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Diffstat (limited to 'common/train.h')
| -rw-r--r-- | common/train.h | 230 |
1 files changed, 230 insertions, 0 deletions
diff --git a/common/train.h b/common/train.h new file mode 100644 index 00000000..42fa704b --- /dev/null +++ b/common/train.h @@ -0,0 +1,230 @@ +// Various helper functions and utilities for training + +#pragma once + +#include <string> +#include <random> +#include <vector> + +#include "ggml.h" +#include "llama.h" + +typedef std::string mt19937_state; + +struct train_state { + struct ggml_opt_context * opt; + + uint64_t train_its; + uint64_t train_samples; + uint64_t train_tokens; + uint64_t train_epochs; + + size_t shuffle_samples_hash; // fn, sample_count, *zip(sample_begins, sample_sizes) + mt19937_state shuffle_rng_state_current; + mt19937_state shuffle_rng_state_next; + size_t shuffle_sample_count; + size_t shuffle_next_sample; +}; + +struct train_params_common { + const char * fn_train_data; + const char * fn_checkpoint_in; + const char * fn_checkpoint_out; + const char * pattern_fn_it; + const char * fn_latest; + + bool print_usage; + + int save_every; + + uint32_t seed; + + int n_ctx; + int n_threads; + int n_batch; + int n_gradient_accumulation; + int n_epochs; + + bool custom_n_ctx; + + bool use_flash; + bool use_checkpointing; + + std::string sample_start; + bool include_sample_start; + bool escape; + bool overlapping_samples; + bool fill_with_next_samples; + bool separate_with_eos; + bool separate_with_bos; + bool sample_random_offsets; + + bool force_reshuffle; + + int warmup; + int cos_decay_steps; + float cos_decay_restart; + float cos_decay_min; + bool enable_restart; + + int opt_past; + float opt_delta; + int opt_max_no_improvement; + + int adam_n_iter; + float adam_alpha; + float adam_min_alpha; + float adam_decay; + int adam_decay_min_ndim; + float adam_beta1; + float adam_beta2; + float adam_gclip; + float adam_eps_f; +}; + +typedef void (*save_train_files_callback)(void * data, struct train_state * train); + +struct train_opt_callback_data { + struct train_params_common * params; + struct train_state * train; + save_train_files_callback save_cb; + void * save_data; + struct llama_context * lctx; + int last_save_iter; + llama_token * tokens_data; + size_t tokens_size; + size_t * samples_begin; + size_t * samples_size; + size_t * shuffled_samples_offs; + size_t * shuffled_samples_begin; + size_t * shuffled_samples_size; + size_t samples_count; + struct ggml_tensor * tokens_input; + struct ggml_tensor * target_probs; + int first_iter; + int first_epoch; + int iter_at_last_epoch; + int64_t last_time; + double millis_per_iter; +}; + +struct train_state * init_train_state(); +void free_train_state(struct train_state * state); + +struct train_params_common get_default_train_params_common(); +void print_common_train_usage(int /*argc*/, char ** argv, const struct train_params_common * params); + +bool consume_common_train_arg(int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param); +void finish_processing_train_args(struct train_params_common * params); + +struct random_normal_distribution; +struct random_uniform_distribution; + +struct random_normal_distribution * init_random_normal_distribution (int seed, float mean, float std, float min, float max); +struct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max); + +void free_random_normal_distribution (struct random_normal_distribution * rnd); +void free_random_uniform_distribution(struct random_uniform_distribution * rnd); + +struct ggml_tensor * randomize_tensor_normal (struct ggml_tensor * tensor, struct random_normal_distribution * rnd); +struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd); + +// generate random float in interval [0,1) +float frand(); +float frand_normal (struct random_normal_distribution * rnd); +float frand_uniform(struct random_uniform_distribution * rnd); + +int clamp (const int v, const int min, const int max); +float fclamp(const float v, const float min, const float max); + +void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0); +void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1); +void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2); +void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3); + +size_t tokenize_file( + struct llama_context * lctx, + const char * filename, + const std::string & sample_start, + bool include_sample_start, + bool overlapping_samples, + unsigned context_length, + std::vector<llama_token> & out_tokens, + std::vector<size_t> & out_samples_begin, + std::vector<size_t> & out_samples_size); + +int64_t get_example_targets_batch( + struct llama_context * lctx, + struct ggml_tensor * tokens_input, + struct ggml_tensor * target_probs, + int64_t example_id, + const size_t * samples_offs, + const size_t * samples_begin, + const size_t * samples_size, + size_t samples_count, + const llama_token * train_data, + size_t n_train_data, + bool separate_with_eos, + bool separate_with_bos, + bool fill_with_next_samples, + bool sample_random_offsets); + + +void mt19937_set_state(std::mt19937& rng, const mt19937_state& rng_state); +mt19937_state mt19937_get_state(const std::mt19937& rng); +mt19937_state mt19937_seed_to_state(unsigned seed); + +mt19937_state shuffle_samples( + const mt19937_state & rng_state, + size_t * shuffled_offs, + size_t * shuffled_begins, + size_t * shuffled_sizes, + const size_t * begins, + const size_t * sizes, + size_t count); + +size_t hash_combine(size_t h1, size_t h2); + +size_t compute_samples_hash( + const char* fn, + const size_t* samples_begin, + const size_t* samples_size, + size_t sample_count); + + +std::string replace_str(const char * s, const char * needle, const char * replacement); + +void print_duration(double milliseconds); + +float cosine_decay( + int64_t step, + int64_t decay_steps, + float minimum); + +float cosine_decay_restart( + int64_t step, + int64_t decay_steps, + float minimum, + float restart_step_mult); + +float learning_schedule( + int64_t step, + int64_t warmup_steps, + int64_t decay_steps, + float learning_rate, + float overall_minimum, + float cos_decay_minimum, + float cos_decay_restart_step_mult, + bool enable_restart); + +void copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name); + +void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt); +void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt); + +bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train); +void save_train_state_gguf(struct gguf_context * fctx, struct train_state * train); + +std::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration); + +void train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel); |