vbatts/llama.cpp
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add ggml_opt_context, so that we can properly resume training

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otherwise the optimizer states, tracking statistics about the error function and its derivates,
will reset to zero each time ggml_opt is called, hindering convergence on resumed training.

now the optimizer context and all its memory is stored in a separate struct.
This commit is contained in:
xaedes 2023-05-21 15:16:07 +02:00
parent 1eee9255e7
commit ec1783c3e0
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GPG key ID: 30030EDD817EA2B1
2 changed files with 278 additions and 108 deletions
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330
ggml.c
View file

@ -14577,6 +14577,7 @@ static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g
static enum ggml_opt_result ggml_opt_adam(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_opt_params params,
struct ggml_tensor * f,
struct ggml_cgraph * gf,
@ -14602,6 +14603,12 @@ static enum ggml_opt_result ggml_opt_adam(
}
}
if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past)) {
int iter = opt->iter;
ggml_opt_init(opt->ctx, opt, params, nx);
opt->iter = iter;
}
// constants
const float sched = params.adam.sched;
const float decay = params.adam.decay * sched;
@ -14610,19 +14617,15 @@ static enum ggml_opt_result ggml_opt_adam(
const float beta2 = params.adam.beta2;
const float eps = params.adam.eps;
float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // view of the parameters
float * g1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient
float * g2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient squared
float * m = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment
float * v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment
float * mh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment hat
float * vh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment hat
float * x = opt->adam.x->data; // view of the parameters
float * g1 = opt->adam.g1->data; // gradient
float * g2 = opt->adam.g2->data; // gradient squared
float * m = opt->adam.m->data; // first moment
float * v = opt->adam.v->data; // second moment
float * mh = opt->adam.mh->data; // first moment hat
float * vh = opt->adam.vh->data; // second moment hat
float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values
// initialize
ggml_vec_set_f32(nx, m, 0.0f);
ggml_vec_set_f32(nx, v, 0.0f);
float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values
// update view
ggml_opt_get_params(np, ps, x);
@ -14632,16 +14635,27 @@ static enum ggml_opt_result ggml_opt_adam(
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(ctx, gb);
float fx_prev = ggml_get_f32_1d(f, 0);
opt->adam.fx_prev = ggml_get_f32_1d(f, 0);
opt->adam.fx_best = opt->adam.fx_prev;
if (pf) {
pf[0] = fx_prev;
pf[opt->iter % params.past] = opt->adam.fx_prev;
}
int n_no_improvement = 0;
float fx_best = fx_prev;
// initialize
if (opt->just_initialized) {
opt->adam.n_no_improvement = 0;
opt->just_initialized = false;
}
float * fx_best = &opt->adam.fx_best;
float * fx_prev = &opt->adam.fx_prev;
int * n_no_improvement = &opt->adam.n_no_improvement;
int iter0 = opt->iter;
// run the optimizer
for (int t = 0; t < params.adam.n_iter; ++t) {
