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