mirror of
https://github.com/aaronpo97/the-biergarten-app.git
synced 2026-10-15 01:54:43 +00:00
Formatting
This commit is contained in:
Aaron Po
@@ -10,207 +10,212 @@
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std::optional<ApplicationOptions> ParseArguments(
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const int argc, char** argv, std::shared_ptr<ILogger> logger) {
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prog_opts::options_description desc("Pipeline Options");
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auto opt = desc.add_options();
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prog_opts::options_description desc("Pipeline Options");
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auto opt = desc.add_options();
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opt("help,h", "Produce help message");
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opt("help,h", "Produce help message");
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auto add_sampling_options = [&]() -> void {
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const SamplingOptions sampling_defaults{};
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opt("temperature",
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prog_opts::value<float>()->default_value(sampling_defaults.temperature),
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"Sampling temperature (higher = more random)");
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opt("top-p",
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prog_opts::value<float>()->default_value(sampling_defaults.top_p),
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"Nucleus sampling top-p in (0,1] (higher = more random)");
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opt("top-k",
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prog_opts::value<uint32_t>()->default_value(sampling_defaults.top_k),
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"Top-k sampling parameter (higher = more candidate tokens)");
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opt("n-ctx",
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prog_opts::value<uint32_t>()->default_value(sampling_defaults.n_ctx),
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"Context window size in tokens");
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opt("seed", prog_opts::value<int>()->default_value(sampling_defaults.seed),
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"Sampler seed: -1 for random, otherwise non-negative integer");
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opt("n-gpu-layers", prog_opts::value<int>()->default_value(0),
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"Number of layers to offload to GPU");
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};
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auto add_sampling_options = [&]() -> void {
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const SamplingOptions sampling_defaults{};
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opt("temperature",
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prog_opts::value<float>()->default_value(
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sampling_defaults.temperature),
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"Sampling temperature (higher = more random)");
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opt("top-p",
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prog_opts::value<float>()->default_value(sampling_defaults.top_p),
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"Nucleus sampling top-p in (0,1] (higher = more random)");
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opt("top-k",
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prog_opts::value<uint32_t>()->default_value(sampling_defaults.top_k),
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"Top-k sampling parameter (higher = more candidate tokens)");
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opt("n-ctx",
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prog_opts::value<uint32_t>()->default_value(sampling_defaults.n_ctx),
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"Context window size in tokens");
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opt("seed",
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prog_opts::value<int>()->default_value(sampling_defaults.seed),
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"Sampler seed: -1 for random, otherwise non-negative integer");
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opt("n-gpu-layers", prog_opts::value<int>()->default_value(0),
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"Number of layers to offload to GPU");
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};
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// --mocked and --model are mutually exclusive; validation is enforced below
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// rather than at registration to produce a clear diagnostic message.
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auto add_generator_options = [&]() -> void {
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opt("mocked", prog_opts::bool_switch(),
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"Use mocked generator for brewery/user data");
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opt("model,m", prog_opts::value<std::string>()->default_value(""),
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"Path to LLM model (gguf)");
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};
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// --mocked and --model are mutually exclusive; validation is enforced below
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// rather than at registration to produce a clear diagnostic message.
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auto add_generator_options = [&]() -> void {
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opt("mocked", prog_opts::bool_switch(),
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"Use mocked generator for brewery/user data");
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opt("model,m", prog_opts::value<std::string>()->default_value(""),
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"Path to LLM model (gguf)");
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};
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auto add_pipeline_options = [&]() -> void {
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opt("output,o", prog_opts::value<std::string>()->default_value("output"),
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"Directory for generated artifacts");
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opt("log-path",
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prog_opts::value<std::string>()->default_value("pipeline.log"),
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"Path for application logs");
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opt("prompt-dir", prog_opts::value<std::string>()->default_value(""),
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"Directory containing named prompt files (e.g. "
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"BREWERY_GENERATION.md)."
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" Required when not using --mocked.");
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opt("location-count", prog_opts::value<uint32_t>()->default_value(10));
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};
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auto add_pipeline_options = [&]() -> void {
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opt("output,o", prog_opts::value<std::string>()->default_value("output"),
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"Directory for generated artifacts");
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opt("log-path",
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prog_opts::value<std::string>()->default_value("pipeline.log"),
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"Path for application logs");
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opt("prompt-dir", prog_opts::value<std::string>()->default_value(""),
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"Directory containing named prompt files (e.g. "
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"BREWERY_GENERATION.md)."
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" Required when not using --mocked.");
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opt("location-count", prog_opts::value<uint32_t>()->default_value(10));
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};
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add_sampling_options();
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add_generator_options();
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add_pipeline_options();
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add_sampling_options();
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add_generator_options();
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add_pipeline_options();
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// No flags provided — treat as a help request rather than an error.
