vbatts/llama.cpp
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add gradient accumulation

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specify number accumulation steps with '--grad-acc N'.
this will simulate a bigger batch size of grad_acc*batch.
This commit is contained in:
xaedes 2023-09-05 01:09:06 +02:00
parent d3afd7131e
commit c1c3b0e0c2
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GPG key ID: 30030EDD817EA2B1
4 changed files with 264 additions and 198 deletions
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185
examples/finetune/finetune.cpp
View file

@ -1730,6 +1730,7 @@ struct train_params {
int n_ctx;
int n_threads;
int n_batch;
int n_gradient_accumulation;
bool custom_n_ctx;
@ -1804,6 +1805,7 @@ struct train_params get_default_train_params() {
params.n_ctx = 128;
params.n_threads = 6;
params.n_batch = 8;
params.n_gradient_accumulation = 1;
params.custom_n_ctx = false;
@ -1880,6 +1882,7 @@ void train_print_usage(int /*argc*/, char ** argv, const struct train_params * p
fprintf(stderr, " -c N, --ctx N Context size used during training (default %d)\n", params->n_ctx);
fprintf(stderr, " -t N, --threads N Number of threads (default %d)\n", params->n_threads);
fprintf(stderr, " -b N, --batch N Parallel batch size (default %d)\n", params->n_batch);
fprintf(stderr, " --grad-acc N Number of gradient accumulation steps (simulates larger batch size of batch*gradacc) (default %d)\n", params->n_gradient_accumulation);
fprintf(stderr, " --norm-rms-eps F RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps);
fprintf(stderr, " --rope-freq-base F Frequency base for ROPE (default %f)\n", params->rope_freq_base);
fprintf(stderr, " --rope-freq-scale F Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
@ -2015,6 +2018,12 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
break;
}
params->n_batch = std::stoi(argv[i]);
} else if (arg == "--grad-acc") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_gradient_accumulation = std::stoi(argv[i]);
} else if (arg == "--norm-rms-eps") {
if (++i >= argc) {
invalid_param = true;
@ -2299,83 +2308,85 @@ void print_duration(float fmillis) {
printf("%02lld:%02lld:%02lld", hours, minutes, seconds);
}
void opt_callback(void * vdata, float * sched) {
void opt_callback(void * vdata, int accum_step, float * sched) {
struct opt_callback_data * data = (struct opt_callback_data *) vdata;
struct train_params * params = data->params;
struct ggml_opt_context * opt = data->opt;
int n_batch = params->n_batch;
int n_ctx = params->n_ctx;
int64_t now = ggml_time_ms();
if (now > data->last_time) {
float dt = now - data->last_time;
if (data->time_per_iter == 0) {
data->time_per_iter = dt;
} else {
const float gain = 0.7f;
data->time_per_iter = data->time_per_iter*(1.0f-gain) + dt*gain;
if (accum_step == 0) {
int64_t now = ggml_time_ms();
if (now > data->last_time) {
float dt = now - data->last_time;
if (data->time_per_iter == 0) {
data->time_per_iter = dt;
} else {
const float gain = 0.7f;
data->time_per_iter = data->time_per_iter*(1.0f-gain) + dt*gain;
}
}
}
data->last_time = now;
float remaining_time = 0;
if (data->time_per_iter > 0) {
const int n_iter = params->use_adam ? params->adam_n_iter : params->lbfgs_n_iter;
const int done_iter = opt->iter - data->first_iter;
const int remaining_iter = n_iter - done_iter;
remaining_time = remaining_iter * data->time_per_iter;
}
const bool save_now = (params->save_every > 0) && (opt->iter - data->last_save_iter >= params->save_every);
if (save_now) {
int new_iters = opt->iter - data->last_save_iter;
data->lora->train_its += new_iters;
data->lora->train_samples += new_iters * n_batch;
data->lora->train_tokens += new_iters * n_batch * n_ctx;
if (strlen(params->fn_checkpoint_out) > 0) {
