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main.cpp
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#include "chat.h"
#include <iomanip>
#include <iostream>
#include <fstream>
#include <thread>
#include <algorithm>
#include <numeric>
#include <functional>
#include <cstring>
#include <climits>
#include <random>
#include <thread>
#include <map>
#include "vectorstore.h"
#if defined(_WIN32)
#include <fcntl.h>
#include <io.h>
#include <windows.h>
#endif
struct Args
{
std::string model_path = "";
std::string embedding_model_path = "";
std::string reranker_model_path = "";
std::string vector_store_in = "";
std::string merge_vs = "";
std::string system = "";
std::string prompt = "你好";
std::string ai_prefix = "";
std::string sampling = "top_p";
chatllm::Pipeline::ExtendingMethod extending = chatllm::Pipeline::ExtendingMethod::Restart;
std::string test_fn = "";
std::string rag_template = "";
std::string rag_context_sep = "";
std::string retrieve_rewrite_template = "";
std::map<std::string, std::string> additional;
std::string layer_spec;
std::string load_session;
std::string save_session;
std::string n_gpu_layers;
std::string cur_vs_name = "default";
std::string dump_dot;
std::string emb_rank_query_sep;
std::map<std::string, std::vector<std::string>> vector_stores;
std::string rpc_endpoints;
std::string serve_rpc;
int max_length = -1;
int max_context_length = 512;
bool interactive = false;
bool show = false;
int top_k = 20;
float top_p = 0.7f;
float temp = 0.7f;
float tfs_z = 0.95f;
float presence_penalty = 1.0f;
int num_threads = 0;
bool multi_line = false;
int seed;
chatllm::ChatFormat format = chatllm::ChatFormat::CHAT;
bool tokenize = false;
DistanceStrategy vc = DistanceStrategy::MaxInnerProduct;
int retrieve_top_n = 2;
int rerank_top_n = 1;
float rerank_score_thres = 0.35f;
int rag_post_extending = 0;
bool hide_reference = false;
bool rag_dump = false;
bool show_banner = true;
bool show_help = false;
bool show_devices = false;
bool rerank_rewrite = false;
bool reversed_role = false;
int save_session_rounds = -1;
int beam_size = -1;
int log_level = 4;
bool moe_on_cpu = false;
};
#define MULTI_LINE_END_MARKER_W L"\\."
#define MULTI_LINE_END_MARKER "\\."
bool has_extending = false;
static chatllm::Pipeline::ExtendingMethod parse_extending_method(const std::string &s)
{
has_extending = true;
if (s == "shift")
return chatllm::Pipeline::ExtendingMethod::Shift;
else if (s == "restart")
return chatllm::Pipeline::ExtendingMethod::Restart;
else
return chatllm::Pipeline::ExtendingMethod::None;
}
void usage(const std::string &prog)
{
std::cout << "Usage: " << prog << " [options]\n"
<< "\n"
<< "Basic options:\n"
<< " -h, --help show this help message and exit [*]\n"
<< " -m, --model PATH model path\n"
<< " -p, --prompt PROMPT prompt to start generation with (default: 你好)\n"
<< " --prompt_file FN prompt from file\n"
<< " -s, --system SYSTEM system prompt (instruction) (default: model specific)\n"
<< " --sys_file FN system prompt (instruction) from file\n"
<< " --ai_prefix AI prefix for generation (default: empty)\n"
<< " -i, --interactive run in interactive mode [*]\n"
<< " --reversed_role AI becomes `user`, user becomes `AI` [#]\n"
<< " -l, --max_length N max total length including prompt and output (default: model specific)\n"
<< " generally, this is used to reduce KV cache size.\n"
<< " for models that does not show its max context window in `config.json`,\n"
<< " use this to enlarge it (use with caution!).\n"
<< " --layer_spec LAYERS select/redesign layers.\n"
<< " LAYERS=S0,S1,.... where S0/S1/... are like slices of Python, `start:stop[:step]`,\n"
<< " negative values in `start` and `stop` can be used referencing layers in reversed order,\n"
<< " `step` is optional, e.g.\n"
