// This file contains functionality for training models using GGML. // It is not strictly needed vs. just vanilla GGML but it provides a more high-level interface for common needs such as datasets. // At the bottom of this file especially there are relatively high-level functions that are suitable use or adaptation in user code. // // Module maintainer: Johannes Gäßler (@JohannesGaessler, johannesg@5d6.de) #pragma once #include "ggml.h" #include "ggml-backend.h" #include #ifdef __cplusplus extern "C" { #endif struct ggml_opt_dataset; struct ggml_opt_context; struct ggml_opt_result; typedef struct ggml_opt_dataset * ggml_opt_dataset_t; typedef struct ggml_opt_context * ggml_opt_context_t; typedef struct ggml_opt_result * ggml_opt_result_t; // ====== Loss ====== // built-in loss types, i.e. the built-in quantities minimized by the optimizer // custom loss types can be defined via mean or sum which simply reduce the outputs for all datapoints to a single value enum ggml_opt_loss_type { GGML_OPT_LOSS_TYPE_MEAN, GGML_OPT_LOSS_TYPE_SUM, GGML_OPT_LOSS_TYPE_CROSS_ENTROPY, GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR, }; // ====== Dataset ====== GGML_API ggml_opt_dataset_t ggml_opt_dataset_init( int64_t ne_datapoint, // number of elements per datapoint int64_t ne_label, // number of elements per label int64_t ndata, // total number of datapoints/labels int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied) GGML_API void ggml_opt_dataset_free(ggml_opt_dataset_t dataset); // get underlying tensors that store the data GGML_API struct ggml_tensor * ggml_opt_dataset_data (ggml_opt_dataset_t dataset); // shape = [ne_datapoint, ndata] GGML_API struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset); // shape = [nd_label, ndata] // shuffle idata first datapoints from dataset with RNG from opt_ctx, shuffle all datapoints if idata is negative GGML_API void ggml_opt_dataset_shuffle(ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, int64_t idata); // get batch at position ibatch from dataset and copy the data to data_batch and labels_batch GGML_API void ggml_opt_dataset_get_batch( ggml_opt_dataset_t dataset, struct ggml_tensor * data_batch, // shape = [ne_datapoint, ndata_batch] struct ggml_tensor * labels_batch, // shape = [ne_label, ndata_batch] int64_t ibatch); // ====== Model / Context ====== enum ggml_opt_build_type { GGML_OPT_BUILD_TYPE_FORWARD, GGML_OPT_BUILD_TYPE_GRAD, GGML_OPT_BUILD_TYPE_OPT, }; // parameters that control which optimizer is used and how said optimizer tries to find the minimal loss struct ggml_opt_optimizer_params { // AdamW optimizer parameters struct { float alpha; // learning rate float beta1; float beta2; float eps; // epsilon for numerical stability float wd; // weight decay for AdamW, use 0.0f to disable } adamw; }; // callback to calculate optimizer parameters prior to a backward pass // userdata can be used to pass arbitrary data typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata); // returns the default optimizer params (constant) // userdata is not used GGML_API struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata); // parameters for initializing a new optimization context struct ggml_opt_params { ggml_backend_sched_t backend_sched; // defines which backends are used to construct the compute graphs struct ggml_context * ctx_compute; // created in user code, holds non-static tensors // the forward graph is defined by inputs and outputs // those tensors and all tensors inbetween are not intended to be reusable between multiple optimization contexts struct ggml_tensor * inputs; struct ggml_tensor * outputs; enum ggml_opt_loss_type loss_type; enum ggml_opt_build_type build_type; int32_t opt_period; // after how many gradient accumulation steps an optimizer step should be done ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters void * get_opt_pars_ud; // userdata for calculating optimizer parameters }; // get parameters for an optimization context with defaults set where possible // parameters for which no sensible defaults exist are supplied as arguments to this function GGML_API ggml_opt_params ggml_opt_default_params( ggml_backend_sched_t backend_sched, struct ggml_context * ctx_compute, struct ggml_tensor * inputs, struct ggml_tensor * outputs, enum ggml_opt_loss_type loss_type); GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params); GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx); // set gradients to zero, initilize loss, and optionally reset the optimizer GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer); // get underlying tensors that store data GGML_API struct ggml_tensor * ggml_opt_inputs( ggml_opt_context_t opt_ctx); // forward graph input tensor GGML_API struct ggml_tensor * ggml_opt_outputs( ggml_opt_context_t opt_ctx); // forward graph output tensor GGML_API struct ggml_tensor * ggml_opt_labels( ggml_opt_context_t opt_ctx); // labels to compare outputs against