save_checkpoint_lora_file(params->fn_checkpoint_out, data->model, data->lora, opt, params->pattern_fn_it, opt->iter, params->fn_latest);
save_checkpoint_lora_file(params->fn_checkpoint_out, data->model, data->lora, opt, params->pattern_fn_it, -1, params->fn_latest);
data->last_time = now;
float remaining_time = 0;
if (data->time_per_iter > 0) {
const int n_iter = params->use_adam ? params->adam_n_iter : params->lbfgs_n_iter;
const int done_iter = opt->iter - data->first_iter;
const int remaining_iter = n_iter - done_iter;
remaining_time = remaining_iter * data->time_per_iter;
}
if (strlen(params->fn_lora_out) > 0) {
save_as_llama_lora(data->lora, params->fn_lora_out, params->pattern_fn_it, opt->iter, params->fn_latest);
save_as_llama_lora(data->lora, params->fn_lora_out, params->pattern_fn_it, -1, params->fn_latest);
const bool save_now = (params->save_every > 0) && (opt->iter - data->last_save_iter >= params->save_every);
if (save_now) {
int new_iters = opt->iter - data->last_save_iter;
data->lora->train_its += new_iters;
data->lora->train_samples += new_iters * n_batch;
data->lora->train_tokens += new_iters * n_batch * n_ctx;
if (strlen(params->fn_checkpoint_out) > 0) {
save_checkpoint_lora_file(params->fn_checkpoint_out, data->model, data->lora, opt, params->pattern_fn_it, opt->iter, params->fn_latest);
save_checkpoint_lora_file(params->fn_checkpoint_out, data->model, data->lora, opt, params->pattern_fn_it, -1, params->fn_latest);
}
if (strlen(params->fn_lora_out) > 0) {
save_as_llama_lora(data->lora, params->fn_lora_out, params->pattern_fn_it, opt->iter, params->fn_latest);
save_as_llama_lora(data->lora, params->fn_lora_out, params->pattern_fn_it, -1, params->fn_latest);
}
data->last_save_iter = opt->iter;
}
data->last_save_iter = opt->iter;
}
*sched = (opt->iter < params->warmup)
? (float) opt->iter / (float) params->warmup
: cosine_decay_restart(
params->cos_decay_steps,
params->cos_decay_min,
opt->iter - params->warmup,
params->cos_decay_restart,
params->enable_restart);
float min_sched = params->adam_min_alpha / params->adam_alpha;
*sched = min_sched + *sched * (1.0f - min_sched);
*sched = (opt->iter < params->warmup)
? (float) opt->iter / (float) params->warmup
: cosine_decay_restart(
params->cos_decay_steps,
params->cos_decay_min,
opt->iter - params->warmup,
params->cos_decay_restart,
params->enable_restart);
float min_sched = params->adam_min_alpha / params->adam_alpha;
*sched = min_sched + *sched * (1.0f - min_sched);
int impr_plot = -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);
if (impr_plot > 0) impr_plot = 0;
if (std::isnan(opt->loss_before) || std::isnan(opt->loss_before)) impr_plot = 0;
printf("%s: iter=%*d sched=%f loss=%f",
__func__, 6, opt->iter, *sched, opt->loss_after);
if (data->time_per_iter > 0) {
printf(" dt=");
print_duration(data->time_per_iter);
printf(" eta=");
print_duration(remaining_time);
}
int impr_plot = -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);
if (impr_plot > 0) impr_plot = 0;
if (std::isnan(opt->loss_before) || std::isnan(opt->loss_before)) impr_plot = 0;
printf("%s: iter=%*d sched=%f loss=%f",
__func__, 6, opt->iter, *sched, opt->loss_after);
if (data->time_per_iter > 0) {
printf(" dt=");
print_duration(data->time_per_iter);
printf(" eta=");
print_duration(remaining_time);
}
float improvement = opt->loss_before - opt->loss_after;
const float plot_scale = 10.0f;
int bar_len = (int)(1 + improvement*plot_scale + 0.5);
printf(" |");
for (int i=0; i<bar_len; ++i) {
printf("-");
float improvement = opt->loss_before - opt->loss_after;
const float plot_scale = 10.0f;
int bar_len = (int)(1 + improvement*plot_scale + 0.5);
printf(" |");
for (int i=0; i<bar_len; ++i) {
printf("-");
}
printf(">");
// printf("improvement: %*d>", impr_plot, (int)0);
printf("\n");
}
printf(">");
// printf("improvement: %*d>", impr_plot, (int)0);
