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llama.cpp/ggml/src/ggml-opt.cpp
Jonathan Graehl 5cdb27e091 finetune: SGD optimizer, more CLI args (#13873) 
* examples/finetune -opt SGD (stochastic gradient descent) memory opt

add unit tested GGML_OPT_OPTIMIZER_SGD to ggml - avoids allocating
m, v tensors.

support finetune.cpp arg -opt SGD (or sgd). (default adamw as before)

llama 3.2-1b-F32 result: observed 11gb gpu ram (41 sec/epoch)
when using SGD instead of 19gb (55 sec/epoch) using adamw.
(wikipedia 100 lines finetune)

(
using the same GPU memory, adamw can only do before OOM 512
batch/context, reaching:
train: [███████▉] data=0000140/0000140 loss=0.02575±0.00099 acc=99.52±0.03% t=00:00:47 ETA=00:00:00
val:   [███████▉] data=0000008/0000008 loss=4.76565±0.28810 acc=41.46±0.77% t=00:00:00 ETA=00:00:00

SGD is superior, though it converges slower, with max before OOM 1728
batch/context (esp see the better validation perf):
train: [███████▉] data=0000039/0000039 loss=0.00371±0.00010 acc=99.96±0.01% t=00:00:41 ETA=00:00:00
val:   [███████▉] data=0000003/0000003 loss=5.11406±0.76034 acc=48.01±0.69% t=00:00:01 ETA=00:00:00
)

note: when finetuning long enough (or w/ enough -lr),
validation accuracy *eventually* drops ('catastrophic forgetting')

-lr-half (halflife) option useful for SGD to avoid oscillation or
super slow underdamped learning (makes setting -lr more forgiving).
terminal -lr for now is set by lr-halvings i.e. if you want at most
1/8 the inital -lr you set -lr-halvings 3.

note: objective loss not directly comparable between adamw, sgd? -
check perplexity or accuracy or consider relative improvements
for convergence

new finetune args -wd 1e-9 to enable weight decay in sgd or adamw,
and max -epochs N (default 2 as before)

cache (1 - wd*alpha) in 'adamw' opt struct -
no noticeable perf benefit, disabled (still done
for new SGD though)

since opt. memory is pre-allocated, the ggml_opt_get_optimizer_params
would probably be able to change between SGD and AdamW with each epoch
but would need to use adamw for the first (unconfirmed - no cmdline arg
to set such a policy yet)

test-opt checks adamw as before and now sgd (except for a few disabled
tests for sgd only; probably just needs logging values and adding
alternate reference values);  tolerance on the 'regression'
test is broader for sgd (so we don't need many more epochs)

* Vulkan: Implement GGML_OP_OPT_STEP_SGD

* tests: Fix OPT_STEP_SGD test-backend-ops

* SGD op param store weight-decay and not 1-alpha*wd

* minor + cosmetic changes

* fix vulkan sgd

* try CI fix

---------

Co-authored-by: 0cc4m <picard12@live.de>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2025-08-14 12:03:57 +02:00