opt->iter = iter0 + t + 1;
GGML_PRINT_DEBUG ("=== iter %d ===\n", t);
GGML_PRINT_DEBUG ("f = %10.6f\n", ggml_get_f32_1d(f, 0));
@ -14683,8 +14697,8 @@ static enum ggml_opt_result ggml_opt_adam(
ggml_vec_cpy_f32 (nx, mh, m);
ggml_vec_cpy_f32 (nx, vh, v);
ggml_vec_scale_f32(nx, mh, alpha/(1.0f - powf(beta1, t + 1)));
ggml_vec_scale_f32(nx, vh, 1.0f/(1.0f - powf(beta2, t + 1)));
ggml_vec_scale_f32(nx, mh, alpha/(1.0f - powf(beta1, opt->iter)));
ggml_vec_scale_f32(nx, vh, 1.0f/(1.0f - powf(beta2, opt->iter)));
ggml_vec_sqrt_f32 (nx, vh, vh);
ggml_vec_acc1_f32 (nx, vh, eps);
@ -14704,7 +14718,7 @@ static enum ggml_opt_result ggml_opt_adam(
const float fx = ggml_get_f32_1d(f, 0);
// check convergence
if (fabsf(fx - fx_prev)/fx < params.adam.eps_f) {
if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) {
GGML_PRINT_DEBUG("converged\n");
return GGML_OPT_OK;
@ -14713,32 +14727,32 @@ static enum ggml_opt_result ggml_opt_adam(
// delta-based convergence test
if (pf != NULL) {
// need at least params.past iterations to start checking for convergence
if (params.past <= t) {
const float rate = (pf[t%params.past] - fx)/fx;
if (params.past <= iter0 + t) {
const float rate = (pf[(iter0 + t)%params.past] - fx)/fx;
if (fabsf(rate) < params.delta) {
return GGML_OPT_OK;
}
}
pf[t%params.past] = fx;
pf[(iter0 + t)%params.past] = fx;
}
// check for improvement
if (params.max_no_improvement > 0) {
if (fx_best > fx) {
fx_best = fx;
n_no_improvement = 0;
if (fx_best[0] > fx) {
fx_best[0] = fx;
n_no_improvement[0] = 0;
} else {
++n_no_improvement;
++n_no_improvement[0];
if (n_no_improvement >= params.max_no_improvement) {
if (n_no_improvement[0] >= params.max_no_improvement) {
return GGML_OPT_OK;
}
}
}
fx_prev = fx;
fx_prev[0] = fx;
{
const int64_t t_end_cpu = ggml_cycles();
@ -14877,6 +14891,7 @@ static enum ggml_opt_result linesearch_backtracking(
static enum ggml_opt_result ggml_opt_lbfgs(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_opt_params params,
struct ggml_tensor * f,
struct ggml_cgraph * gf,
@ -14909,31 +14924,32 @@ static enum ggml_opt_result ggml_opt_lbfgs(
}
}
float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current parameters
float * xp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous parameters
float * g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current gradient
float * gp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous gradient
float * d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // search direction
if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past) || (opt->params.lbfgs.m != params.lbfgs.m)) {
int iter = opt->iter;
ggml_opt_init(ctx, opt, params, nx);
opt->iter = iter;
}
float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values
float * x = opt->lbfgs.x->data; // current parameters
float * xp = opt->lbfgs.xp->data; // previous parameters
float * g = opt->lbfgs.g->data; // current gradient
float * gp = opt->lbfgs.gp->data; // previous gradient
float * d = opt->lbfgs.d->data; // search direction
float * pf = params.past > 0 ? opt->lbfgs.pf->data : NULL; // past function values
float fx = 0.0f; // cost function value
float xnorm = 0.0f; // ||x||
float gnorm = 0.0f; // ||g||
float step = 0.0f;
// initialize x from the graph nodes
ggml_opt_get_params(np, ps, x);
// the L-BFGS memory
