Use unsigned for random seed (#2006)

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

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

examples/common.cpp

@@ -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;

examples/common.h

@@ -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

examples/embedding/embedding.cpp

@@ -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) {

examples/main/README.md

@@ -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.
 

examples/main/main.cpp

@@ -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) {

examples/perplexity/perplexity.cpp

@@ -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) {

examples/server/README.md

@@ -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).
 

examples/train-text-from-scratch/train-text-from-scratch.cpp

@@ -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();

llama.cpp

@@ -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);

llama.h

@@ -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