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#include "ggml-opt.h"
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "ggml-impl.h"
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <cinttypes>
#include <map>
#include <random>
#include <vector>
struct ggml_opt_dataset {
struct ggml_context * ctx = nullptr;
ggml_backend_buffer_t buf = nullptr;
struct ggml_tensor * data = nullptr;
struct ggml_tensor * labels = nullptr;
int64_t ndata = -1;
int64_t ndata_shard = -1;
size_t nbs_data = -1;
size_t nbs_labels = -1;
std::vector<int64_t> permutation;
};
struct ggml_opt_context {
ggml_backend_sched_t backend_sched = nullptr;
ggml_cgraph * allocated_graph = nullptr;
ggml_cgraph * allocated_graph_copy = nullptr;
struct ggml_context * ctx_static = nullptr;
struct ggml_context * ctx_cpu = nullptr;
struct ggml_context * ctx_compute = nullptr;
struct ggml_context * ctx_copy = nullptr;
ggml_backend_buffer_t buf_static = nullptr;
ggml_backend_buffer_t buf_cpu = nullptr;
std::mt19937 rng;
enum ggml_opt_loss_type loss_type;
enum ggml_opt_build_type build_type;
enum ggml_opt_build_type build_type_alloc;
struct ggml_tensor * inputs = nullptr;
struct ggml_tensor * outputs = nullptr;
struct ggml_tensor * labels = nullptr;
struct ggml_tensor * loss = nullptr;
struct ggml_tensor * pred = nullptr;
struct ggml_tensor * ncorrect = nullptr;
struct ggml_cgraph * gf = nullptr;
struct ggml_cgraph * gb_grad = nullptr;
struct ggml_cgraph * gb_opt = nullptr;
bool static_graphs = false;
bool eval_ready = false;
std::vector<struct ggml_tensor *> grad_accs;
std::vector<struct ggml_tensor *> grad_m;
std::vector<struct ggml_tensor *> grad_v;
int64_t iter = 1;
int32_t opt_period = 1;
int32_t opt_i = 0;
bool loss_per_datapoint = false;
ggml_opt_get_optimizer_params get_opt_pars = nullptr;
void * get_opt_pars_ud = nullptr;
struct ggml_tensor * opt_step_params = nullptr; // Stores output of get_opt_pars.
enum ggml_opt_optimizer_type optimizer = GGML_OPT_OPTIMIZER_TYPE_ADAMW;
};
struct ggml_opt_result {
int64_t ndata = 0;
std::vector<float> loss;
std::vector<int32_t> pred;
int64_t ncorrect = 0;
int64_t opt_period = -1;
bool loss_per_datapoint = false;
};
// ====== Dataset ======
ggml_opt_dataset_t ggml_opt_dataset_init(
enum ggml_type type_data,
enum ggml_type type_label,
int64_t ne_datapoint,
int64_t ne_label,
int64_t ndata,
int64_t ndata_shard) {
GGML_ASSERT(ne_datapoint > 0);
GGML_ASSERT(ne_label >= 0);
GGML_ASSERT(ndata > 0);
GGML_ASSERT(ndata_shard > 0);
ggml_opt_dataset_t result = new ggml_opt_dataset;
result->ndata = ndata;
result->ndata_shard = ndata_shard;
{
struct ggml_init_params params = {
/*.mem_size =*/ 2*ggml_tensor_overhead(),
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
result->ctx = ggml_init(params);
}
result->data = ggml_new_tensor_2d(result->ctx, type_data, ne_datapoint, ndata);
result->nbs_data = ggml_nbytes(result->data) * ndata_shard/ndata;
if (ne_label > 0) {
result->labels = ggml_new_tensor_2d(result->ctx, type_label, ne_label, ndata);
result->nbs_labels = ggml_nbytes(result->labels) * ndata_shard/ndata;
} else {
result->labels = nullptr;
result->nbs_labels = 0;
}
result->buf = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx, ggml_backend_cpu_buffer_type());
const int64_t nshards = ndata/ndata_shard;
result->permutation.resize(nshards);
for (int64_t i = 0; i < nshards; ++i) {
result->permutation[i] = i;
}
return result;
}
void ggml_opt_dataset_free(ggml_opt_dataset_t dataset) {
ggml_backend_buffer_free(dataset->buf);
ggml_free(dataset->ctx);
delete dataset;
}
int64_t ggml_opt_dataset_ndata(ggml_opt_dataset_t dataset) {
return dataset->ndata;
}
struct ggml_tensor * ggml_opt_dataset_data(ggml_opt_dataset_t dataset) {
return dataset->data;
}
struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset) {
return dataset->labels;
}
void ggml_opt_dataset_shuffle(ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, int64_t idata) {
GGML_ASSERT(idata <= dataset->ndata);
if (idata < 0) {
std::shuffle(dataset->permutation.begin(), dataset->permutation.end(), opt_ctx->rng);
return;
}
GGML_ASSERT(idata % dataset->ndata_shard == 0);
const int64_t ishard_max = idata / dataset->ndata_shard;
std::shuffle(dataset->permutation.begin(), dataset->permutation.begin() + ishard_max, opt_ctx->rng);
}
void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor * data_batch, struct ggml_tensor * labels_batch, int64_t ibatch) {
GGML_ASSERT( data_batch && ggml_is_contiguous(data_batch));
GGML_ASSERT(!labels_batch || ggml_is_contiguous(labels_batch));
GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr));
GGML_ASSERT( data_batch->type == dataset->data->type);
GGML_ASSERT(!labels_batch || labels_batch->type == dataset->labels->type);
const size_t nb_data_batch = ggml_nbytes(data_batch);
GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0);
const int64_t shards_per_batch = nb_data_batch / dataset->nbs_data;
if (labels_batch) {