<< " --layer_spec 0:3,1:4 (3 + 3 = 6 layers are selected, layer #1/2 are used twice)\n"
<< " layer structure: 0->1->2->1->2->3\n"
<< " -c, --max_context_length N\n"
<< " max context length (default: 512)\n"
<< " --extending EXT context extending method (EXT = restart | shift | none)\n"
<< " (default: none if `--load_session` is specified, otherwise restart)\n"
<< " --multi enabled multiple lines of input [*]\n"
<< " when enabled, `" << MULTI_LINE_END_MARKER << "` marks the end of your input.\n"
<< " --format FMT conversion format (model specific, FMT = chat | completion | qa) (default: chat)\n"
<< "Performance options:\n"
<< " -n, --threads N number of threads for inference (default: number of cores)\n"
<< " -ngl, --n_gpu_layers N number of model layers to offload to a backend device (GPU) (default: GPU not used)\n"
<< " N ::= one_spec;...\n"
<< " one_spec ::= [id:]spec, where spec ::= [n|epilog|prolog|all]\n"
<< " +moe_on_cpu alway use CPU for sparse operations (MoE) (default: off)\n"
<< " --rpc_endpoints EP.. RPC endpoints (i.e. servers) for distributed inference (default: empty)\n"
<< " EP1;EP2, where EP ::= host:port\n"
<< "Sampling options:\n"
<< " --sampling ALG sampling algorithm (ALG = greedy | top_p | tfs) (default: top_p) \n"
<< " where, tfs = Tail Free Sampling\n"
<< " -t, --temp T temperature (default: 0.7) (Note: `-t 0` also sets sampling algorithm to greedy)\n"
<< " --top_k N top-k sampling (default: 20)\n"
<< " --top_p N top-p sampling (default: 0.7)\n"
<< " --tfs_z Z Z param for TFS (default: 0.95)\n"
<< " --presence_penalty N presence repetition penalty (default: 1.0, no penalty)\n"
<< " --seed N seed for random generator (default: random)\n"
<< " --beam_size N beam size for generation (default: -1, disabled)\n"
<< " functionality of beam search limited.\n"
<< "RAG options:\n"
<< " --set_vs_name set vector store name.\n"
<< " all following vector store files are merged into this vector store. (optional. default: `default`)\n"
<< " Note: command line RAG chat will always the first store.\n"
<< " --vector_store FILE append a vector store file (when RAG enabled, at lease one is required)\n"
<< " --embedding_model PATH embedding model path (when set, RAG is enabled)\n"
<< " --distance_strategy DS distance strategy (model dependent, default: MaxInnerProduct)\n"
<< " DS = EuclideanDistance | MaxInnerProduct | InnerProduct | CosineSimilarity\n"
<< " --retrieve_top_n N number of retrieved items using embedding model (default: 2)\n"
<< " --retrieve_rewrite_template ...\n"
<< " prompt template to ask LLM to rewrite a query for retrieving (optional).\n"
<< " (default: \"\", i.e. disabled, the original prompt is used for retrieving)\n"
<< " macros: {question}. this may NOT WORK. Example:\n"
<< " Extract keywords for querying: {question}\n"
<< " --reranker_model PATH reranker model path (optional)\n"
<< " --rerank_score_thres reranking score threshold (default: 0.35)\n"
<< " items with a lower score are discarded.\n"
<< " --rerank_top_n N number of selected items using reranker model (default: 1)\n"
<< " +rerank_rewrite reranker use the rewritten query (default: OFF, i.e. use the original user input)\n"
<< " --emb_rank_query_sep separator for embedding & rerank query (default: \"\", i.e. disabled)\n"
<< " only used without main model\n"
<< " --hide_reference do not show references (default: false) [*]\n"
<< " --rag_template ... prompt template for RAG (macros: {context}, {question}) (optional).\n"
<< " Support some C escape sequences (\\n). Example:\n"
<< " Answer the question according to below information:\n"
<< " ---\n"
<< " {context}\n"
<< " ---\n"
<< " Question: {question}\n"
<< " --rag_context_sep context separator (default: '\\n```\\n')\n"
