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Use unsigned for random seed (#2006)

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* Use unsigned for random seed. Keep -1 as the value to use a time based seed.

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Howard Su 2023-06-29 21:15:15 +08:00 committed by GitHub
parent 96a712ca1b
commit b8c8dda75f
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GPG Key ID: 4AEE18F83AFDEB23
10 changed files with 25 additions and 23 deletions
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2
examples/common.cpp
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@ -110,7 +110,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
invalid_param = true;
break;
}
params.seed = std::stoi(argv[i]);
params.seed = std::stoul(argv[i]);
} else if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
invalid_param = true;

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 == LLAMA_DEFAULT_SEED) {
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
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@ -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
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@ -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 == LLAMA_DEFAULT_SEED) {
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
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@ -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 == LLAMA_DEFAULT_SEED) {
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
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@ -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
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@ -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 == LLAMA_DEFAULT_SEED) {
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();

8
llama.cpp
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@ -777,7 +777,7 @@ static bool kv_cache_init(
struct llama_context_params llama_context_default_params() {
struct llama_context_params result = {
/*.seed =*/ -1,
/*.seed =*/ LLAMA_DEFAULT_SEED,
/*.n_ctx =*/ 512,
/*.n_batch =*/ 512,
/*.gpu_layers =*/ 0,
@ -2541,7 +2541,7 @@ struct llama_context * llama_new_context_with_model(
llama_context * ctx = new llama_context(*model, model->vocab);
if (params.seed < 0) {
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
@ -2974,8 +2974,8 @@ 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, uint32_t seed) {
if (seed == LLAMA_DEFAULT_SEED) {
seed = time(NULL);
}
ctx->rng.seed(seed);

14
llama.h
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@ -46,6 +46,8 @@
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 1
#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
#define LLAMA_SUPPORTS_GPU_OFFLOAD
@ -81,11 +83,11 @@ extern "C" {
typedef void (*llama_progress_callback)(float progress, void *ctx);
struct llama_context_params {
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
int main_gpu; // the GPU that is used for scratch and small tensors
uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context
int32_t n_batch; // prompt processing batch size
int32_t n_gpu_layers; // number of layers to store in VRAM
int32_t main_gpu; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
// called with a progress value between 0 and 1, pass NULL to disable
llama_progress_callback progress_callback;
@ -196,7 +198,7 @@ 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, uint32_t seed);
// Returns the maximum size in bytes of the state (rng, logits, embedding
// and kv_cache) - will often be smaller after compacting tokens