const size_t nb_labels_batch = ggml_nbytes(labels_batch);
GGML_ASSERT(nb_labels_batch == shards_per_batch*dataset->nbs_labels);
}
GGML_ASSERT((ibatch + 1)*shards_per_batch <= int64_t(dataset->permutation.size()));
for (int64_t ishard_batch = 0; ishard_batch < shards_per_batch; ++ishard_batch) {
const int64_t ishard = dataset->permutation[ibatch*shards_per_batch + ishard_batch];
const char * ptr_data = (const char *) dataset->data->data + ishard*dataset->nbs_data;
ggml_backend_tensor_set(data_batch, ptr_data, ishard_batch*dataset->nbs_data, dataset->nbs_data);
if (!labels_batch) {
continue;
}
const char * ptr_labels = (const char *) dataset->labels->data + ishard*dataset->nbs_labels;
ggml_backend_tensor_set(labels_batch, ptr_labels, ishard_batch*dataset->nbs_labels, dataset->nbs_labels);
}
}
void ggml_opt_dataset_get_batch_host(ggml_opt_dataset_t dataset, void * data_batch, size_t nb_data_batch, void * labels_batch, int64_t ibatch) {
GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr));
GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0);
const int64_t shards_per_batch = nb_data_batch / dataset->nbs_data;
GGML_ASSERT((ibatch + 1)*shards_per_batch <= int64_t(dataset->permutation.size()));
for (int64_t ishard_batch = 0; ishard_batch < shards_per_batch; ++ishard_batch) {
const int64_t ishard = dataset->permutation[ibatch*shards_per_batch + ishard_batch];
const char * ptr_data = (const char *) dataset->data->data + ishard *dataset->nbs_data;
char * ptr_data_batch = (char *) data_batch + ishard_batch*dataset->nbs_data;
memcpy(ptr_data_batch, ptr_data, dataset->nbs_data);
if (!labels_batch) {
continue;
}
const char * ptr_labels = (const char *) dataset->labels->data + ishard *dataset->nbs_labels;
char * ptr_labels_batch = (char *) labels_batch + ishard_batch*dataset->nbs_labels;
memcpy(ptr_labels_batch, ptr_labels, dataset->nbs_labels);
}
}
// ====== Model / Context ======
struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata) {
GGML_UNUSED(userdata);
ggml_opt_optimizer_params result;
result.adamw.alpha = 0.001f;
result.adamw.beta1 = 0.9f;
result.adamw.beta2 = 0.999f;
result.adamw.eps = 1e-8f;
result.adamw.wd = 0.0f;
result.sgd.alpha = 1e-3f;
result.sgd.wd = 0.0f;
return result;
}
struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata) {
return *((struct ggml_opt_optimizer_params *) userdata);
}
struct ggml_opt_params ggml_opt_default_params(
ggml_backend_sched_t backend_sched,
enum ggml_opt_loss_type loss_type) {
return {
/*backend_sched =*/ backend_sched,
/*ctx_compute =*/ nullptr,
/*inputs =*/ nullptr,
/*logits =*/ nullptr,
/*loss_type =*/ loss_type,
/*build_type =*/ GGML_OPT_BUILD_TYPE_OPT,
/*opt_period =*/ 1,
/*get_opt_pars =*/ ggml_opt_get_default_optimizer_params,
/*get_opt_pars_ud =*/ nullptr,
/*optimizer =*/ GGML_OPT_OPTIMIZER_TYPE_ADAMW,
};
}
static ggml_tensor * map_tensor(std::map<ggml_tensor *, ggml_tensor *> & tensor_map, ggml_context * ctx, ggml_tensor * tensor) {
if (!tensor) {
return nullptr;
}
if (tensor_map.find(tensor) != tensor_map.end()) {
return tensor_map[tensor];
}
ggml_tensor * new_tensor = ggml_dup_tensor(ctx, tensor);
tensor_map[tensor] = new_tensor;
new_tensor->op = tensor->op;
for (int i = 0; i < GGML_MAX_DIMS; i++) {
new_tensor->nb[i] = tensor->nb[i];
}
new_tensor->flags = tensor->flags;
memcpy(new_tensor->op_params, tensor->op_params, sizeof(tensor->op_params));
strcpy(new_tensor->name, tensor->name);
new_tensor->data = tensor->data;
new_tensor->buffer = tensor->buffer;
new_tensor->extra = tensor->extra;
new_tensor->view_offs = tensor->view_offs;
new_tensor->view_src = map_tensor(tensor_map, ctx, tensor->view_src);
for (int i = 0; i < GGML_MAX_SRC; i++) {
new_tensor->src[i] = map_tensor(tensor_map, ctx, tensor->src[i]);
}
return new_tensor;
}
static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * src) {
std::map<ggml_tensor *, ggml_tensor *> tensor_map;
ggml_cgraph * dst = ggml_new_graph_custom(ctx, src->size, /*grads =*/ true);
for (int i = 0; i < src->n_leafs; i++) {
ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->leafs[i]));
}
GGML_ASSERT(dst->n_leafs == src->n_leafs);
for (int i = 0; i < src->n_nodes; i++) {
ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->nodes[i]));
}
GGML_ASSERT(dst->n_nodes == src->n_nodes);
for (int i = 0; i < src->n_nodes; ++i) {
const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]);
const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]);
GGML_ASSERT(igrad_src != GGML_HASHSET_FULL);
GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src));
GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL);
GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst));
dst->grads[igrad_dst] = src->grads[igrad_src];
dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src];
}
return dst;
}
static void ggml_opt_build(ggml_opt_context_t opt_ctx) {
GGML_ASSERT(opt_ctx->ctx_compute && "no compute context set, either use static graphs or set one with ggml_opt_prepare_alloc");
