mirror of
https://github.com/jart/cosmopolitan.git
synced 2025-02-07 15:03:34 +00:00
8fdb31681a
llama.com can now load weights that use the new file format which was introduced a few weeks ago. Note that, unlike llama.cpp, we will keep support for old file formats in our tool so you don't need to convert your weights when the upstream project makes breaking changes. Please note that using ggjt v3 does make avx2 inference go 5% faster for me.
137 lines
5.3 KiB
C++
137 lines
5.3 KiB
C++
// -*- c++; c-basic-offset:4 -*-
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#ifndef COSMOPOLITAN_THIRD_PARTY_GGML_COMMON_H_
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#define COSMOPOLITAN_THIRD_PARTY_GGML_COMMON_H_
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#include "libc/calls/struct/termios.h"
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#include "libc/runtime/runtime.h"
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#include "libc/stdio/stdio.h"
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#include "third_party/ggml/llama.h"
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#include "third_party/libcxx/random"
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#include "third_party/libcxx/string"
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#include "third_party/libcxx/thread"
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#include "third_party/libcxx/unordered_map"
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#include "third_party/libcxx/vector"
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#if !(__ASSEMBLER__ + __LINKER__ + 0)
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// clang-format off
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// Various helper functions and utilities
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//
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// CLI argument parsing
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//
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struct gpt_params {
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int32_t seed = -1; // RNG seed
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int32_t verbose = 0; // Logging verbosity
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int32_t n_threads = std::max(1, _getcpucount() >> 1);
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 32; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_gpu_layers = 0; // number of layers to store in VRAM
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// sampling parameters
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std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
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int32_t top_k = 40; // <= 0 to use vocab size
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float top_p = 0.95f; // 1.0 = disabled
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float tfs_z = 1.00f; // 1.0 = disabled
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float typical_p = 1.00f; // 1.0 = disabled
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float temp = 0.80f; // 1.0 = disabled
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float repeat_penalty = 1.10f; // 1.0 = disabled
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int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
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float frequency_penalty = 0.00f; // 0.0 = disabled
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float presence_penalty = 0.00f; // 0.0 = disabled
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int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
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float mirostat_tau = 5.00f; // target entropy
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float mirostat_eta = 0.10f; // learning rate
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std::string model = "models/lamma-7B/ggml-model.bin"; // model path
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std::string prompt = "";
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std::string prompt_path = ".prompt.jtlp";
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std::string input_prefix = ""; // string to prefix user inputs with
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std::string n_keep_str = ""; // substring in prompt used to override n_keep == 0
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std::string input_suffix = ""; // string to suffix user inputs with
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std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
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std::string lora_adapter = ""; // lora adapter path
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std::string lora_base = ""; // base model path for the lora adapter
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bool memory_f16 = true; // use f16 instead of f32 for memory kv
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bool random_prompt = false; // do not randomize prompt if none provided
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bool use_color = isatty(1) == 1; // use color to distinguish generations and inputs
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bool interactive = false; // interactive mode
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bool embedding = false; // get only sentence embedding
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bool interactive_first = false; // wait for user input immediately
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bool multiline_input = false; // reverse the usage of `\`
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bool instruct = false; // instruction mode (used for Alpaca models)
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bool penalize_nl = true; // consider newlines as a repeatable token
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bool perplexity = false; // compute perplexity over the prompt
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bool use_mmap = true; // use mmap for faster loads
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bool use_mlock = false; // use mlock to keep model in memory
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bool mem_test = false; // compute maximum memory usage
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bool verbose_prompt = false; // print prompt tokens before generation
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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void gpt_print_usage(FILE *f, int argc, char ** argv, const gpt_params & params);
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std::string gpt_random_prompt(std::mt19937 & rng);
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//
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// Vocab utils
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//
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std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos);
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//
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// Model utils
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//
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struct llama_context * llama_init_from_gpt_params(const gpt_params & params);
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//
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// Console utils
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//
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#define ANSI_COLOR_RED "\x1b[31m"
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#define ANSI_COLOR_GREEN "\x1b[32m"
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#define ANSI_COLOR_YELLOW "\x1b[33m"
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#define ANSI_COLOR_BLUE "\x1b[34m"
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#define ANSI_COLOR_MAGENTA "\x1b[35m"
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#define ANSI_COLOR_CYAN "\x1b[36m"
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#define ANSI_COLOR_RESET "\x1b[0m"
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#define ANSI_BOLD "\x1b[1m"
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enum console_color_t {
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CONSOLE_COLOR_DEFAULT=0,
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CONSOLE_COLOR_PROMPT,
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CONSOLE_COLOR_USER_INPUT
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};
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struct console_state {
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bool multiline_input = false;
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bool use_color = false;
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console_color_t color = CONSOLE_COLOR_DEFAULT;
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FILE* out = stdout;
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#if defined (_WIN32)
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void* hConsole;
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#else
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FILE* tty = nullptr;
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termios prev_state;
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#endif
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};
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void console_init(console_state & con_st);
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void console_cleanup(console_state & con_st);
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void console_set_color(console_state & con_st, console_color_t color);
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bool console_readline(console_state & con_st, std::string & line);
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#if defined (_WIN32)
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void win32_console_init(bool enable_color);
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void win32_utf8_encode(const std::wstring & wstr, std::string & str);
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#endif
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#endif /* !(__ASSEMBLER__ + __LINKER__ + 0) */
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#endif /* COSMOPOLITAN_THIRD_PARTY_GGML_COMMON_H_ */
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