<< " Support some C escape sequences (\\n).\n"
<< " --rag_post_extending N extend selected items with pre & post N chunks with same metadata. (default: 0)\n"
<< " this may be useful when context length of embedding/reranker models is limited.\n"
<< " +rag_dump (debug) dump retrieved/re-ranking results\n"
<< "Session:\n"
<< " --save_session N FILE save session to FILE after N round(s) of chatting (N >= 0) and quit [*]\n"
<< " when N = 0, system prompt is evaluated.\n"
<< " --load_session FILE load session from FILE [*]\n"
<< "Misc:\n"
<< " --init_vs FILE init vector store file from input [*]\n"
<< " --merge_vs FILE merge multiple vector store files into a single one [*]\n"
<< " --tokenize (debug) tokenize `prompt` and exit [*]\n"
<< " --test FILE test against inputs from a file and exit [*]\n"
<< " --hide_banner hide banner [*]\n"
<< " --show show model info and quit [*]\n"
<< " --show_devices show info about backends and devices, then quit [*]\n"
<< " --dump_dot FILE dump sched splits to a DOT file, and exit with -1\n"
<< " --log_level log level. (default: 4 - ERROR)\n"
<< " --serve_rpc [H:]P[@id] as a RPC server on host:port (optional: host default to 127.0.0.1, id defaults to 0) [#]\n"
<< "Additional key-value args:\n"
<< " --kv start of additional args. all following options are interpreted as k-v pairs\n"
<< " key value a key-value pair of args\n"
<< "\n------------------------\n"
<< "*: implemented by front end (i.e. `main.cpp` or apps using bindings)\n"
<< "#: implemented by front end & backend\n"
<< std::endl;
}
static std::string load_txt(const std::string &fn)
{
std::ifstream f(fn);
std::ostringstream sstr;
if (f.is_open())
{
sstr << f.rdbuf();
f.close();
}
return sstr.str();
}
static size_t parse_args(Args &args, const std::vector<std::string> &argv)
{
std::random_device rd;
args.seed = rd();
const size_t argc = argv.size();
#define handle_para0(fmt1, field, f) \
else if ((strcmp(arg, fmt1) == 0)) \
{ \
c++; \
if (c < argc) \
args.field = f(argv[c].c_str()); \
}
#define handle_param(fmt1, fmt2, field, f) \
else if ((strcmp(arg, fmt1) == 0) || (strcmp(arg, fmt2) == 0)) \
{ \
c++; \
if (c < argc) \
args.field = f(argv[c].c_str()); \
}
#define append_param(fmt1, field, f) \
else if ((strcmp(arg, fmt1) == 0)) \
{ \
c++; \
if (c < argc) \
args.field.push_back(f(argv[c].c_str())); \
}
#define handle_flag(field) \
else if (((strcmp(arg, "+" #field) == 0)) || ((strcmp(arg, "--" #field) == 0))) \
{ \
args.field = true; \
}
size_t c = 1;
try
{
while (c < argc)
{
const char *arg = argv[c].c_str();
if ((strcmp(arg, "--help") == 0) || (strcmp(arg, "-h") == 0) || (strcmp(arg, "-?") == 0))
{
args.show_help = true;
}
else if ((strcmp(arg, "--interactive") == 0) || (strcmp(arg, "-i") == 0))
{
args.interactive = true;
}
else if (strcmp(arg, "--multi") == 0)
{
args.multi_line = true;
}
else if (strcmp(arg, "--hide_banner") == 0)
{
args.show_banner = false;
}
handle_flag(tokenize)
handle_flag(hide_reference)
handle_flag(show)
handle_flag(show_devices)
handle_flag(reversed_role)
handle_flag(rag_dump)
handle_flag(rerank_rewrite)
handle_flag(moe_on_cpu)
else if (strcmp(arg, "--format") == 0)
{
c++;
if (c < argc)
{
if (argv[c] == "completion")
args.format = chatllm::ChatFormat::COMPLETION;
else if (argv[c] == "qa")
args.format = chatllm::ChatFormat::QA;
else
args.format = chatllm::ChatFormat::CHAT;
}
}
else if (strcmp(arg, "--save_session") == 0)
{
c++;
if (c + 1 < argc)
{
args.save_session_rounds = std::stoi(argv[c]);
args.save_session = argv[c + 1];
c++;
}
}
else if (strcmp(arg, "--kv") == 0)
{
while (c + 2 < argc)
{
args.additional.insert_or_assign(argv[c + 1], argv[c + 2]);
c += 2;
}
}
else if (strcmp(arg, "--vector_store") == 0)
{
c++;
if (c < argc)
{
if (args.vector_stores.find(args.cur_vs_name) == args.vector_stores.end())