GGML_ASSERT((!opt_ctx->static_graphs || opt_ctx->inputs->data) && "when using static graphs the inputs must be allocated statically");
const enum ggml_opt_optimizer_type optimizer = opt_ctx->optimizer;
const bool accumulate = opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD &&
!(opt_ctx->static_graphs && opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT && opt_ctx->opt_period == 1);
const bool need_momenta = opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT &&
opt_ctx->optimizer == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
ggml_set_input(opt_ctx->inputs);
ggml_set_output(opt_ctx->outputs);
int n_param = 0;
for (int i = 0; i < opt_ctx->gf->n_nodes; ++i) {
const struct ggml_tensor * node = opt_ctx->gf->nodes[i];
if (node->flags & GGML_TENSOR_FLAG_PARAM) {
n_param++;
}
GGML_ASSERT(!(node->flags & GGML_TENSOR_FLAG_LOSS) && "support for extra loss terms not implemented");
}
if (!opt_ctx->ctx_static) {
// The static context is used for:
// - gradients (1 per loss, 1 tensor per param if using gradient accumulation)
// - optimizer momenta (2 tensors per param)
// - labels (if using static graphs)
// - loss (if using static graphs, up to 5 tensors)
// - pred (if using static graphs)
// - ncorrect (if using static graphs, 2 tensors).
constexpr size_t n_loss = 1;
const size_t tensors_per_param = (accumulate ? 1 : 0) + (need_momenta ? 2 : 0);
const size_t tensors_const = opt_ctx->static_graphs ? 9 : 0;
const size_t size_meta = (n_loss + tensors_per_param*n_param + tensors_const) * ggml_tensor_overhead();
struct ggml_init_params params = {
/*.mem_size =*/ size_meta,
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
opt_ctx->ctx_static = ggml_init(params);
}
GGML_ASSERT(opt_ctx->build_type <= opt_ctx->build_type_alloc);
{
// The cpu context is allocated statically if using static graphs, dynamically otherwise.
// It is used for:
// - optimizer parameters (1 shared for all optimizer invocations)
const size_t size_meta = 1 * ggml_tensor_overhead();
struct ggml_init_params params = {
/*.mem_size =*/ size_meta,
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
ggml_free(opt_ctx->ctx_cpu);
opt_ctx->ctx_cpu = ggml_init(params);
ggml_backend_buffer_free(opt_ctx->buf_cpu);
opt_ctx->buf_cpu = nullptr;
}
struct ggml_context * ctx_results = opt_ctx->static_graphs ? opt_ctx->ctx_static : opt_ctx->ctx_compute;
switch (opt_ctx->loss_type) {
case GGML_OPT_LOSS_TYPE_MEAN: {
opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->outputs);
ggml_set_name(opt_ctx->loss, "loss_sum");
const float scale = 1.0f / (opt_ctx->opt_period * ggml_nelements(opt_ctx->outputs));
opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, scale);
ggml_set_name(opt_ctx->loss, "loss_mean");
opt_ctx->loss_per_datapoint = true;
break;
}
case GGML_OPT_LOSS_TYPE_SUM: {
opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->outputs);
ggml_set_name(opt_ctx->loss, "loss_sum");
opt_ctx->loss_per_datapoint = false;
break;
}
case GGML_OPT_LOSS_TYPE_CROSS_ENTROPY: {
opt_ctx->labels = ggml_dup_tensor(ctx_results, opt_ctx->outputs);
ggml_set_input(opt_ctx->labels);
ggml_set_name(opt_ctx->labels, "labels");
opt_ctx->loss = ggml_cross_entropy_loss(ctx_results, opt_ctx->outputs, opt_ctx->labels);
ggml_set_name(opt_ctx->loss, "loss_cross_entropy");
if (opt_ctx->opt_period > 1) {
opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, 1.0f / opt_ctx->opt_period);
ggml_set_name(opt_ctx->loss, "loss_cross_entropy_scaled");
}
opt_ctx->loss_per_datapoint = true;
break;
}
case GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR: {
opt_ctx->labels = ggml_dup_tensor(ctx_results, opt_ctx->outputs);
ggml_set_input(opt_ctx->labels);
ggml_set_name(opt_ctx->labels, "labels");
opt_ctx->loss = ggml_sub(ctx_results, opt_ctx->outputs, opt_ctx->labels);
ggml_set_name(opt_ctx->loss, "loss_error");
opt_ctx->loss = ggml_sqr(ctx_results, opt_ctx->loss);
ggml_set_name(opt_ctx->loss, "loss_squared_error");
opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->loss);
ggml_set_name(opt_ctx->loss, "loss_sum_squared_error");
const float scale = 1.0f / (opt_ctx->opt_period * ggml_nelements(opt_ctx->outputs));
opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, scale);
ggml_set_name(opt_ctx->loss, "loss_mean_squared_error");
opt_ctx->loss_per_datapoint = true;
break;
}
}
ggml_set_output(opt_ctx->loss);
ggml_set_loss(opt_ctx->loss);
ggml_build_forward_expand(opt_ctx->gf, opt_ctx->loss);
if (opt_ctx->loss_type == GGML_OPT_LOSS_TYPE_CROSS_ENTROPY) {
opt_ctx->pred = ggml_argmax(ctx_results, opt_ctx->outputs);
ggml_set_name(opt_ctx->pred, "pred");
ggml_set_output(opt_ctx->pred);
ggml_build_forward_expand(opt_ctx->gf, opt_ctx->pred);
opt_ctx->ncorrect = ggml_count_equal(ctx_results, opt_ctx->pred, ggml_argmax(ctx_results, opt_ctx->labels));
ggml_set_name(opt_ctx->ncorrect, "ncorrect");
ggml_set_output(opt_ctx->ncorrect);
ggml_build_forward_expand(opt_ctx->gf, opt_ctx->ncorrect);
}