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if (argc == 1) {
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const std::string title = "Biergarten Pipeline";
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const std::string usage = ([&] {
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std::stringstream usage_stream;
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usage_stream << "\nUsage: biergarten-pipeline [options]\n\n" << desc;
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return usage_stream.str();
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})();
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Info,
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.phase = PipelinePhase::Startup,
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.message = title});
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logger->Log(LogDTO{.level = LogLevel::Info,
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.phase = PipelinePhase::Startup,
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.message = usage});
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}
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return std::nullopt;
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}
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try {
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prog_opts::variables_map var_map;
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prog_opts::store(prog_opts::parse_command_line(argc, argv, desc), var_map);
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prog_opts::notify(var_map);
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if (var_map.contains("help")) {
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std::stringstream help_stream;
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help_stream << "\n" << desc;
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// No flags provided — treat as a help request rather than an error.
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if (argc == 1) {
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const std::string title = "Biergarten Pipeline";
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const std::string usage = ([&] {
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std::stringstream usage_stream;
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usage_stream << "\nUsage: biergarten-pipeline [options]\n\n" << desc;
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return usage_stream.str();
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})();
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Info,
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.phase = PipelinePhase::Startup,
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.message = help_stream.str()});
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logger->Log(LogDTO{.level = LogLevel::Info,
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.phase = PipelinePhase::Startup,
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.message = title});
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logger->Log(LogDTO{.level = LogLevel::Info,
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.phase = PipelinePhase::Startup,
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.message = usage});
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}
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return std::nullopt;
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}
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}
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ApplicationOptions options;
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try {
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prog_opts::variables_map var_map;
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prog_opts::store(prog_opts::parse_command_line(argc, argv, desc),
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var_map);
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prog_opts::notify(var_map);
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options.pipeline.output_path = var_map["output"].as<std::string>();
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options.pipeline.log_path = var_map["log-path"].as<std::string>();
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options.pipeline.prompt_dir = var_map["prompt-dir"].as<std::string>();
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options.pipeline.location_count = var_map["location-count"].as<uint32_t>();
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const bool use_mocked = var_map["mocked"].as<bool>();
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const std::string model_path = var_map["model"].as<std::string>();
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const int n_gpu_layers = var_map["n-gpu-layers"].as<int>();
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// Enforce mutual exclusivity before any further configuration is
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// applied.
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if (use_mocked && !model_path.empty()) {
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const std::string msg =
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"Invalid arguments: --mocked and --model are mutually "
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"exclusive";
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Error,
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.phase = PipelinePhase::Startup,
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.message = msg});
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} else {
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std::cerr << msg << std::endl;
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if (var_map.contains("help")) {
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std::stringstream help_stream;
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help_stream << "\n" << desc;
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Info,
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.phase = PipelinePhase::Startup,
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.message = help_stream.str()});
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}
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return std::nullopt;
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}
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return std::nullopt;
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}
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if (!use_mocked && model_path.empty()) {
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const std::string msg =
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"Invalid arguments: either --mocked or --model must be "
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"specified";
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Error,
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.phase = PipelinePhase::Startup,
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.message = msg});
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} else {
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std::cerr << msg << std::endl;
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ApplicationOptions options;
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options.pipeline.output_path = var_map["output"].as<std::string>();
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options.pipeline.log_path = var_map["log-path"].as<std::string>();
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options.pipeline.prompt_dir = var_map["prompt-dir"].as<std::string>();
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options.pipeline.location_count =
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var_map["location-count"].as<uint32_t>();
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const bool use_mocked = var_map["mocked"].as<bool>();
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const std::string model_path = var_map["model"].as<std::string>();
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const int n_gpu_layers = var_map["n-gpu-layers"].as<int>();
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// Enforce mutual exclusivity before any further configuration is
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// applied.
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if (use_mocked && !model_path.empty()) {
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const std::string msg =
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"Invalid arguments: --mocked and --model are mutually "
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"exclusive";
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Error,
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.phase = PipelinePhase::Startup,
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.message = msg});
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} else {
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std::cerr << msg << std::endl;
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}
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return std::nullopt;
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}
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return std::nullopt;
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}
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// Prompt directory is only meaningful for live inference — the mock
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// generator has no use for it and should not require it to be present.
