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main.cpp
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main.cpp
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#include "qwen.h"
#include <iomanip>
#include <iostream>
enum InferenceMode {
INFERENCE_MODE_CHAT,
INFERENCE_MODE_GENERATE,
};
static inline auto to_inference_mode(const std::string &s) -> InferenceMode {
static std::unordered_map<std::string, InferenceMode> m{{"chat", INFERENCE_MODE_CHAT},
{"generate", INFERENCE_MODE_GENERATE}};
return m.at(s);
}
struct Args {
std::string model_path = "qwen-ggml.bin";
std::string tiktoken_path = "qwen.tiktoken";
InferenceMode mode = INFERENCE_MODE_CHAT;
std::string prompt = "你好";
int max_length = 2048;
int max_context_length = 512;
bool interactive = false;
int top_k = 0;
float top_p = 0.5;
float temp = 0.95;
float repeat_penalty = 1.0;
int num_threads = 0;
bool verbose = false;
};
static auto usage(const std::string &prog) -> void {
std::cout << "Usage: " << prog << " [options]\n"
<< "\n"
<< "options:\n"
<< " -h, --help show this help message and exit\n"
<< " -m, --model PATH model path (default: qwen-ggml.bin)\n"
<< " --mode inference mode chose from {chat, generate} (default: chat)\n"
<< " -p, --prompt PROMPT prompt to start generation with (default: 你好)\n"
<< " -i, --interactive run in interactive mode\n"
<< " -l, --max_length N max total length including prompt and output (default: 2048)\n"
<< " -c, --max_context_length N\n"
<< " max context length (default: 512)\n"
<< " --top_k N top-k sampling (default: 0)\n"
<< " --top_p N top-p sampling (default: 0.7)\n"
<< " --temp N temperature (default: 0.95)\n"
<< " --repeat_penalty N penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled)\n"
<< " -t, --threads N number of threads for inference\n"
<< " -v, --verbose display verbose output including config/system/performance info\n";
}
static auto parse_args(const std::vector<std::string> &argv) -> Args {
Args args;
for (size_t i = 1; i < argv.size(); i++) {
const std::string &arg = argv[i];
if (arg == "-h" || arg == "--help") {
usage(argv[0]);
exit(EXIT_SUCCESS);
} else if (arg == "-m" || arg == "--model") {
args.model_path = argv[++i];
} else if (arg == "--tiktoken") {
args.tiktoken_path = argv[++i];
} else if (arg == "--mode") {
args.mode = to_inference_mode(argv[++i]);
} else if (arg == "-p" || arg == "--prompt") {
args.prompt = argv[++i];
} else if (arg == "-i" || arg == "--interactive") {
args.interactive = true;
} else if (arg == "-l" || arg == "--max_length") {
args.max_length = std::stoi(argv[++i]);
} else if (arg == "-c" || arg == "--max_context_length") {
args.max_context_length = std::stoi(argv[++i]);
} else if (arg == "--top_k") {
args.top_k = std::stoi(argv[++i]);
} else if (arg == "--top_p") {
args.top_p = std::stof(argv[++i]);
} else if (arg == "--temp") {
args.temp = std::stof(argv[++i]);
} else if (arg == "--repeat_penalty") {
args.repeat_penalty = std::stof(argv[++i]);
} else if (arg == "-t" || arg == "--threads") {
args.num_threads = std::stoi(argv[++i]);
} else if (arg == "-v" || arg == "--verbose") {
args.verbose = true;
} else {
std::cerr << "Unknown argument: " << arg << std::endl;
usage(argv[0]);
exit(EXIT_FAILURE);
}
}
return args;
}
static auto parse_args(int argc, char **argv) -> Args {
std::vector<std::string> argv_vec;
argv_vec.reserve(argc);
for (int i = 0; i < argc; i++) {
argv_vec.emplace_back(argv[i]);
}
return parse_args(argv_vec);
}
static auto get_utf8_line(std::string &line) -> bool {
return !!std::getline(std::cin, line);
}
static auto chat(Args &args) -> void {
ggml_time_init();