if (opt_ctx->buf_static) {
if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_FORWARD) {
return;
}
} else if (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_FORWARD) {
opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(
opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0));
return;
}
if (opt_ctx->grad_accs.empty()) {
GGML_ASSERT(opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD);
const int n_nodes = opt_ctx->gf->n_nodes;
opt_ctx->grad_accs.resize(n_nodes);
for (int i = 0; i < n_nodes; ++i) {
ggml_tensor * node = opt_ctx->gf->nodes[i];
if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) {
opt_ctx->grad_accs[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
} else {
opt_ctx->grad_accs[i] = nullptr;
}
}
if (need_momenta && opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_OPT) {
opt_ctx->grad_m.resize(n_nodes);
opt_ctx->grad_v.resize(n_nodes);
for (int i = 0; i < n_nodes; ++i) {
ggml_tensor * node = opt_ctx->gf->nodes[i];
if (node->flags & GGML_TENSOR_FLAG_PARAM) {
opt_ctx->grad_m[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
opt_ctx->grad_v[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
} else {
opt_ctx->grad_m[i] = nullptr;
opt_ctx->grad_v[i] = nullptr;
}
}
}
}
// gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients.
opt_ctx->gb_grad = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gf, /*force_grads =*/ true);
ggml_build_backward_expand(opt_ctx->ctx_compute, opt_ctx->gb_grad, opt_ctx->grad_accs.data());
if (opt_ctx->buf_static) {
if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_GRAD) {
return;
}
} else if (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_GRAD) {
opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0));
ggml_graph_reset(opt_ctx->gb_grad);
}
GGML_ASSERT(opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT);
// gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step.
opt_ctx->gb_opt = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gb_grad, /*force_grads =*/ true);
opt_ctx->opt_step_params = ggml_new_tensor_1d(opt_ctx->ctx_cpu, GGML_TYPE_F32, need_momenta ? 7 : 2);
ggml_tensor * adamw_params = opt_ctx->opt_step_params;
ggml_set_input(adamw_params);
const char * optimizer_name = ggml_opt_optimizer_name(opt_ctx->optimizer);
ggml_format_name(adamw_params, "%s_params", optimizer_name);
for (int i = opt_ctx->gf->n_nodes-1; i >= 0; --i) {
struct ggml_tensor * node = opt_ctx->gb_opt->nodes[i];
struct ggml_tensor * grad = ggml_graph_get_grad(opt_ctx->gb_opt, node);
if (grad && (node->flags & GGML_TENSOR_FLAG_PARAM)) {
struct ggml_tensor * m = nullptr;
struct ggml_tensor * v = nullptr;
if (need_momenta) {
m = opt_ctx->grad_m[i];
v = opt_ctx->grad_v[i];
ggml_format_name(m, "AdamW m for %s", node->name);
ggml_format_name(v, "AdamW v for %s", node->name);
}
struct ggml_tensor * opt_step;
switch (optimizer) {
case GGML_OPT_OPTIMIZER_TYPE_ADAMW:
opt_step = ggml_opt_step_adamw(opt_ctx->ctx_compute, node, grad, m, v, adamw_params);
break;
case GGML_OPT_OPTIMIZER_TYPE_SGD:
opt_step = ggml_opt_step_sgd(opt_ctx->ctx_compute, node, grad, adamw_params);
break;
default:
GGML_ABORT("fatal error");
}
ggml_format_name(opt_step, "%s step for %s", optimizer_name, node->name);
ggml_build_forward_expand(opt_ctx->gb_opt, opt_step);
}
}
if (!opt_ctx->buf_static) {
opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(
opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0));
ggml_graph_reset(opt_ctx->gb_opt);
}
opt_ctx->buf_cpu = ggml_backend_alloc_ctx_tensors_from_buft(opt_ctx->ctx_cpu, ggml_backend_cpu_buffer_type());
}
ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
ggml_opt_context_t result = new struct ggml_opt_context;
result->backend_sched = params.backend_sched;
result->ctx_compute = params.ctx_compute;
result->loss_type = params.loss_type;
result->build_type = params.build_type;
result->build_type_alloc = params.build_type;
result->inputs = params.inputs;
result->outputs = params.outputs;
result->opt_period = params.opt_period;
result->get_opt_pars = params.get_opt_pars;
result->get_opt_pars_ud = params.get_opt_pars_ud;
result->optimizer = params.optimizer;
GGML_ASSERT(result->opt_period >= 1);
result->static_graphs = result->ctx_compute;
if (!result->static_graphs) {
GGML_ASSERT(!result->inputs);
GGML_ASSERT(!result->outputs);
return result;
}
GGML_ASSERT(result->inputs);
GGML_ASSERT(result->outputs);
result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass.
ggml_build_forward_expand(result->gf, result->outputs);
ggml_opt_build(result);
return result;
}
void ggml_opt_free(ggml_opt_context_t opt_ctx) {
if (opt_ctx == nullptr) {
return;
}
ggml_backend_buffer_free(opt_ctx->buf_static);
ggml_backend_buffer_free(opt_ctx->buf_cpu);
ggml_free(opt_ctx->ctx_static);
ggml_free(opt_ctx->ctx_cpu);
delete opt_ctx;
}
void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer) {
if (optimizer) {
ggml_graph_reset(opt_ctx->gb_opt);
opt_ctx->iter = 1;
