vbatts/llama.cpp
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also save latest finetune output with ITERATION="LATEST" and print where files are saved

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saving with LATEST makes it easier to resume training from the latest checkpoint
the string "LATEST" can be configured with command line option "--fn-latest STR"
This commit is contained in:
xaedes 2023-08-21 02:24:25 +02:00
parent 27c24ffa1b
commit 8b4106ae33
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GPG key ID: 30030EDD817EA2B1
1 changed files with 26 additions and 11 deletions
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37
examples/finetune/finetune.cpp
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@ -1865,9 +1865,10 @@ std::string replace_str(const char * s, const char * needle, const char * replac
return str;
}
void save_checkpoint(struct my_llama_model * model, struct my_llama_lora * lora, struct ggml_opt_context * opt, const char * filename, const char * pattern_it, int iteration) {
std::string sit = std::to_string(iteration);
void save_checkpoint(struct my_llama_model * model, struct my_llama_lora * lora, struct ggml_opt_context * opt, const char * filename, const char * pattern_it, int iteration, const char * latest) {
std::string sit = (iteration >= 0) ? std::to_string(iteration) : std::string(latest);
std::string fn = replace_str(filename, pattern_it, sit.c_str());
printf("%s: saving to %s\n", __func__, fn.c_str());
struct llama_file file(fn.c_str(), "wb");
if (file.fp == NULL) {
return;
@ -2032,9 +2033,10 @@ bool load_checkpoint(struct my_llama_model * model, struct my_llama_lora * lora,
return (file.fp != NULL);
}
void save_as_llama_lora(struct my_llama_lora * lora, const char * filename, const char * pattern_it, int iteration) {
std::string sit = std::to_string(iteration);
void save_as_llama_lora(struct my_llama_lora * lora, const char * filename, const char * pattern_it, int iteration, const char * latest) {
std::string sit = (iteration >= 0) ? std::to_string(iteration) : std::string(latest);
std::string fn = replace_str(filename, pattern_it, sit.c_str());
printf("%s: saving to %s\n", __func__, fn.c_str());
struct llama_file file(fn.c_str(), "wb");
if (file.fp == NULL) {
return;
@ -2102,6 +2104,7 @@ struct train_params {
const char * fn_checkpoint_out;
const char * fn_lora_out;
const char * pattern_fn_it;
const char * fn_latest;
int save_every;
@ -2173,6 +2176,7 @@ struct train_params get_default_train_params() {
params.fn_checkpoint_out = "checkpoint-ITERATION.bin";
params.fn_lora_out = "ggml-lora-ITERATION-f32.bin";
params.pattern_fn_it = "ITERATION";
params.fn_latest = "LATEST";
params.save_every = 10;
@ -2248,7 +2252,8 @@ void train_print_usage(int /*argc*/, char ** argv, const struct train_params * p
fprintf(stderr, " --checkpoint-out FNAME path to save training checkpoint (default '%s')\n", params->fn_checkpoint_out);
fprintf(stderr, " --lora-out FNAME path to save llama lora (default '%s')\n", params->fn_lora_out);
fprintf(stderr, " --pattern-fn-it STR pattern in output filenames to be replaced by iteration number (default '%s')\n", params->pattern_fn_it);
fprintf(stderr, " --save-every N save checkpoint and lora every N iterations. Disabled when N <= 0. (default '%s')\n", params->save_every);
fprintf(stderr, " --fn-latest STR string to use instead of iteration number for saving latest output (default '%s')\n", params->fn_latest);
fprintf(stderr, " --save-every N save checkpoint and lora every N iterations. Disabled when N <= 0. (default '%d')\n", params->save_every);
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for -1)\n");
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);
@ -2352,6 +2357,12 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
break;
}
params->pattern_fn_it = argv[i];
} else if (arg == "--fn-latest") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_latest = argv[i];
} else if (arg == "--save-every") {
if (++i >= argc) {
invalid_param = true;
@ -2669,11 +2680,13 @@ void opt_callback(void * vdata, float * sched) {
const bool save_now = (params->save_every > 0) && (opt->iter - data->last_save_iter >= params->save_every);
if (save_now) {
if (strlen(params->fn_checkpoint_out) > 0) {
save_checkpoint(data->model, data->lora, opt, params->fn_checkpoint_out, params->pattern_fn_it, opt->iter);
}
save_checkpoint(data->model, data->lora, opt, params->fn_checkpoint_out, params->pattern_fn_it, opt->iter, params->fn_latest);
save_checkpoint(data->model, data->lora, opt, params->fn_checkpoint_out, 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);
}
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;
}
@ -3038,11 +3051,13 @@ int main(int argc, char ** argv) {
printf("%s: total training time=%f seconds\n", __func__, dd);
if (params.n_examples > 0) {
save_checkpoint(&model, &lora, opt, params.fn_checkpoint_out, params.pattern_fn_it, opt->iter);
save_checkpoint(&model, &lora, opt, params.fn_checkpoint_out, params.pattern_fn_it, opt->iter, params.fn_latest);
save_checkpoint(&model, &lora, opt, params.fn_checkpoint_out, params.pattern_fn_it, -1, params.fn_latest);
}
if (strlen(params.fn_lora_out) > 0) {
save_as_llama_lora(&lora, params.fn_lora_out, params.pattern_fn_it, opt->iter);
save_as_llama_lora(&lora, params.fn_lora_out, params.pattern_fn_it, opt->iter, params.fn_latest);
save_as_llama_lora(&lora, params.fn_lora_out, params.pattern_fn_it, -1, params.fn_latest);
}
{