mirror of
https://github.com/ggml-org/llama.cpp.git
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88b5769487
* gguf : better type names * dedup : CPU + Metal is working * ggml : fix warnings about unused results * llama.cpp : fix line feed and compiler warning * llama : fix strncpy warning + note token_to_str does not write null * llama : restore the original load/save session implementation Will migrate this to GGUF in the future * convert-llama-h5-to-gguf.py : support alt ctx param name * ggml : assert when using ggml_mul with non-F32 src1 * examples : dedup simple --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
108 lines
5.6 KiB
C++
108 lines
5.6 KiB
C++
// Various helper functions and utilities
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#pragma once
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#define LLAMA_API_CPP // TODO: eliminate me
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#include "llama.h"
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#include <string>
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#include <vector>
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#include <random>
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#include <thread>
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#include <unordered_map>
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#include <tuple>
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//
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// CLI argument parsing
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//
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int32_t get_num_physical_cores();
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struct gpt_params {
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uint32_t seed = -1; // RNG seed
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int32_t n_threads = get_num_physical_cores();
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // 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_chunks = -1; // max number of chunks to process (-1 = unlimited)
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int32_t n_gpu_layers = 0; // number of layers to store in VRAM
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int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
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float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
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int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
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float rope_freq_base = 10000.0f; // RoPE base frequency
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float rope_freq_scale = 1.0f; // RoPE frequency scaling factor
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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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int32_t 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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// Classifier-Free Guidance
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// https://arxiv.org/abs/2306.17806
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std::string cfg_negative_prompt; // string to help guidance
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float cfg_scale = 1.f; // How strong is guidance
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std::string model = "models/7B/ggml-model.bin"; // model path
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std::string model_alias = "unknown"; // model alias
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std::string prompt = "";
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std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
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std::string input_prefix = ""; // string to prefix user inputs with
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std::string input_suffix = ""; // string to suffix user inputs with
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std::string grammar = ""; // optional BNF-like grammar to constrain sampling
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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 hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
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size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
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bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
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bool mul_mat_q = false; // if true, use experimental mul_mat_q kernels
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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 = false; // use color to distinguish generations and inputs
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bool interactive = false; // interactive mode
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bool prompt_cache_all = false; // save user input and generations to prompt cache
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bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it
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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 simple_io = false; // improves compatibility with subprocesses and limited consoles
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bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
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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 numa = false; // attempt optimizations that help on some NUMA systems
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bool export_cgraph = false; // export the computation graph
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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(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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// Model utils
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//
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std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(const gpt_params & params);
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struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
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