} else {
ggml_graph_reset(opt_ctx->gb_grad);
}
}
bool ggml_opt_static_graphs(ggml_opt_context_t opt_ctx) {
return opt_ctx->static_graphs;
}
struct ggml_tensor * ggml_opt_inputs(ggml_opt_context_t opt_ctx) {
return opt_ctx->inputs;
}
struct ggml_tensor * ggml_opt_outputs(ggml_opt_context_t opt_ctx) {
return opt_ctx->outputs;
}
struct ggml_tensor * ggml_opt_labels(ggml_opt_context_t opt_ctx) {
return opt_ctx->labels;
}
struct ggml_tensor * ggml_opt_loss(ggml_opt_context_t opt_ctx) {
return opt_ctx->loss;
}
struct ggml_tensor * ggml_opt_pred(ggml_opt_context_t opt_ctx) {
return opt_ctx->pred;
}
struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx) {
return opt_ctx->ncorrect;
}
struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node) {
return ggml_graph_get_grad_acc(opt_ctx->gb_opt, node);
}
// ====== Optimization Result ======
ggml_opt_result_t ggml_opt_result_init() {
return new ggml_opt_result;
}
void ggml_opt_result_free(ggml_opt_result_t result) {
delete result;
}
void ggml_opt_result_reset(ggml_opt_result_t result) {
result->ndata = 0;
result->loss.clear();
result->pred.clear();
result->ncorrect = 0;
}
void ggml_opt_result_ndata(ggml_opt_result_t result, int64_t * ndata) {
*ndata = result->ndata;
}
void ggml_opt_result_loss(ggml_opt_result_t result, double * loss, double * unc) {
const int64_t nbatches = result->loss.size(); // Number of physical batches.
if (nbatches == 0) {
*loss = 0.0;
*unc = NAN;
return;
}
double sum = 0.0;
double sum_squared = 0.0;
for (const float & loss : result->loss) {
// If the loss is per datapoint it was scaled by 1.0f/opt_period for each physical batch.
const float loss_scaled = result->loss_per_datapoint ? loss*result->opt_period : loss;
sum += loss_scaled;
sum_squared += loss_scaled*loss_scaled;
}
const double mean = sum/nbatches;
*loss = result->loss_per_datapoint ? mean : sum;
if (!unc) {
return;
}
if (nbatches < 2) {
*unc = NAN;
return;
}
const double var_sum = sum_squared/nbatches - mean*mean; // variance without Bessel's correction, i.e. nbatches/(nbatches-1)
*unc = result->loss_per_datapoint ? sqrt(var_sum / (nbatches - 1)) : sqrt(var_sum * nbatches/(nbatches - 1));
}
void ggml_opt_result_pred(ggml_opt_result_t result, int32_t * pred) {
for (size_t i = 0; i < result->pred.size(); ++i) {
pred[i] = result->pred[i];
}
}
void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, double * unc) {
*accuracy = result->ncorrect >= 0 ? double(result->ncorrect) / double(result->ndata) : NAN;
if (!unc) {
return;
}
*unc = result->ncorrect >= 0 && result->ndata >= 2 ?
sqrt((*accuracy) * (1.0 - (*accuracy)) / double(result->ndata - 1)) : NAN;
}
// ====== Computation ======
void ggml_opt_prepare_alloc(
ggml_opt_context_t opt_ctx,
struct ggml_context * ctx_compute,
struct ggml_cgraph * gf,
struct ggml_tensor * inputs,
struct ggml_tensor * outputs) {
GGML_ASSERT(!opt_ctx->static_graphs);
opt_ctx->ctx_compute = ctx_compute;
opt_ctx->gf = gf;
opt_ctx->inputs = inputs;
opt_ctx->outputs = outputs;
}
void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward) {
GGML_ASSERT(!opt_ctx->eval_ready);
if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_OPT && opt_ctx->opt_period > 1 && opt_ctx->opt_i == 0) {
ggml_graph_reset(opt_ctx->gb_grad);
}
if (backward) {
const int32_t opt_i_next = (opt_ctx->opt_i + 1) % opt_ctx->opt_period;
opt_ctx->build_type = opt_i_next == 0 ? GGML_OPT_BUILD_TYPE_OPT : GGML_OPT_BUILD_TYPE_GRAD;
} else {
opt_ctx->build_type = GGML_OPT_BUILD_TYPE_FORWARD;
}
if (!opt_ctx->static_graphs) {
ggml_opt_build(opt_ctx);
}
struct ggml_cgraph * graph = nullptr;
switch (opt_ctx->build_type) {
case GGML_OPT_BUILD_TYPE_FORWARD: {
graph = opt_ctx->gf;
} break;
case GGML_OPT_BUILD_TYPE_GRAD: {
graph = opt_ctx->gb_grad;
} break;
case GGML_OPT_BUILD_TYPE_OPT: {
graph = opt_ctx->gb_opt;
} break;
}
GGML_ASSERT(graph);
if (opt_ctx->allocated_graph == graph) {
opt_ctx->eval_ready = true;
return;
}
ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph
if (opt_ctx->static_graphs) {
ggml_init_params params = {
/*.mem_size =*/ graph->size*ggml_tensor_overhead() + ggml_graph_overhead_custom(graph->size, graph->grads),
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
ggml_free(opt_ctx->ctx_copy);
opt_ctx->ctx_copy = ggml_init(params);
opt_ctx->allocated_graph_copy = dup_graph(opt_ctx->ctx_copy, graph);
} else {
opt_ctx->allocated_graph_copy = graph;
}
ggml_backend_sched_alloc_graph(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy);
opt_ctx->allocated_graph = graph;
opt_ctx->eval_ready = true;
}
void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result) {
GGML_ASSERT(opt_ctx->eval_ready);
if (opt_ctx->allocated_graph == opt_ctx->gb_opt) {
const ggml_opt_optimizer_params & opt_pars = opt_ctx->get_opt_pars(opt_ctx->get_opt_pars_ud);
switch (opt_ctx->optimizer) {
case GGML_OPT_OPTIMIZER_TYPE_ADAMW: {
GGML_ASSERT(opt_pars.adamw.alpha > 0.0f);
GGML_ASSERT(opt_pars.adamw.beta1 >= 0.0f);
