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5cdb27e091
* 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>
97 lines
3.3 KiB
C++
97 lines
3.3 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "log.h"
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#include "llama.h"
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <vector>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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int main(int argc, char ** argv) {
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common_params params;
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params.escape = false;
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if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_FINETUNE)) {
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return 1;
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}
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if (params.use_mmap) {
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LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n",
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__func__);
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params.use_mmap = false;
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}
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if (params.cache_type_k != GGML_TYPE_F32) {
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LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
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params.cache_type_k = GGML_TYPE_F32;
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}
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if (params.cache_type_v != GGML_TYPE_F32) {
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LOG_INF("%s: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
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params.cache_type_v = GGML_TYPE_F32;
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}
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common_init();
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llama_backend_init();
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llama_numa_init(params.numa);
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// load the model and apply lora adapter, if any
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common_init_result llama_init = common_init_from_params(params);
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llama_model_ptr & model = llama_init.model;
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llama_context_ptr & ctx = llama_init.context;
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if (model == NULL) {
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LOG_ERR("%s: unable to load model\n", __func__);
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return 1;
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}
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// print system information
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{
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LOG_INF("\n");
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LOG_INF("%s\n", common_params_get_system_info(params).c_str());
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}
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std::vector<llama_token> tokens = common_tokenize(ctx.get(), params.prompt, true);
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ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get()) / 2);
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struct lr_opt & lr = params.lr;
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LOG_INF("-optimizer %s -lr0 %.2g -wd %.2g -lr-min %.2g -min-epochs %.2g -epochs %d -period %.2g -val %.2g\n",
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ggml_opt_optimizer_name(params.optimizer), (double) lr.lr0, (double) lr.wd, (double) lr.lr_min, (double) lr.decay_epochs,
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(unsigned) lr.epochs, (double) params.n_batch / params.n_ubatch, (double) params.val_split);
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struct llama_opt_params lopt_params{
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/*n_ctx_train =*/0,
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/*param_filter =*/llama_opt_param_filter_all,
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/*param_filter_ud =*/nullptr,
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/*get_opt_pars =*/common_opt_lr_pars,
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/*get_opt_pars_ud =*/¶ms.lr,
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/*optimizer_type =*/params.optimizer,
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};
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llama_opt_init(ctx.get(), model.get(), lopt_params);
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const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - params.val_split);
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ggml_opt_result_t result_train = ggml_opt_result_init();
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ggml_opt_result_t result_eval = ggml_opt_result_init();
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for (lr.epoch = 0; lr.epoch < lr.epochs; ++lr.epoch) {
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llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
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ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
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fprintf(stderr, "\n");
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ggml_opt_result_reset(result_train);
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ggml_opt_result_reset(result_eval);
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}
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ggml_opt_result_free(result_train);
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ggml_opt_result_free(result_eval);
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llama_model_save_to_file(model.get(), params.out_file.c_str());
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llama_backend_free();
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return 0;
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}
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