{
args.vector_stores.insert(std::pair(args.cur_vs_name, std::vector<std::string>()));
}
args.vector_stores.at(args.cur_vs_name).push_back(argv[c]);
}
}
handle_param("--model", "-m", model_path, std::string)
handle_param("--prompt", "-p", prompt, std::string)
handle_para0("--prompt_file", prompt, load_txt)
handle_param("--system", "-s", system, std::string)
handle_para0("--sys_file", system, load_txt)
handle_para0("--ai_prefix", ai_prefix, std::string)
handle_param("--max_length", "-l", max_length, std::stoi)
handle_param("--max_context_length", "-c", max_context_length, std::stoi)
handle_para0("--extending", extending, parse_extending_method)
handle_para0("--sampling", sampling, std::string)
handle_param("--top_k", "-k", top_k, std::stoi)
handle_param("--top_p", "-q", top_p, std::stof)
handle_para0("--tfs_z", tfs_z, std::stof)
handle_param("--temp", "-t", temp, std::stof)
handle_para0("--presence_penalty", presence_penalty, std::stof)
handle_param("--threads", "-n", num_threads, std::stoi)
handle_param("--n_gpu_layers", "-ngl", n_gpu_layers, std::string)
handle_para0("--seed", seed, std::stoi)
handle_para0("--test", test_fn, std::string)
handle_para0("--set_vs_name", cur_vs_name, std::string)
handle_para0("--embedding_model", embedding_model_path, std::string)
handle_para0("--distance_strategy", vc, ParseDistanceStrategy)
handle_para0("--retrieve_top_n", retrieve_top_n, std::stoi)
handle_para0("--reranker_model", reranker_model_path, std::string)
handle_para0("--retrieve_rewrite_template", retrieve_rewrite_template, std::string)
handle_para0("--rerank_score_thres", rerank_score_thres, std::stof)
handle_para0("--rerank_top_n", rerank_top_n, std::stoi)
handle_para0("--rag_post_extending", rag_post_extending, std::stoi)
handle_para0("--rag_template", rag_template, std::string)
handle_para0("--rag_context_sep", rag_context_sep, std::string)
handle_para0("--emb_rank_query_sep", emb_rank_query_sep, std::string)
handle_para0("--init_vs", vector_store_in, std::string)
handle_para0("--merge_vs", merge_vs, std::string)
handle_para0("--layer_spec", layer_spec, std::string)
handle_para0("--load_session", load_session, std::string)
handle_para0("--dump_dot", dump_dot, std::string)
handle_para0("--beam_size", beam_size, std::stoi)
handle_para0("--log_level", log_level, std::stoi)
handle_para0("--rpc_endpoints", rpc_endpoints, std::string)
handle_para0("--serve_rpc", serve_rpc, std::string)
else
break;
c++;
}
}
catch(const std::exception& e)
{
std::cerr << e.what() << '\n';
return c;
}
#undef append_param
if (!has_extending && (args.load_session.size() > 0))
args.extending = chatllm::Pipeline::ExtendingMethod::None;
return c;
}
#if defined(_WIN32)
static void append_utf8(char32_t ch, std::string &out)
{
if (ch <= 0x7F)
{
out.push_back(static_cast<unsigned char>(ch));
}
else if (ch <= 0x7FF)
{
out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
}
else if (ch <= 0xFFFF)
{
out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
}
else if (ch <= 0x10FFFF)
{
out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
}
else
{
// Invalid Unicode code point
}
}
static bool get_utf8_line(std::string &line, bool multi_line)
{
std::wstring marker(MULTI_LINE_END_MARKER_W);
do
{
std::wstring prompt;
std::getline(std::wcin, prompt);
if (multi_line)
{
if (prompt == marker)
return true;
if (line.size() > 0)
append_utf8('\n', line);
}
for (auto wc : prompt)
append_utf8(wc, line);
} while (multi_line);
return true;
}
#else
static bool get_utf8_line(std::string &line, bool multi_line)
{
do
{
std::string prompt;
std::getline(std::cin, prompt);
if (multi_line)
{
if (prompt == MULTI_LINE_END_MARKER)
return true;
if (line.size() > 0)
line.push_back('\n');
}