printf("\n");
if (data->shuffle_countdown < n_batch) {
printf("%s: reshuffle samples\n", __func__);
@ -2491,30 +2502,32 @@ int main(int argc, char ** argv) {
// set opt params from command line
if (params.use_adam) {
opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
opt->params.print_forward_graph = false;
opt->params.print_backward_graph = false;
opt->params.n_threads = params.n_threads;
opt->params.past = params.opt_past;
opt->params.delta = params.opt_delta;
opt->params.max_no_improvement = params.opt_max_no_improvement;
opt->params.adam.n_iter = params.adam_n_iter;
opt->params.adam.sched = 1.0f;
opt->params.adam.alpha = params.adam_alpha;
opt->params.adam.decay = params.adam_decay;
opt->params.adam.decay_min_ndim = params.adam_decay_min_ndim;
opt->params.adam.beta1 = params.adam_beta1;
opt->params.adam.beta2 = params.adam_beta2;
opt->params.adam.gclip = params.adam_gclip;
opt->params.adam.eps_f = params.adam_eps_f;
opt->params.print_forward_graph = false;
opt->params.print_backward_graph = false;
opt->params.n_threads = params.n_threads;
opt->params.past = params.opt_past;
opt->params.delta = params.opt_delta;
opt->params.max_no_improvement = params.opt_max_no_improvement;
opt->params.n_gradient_accumulation = params.n_gradient_accumulation;
opt->params.adam.n_iter = params.adam_n_iter;
opt->params.adam.sched = 1.0f;
opt->params.adam.alpha = params.adam_alpha;
opt->params.adam.decay = params.adam_decay;
opt->params.adam.decay_min_ndim = params.adam_decay_min_ndim;
opt->params.adam.beta1 = params.adam_beta1;
opt->params.adam.beta2 = params.adam_beta2;
opt->params.adam.gclip = params.adam_gclip;
opt->params.adam.eps_f = params.adam_eps_f;
} else {
opt->params = ggml_opt_default_params(GGML_OPT_LBFGS);
opt->params.print_forward_graph = false;
opt->params.print_backward_graph = false;
opt->params.n_threads = params.n_threads;
opt->params.past = params.opt_past;
opt->params.delta = params.opt_delta;
opt->params.max_no_improvement = params.opt_max_no_improvement;
opt->params.lbfgs.n_iter = params.lbfgs_n_iter;
opt->params.print_forward_graph = false;
opt->params.print_backward_graph = false;
opt->params.n_threads = params.n_threads;
opt->params.past = params.opt_past;
opt->params.delta = params.opt_delta;
opt->params.max_no_improvement = params.opt_max_no_improvement;
opt->params.n_gradient_accumulation = params.n_gradient_accumulation;
opt->params.lbfgs.n_iter = params.lbfgs_n_iter;
}
ggml_allocr * alloc = NULL;

122
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -1299,8 +1299,9 @@ struct train_params {
int n_ff;
int n_threads;
int n_batch;
int n_examples;
int n_batch;
int n_gradient_accumulation;
float f_norm_rms_eps;
float rope_freq_base;
@ -1362,8 +1363,9 @@ struct train_params get_default_train_params() {
params.n_ff = 768;
params.n_threads = 6;
params.n_batch = 8;
params.n_examples = 1;
params.n_batch = 8;
params.n_gradient_accumulation = 1;
params.f_norm_rms_eps = 1e-5f;
params.rope_freq_base = 10000.0f;
@ -1428,8 +1430,9 @@ void train_print_usage(int /*argc*/, char ** argv, const struct train_params * p
fprintf(stderr, " --rope-freq-base F Frequency base for ROPE (default %f)\n", params->rope_freq_base);
fprintf(stderr, " --rope-freq-scale F Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
fprintf(stderr, " -t N, --threads N Number of threads (default %d)\n", params->n_threads);
fprintf(stderr, " -b N, --batch N Parallel batch size (default %d)\n", params->n_batch);
fprintf(stderr, " -n N, --examples N Number of examples to train (default %d)\n", params->n_examples);
fprintf(stderr, " -b N, --batch N Parallel batch size (default %d)\n", params->n_batch);
fprintf(stderr, " --grad-acc N Number of gradient accumulation steps (simulates larger batch size of batch*gradacc) (default %d)\n", params->n_gradient_accumulation);