GGML_ASSERT(opt_pars.adamw.beta1 <= 1.0f);
GGML_ASSERT(opt_pars.adamw.beta2 >= 0.0f);
GGML_ASSERT(opt_pars.adamw.beta2 <= 1.0f);
GGML_ASSERT(opt_pars.adamw.eps >= 0.0f);
GGML_ASSERT(opt_pars.adamw.wd >= 0.0f);
GGML_ASSERT(opt_pars.adamw.wd <= 1.0f);
// beta1, beta2 after applying warmup
const float beta1h = 1.0f / (1.0f - powf(opt_pars.adamw.beta1, opt_ctx->iter));
const float beta2h = 1.0f / (1.0f - powf(opt_pars.adamw.beta2, opt_ctx->iter));
float * adamw_par_data = ggml_get_data_f32(opt_ctx->opt_step_params);
adamw_par_data[0] = opt_pars.adamw.alpha;
adamw_par_data[1] = opt_pars.adamw.beta1;
adamw_par_data[2] = opt_pars.adamw.beta2;
adamw_par_data[3] = opt_pars.adamw.eps;
adamw_par_data[4] = opt_pars.adamw.wd;
adamw_par_data[5] = beta1h;
adamw_par_data[6] = beta2h;
} break;
case GGML_OPT_OPTIMIZER_TYPE_SGD: {
GGML_ASSERT(opt_pars.sgd.alpha > 0.0f);
GGML_ASSERT(opt_pars.sgd.wd >= 0.0f);
GGML_ASSERT(opt_pars.sgd.wd <= 1.0f);
float * sgd = ggml_get_data_f32(opt_ctx->opt_step_params);
sgd[0] = opt_pars.sgd.alpha;
sgd[1] = opt_pars.sgd.wd;
} break;
default:
GGML_ABORT("fatal error");
}
}
ggml_backend_sched_graph_compute(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy);
opt_ctx->iter += opt_ctx->allocated_graph == opt_ctx->gb_opt;
opt_ctx->opt_i = (opt_ctx->opt_i + 1) % opt_ctx->opt_period;
if (!opt_ctx->static_graphs) {
opt_ctx->gf = nullptr;
opt_ctx->gb_grad = nullptr;
opt_ctx->gb_opt = nullptr;
opt_ctx->allocated_graph = nullptr;
opt_ctx->allocated_graph_copy = nullptr;
}
opt_ctx->eval_ready = false;
if (!result) {
return;
}
if (result->ndata == 0) {
result->loss_per_datapoint = opt_ctx->loss_per_datapoint;
result->opt_period = opt_ctx->opt_period;
} else {
GGML_ASSERT(result->loss_per_datapoint == opt_ctx->loss_per_datapoint);
GGML_ASSERT(result->opt_period == opt_ctx->opt_period);
}
const int64_t ndata = opt_ctx->outputs->ne[1];
GGML_ASSERT(result->ndata == ndata*int64_t(result->loss.size()) && "varying batch size not supported");
result->ndata += ndata;
GGML_ASSERT(ggml_is_scalar(opt_ctx->loss));
GGML_ASSERT(opt_ctx->loss->type == GGML_TYPE_F32);
float loss;
ggml_backend_tensor_get(opt_ctx->loss, &loss, 0, ggml_nbytes(opt_ctx->loss));
result->loss.push_back(loss);
if (opt_ctx->pred) {
GGML_ASSERT(opt_ctx->pred->type == GGML_TYPE_I32);
std::vector<int32_t> pred(ndata);
ggml_backend_tensor_get(opt_ctx->pred, pred.data(), 0, ggml_nbytes(opt_ctx->pred));
result->pred.insert(result->pred.end(), pred.begin(), pred.end());
}
if (!opt_ctx->ncorrect || result->ncorrect < 0) {
result->ncorrect = -1;
return;
}
GGML_ASSERT(ggml_is_scalar(opt_ctx->ncorrect));
GGML_ASSERT(opt_ctx->ncorrect->type == GGML_TYPE_I64);
int64_t ncorrect;
ggml_backend_tensor_get(opt_ctx->ncorrect, &ncorrect, 0, ggml_nbytes(opt_ctx->ncorrect));
result->ncorrect += ncorrect;
}
// ====== High-Level Functions ======
void ggml_opt_epoch(
ggml_opt_context_t opt_ctx,
ggml_opt_dataset_t dataset,
ggml_opt_result_t result_train,
ggml_opt_result_t result_eval,
int64_t idata_split,
ggml_opt_epoch_callback callback_train,
ggml_opt_epoch_callback callback_eval) {
GGML_ASSERT(ggml_opt_static_graphs(opt_ctx) && "ggml_opt_epoch requires static graphs");
struct ggml_tensor * inputs = ggml_opt_inputs(opt_ctx);
struct ggml_tensor * labels = ggml_opt_labels(opt_ctx);
struct ggml_tensor * data = ggml_opt_dataset_data(dataset);
GGML_ASSERT(data->ne[0] == inputs->ne[0]);
const int64_t ndata = data->ne[1];
const int64_t ndata_batch = inputs->ne[1];
GGML_ASSERT(data->ne[1] % inputs->ne[1] == 0);
const int64_t nbatches = ndata/ndata_batch;
idata_split = idata_split < 0 ? ndata : idata_split;
GGML_ASSERT(idata_split % ndata_batch == 0);
const int64_t ibatch_split = idata_split / ndata_batch;
int64_t ibatch = 0;
int64_t t_loop_start = ggml_time_us();
for (; ibatch < ibatch_split; ++ibatch) {
ggml_opt_alloc(opt_ctx, /*backward =*/ true);
ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch);
ggml_opt_eval(opt_ctx, result_train);
if (callback_train) {
callback_train(true, opt_ctx, dataset, result_train, ibatch+1, ibatch_split, t_loop_start);
}
}
t_loop_start = ggml_time_us();
for (; ibatch < nbatches; ++ibatch) {
ggml_opt_alloc(opt_ctx, /*backward =*/ false);
ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch);
ggml_opt_eval(opt_ctx, result_eval);
if (callback_eval) {
callback_eval(false, opt_ctx, dataset, result_eval, ibatch+1-ibatch_split, nbatches-ibatch_split, t_loop_start);
}
}
}
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) {
fprintf(stderr, "%s[", train ? "train: " : "val: ");
// The progress bar consists of partially filled blocks, unicode has 8 separate fill levels.
constexpr int64_t bar_length = 8;
const int64_t ibatch8 = 8 * ibatch;
for (int64_t j = 0; j < bar_length; ++j) {
if (ibatch_max * (8*j + 8) / bar_length < ibatch8) {
fprintf(stderr, "\u2588"); // full block
} else if (ibatch_max * (8*j + 7) / bar_length < ibatch8) {
fprintf(stderr, "\u2589"); // 7/8 filled