GGML_API struct ggml_tensor * ggml_opt_loss( ggml_opt_context_t opt_ctx); // scalar tensor that contains the loss GGML_API struct ggml_tensor * ggml_opt_pred( ggml_opt_context_t opt_ctx); // predictions made by outputs GGML_API struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx); // number of matching predictions between outputs and labels GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node); // ====== Optimization Result ====== GGML_API ggml_opt_result_t ggml_opt_result_init(); GGML_API void ggml_opt_result_free(ggml_opt_result_t result); GGML_API void ggml_opt_result_reset(ggml_opt_result_t result); // get data from result, uncertainties are optional and can be ignored by passing NULL GGML_API void ggml_opt_result_ndata( ggml_opt_result_t result, int64_t * ndata); // writes 1 value, number of datapoints GGML_API void ggml_opt_result_loss( ggml_opt_result_t result, double * loss, double * unc); // writes 1 value GGML_API void ggml_opt_result_pred( ggml_opt_result_t result, int32_t * pred); // writes ndata values GGML_API void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, double * unc); // writes 1 value // ====== Computation ====== // do forward pass, increment result if not NULL GGML_API void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result); // do forward pass, increment result if not NULL, do backward pass GGML_API void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result); // ############################################################################ // ## The high-level functions start here. They do not depend on any private ## // ## functions or structs and can be copied to and adapted for user code. ## // ############################################################################ // ====== Intended Usage ====== // // 1. Select the appropriate loss for your problem. // 2. Create a dataset and set the data for the "data" tensor. Also set the "labels" tensor if your loss needs them. // Setting the shard size to 1 will be fine, it's the granularity with which data is shuffled/loaded (bigger values are faster). // 3. Create a GGML graph for your model with no_alloc == true. Use two separate contexts for the tensors. // The first context should contain the model parameters and inputs and be allocated statically in user code. // The second context should contain all other tensors and will be (re)allocated automatically. // Due to this automated allocation the data of the second context is not defined when accessed in user code. // Note that the second dimension of the inputs/outputs are interpreted as the number of datapoints in those tensors. // 4. Call ggml_opt_fit. If you need more control you can use ggml_opt_epoch instead. // signature for a callback while evaluating opt_ctx on dataset, called after an evaluation typedef void (*ggml_opt_epoch_callback)( bool train, // true after training evaluation, false after validation evaluation ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, ggml_opt_result_t result, // result associated with the dataset subsection int64_t ibatch, // number of batches that have been evaluated so far int64_t ibatch_max, // total number of batches in this dataset subsection int64_t t_start_us); // time at which the evaluation on the dataset subsection was started // do training on front of dataset, do evaluation only on back of dataset GGML_API void ggml_opt_epoch( ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, ggml_opt_result_t result_train, // result to increment during training, ignored if NULL ggml_opt_result_t result_eval, // result to increment during evaluation, ignored if NULL int64_t idata_split, // data index at which to split training and evaluation ggml_opt_epoch_callback callback_train, ggml_opt_epoch_callback callback_eval); // callback that prints a progress bar on stderr GGML_API void ggml_opt_epoch_callback_progress_bar( bool train, ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, ggml_opt_result_t result, int64_t ibatch, int64_t ibatch_max, int64_t t_start_us); // fit model defined by inputs and outputs to dataset GGML_API void ggml_opt_fit( ggml_backend_sched_t backend_sched, // backend scheduler for constructing the compute graphs ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch] ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used ggml_opt_dataset_t dataset, // dataset with data and optionally also labels enum ggml_opt_loss_type loss_type, // loss to minimize ggml_opt_get_optimizer_params get_opt_pars, // callback to get optimizer params, userdata is pointer to epoch (of type int64_t) int64_t nepoch, // how many times the dataset should be iterated over int64_t nbatch_logical, // datapoints optimizer step, must be a multiple of ndata_batch in inputs/outputs float val_split, // fraction of the dataset to use for validation, must be in [0.0f, 1.0f) bool silent); // whether or not info prints to stderr should be suppressed #ifdef __cplusplus } #endif