fprintf(stderr, " --print-info-interval N Print infos during training each N examples (default %d)\n", params->print_info_interval);
fprintf(stderr, " --samples-after-nl Training samples start after newlines. (default %s)\n", params->samples_start_after_nl ? "on" : "off");
fprintf(stderr, " --use-lbfgs Use LBFGS optimizer instead of default Adam\n");
@ -1591,6 +1594,12 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
break;
}
params->n_batch = std::stoi(argv[i]);
} else if (arg == "--grad-acc") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_gradient_accumulation = std::stoi(argv[i]);
} else if (arg == "-n" || arg == "--examples") {
if (++i >= argc) {
invalid_param = true;
@ -1779,46 +1788,49 @@ struct opt_callback_data {
struct ggml_tensor * target_probs;
};
void opt_callback(void * vdata, float * sched) {
void opt_callback(void * vdata, int accum_step, float * sched) {
struct opt_callback_data * data = (struct opt_callback_data *) vdata;
struct train_params * params = data->params;
struct ggml_opt_context * opt = data->opt;
int n_batch = params->n_batch;
int n_ctx = params->n_ctx;
const bool save_now = (params->save_every > 0) && (opt->iter - data->last_save_iter >= params->save_every);
if (save_now) {
int new_iters = opt->iter - data->last_save_iter;
data->model->train_its += new_iters;
data->model->train_samples += new_iters * n_batch;
data->model->train_tokens += new_iters * n_batch * n_ctx;
if (accum_step == 0) {
const bool save_now = (params->save_every > 0) && (opt->iter - data->last_save_iter >= params->save_every);
if (save_now) {
int new_iters = opt->iter - data->last_save_iter;
data->model->train_its += new_iters;
data->model->train_samples += new_iters * n_batch;
data->model->train_tokens += new_iters * n_batch * n_ctx;
if (strlen(params->fn_checkpoint_out) > 0) {
save_checkpoint_file(params->fn_checkpoint_out, params->fn_vocab_model, data->model, opt, params->pattern_fn_it, opt->iter, params->fn_latest);
save_checkpoint_file(params->fn_checkpoint_out, params->fn_vocab_model, data->model, opt, params->pattern_fn_it, -1, params->fn_latest);
if (strlen(params->fn_checkpoint_out) > 0) {
save_checkpoint_file(params->fn_checkpoint_out, params->fn_vocab_model, data->model, opt, params->pattern_fn_it, opt->iter, params->fn_latest);
save_checkpoint_file(params->fn_checkpoint_out, params->fn_vocab_model, data->model, opt, params->pattern_fn_it, -1, params->fn_latest);
}
if (strlen(params->fn_model_out) > 0) {
save_llama_model_file(params->fn_model_out, params->fn_vocab_model, data->model, params->pattern_fn_it, opt->iter, params->fn_latest);
save_llama_model_file(params->fn_model_out, params->fn_vocab_model, data->model, params->pattern_fn_it, -1, params->fn_latest);
}
data->last_save_iter = opt->iter;
}
if (strlen(params->fn_model_out) > 0) {
save_llama_model_file(params->fn_model_out, params->fn_vocab_model, data->model, params->pattern_fn_it, opt->iter, params->fn_latest);
save_llama_model_file(params->fn_model_out, params->fn_vocab_model, data->model, params->pattern_fn_it, -1, params->fn_latest);
}
data->last_save_iter = opt->iter;
*sched = (opt->iter < params->warmup)
? (float) opt->iter / (float) params->warmup
: cosine_decay_restart(
params->cos_decay_steps,
params->cos_decay_min,
opt->iter - params->warmup,
params->cos_decay_restart,
params->enable_restart);
float min_sched = params->adam_min_alpha / params->adam_alpha;
*sched = min_sched + *sched * (1.0f - min_sched);
int impr_plot = std::isnan(opt->loss_after) ? 0 : -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);
printf("%s: iter=%*d, sched=%f loss0=%f loss=%f | improvement: %*d>\n", __func__, 6, opt->iter, *sched, opt->loss_before, opt->loss_after, impr_plot, (int)0);
}
*sched = (opt->iter < params->warmup)