} else if (ibatch_max * (8*j + 6) / bar_length < ibatch8) {
fprintf(stderr, "\u258A"); // 6/8 filled
} else if (ibatch_max * (8*j + 5) / bar_length < ibatch8) {
fprintf(stderr, "\u258B"); // 5/8 filled
} else if (ibatch_max * (8*j + 4) / bar_length < ibatch8) {
fprintf(stderr, "\u258C"); // 4/8 filled
} else if (ibatch_max * (8*j + 3) / bar_length < ibatch8) {
fprintf(stderr, "\u258D"); // 3/8 filled
} else if (ibatch_max * (8*j + 2) / bar_length < ibatch8) {
fprintf(stderr, "\u258E"); // 2/8 filled
} else if (ibatch_max * (8*j + 1) / bar_length < ibatch8) {
fprintf(stderr, "\u258F"); // 1/8 filled
} else {
fprintf(stderr, " ");
}
}
const int64_t batch_size = ggml_opt_inputs(opt_ctx)->ne[1];
const int64_t idata = ibatch*batch_size;
const int64_t idata_max = ibatch_max*batch_size;
double loss;
double loss_unc;
ggml_opt_result_loss(result, &loss, &loss_unc);
double accuracy;
double accuracy_unc;
ggml_opt_result_accuracy(result, &accuracy, &accuracy_unc);
const int64_t t_ibatch_us = ggml_time_us() - t_start_us;
int64_t t_ibatch_s = t_ibatch_us / 1000000;
const int64_t t_ibatch_h = t_ibatch_s / 3600;
t_ibatch_s -= t_ibatch_h * 3600;
const int64_t t_ibatch_m = t_ibatch_s / 60;
t_ibatch_s -= t_ibatch_m * 60;
const int64_t t_eta_us = t_ibatch_us * (ibatch_max - ibatch)/ibatch;
int64_t t_eta_s = t_eta_us / 1000000;
const int64_t t_eta_h = t_eta_s / 3600;
t_eta_s -= t_eta_h * 3600;
const int64_t t_eta_m = t_eta_s / 60;
t_eta_s -= t_eta_m * 60;
fprintf(stderr, "] data=%07" PRId64 "/%07" PRId64 " loss=%.5lf±%.5lf acc=%.2lf±%.2lf%% "
"t=%02" PRId64 ":%02" PRId64 ":%02" PRId64 " ETA=%02" PRId64 ":%02" PRId64 ":%02" PRId64 " \r",
idata, idata_max, loss, loss_unc, 100.0*accuracy, 100.0*accuracy_unc,
t_ibatch_h, t_ibatch_m, t_ibatch_s, t_eta_h, t_eta_m, t_eta_s);
if (ibatch == ibatch_max) {
fprintf(stderr, "\n");
}
fflush(stderr);
GGML_UNUSED(dataset);
}
void ggml_opt_fit(
ggml_backend_sched_t backend_sched,
ggml_context * ctx_compute,
ggml_tensor * inputs,
ggml_tensor * outputs,
ggml_opt_dataset_t dataset,
enum ggml_opt_loss_type loss_type,
enum ggml_opt_optimizer_type optimizer,
ggml_opt_get_optimizer_params get_opt_pars,
int64_t nepoch,
int64_t nbatch_logical,
float val_split,
bool silent) {
ggml_time_init();
const int64_t t_start_us = ggml_time_us();
const int64_t ndata = ggml_opt_dataset_data(dataset)->ne[1];
const int64_t nbatch_physical = inputs->ne[1];
GGML_ASSERT(ndata % nbatch_logical == 0);
GGML_ASSERT(nbatch_logical % nbatch_physical == 0);
const int64_t opt_period = nbatch_logical / nbatch_physical;
const int64_t nbatches_logical = ndata / nbatch_logical;
GGML_ASSERT(val_split >= 0.0f);
GGML_ASSERT(val_split < 1.0f);
const int64_t ibatch_split = int64_t(((1.0f - val_split) * nbatches_logical)) * opt_period; // train <-> val split index (physical)
const int64_t idata_split = ibatch_split * nbatch_physical;
int64_t epoch = 1;
ggml_opt_params params = ggml_opt_default_params(backend_sched, loss_type);
params.ctx_compute = ctx_compute;
params.inputs = inputs;
params.outputs = outputs;
params.opt_period = opt_period;
params.get_opt_pars = get_opt_pars;
params.get_opt_pars_ud = &epoch;
params.optimizer = optimizer;
ggml_opt_context_t opt_ctx = ggml_opt_init(params);
// Shuffling the data is generally useful but there is only a point if not all data is used in a single batch.
if (nbatch_logical < ndata) {
ggml_opt_dataset_shuffle(opt_ctx, dataset, -1); // Shuffle all data (train + validation).
}
ggml_opt_result_t result_train = ggml_opt_result_init();
ggml_opt_result_t result_val = ggml_opt_result_init();
ggml_opt_epoch_callback epoch_callback = silent ? nullptr : ggml_opt_epoch_callback_progress_bar;
for (; epoch <= nepoch; ++epoch) {
if (nbatch_logical < idata_split) {
ggml_opt_dataset_shuffle(opt_ctx, dataset, idata_split);
}
ggml_opt_result_reset(result_train);
ggml_opt_result_reset(result_val);
if (!silent) {
fprintf(stderr, "%s: epoch %04" PRId64 "/%04" PRId64 ":\n", __func__, epoch, nepoch);
}
ggml_opt_epoch(opt_ctx, dataset, result_train, result_val, idata_split, epoch_callback, epoch_callback);
if (!silent) {
fprintf(stderr, "\n");
}
}
if (!silent) {
int64_t t_total_s = (ggml_time_us() - t_start_us) / 1000000;
const int64_t t_total_h = t_total_s / 3600;
t_total_s -= t_total_h * 3600;
const int64_t t_total_m = t_total_s / 60;
t_total_s -= t_total_m * 60;
fprintf(stderr, "%s: training took %02" PRId64 ":%02" PRId64 ":%02" PRId64 "\n", __func__, t_total_h, t_total_m, t_total_s);
}
ggml_opt_free(opt_ctx);
ggml_opt_result_free(result_train);
ggml_opt_result_free(result_val);
}
enum ggml_opt_optimizer_type ggml_opt_context_optimizer_type(ggml_opt_context_t c) {
return c->optimizer;
}
GGML_API const char * ggml_opt_optimizer_name(enum ggml_opt_optimizer_type o) {
switch (o) {
case GGML_OPT_OPTIMIZER_TYPE_ADAMW:
return "adamw";
case GGML_OPT_OPTIMIZER_TYPE_SGD:
return "sgd";
default:
return "undefined";
};
}
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