line.append(prompt.begin(), prompt.end());
} while (multi_line);
return true;
}
#endif
static inline int get_num_physical_cores()
{
unsigned int n_threads = std::thread::hardware_concurrency();
return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
}
static void trim(std::string &s)
{
size_t l = s.size();
while (l > 0)
{
if ((s[l - 1] == '\r') || (s[l - 1] == '\n'))
l--;
else
break;
}
s.resize(l);
}
// reference: https://github.com/huggingface/transformers/blob/main/src/transformers/generation/streamers.py
class TextStreamer : public chatllm::BaseStreamer
{
public:
TextStreamer(chatllm::BaseTokenizer *tokenizer) :
BaseStreamer(tokenizer),
cout(std::cout),
reference_tag("Reference:"), ref_count(0) {}
void put_chunk(bool first, const std::string &chunk) override;
void putln(const std::string &line, TextType type = TextType::META) override;
void end() override;
public:
std::ostream &cout;
std::string reference_tag;
int ref_count;
};
static void print_timing(char *str, const char *prefix, size_t tok_number, double duration_sec)
{
sprintf(str, "%s = %12.2f ms / %5zd tokens ( %8.2f ms per token, %8.2f tokens per second)", prefix, duration_sec, tok_number,
duration_sec / tok_number,
tok_number / duration_sec * 1000);
}
static void show_stat(chatllm::Pipeline &pipeline, chatllm::BaseStreamer &streamer)
{
streamer.putln("");
chatllm::ModelPerfInfo *perf = &pipeline.performance;
char str[1024];
print_timing(str, "timings: prompt eval time", perf->timings[chatllm::ModelPerfInfo::Type::Prompt].tok_count, perf->timings[chatllm::ModelPerfInfo::Type::Prompt].duration_ms);
streamer.putln(str);
print_timing(str, "timings: eval time", perf->timings[chatllm::ModelPerfInfo::Type::Generation].tok_count, perf->timings[chatllm::ModelPerfInfo::Type::Generation].duration_ms);
streamer.putln(str);
sprintf(str, "timings: total time = %12.2f ms / %5zd tokens",
(perf->timings[chatllm::ModelPerfInfo::Type::Generation].duration_ms + perf->timings[chatllm::ModelPerfInfo::Type::Prompt].duration_ms),
perf->timings[chatllm::ModelPerfInfo::Type::Generation].tok_count + perf->timings[chatllm::ModelPerfInfo::Type::Prompt].tok_count);
streamer.putln(str);
}
static void run_file(Args &args, chatllm::Pipeline &pipeline, TextStreamer &streamer, const chatllm::GenerationConfig &gen_config)
{
chatllm::Messages history;
std::string input;
std::ifstream f(args.test_fn);
if (f.is_open())
{
while (std::getline(f, input))
{
trim(input);
streamer.cout << "You > " << input << std::endl;
history.push_back(input, chatllm::MsgRole::User);
streamer.cout << "A.I. > " << std::flush;
std::string output = pipeline.chat(history, gen_config, &streamer);
history.push_back(output, chatllm::MsgRole::Assistant);
}
}
f.close();
streamer.cout << std::endl << pipeline.model->get_n_past() << " tokens are processed/generated. Bye" << std::endl;
show_stat(pipeline, streamer);
}
static void show_banner(chatllm::Pipeline &pipeline, bool show, chatllm::BaseStreamer *streamer)
{
std::ostringstream oss;
if (!show) return;
if (pipeline.is_loaded())
{
#define MODEL_INFO() "You are served by " << std::left << std::setw(28) << pipeline.model->type_name() + ","
#define SHOW_NATIVE() if (pipeline.model->native_name().size() > 0) { oss << "(" << pipeline.model->native_name() << ")"; }
const int64_t total_param_num = pipeline.model->get_param_num(false);
const int64_t total_effective_param_num = pipeline.model->get_param_num(true);
oss << R"( ________ __ __ __ __ ___ )"; SHOW_NATIVE(); oss << '\n'
<< R"( / ____/ /_ ____ _/ /_/ / / / / |/ /_________ ____ )" << '\n'
<< R"( / / / __ \/ __ `/ __/ / / / / /|_/ // ___/ __ \/ __ \ )" << '\n'
<< R"( / /___/ / / / /_/ / /_/ /___/ /___/ / / // /__/ /_/ / /_/ / )" << '\n'
<< R"( \____/_/ /_/\__,_/\__/_____/_____/_/ /_(_)___/ .___/ .___/ )" << '\n';
