vbatts/llama.cpp
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Use unsigned for random seed

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This commit is contained in:
Howard Su 2023-06-26 22:58:22 +08:00
parent 0be54f75a6
commit e67b15f5c2
9 changed files with 17 additions and 17 deletions
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2
examples/common.h
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@ -22,7 +22,7 @@
int32_t get_num_physical_cores();
struct gpt_params {
int32_t seed = -1; // RNG seed
uint32_t seed = -1; // RNG seed
int32_t n_threads = get_num_physical_cores();
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 512; // context size

4
examples/embedding/embedding.cpp
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@ -24,11 +24,11 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
if (params.seed == -1) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {

2
examples/main/README.md
View file

@ -242,7 +242,7 @@ Example usage: `--logit-bias 29905-inf`
### RNG Seed
- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).
- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.

4
examples/main/main.cpp
View file

@ -94,11 +94,11 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
if (params.seed == -1) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {

4
examples/perplexity/perplexity.cpp
View file

@ -136,11 +136,11 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
if (params.seed == -1) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {

2
examples/server/README.md
View file

@ -152,7 +152,7 @@ node .
`mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
`seed`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).
`seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
`ignore_eos`: Ignore end of stream token and continue generating (default: false).

6
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -2768,7 +2768,7 @@ void train_print_usage(int /*argc*/, char ** argv, const struct train_params * p
fprintf(stderr, " --checkpoint-in FNAME path from which to load training checkpoint (default '%s')\n", params->fn_checkpoint_in);
fprintf(stderr, " --checkpoint-out FNAME path to save training checkpoint (default '%s')\n", params->fn_checkpoint_out);
fprintf(stderr, " --model-out FNAME path to save ggml model (default '%s')\n", params->fn_model_out);
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
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, " --embd N Embedding size used for new models (default %d)\n", params->n_embd);
fprintf(stderr, " --mult N Mult size used for new models, influences feedforward size. (default %d)\n", params->n_mult);
@ -3034,10 +3034,10 @@ int main(int argc, char ** argv) {
return 1;
}
if (params.seed < 0) {
if (params.seed == -1) {
params.seed = time(NULL);
}
printf("%s: seed: %d\n", __func__, params.seed);
printf("%s: seed: %u\n", __func__, params.seed);
srand(params.seed);
struct llama_context_params llama_params = llama_context_default_params();

6
llama.cpp
View file

@ -938,7 +938,7 @@ static bool kv_cache_init(
struct llama_context_params llama_context_default_params() {
struct llama_context_params result = {
/*.seed =*/ -1,
/*.seed =*/ (unsigned int)-1,
/*.n_ctx =*/ 512,
/*.n_batch =*/ 512,
/*.gpu_layers =*/ 0,
@ -3091,8 +3091,8 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
#define LLAMA_MAX_RNG_STATE (64*1024)
void llama_set_rng_seed(struct llama_context * ctx, int seed) {
if (seed < 0) {
void llama_set_rng_seed(struct llama_context * ctx, unsigned int seed) {
if (seed == -1) {
seed = time(NULL);
}
ctx->rng.seed(seed);

4
llama.h
View file

@ -81,7 +81,7 @@ extern "C" {
typedef void (*llama_progress_callback)(float progress, void *ctx);
struct llama_context_params {
int seed; // RNG seed, -1 for random
unsigned int seed; // RNG seed, -1 for random
int n_ctx; // text context
int n_batch; // prompt processing batch size
int n_gpu_layers; // number of layers to store in VRAM
@ -196,7 +196,7 @@ extern "C" {
LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
// Sets the current rng seed.
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, unsigned int seed);
// Returns the maximum size in bytes of the state (rng, logits, embedding
// and kv_cache) - will often be smaller after compacting tokens