL_ERROR) {
- fprintf(stderr, "%s", text);
+ printe("%s", text);
}
}
static bool is_stdin_a_terminal() {
#if defined(_WIN32)
HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE);
- DWORD mode;
+ DWORD mode;
return GetConsoleMode(hStdin, &mode);
#else
return isatty(STDIN_FILENO);
}
int main(int argc, const char ** argv) {
- Options opt;
- if (parse_arguments(argc, argv, opt)) {
+ Opt opt;
+ const int ret = opt.init(argc, argv);
+ if (ret == 2) {
+ return 0;
+ } else if (ret) {
return 1;
}
if (!is_stdin_a_terminal()) {
- if (!opt.prompt_non_interactive.empty()) {
- opt.prompt_non_interactive += "\n\n";
+ if (!opt.user_.empty()) {
+ opt.user_ += "\n\n";
}
- opt.prompt_non_interactive += read_pipe_data();
+ opt.user_ += read_pipe_data();
}
llama_log_set(log_callback, nullptr);
return 1;
}
- if (chat_loop(llama_data, opt.prompt_non_interactive)) {
+ if (chat_loop(llama_data, opt.user_)) {
return 1;
}
overview / pkg / ggml / sources / llama.cpp / commitdiff