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if (!use_mocked && options.pipeline.prompt_dir.empty()) {
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const std::string msg =
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"Invalid arguments: --prompt-dir is required when not using "
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"--mocked";
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if (logger) {
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logger->Log({.level = LogLevel::Error,
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.phase = PipelinePhase::Startup,
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.message = msg});
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} else {
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std::cerr << msg << std::endl;
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if (!use_mocked && model_path.empty()) {
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const std::string msg =
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"Invalid arguments: either --mocked or --model must be "
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"specified";
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Error,
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.phase = PipelinePhase::Startup,
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.message = msg});
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} else {
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std::cerr << msg << std::endl;
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}
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return std::nullopt;
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}
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return std::nullopt;
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}
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options.generator.use_mocked = use_mocked;
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options.generator.model_path = model_path;
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// Only populate sampling config when the user explicitly overrides at
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// least one value. Leaving it as std::nullopt lets LlamaGenerator fall
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// back to its own SamplingOptions{} defaults, keeping the two paths
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// consistent without redundant copies.
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const bool user_provided_sampling =
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!var_map["temperature"].defaulted() || !var_map["top-p"].defaulted() ||
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!var_map["top-k"].defaulted() || !var_map["n-ctx"].defaulted() ||
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!var_map["seed"].defaulted() || !var_map["n_gpu_layers"].defaulted();
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if (user_provided_sampling) {
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// Warn but do not fail — the run is still valid, the flags are just
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// silently irrelevant when no model is loaded.
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if (use_mocked) {
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const std::string msg =
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"Sampling parameters are ignored when using --mocked";
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Warn,
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.phase = PipelinePhase::Startup,
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.message = msg});
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} else {
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std::cerr << msg << std::endl;
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}
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} else {
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SamplingOptions sampling;
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sampling.temperature = var_map["temperature"].as<float>();
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sampling.top_p = var_map["top-p"].as<float>();
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sampling.top_k = var_map["top-k"].as<uint32_t>();
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sampling.n_ctx = var_map["n-ctx"].as<uint32_t>();
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sampling.seed = var_map["seed"].as<int>();
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sampling.n_gpu_layers = var_map["n-gpu-layers"].as<int>();
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options.generator.sampling = sampling;
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}
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}
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return options;
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} catch (const std::exception& exception) {
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const std::string msg =
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std::string("Failed to parse command-line arguments: ") +
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exception.what();
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Error,
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// Prompt directory is only meaningful for live inference — the mock
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// generator has no use for it and should not require it to be present.
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if (!use_mocked && options.pipeline.prompt_dir.empty()) {
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const std::string msg =
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"Invalid arguments: --prompt-dir is required when not using "
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"--mocked";
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if (logger) {
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logger->Log({.level = LogLevel::Error,
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.phase = PipelinePhase::Startup,
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.message = msg});
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}
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return std::nullopt;
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} catch (...) {
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const std::string msg =
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"Failed to parse command-line arguments: unknown error";
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Error,
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.phase = PipelinePhase::Startup,
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.message = msg});
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}
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return std::nullopt;
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}
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} else {
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std::cerr << msg << std::endl;
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}
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return std::nullopt;
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}
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options.generator.use_mocked = use_mocked;
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options.generator.model_path = model_path;
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// Only populate sampling config when the user explicitly overrides at
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// least one value. Leaving it as std::nullopt lets LlamaGenerator fall
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// back to its own SamplingOptions{} defaults, keeping the two paths
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// consistent without redundant copies.
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const bool user_provided_sampling =
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!var_map["temperature"].defaulted() ||
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!var_map["top-p"].defaulted() || !var_map["top-k"].defaulted() ||
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!var_map["n-ctx"].defaulted() || !var_map["seed"].defaulted() ||
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!var_map["n_gpu_layers"].defaulted();
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if (user_provided_sampling) {
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// Warn but do not fail — the run is still valid, the flags are just
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// silently irrelevant when no model is loaded.
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if (use_mocked) {
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const std::string msg =
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"Sampling parameters are ignored when using --mocked";
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Warn,
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.phase = PipelinePhase::Startup,
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.message = msg});
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} else {
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std::cerr << msg << std::endl;
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}
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} else {
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SamplingOptions sampling;
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sampling.temperature = var_map["temperature"].as<float>();
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sampling.top_p = var_map["top-p"].as<float>();
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sampling.top_k = var_map["top-k"].as<uint32_t>();
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sampling.n_ctx = var_map["n-ctx"].as<uint32_t>();
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sampling.seed = var_map["seed"].as<int>();
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sampling.n_gpu_layers = var_map["n-gpu-layers"].as<int>();
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options.generator.sampling = sampling;
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}
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}
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return options;
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} catch (const std::exception& exception) {
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const std::string msg =
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std::string("Failed to parse command-line arguments: ") +
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exception.what();
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Error,
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.phase = PipelinePhase::Startup,
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.message = msg});
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}
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return std::nullopt;
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} catch (...) {
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const std::string msg =
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"Failed to parse command-line arguments: unknown error";
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if (logger) {
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logger->Log(LogDTO{.level = LogLevel::Error,
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.phase = PipelinePhase::Startup,
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.message = msg});
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}
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return std::nullopt;
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}
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}
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