oss << MODEL_INFO() << R"(/_/ /_/ )" << '\n';
if (total_param_num == total_effective_param_num)
oss << "with " << total_param_num << " (" << std::fixed << std::setprecision(1) << (double)total_param_num / 1000000000. << "B) parameters." << '\n';
else
oss << "with " << total_param_num << " (" << std::fixed << std::setprecision(1) << (double)total_effective_param_num / 1000000000. << "B effect.) parameters." << '\n';
}
else
{
oss << R"( ________ __ __ __ __ ___ )" << '\n'
<< R"( / ____/ /_ ____ _/ /_/ / / / / |/ /_________ ____ )" << '\n'
<< R"( / / / __ \/ __ `/ __/ / / / / /|_/ // ___/ __ \/ __ \ )" << '\n'
<< R"( / /___/ / / / /_/ / /_/ /___/ /___/ / / // /__/ /_/ / /_/ / )" << '\n'
<< R"( \____/_/ /_/\__,_/\__/_____/_____/_/ /_(_)___/ .___/ .___/ )" << '\n';
oss << R"(No LLM is loaded. /_/ /_/ )" << '\n';
}
auto additional = pipeline.get_additional_description();
if (additional.size() > 0)
{
oss << additional << std::endl;
}
streamer->putln(oss.str());
}
static void print_embedding(const std::vector<float> &data, std::ostream &cout)
{
for (size_t i = 0; i < data.size(); i++)
{
if ((i % 8) == 0) cout << std::endl;
cout << std::setw(14) << std::fixed << std::setprecision(8) << data[i] << " ";
}
cout << std::endl;
}
static void run_text_embedding(Args &args, chatllm::Pipeline &pipeline, TextStreamer &streamer, const chatllm::GenerationConfig &gen_config)
{
std::vector<float> result;
if (!args.interactive)
{
pipeline.text_embedding(args.prompt, gen_config, result);
print_embedding(result, streamer.cout);
return;
}
while (1)
{
streamer.cout << "Input > " << std::flush;
std::string input;
if (!get_utf8_line(input, args.multi_line))
{
streamer.cout << "FAILED to read line." << std::endl;
break;
}
if (input.empty()) continue;
result.clear();
pipeline.text_embedding(input, gen_config, result);
streamer.cout << " > ";
print_embedding(result, streamer.cout);
}
streamer.cout << "Bye\n";
}
static void run_qa_ranker(Args &args, chatllm::Pipeline &pipeline, TextStreamer &streamer, const chatllm::GenerationConfig &gen_config)
{
while (1)
{
streamer.cout << "Answer > " << std::flush;
std::string answer;
if (!get_utf8_line(answer, args.multi_line))
{
streamer.cout << "FAILED to read line." << std::endl;
break;
}
if (answer.empty()) continue;
float rank = pipeline.qa_rank(args.prompt, answer, gen_config);
streamer.cout << std::setw(14) << std::fixed << std::setprecision(8) << rank << std::endl;
}
streamer.cout << "Bye\n";
}
#define DEF_GenerationConfig(gen_config, args) chatllm::GenerationConfig gen_config(args.max_length, args.max_context_length, args.temp > 0, args.reversed_role, \
args.top_k, args.top_p, args.temp, args.num_threads, args.sampling, args.presence_penalty, args.tfs_z); \
gen_config.set_ai_prefix(args.ai_prefix); gen_config.dump_dot = args.dump_dot; \
gen_config.emb_rank_query_sep = args.emb_rank_query_sep;
#define DEF_ExtraArgs(pipe_args, args) \
chatllm::ModelObject::extra_args pipe_args(args.max_length, args.layer_spec, args.n_gpu_layers, args.moe_on_cpu, args.num_threads)
chatllm::BaseStreamer *get_streamer_for_log(void);
void log_internal(int level, const char * text)
{
chatllm::BaseStreamer *streamer = get_streamer_for_log();
if (nullptr == streamer) return;
std::ostringstream oss;
static const char tags[] = {' ', 'D', 'I', 'W', 'E', '.'};
if (level < streamer->log_level) return;
if ((0 <= level) && (level < sizeof(tags)))
oss << tags[level];
else
oss << '?';
std::string s(text);
s.erase(s.find_last_not_of('\n') + 1);
oss << s;
streamer->putln(oss.str(), chatllm::BaseStreamer::LOGGING);
}
static void _ggml_log_callback(enum ggml_log_level level, const char * text, void * user_data)
{
log_internal(level, text);
}