? (float) opt->iter / (float) params->warmup
: cosine_decay_restart(
params->cos_decay_steps,
params->cos_decay_min,
opt->iter - params->warmup,
params->cos_decay_restart,
params->enable_restart);
float min_sched = params->adam_min_alpha / params->adam_alpha;
*sched = min_sched + *sched * (1.0f - min_sched);
int impr_plot = std::isnan(opt->loss_after) ? 0 : -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);
printf("%s: iter=%*d, sched=%f loss0=%f loss=%f | improvement: %*d>\n", __func__, 6, opt->iter, *sched, opt->loss_before, opt->loss_after, impr_plot, (int)0);
if (data->shuffle_countdown < n_batch) {
printf("%s: reshuffle samples\n", __func__);
shuffle_ints(data->samples_data, data->samples_data + data->samples_size);
@ -1917,29 +1929,31 @@ int main(int argc, char ** argv) {
struct ggml_opt_params opt_params_adam = ggml_opt_default_params(GGML_OPT_ADAM);
struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS);
opt_params_adam.print_forward_graph = false;
opt_params_adam.print_backward_graph = false;
opt_params_adam.n_threads = params.n_threads;
opt_params_adam.past = params.opt_past;
opt_params_adam.delta = params.opt_delta;
opt_params_adam.max_no_improvement = params.opt_max_no_improvement;
opt_params_adam.adam.n_iter = params.adam_n_iter;
opt_params_adam.adam.sched = 1.0f;
opt_params_adam.adam.alpha = params.adam_alpha;
opt_params_adam.adam.decay = params.adam_decay;
opt_params_adam.adam.decay_min_ndim = params.adam_decay_min_ndim;
opt_params_adam.adam.beta1 = params.adam_beta1;
opt_params_adam.adam.beta2 = params.adam_beta2;
opt_params_adam.adam.gclip = params.adam_gclip;
opt_params_adam.adam.eps_f = params.adam_eps_f;
opt_params_adam.print_forward_graph = false;
opt_params_adam.print_backward_graph = false;
opt_params_adam.n_threads = params.n_threads;
opt_params_adam.past = params.opt_past;
opt_params_adam.delta = params.opt_delta;
opt_params_adam.max_no_improvement = params.opt_max_no_improvement;
opt_params_adam.n_gradient_accumulation = params.n_gradient_accumulation;
opt_params_adam.adam.n_iter = params.adam_n_iter;
opt_params_adam.adam.sched = 1.0f;
opt_params_adam.adam.alpha = params.adam_alpha;
opt_params_adam.adam.decay = params.adam_decay;
opt_params_adam.adam.decay_min_ndim = params.adam_decay_min_ndim;
opt_params_adam.adam.beta1 = params.adam_beta1;
opt_params_adam.adam.beta2 = params.adam_beta2;
opt_params_adam.adam.gclip = params.adam_gclip;
opt_params_adam.adam.eps_f = params.adam_eps_f;
opt_params_lbfgs.print_forward_graph = false;
opt_params_lbfgs.print_backward_graph = false;
opt_params_lbfgs.n_threads = params.n_threads;
opt_params_adam.past = params.opt_past;
opt_params_adam.delta = params.opt_delta;
opt_params_adam.max_no_improvement = params.opt_max_no_improvement;
opt_params_lbfgs.lbfgs.n_iter = params.lbfgs_n_iter;
opt_params_lbfgs.print_forward_graph = false;
opt_params_lbfgs.print_backward_graph = false;
opt_params_lbfgs.n_threads = params.n_threads;
opt_params_lbfgs.past = params.opt_past;
opt_params_lbfgs.delta = params.opt_delta;
opt_params_lbfgs.max_no_improvement = params.opt_max_no_improvement;
opt_params_lbfgs.n_gradient_accumulation = params.n_gradient_accumulation;
opt_params_lbfgs.lbfgs.n_iter = params.lbfgs_n_iter;
opt->ctx = model.ctx;
opt->params = params.use_adam ? opt_params_adam : opt_params_lbfgs;

150
ggml.c
View file

@ -19112,7 +19112,7 @@ static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float *
}
static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) {
int i = 0;
int64_t i = 0;
for (int p = 0; p < np; ++p) {
const int64_t ne = ggml_nelements(ps[p]) ;
// TODO: add function to get all elements at once
@ -19122,6 +19122,17 @@ static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g
}
}
static void ggml_opt_acc_grad(int np, struct ggml_tensor * const ps[], float * g, float scale) {