struct ggml_lbfgs_iteration_data * lm = alloca(sizeof(struct ggml_lbfgs_iteration_data)*m);
for (int i = 0; i < m; ++i) {
lm[i].alpha = 0.0f;
lm[i].ys = 0.0f;
lm[i].s = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data;
lm[i].y = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data;
}
float * lm_alpha = opt->lbfgs.lmal->data;
float * lm_ys = opt->lbfgs.lmys->data;
float * lm_s = opt->lbfgs.lms->data;
float * lm_y = opt->lbfgs.lmy->data;
// evaluate the function value and its gradient
{
@ -14948,12 +14964,6 @@ static enum ggml_opt_result ggml_opt_lbfgs(
fx = ggml_get_f32_1d(f, 0);
}
if (pf) {
pf[0] = fx;
}
float fx_best = fx;
// search direction = -gradient
ggml_vec_neg_f32(nx, d, g);
@ -14970,26 +14980,43 @@ static enum ggml_opt_result ggml_opt_lbfgs(
return GGML_OPT_OK;
}
// initial step
ggml_vec_norm_inv_f32(nx, &step, d);
if (opt->just_initialized) {
if (pf) {
pf[0] = fx;
}
opt->lbfgs.fx_best = fx;
int j = 0;
int k = 1;
int ls = 0;
int end = 0;
int bound = 0;
int n_no_improvement = 0;
// initial step
ggml_vec_norm_inv_f32(nx, &opt->lbfgs.step, d);
opt->lbfgs.j = 0;
opt->lbfgs.k = 1;
opt->lbfgs.end = 0;
opt->lbfgs.n_no_improvement = 0;
opt->just_initialized = false;
}
float * fx_best = &opt->lbfgs.fx_best;
float * step = &opt->lbfgs.step;
int * j = &opt->lbfgs.j;
int * k = &opt->lbfgs.k;
int * end = &opt->lbfgs.end;
int * n_no_improvement = &opt->lbfgs.n_no_improvement;
int ls = 0;
int bound = 0;
float ys = 0.0f;
float yy = 0.0f;
float beta = 0.0f;
int it = 0;
while (true) {
// store the current position and gradient vectors
ggml_vec_cpy_f32(nx, xp, x);
ggml_vec_cpy_f32(nx, gp, g);
ls = linesearch_backtracking(ctx, &params, nx, x, &fx, g, d, &step, xp, f, gf, gb, np, ps);
ls = linesearch_backtracking(ctx, &params, nx, x, &fx, g, d, step, xp, f, gf, gb, np, ps);
if (ls < 0) {
// linesearch failed - go back to the previous point and return
@ -15015,32 +15042,32 @@ static enum ggml_opt_result ggml_opt_lbfgs(
// delta-based convergence test
if (pf != NULL) {
// need at least params.past iterations to start checking for convergence
if (params.past <= k) {
const float rate = (pf[k%params.past] - fx)/fx;
if (params.past <= k[0]) {
const float rate = (pf[k[0]%params.past] - fx)/fx;
if (fabsf(rate) < params.delta) {
return GGML_OPT_OK;
}
}
pf[k%params.past] = fx;
pf[k[0]%params.past] = fx;
}
// check for improvement
if (params.max_no_improvement > 0) {
if (fx < fx_best) {
fx_best = fx;
n_no_improvement = 0;
if (fx < fx_best[0]) {
fx_best[0] = fx;
n_no_improvement[0] = 0;
} else {
n_no_improvement++;
n_no_improvement[0]++;
if (n_no_improvement >= params.max_no_improvement) {
if (n_no_improvement[0] >= params.max_no_improvement) {
return GGML_OPT_OK;
}
}
}
if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < k + 1) {
if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) {
// reached the maximum number of iterations
return GGML_OPT_DID_NOT_CONVERGE;
}
@ -15049,50 +15076,51 @@ static enum ggml_opt_result ggml_opt_lbfgs(
// s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}.
// y_{k+1} = g_{k+1} - g_{k}.
//
ggml_vec_sub_f32(nx, lm[end].s, x, xp);
ggml_vec_sub_f32(nx, lm[end].y, g, gp);
ggml_vec_sub_f32(nx, &lm_s[end[0]*nx], x, xp);
ggml_vec_sub_f32(nx, &lm_y[end[0]*nx], g, gp);
// compute scalars ys and yy:
// ys = y^t \cdot s -> 1 / \rho.
// yy = y^t \cdot y.