void chat(Args &args, chatllm::Pipeline &pipeline, TextStreamer &streamer)
{
streamer.set_tokenizer(pipeline.tokenizer);
if (args.system.size() > 0)
pipeline.set_system_prompt(args.system);
if (pipeline.is_loaded())
{
pipeline.model->seed(args.seed);
args.max_length = pipeline.model->get_max_length();
pipeline.set_extending_method(args.extending);
pipeline.tokenizer->set_chat_format(args.format);
}
if (args.tokenize)
{
auto ids = pipeline.tokenizer->encode(args.prompt);
streamer.cout << "ID: ";
for (auto x : ids)
streamer.cout << x << ", ";
streamer.cout << std::endl;
return;
}
pipeline.set_additional_args(args.additional);
const std::string ai_prompt = "A.I.";
const std::string user_prompt = "You ";
auto show_msg_role = [&](chatllm::MsgRole role) -> std::string
{
switch (role)
{
case chatllm::MsgRole::Assistant:
return ai_prompt;
case chatllm::MsgRole::User:
return user_prompt;
case chatllm::MsgRole::Tool:
return "Tool";
default:
return "????";
}
};
DEF_GenerationConfig(gen_config, args);
chatllm::Messages history;
show_banner(pipeline, args.interactive && args.show_banner, &streamer);
if (pipeline.is_loaded())
{
switch (pipeline.model->get_purpose())
{
case chatllm::ModelPurpose::TextEmbedding:
run_text_embedding(args, pipeline, streamer, gen_config);
return;
case chatllm::ModelPurpose::Ranker:
run_qa_ranker(args, pipeline, streamer, gen_config);
return;
default:
break;
}
}
if (args.test_fn.size() > 0)
{
run_file(args, pipeline, streamer, gen_config);
return;
}
if (!args.interactive)
{
history.push_back(args.prompt, chatllm::MsgRole::User);
pipeline.chat(history, gen_config, &streamer);
show_stat(pipeline, streamer);
return;
}
if (0 == args.save_session_rounds)
{
std::cout << std::endl << "evaluating system prompt... ";
pipeline.eval_sys_prompt(gen_config);
std::cout << "saving session..." << std::endl;
pipeline.save_session(history, args.save_session);
return;
}
if (args.load_session.size() > 0)
{
CHATLLM_CHECK(pipeline.load_session(history, args.load_session, nullptr) == 0) << "failed to load session file";
for (int i = 0; i < (int)history.size(); i++)
{
auto &m = history[i];
streamer.cout << show_msg_role(m.role)
<< " > "
<< m.content << std::endl << std::flush;
}
if (history.size() > 0)
{
auto &last = history[history.size() - 1];
if (last.role != chatllm::MsgRole::Assistant)
{
std::string output = pipeline.chat(history, gen_config, &streamer);
history.push_back(output, chatllm::MsgRole::Assistant);
}
}
}
if (args.reversed_role)
{
CHATLLM_CHECK(args.save_session_rounds < 0) << "TODO: save_session_rounds for reversed_role";
streamer.cout << ai_prompt << " > " << args.prompt << std::endl << std::flush;
history.push_back(args.prompt, chatllm::MsgRole::User);
while (1)
{
streamer.cout << user_prompt << " > " << std::flush;
std::string input;
if (!get_utf8_line(input, args.multi_line))
{
streamer.cout << "FAILED to read line." << std::endl;
break;
}
if (input.empty()) continue;
history.push_back(input, chatllm::MsgRole::Assistant);
streamer.cout << ai_prompt << " > " << std::flush;
std::string output = pipeline.chat(history, gen_config, &streamer);
history.push_back(output, chatllm::MsgRole::User);
}
}
else
{
while (1)
{
if ((args.save_session_rounds > 0) && ((int)(history.size() / 2) == args.save_session_rounds))
{
std::cout << std::endl << "saving session..." << std::endl;
pipeline.save_session(history, args.save_session);
break;
}
streamer.cout << user_prompt << " > " << std::flush;
std::string input;
if (!get_utf8_line(input, args.multi_line))
{
streamer.cout << "FAILED to read line." << std::endl;
break;
}
if (input.empty()) continue;
history.push_back(input, chatllm::MsgRole::User);
streamer.cout << ai_prompt << " > " << std::flush;
std::string output = pipeline.chat(history, gen_config, &streamer);