int64_t start_load_us = ggml_time_us();
qwen::Pipeline pipeline(args.model_path, args.tiktoken_path);
int64_t end_load_us = ggml_time_us();
std::string model_name = "qwen";
auto text_streamer = std::make_shared<qwen::TextStreamer>(std::cout, pipeline.tokenizer.get());
auto perf_streamer = std::make_shared<qwen::PerfStreamer>();
auto streamer = std::make_shared<qwen::StreamerGroup>(
std::vector<std::shared_ptr<qwen::BaseStreamer>>{text_streamer, perf_streamer});
qwen::GenerationConfig gen_config(args.max_length, args.max_context_length, args.temp > 0, args.top_k,
args.top_p, args.temp, args.repeat_penalty, args.num_threads);
if (args.verbose) {
std::cout << "system info: | "
<< "AVX = " << ggml_cpu_has_avx() << " | "
<< "AVX2 = " << ggml_cpu_has_avx2() << " | "
<< "AVX512 = " << ggml_cpu_has_avx512() << " | "
<< "AVX512_VBMI = " << ggml_cpu_has_avx512_vbmi() << " | "
<< "AVX512_VNNI = " << ggml_cpu_has_avx512_vnni() << " | "
<< "FMA = " << ggml_cpu_has_fma() << " | "
<< "NEON = " << ggml_cpu_has_neon() << " | "
<< "ARM_FMA = " << ggml_cpu_has_arm_fma() << " | "
<< "F16C = " << ggml_cpu_has_f16c() << " | "
<< "FP16_VA = " << ggml_cpu_has_fp16_va() << " | "
<< "WASM_SIMD = " << ggml_cpu_has_wasm_simd() << " | "
<< "BLAS = " << ggml_cpu_has_blas() << " | "
<< "SSE3 = " << ggml_cpu_has_sse3() << " | "
<< "VSX = " << ggml_cpu_has_vsx() << " |\n";
std::cout << "inference config: | "
<< "max_length = " << args.max_length << " | "
<< "max_context_length = " << args.max_context_length << " | "
<< "top_k = " << args.top_k << " | "
<< "top_p = " << args.top_p << " | "
<< "temperature = " << args.temp << " | "
<< "num_threads = " << args.num_threads << " |\n";
std::cout << "loaded qwen model from " << args.model_path
<< " within: " << (end_load_us - start_load_us) / 1000.f << " ms\n";
std::cout << std::endl;
}
if (args.mode != INFERENCE_MODE_CHAT && args.interactive) {
std::cerr << "interactive demo is only supported for chat mode, falling back to non-interactive one\n";
args.interactive = false;
}
if (args.interactive) {
std::cout << R"( _____ )" << '\n'
<< R"(| _ | )" << '\n'
<< R"(| | | | __ __ ___ _ __ ___ _ __ _ __ )" << '\n'
<< R"(| | | | \ \ /\ / / / _ \ | '_ \ / __| | '_ \ | '_ \ )" << '\n'
<< R"(\ \/' / \ V V / | __/ | | | | _ | (__ | |_) | | |_) |)" << '\n'
<< R"( \_/\_\ \_/\_/ \___| |_| |_| (_) \___| | .__/ | .__/ )" << '\n'
<< R"( | | | | )" << '\n'
<< R"( |_| |_| )" << '\n'
<< '\n';
std::cout
<< "Welcome to Qwen.cpp! Ask whatever you want. Type 'clear' to clear context. Type 'stop' to exit.\n"
<< "\n";
std::vector<std::string> history;
while (1) {
std::cout << std::setw(model_name.size()) << std::left << "Prompt"
<< " > " << std::flush;
std::string prompt;
if (!get_utf8_line(prompt) || prompt == "stop") {
break;
}
if (prompt.empty()) {
continue;
}
if (prompt == "clear") {
history.clear();
continue;
}
history.emplace_back(std::move(prompt));
std::cout << model_name << " > ";
std::string output = pipeline.chat(history, gen_config, streamer.get());
history.emplace_back(std::move(output));
if (args.verbose) {
std::cout << "\n" << perf_streamer->to_string() << "\n\n";
}
perf_streamer->reset();
}
std::cout << "Bye\n";
} else {
if (args.mode == INFERENCE_MODE_CHAT) {
pipeline.chat({args.prompt}, gen_config, streamer.get());
} else {
pipeline.generate(args.prompt, gen_config, streamer.get());
}
if (args.verbose) {
std::cout << "\n" << perf_streamer->to_string() << "\n\n";
}
}
}
int main(int argc, char **argv) {
try {
Args args = parse_args(argc, argv);
chat(args);
} catch (std::exception &e) {
std::cerr << e.what() << std::endl;
exit(EXIT_FAILURE);
}
return 0;
}