int64_t i = 0;
for (int p = 0; p < np; ++p) {
const int64_t ne = ggml_nelements(ps[p]) ;
// TODO: add function to get all elements at once
for (int64_t j = 0; j < ne; ++j) {
g[i++] += ggml_get_f32_1d(ps[p]->grad, j) * scale;
}
}
}
//
// ADAM
//
@ -19170,26 +19181,37 @@ static enum ggml_opt_result ggml_opt_adam(
const float eps = params.adam.eps;
const float gclip = params.adam.gclip;
const int decay_min_ndim = params.adam.decay_min_ndim;
const int n_accum = MAX(1, params.n_gradient_accumulation);
const float accum_norm = 1.0f / (float) n_accum;
float * g = opt->adam.g->data; // gradients
float * m = opt->adam.m->data; // first moment
float * v = opt->adam.v->data; // second moment
float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values
if (callback) {
callback(callback_data, &sched);
}
// compute the function value
// ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
ggml_graph_compute(gb, &cplan);
opt->adam.fx_prev = ggml_get_f32_1d(f, 0);
// compute the function value
float fx = 0;
ggml_set_zero(opt->adam.g);
for (int accum_step = 0; accum_step < n_accum; ++accum_step) {
if (callback) {
callback(callback_data, accum_step, &sched);
}
// ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(gb, &cplan);
ggml_opt_acc_grad(np, ps, g, accum_norm);
fx += ggml_get_f32_1d(f, 0);
}
fx *= accum_norm;
opt->adam.fx_prev = fx;
opt->adam.fx_best = opt->adam.fx_prev;
if (pf) {
pf[opt->iter % params.past] = opt->adam.fx_prev;
@ -19234,12 +19256,8 @@ static enum ggml_opt_result ggml_opt_adam(
if (gclip > 0.0f) {
// gradient clipping
ggml_float sum = 0.0;
for (int p = 0; p < np; ++p) {
const int64_t ne = ggml_nelements(ps[p]);
for (int64_t j = 0; j < ne; ++j) {
float g = ggml_get_f32_1d(ps[p]->grad, j);
sum += (ggml_float)(g*g);
}
for (int64_t i = 0; i < nx; ++i) {
sum += (ggml_float)(g[i]*g[i]);
}
ggml_float norm = sqrt(sum);
if (norm > (ggml_float) gclip) {
@ -19253,10 +19271,10 @@ static enum ggml_opt_result ggml_opt_adam(
const int64_t ne = ggml_nelements(ps[p]);
const float p_decay = ((ps[p]->n_dims >= decay_min_ndim) ? decay : 0.0f) * sched;
for (int64_t j = 0; j < ne; ++j) {
float x = ggml_get_f32_1d(ps[p], j);
float g = ggml_get_f32_1d(ps[p]->grad, j)*gnorm;
m[i] = m[i]*beta1 + g*(1.0f - beta1);
v[i] = v[i]*beta2 + g*g*(1.0f - beta2);
float x = ggml_get_f32_1d(ps[p], j);
float g_ = g[i]*gnorm;
m[i] = m[i]*beta1 + g_*(1.0f - beta1);
v[i] = v[i]*beta2 + g_*g_*(1.0f - beta2);
float mh = m[i]*beta1h;
float vh = v[i]*beta2h;
vh = sqrtf(vh) + eps;
@ -19267,16 +19285,20 @@ static enum ggml_opt_result ggml_opt_adam(
}
}
if (callback) {
callback(callback_data, &sched);
fx = 0;
ggml_set_zero(opt->adam.g);
for (int accum_step = 0; accum_step < n_accum; ++accum_step) {
if (callback) {
callback(callback_data, accum_step, &sched);
}
// ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(gb, &cplan);
ggml_opt_acc_grad(np, ps, g, accum_norm);
fx += ggml_get_f32_1d(f, 0);
}
fx *= accum_norm;
// ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(gb, &cplan);
const float fx = ggml_get_f32_1d(f, 0);
opt->loss_after = fx;
@ -19373,6 +19395,9 @@ static enum ggml_opt_result linesearch_backtracking(
const float dec = 0.5f;
const float inc = 2.1f;
const int n_accum = MAX(1, params->n_gradient_accumulation);
const float accum_norm = 1.0f / (float) n_accum;
if (*step <= 0.f) {
return GGML_LINESEARCH_INVALID_PARAMETERS;
}
@ -19390,12 +19415,6 @@ static enum ggml_opt_result linesearch_backtracking(
dgtest = params->lbfgs.ftol*dginit;
while (true) {
if (callback) {
// LBFG-S does not support learning rate -> ignore learning schedule
float sched = 0;
callback(callback_data, &sched);
}
ggml_vec_cpy_f32(nx, x, xp);
ggml_vec_mad_f32(nx, x, d, *step);
@ -19403,14 +19422,22 @@ static enum ggml_opt_result linesearch_backtracking(