history.push_back(output, chatllm::MsgRole::Assistant);
}
}
streamer.cout << "Bye\n";
}
void TextStreamer::putln(const std::string &line, TextType type)
{
switch (type)
{
case TextType::ERR:
cout << "ERROR: " << line << std::endl << std::flush;
break;
case TextType::REF:
if (ref_count == 0)
{
putln("");
putln(reference_tag);
}
ref_count++;
cout << ref_count << ". " << line << std::endl << std::flush;
break;
case TextType::REWRITTEN_QUERY:
cout << "Searching " << line << " ..." << std::endl << std::flush;
break;
case TextType::HISTORY_USER:
case TextType::HISTORY_AI:
break;
case TextType::TOOL_CALLING:
cout << " <TOOL_CALLING> Run this tool and tell AI the result: " << line << std::endl << std::flush;
break;
default:
cout << line << std::endl << std::flush;
break;
}
}
void TextStreamer::put_chunk(bool first, const std::string &chunk)
{
cout << chunk << std::flush;
}
void TextStreamer::end()
{
BaseStreamer::end();
ref_count = 0;
cout << std::endl;
}
static void start_rpc_server(std::string endpoint)
{
int device = 0;
auto pos = endpoint.find('@');
if (pos != std::string::npos)
{
device = std::stoi(endpoint.substr(pos + 1));
endpoint = endpoint.substr(0, pos);
}
if (!chatllm::ComputeManager::start_rpc_server(device, endpoint.c_str()))
{
chatllm::ggml::log(GGML_LOG_LEVEL_ERROR, "Failed to start RPC server at %s@%d", endpoint.c_str(), device);
}
else;
}
static void prepare_rpc_devices(const Args &args)
{
if (!chatllm::ComputeManager::prepare_rpc_devices(args.rpc_endpoints))
{
chatllm::ggml::log(GGML_LOG_LEVEL_ERROR, "Failed to prepare RPC devices");
}
}
#if defined(_WIN32)
std::string wstr_to_utf8(const wchar_t* wstr)
{
int s = WideCharToMultiByte(CP_UTF8, 0, wstr, (int)wcslen(wstr), NULL, 0, NULL, NULL);
std::string str;
str.resize(s);
WideCharToMultiByte(CP_UTF8, 0, wstr, (int)wcslen(wstr), LPSTR(str.data()), s, NULL, NULL);
return str;
}
#endif
#ifndef CHATLLM_SHARED_LIB
static chatllm::BaseStreamer *log_streamer = nullptr;
chatllm::BaseStreamer *get_streamer_for_log(void)
{
return log_streamer;
}
static int init_vector_store(Args &args)
{
chatllm::Pipeline pipeline(args.embedding_model_path);
args.max_length = pipeline.model->get_max_length();
DEF_GenerationConfig(gen_config, args);
std::vector<float> r;
CVectorStore vs(args.vc, pipeline.get_text_embedding_dim(),
[&pipeline, &gen_config, &r](const std::string &s, float *emb)
{
pipeline.text_embedding(s, gen_config, r);
CHATLLM_CHECK((int)r.size() == pipeline.get_text_embedding_dim()) << "embedding dim mismatch";
memcpy(emb, r.data(), r.size() * sizeof(float));
},
args.vector_store_in.c_str());
vs.ExportDB((args.vector_store_in + ".vsdb").c_str());
printf("Vector store saved to: %s\n", (args.vector_store_in + ".vsdb").c_str());
return 0;
}
static int merge_vector_store(Args &args)
{
std::vector<std::string> files;
for (auto x : args.vector_stores)
{
files.insert(files.end(), x.second.begin(), x.second.end());
}
CVectorStore vs(args.vc, files);
vs.ExportDB(args.merge_vs.c_str());
printf("Vector store saved to: %s\n", args.merge_vs.c_str());
return 0;
}
static void show_devices(void)
{
std::vector<chatllm::ComputeManager::DeviceInfo> devs;
chatllm::ComputeManager::get_devices_info(devs);
for (size_t i = 0; i < devs.size(); i++)
{
auto &dev = devs[i];
printf("%2zd: %s - %s (%s)\n", i, dev.backend_name.c_str(), dev.name.c_str(), dev.description.c_str());
printf(" type: %s\n", chatllm::ComputeManager::dev_type_to_str(dev.type).c_str());
printf(" memory total: %zd B\n", dev.total_memory);
printf(" memory free : %zd B\n", dev.free_memory);
}
}
#if defined(_WIN32)
int wmain(int argc, const wchar_t **wargv)
{
std::vector<std::string> utf_args;
for (int i = 0; i < argc; i++)
utf_args.push_back(wstr_to_utf8(wargv[i]));