//
ggml_vec_dot_f32(nx, &ys, lm[end].y, lm[end].s);
ggml_vec_dot_f32(nx, &yy, lm[end].y, lm[end].y);
ggml_vec_dot_f32(nx, &ys, &lm_y[end[0]*nx], &lm_s[end[0] *nx]);
ggml_vec_dot_f32(nx, &yy, &lm_y[end[0]*nx], &lm_y[end[0]*nx]);
lm[end].ys = ys;
lm_ys[end[0]] = ys;
// find new search direction
// ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS
bound = (m <= k) ? m : k;
k++;
end = (end + 1)%m;
bound = (m <= k[0]) ? m : k[0];
k[0]++;
it++;
end[0] = (end[0] + 1)%m;
// initialize search direction with -g
ggml_vec_neg_f32(nx, d, g);
j = end;
j[0] = end[0];
for (int i = 0; i < bound; ++i) {
j = (j + m - 1) % m;
j[0] = (j[0] + m - 1) % m;
// \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1}
ggml_vec_dot_f32(nx, &lm[j].alpha, lm[j].s, d);
lm[j].alpha /= lm[j].ys;
ggml_vec_dot_f32(nx, &lm_alpha[j[0]], &lm_s[j[0]*nx], d);
lm_alpha[j[0]] /= lm_ys[j[0]];
// q_{i} = q_{i+1} - \alpha_{i} y_{i}
ggml_vec_mad_f32(nx, d, lm[j].y, -lm[j].alpha);
ggml_vec_mad_f32(nx, d, &lm_y[j[0]*nx], -lm_alpha[j[0]]);
}
ggml_vec_scale_f32(nx, d, ys/yy);
for (int i = 0; i < bound; ++i) {
// \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i}
ggml_vec_dot_f32(nx, &beta, lm[j].y, d);
beta /= lm[j].ys;
ggml_vec_dot_f32(nx, &beta, &lm_y[j[0]*nx], d);
beta /= lm_ys[j[0]];
// \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j}
ggml_vec_mad_f32(nx, d, lm[j].s, lm[j].alpha - beta);
j = (j + 1)%m;
ggml_vec_mad_f32(nx, d, &lm_s[j[0]*nx], lm_alpha[j[0]] - beta);
j[0] = (j[0] + 1)%m;
}
step = 1.0;
step[0] = 1.0;
}
return GGML_OPT_DID_NOT_CONVERGE;
@ -15161,6 +15189,71 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
return result;
}
GGML_API void ggml_opt_init(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_opt_params params,
int64_t nx) {
opt->ctx = ctx;
opt->params = params;
opt->iter = 0;
opt->nx = nx;
opt->just_initialized = true;
switch (opt->params.type) {
case GGML_OPT_ADAM:
{
opt->adam.x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->adam.g1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->adam.g2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->adam.m = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->adam.v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->adam.mh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->adam.vh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->adam.pf = params.past > 0
? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)
: NULL;
ggml_set_zero(opt->adam.x);
ggml_set_zero(opt->adam.g1);
ggml_set_zero(opt->adam.g2);
ggml_set_zero(opt->adam.m);
ggml_set_zero(opt->adam.v);
ggml_set_zero(opt->adam.mh);
ggml_set_zero(opt->adam.vh);
if (opt->adam.pf) {
ggml_set_zero(opt->adam.pf);
}
} break;
case GGML_OPT_LBFGS:
{
opt->lbfgs.x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->lbfgs.xp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->lbfgs.g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->lbfgs.gp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->lbfgs.d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
opt->lbfgs.pf = params.past > 0
? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)
: NULL;
opt->lbfgs.lmal = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.lbfgs.m);
opt->lbfgs.lmys = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.lbfgs.m);
opt->lbfgs.lms = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, params.lbfgs.m);
opt->lbfgs.lmy = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, params.lbfgs.m);
ggml_set_zero(opt->lbfgs.x);
ggml_set_zero(opt->lbfgs.xp);
ggml_set_zero(opt->lbfgs.g);
ggml_set_zero(opt->lbfgs.gp);
ggml_set_zero(opt->lbfgs.d);
ggml_set_zero(opt->lbfgs.pf);
if (opt->lbfgs.pf) {
ggml_set_zero(opt->lbfgs.pf);
}
ggml_set_zero(opt->lbfgs.lmal);
ggml_set_zero(opt->lbfgs.lmys);
ggml_set_zero(opt->lbfgs.lms);
ggml_set_zero(opt->lbfgs.lmy);
} break;
}
}
enum ggml_opt_result ggml_opt(
struct ggml_context * ctx,
struct ggml_opt_params params,
@ -15183,30 +15276,10 @@ enum ggml_opt_result ggml_opt(