{
ggml_opt_set_params(np, ps, x);
//ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
*fx = 0;
memset(g, 0, sizeof(float)*nx);
for (int accum_step = 0; accum_step < n_accum; ++accum_step) {
if (callback) {
// LBFG-S does not support learning rate -> ignore learning schedule
float sched = 0;
callback(callback_data, accum_step, &sched);
}
// ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(gb, cplan);
ggml_opt_acc_grad(np, ps, g, accum_norm);
*fx += ggml_get_f32_1d(f, 0);
}
*fx *= accum_norm;
ggml_graph_compute(gb, cplan);
ggml_opt_get_grad(np, ps, g);
*fx = ggml_get_f32_1d(f, 0);
}
++count;
@ -19512,6 +19539,9 @@ static enum ggml_opt_result ggml_opt_lbfgs(
float * pf = params.past > 0 ? opt->lbfgs.pf->data : NULL; // past function values
const int n_accum = MAX(1, params.n_gradient_accumulation);
const float accum_norm = 1.0f / (float) n_accum;
float fx = 0.0f; // cost function value
float xnorm = 0.0f; // ||x||
float gnorm = 0.0f; // ||g||
@ -19525,24 +19555,25 @@ static enum ggml_opt_result ggml_opt_lbfgs(
float * lm_s = opt->lbfgs.lms->data;
float * lm_y = opt->lbfgs.lmy->data;
if (callback) {
// LBFG-S does not support learning rate -> ignore learning schedule
float sched = 0;
callback(callback_data, &sched);
}
// evaluate the function value and its gradient
{
ggml_opt_set_params(np, ps, x);
//ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(gb, &cplan);
ggml_opt_get_grad(np, ps, g);
fx = ggml_get_f32_1d(f, 0);
fx = 0;
memset(g, 0, sizeof(float)*nx);
for (int accum_step = 0; accum_step < n_accum; ++accum_step) {
if (callback) {
// LBFG-S does not support learning rate -> ignore learning schedule
float sched = 0;
callback(callback_data, accum_step, &sched);
}
// ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(gb, &cplan);
ggml_opt_acc_grad(np, ps, g, accum_norm);
fx += ggml_get_f32_1d(f, 0);
}
fx *= accum_norm;
opt->loss_before = fx;
opt->loss_after = fx;
@ -19729,6 +19760,8 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
.print_forward_graph = true,
.print_backward_graph = true,
.n_gradient_accumulation = 1,
.adam = {
.n_iter = 10000,
.sched = 1.000f,
@ -19757,6 +19790,8 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
.print_forward_graph = true,
.print_backward_graph = true,
.n_gradient_accumulation = 1,
.lbfgs = {
.m = 6,
.n_iter = 100,
@ -19790,7 +19825,7 @@ GGML_API void ggml_opt_init(
if (opt->ctx == NULL) {
struct ggml_init_params ctx_opt_params;
if (opt->params.type == GGML_OPT_ADAM) {
ctx_opt_params.mem_size = GGML_MEM_ALIGN*2 + ggml_tensor_overhead()*2 + ggml_type_size(GGML_TYPE_F32)*nx*2;
ctx_opt_params.mem_size = GGML_MEM_ALIGN*3 + ggml_tensor_overhead()*3 + ggml_type_size(GGML_TYPE_F32)*nx*3;
if (opt->params.past > 0) {
ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past;
}
@ -19808,6 +19843,7 @@ GGML_API void ggml_opt_init(
switch (opt->params.type) {
case GGML_OPT_ADAM:
{
opt->adam.g = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
opt->adam.m = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
opt->adam.v = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
opt->adam.pf = params.past > 0

5
ggml.h
View file

@ -1708,7 +1708,7 @@ extern "C" {
GGML_LINESEARCH_INVALID_PARAMETERS,
};
typedef void (*ggml_opt_callback)(void * data, float * sched);
typedef void (*ggml_opt_callback)(void * data, int accum_step, float * sched);
// optimization parameters
//
@ -1739,6 +1739,8 @@ extern "C" {
bool print_forward_graph;
bool print_backward_graph;
int n_gradient_accumulation;
// ADAM parameters
struct {
int n_iter;
@ -1784,6 +1786,7 @@ extern "C" {
float loss_after;
struct {
struct ggml_tensor * g; // current gradient
struct ggml_tensor * m; // first moment
struct ggml_tensor * v; // second moment
struct ggml_tensor * pf; // past function values