enum ggml_opt_result result = GGML_OPT_OK;
// build forward + backward compute graphs
struct ggml_cgraph gf = ggml_build_forward (f);
struct ggml_cgraph gb = ggml_build_backward(ctx, &gf, true);
struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context));
switch (params.type) {
case GGML_OPT_ADAM:
{
result = ggml_opt_adam(ctx, params, f, &gf, &gb);
} break;
case GGML_OPT_LBFGS:
{
result = ggml_opt_lbfgs(ctx, params, f, &gf, &gb);
} break;
}
if (params.print_forward_graph) {
ggml_graph_print (&gf);
ggml_graph_dump_dot(&gf, NULL, "opt-forward.dot");
}
if (params.print_backward_graph) {
ggml_graph_print (&gb);
ggml_graph_dump_dot(&gb, &gf, "opt-backward.dot");
}
ggml_opt_init(ctx, opt, params, 0);
result = ggml_opt_resume(ctx, opt, f);
if (free_ctx) {
ggml_free(ctx);
@ -15215,6 +15288,47 @@ enum ggml_opt_result ggml_opt(
return result;
}
enum ggml_opt_result ggml_opt_resume(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_tensor * f) {
// build forward + backward compute graphs
struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / GGML_TYPE_SIZE[GGML_TYPE_I32]+ (sizeof(struct ggml_cgraph) % GGML_TYPE_SIZE[GGML_TYPE_I32] ? 1 : 0));
struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / GGML_TYPE_SIZE[GGML_TYPE_I32]+ (sizeof(struct ggml_cgraph) % GGML_TYPE_SIZE[GGML_TYPE_I32] ? 1 : 0));
struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data;
struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data;
*gf = ggml_build_forward (f);
*gb = ggml_build_backward(ctx, gf, true);
enum ggml_opt_result result = GGML_OPT_OK;
switch (opt->params.type) {
case GGML_OPT_ADAM:
{
result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb);
} break;
case GGML_OPT_LBFGS:
{
result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb);
} break;
}
if (opt->params.print_forward_graph) {
ggml_graph_print (gf);
ggml_graph_dump_dot(gf, NULL, "opt-forward.dot");
}
if (opt->params.print_backward_graph) {
ggml_graph_print (gb);
ggml_graph_dump_dot(gb, gf, "opt-backward.dot");
}
return result;
}
////////////////////////////////////////////////////////////////////////////////
size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) {

56
ggml.h
View file

@ -1081,6 +1081,49 @@ extern "C" {
} lbfgs;
};
struct ggml_opt_context {
struct ggml_context * ctx;
struct ggml_opt_params params;
int iter;
int64_t nx; // number of parameter elements
bool just_initialized;
struct {
struct ggml_tensor * x; // view of the parameters
struct ggml_tensor * g1; // gradient
struct ggml_tensor * g2; // gradient squared
struct ggml_tensor * m; // first moment
struct ggml_tensor * v; // second moment
struct ggml_tensor * mh; // first moment hat
struct ggml_tensor * vh; // second moment hat
struct ggml_tensor * pf; // past function values
float fx_best;
float fx_prev;
int n_no_improvement;
} adam;
struct {
struct ggml_tensor * x; // current parameters
struct ggml_tensor * xp; // previous parameters
struct ggml_tensor * g; // current gradient
struct ggml_tensor * gp; // previous gradient
struct ggml_tensor * d; // search direction
struct ggml_tensor * pf; // past function values
struct ggml_tensor * lmal; // the L-BFGS memory alpha
struct ggml_tensor * lmys; // the L-BFGS memory ys
struct ggml_tensor * lms; // the L-BFGS memory s
struct ggml_tensor * lmy; // the L-BFGS memory y
float fx_best;
float step;
int j;
int k;
int end;
int n_no_improvement;
} lbfgs;
};
GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
// optimize the function defined by the tensor f
@ -1089,6 +1132,19 @@ extern "C" {
struct ggml_opt_params params,
struct ggml_tensor * f);
// initialize optimizer context
GGML_API void ggml_opt_init(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_opt_params params,
int64_t nx);
// continue optimizing the function defined by the tensor f
GGML_API enum ggml_opt_result ggml_opt_resume(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_tensor * f);
//
// quantization
//