From ff966e7ca6af127c9405523cdb07ef8fa01bf6d6 Mon Sep 17 00:00:00 2001
From: Borislav Stanimirov <b.stanimirov@abv.bg>
Date: Fri, 4 Aug 2023 13:07:21 +0300
Subject: [PATCH] build : fix several cast and printf warnings (#2499)

---
 examples/embd-input/embd-input-lib.cpp | 2 +-
 examples/grammar-parser.cpp            | 2 +-
 examples/perplexity/perplexity.cpp     | 8 ++++----
 examples/simple/simple.cpp             | 2 +-
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp
index 26563821a..2185b9b0e 100644
--- a/examples/embd-input/embd-input-lib.cpp
+++ b/examples/embd-input/embd-input-lib.cpp
@@ -30,7 +30,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) {
     fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
-        params.seed = time(NULL);
+        params.seed = uint32_t(time(NULL));
     }
     fprintf(stderr, "%s: seed  = %d\n", __func__, params.seed);
 
diff --git a/examples/grammar-parser.cpp b/examples/grammar-parser.cpp
index 019d5e1bf..e76bd11c3 100644
--- a/examples/grammar-parser.cpp
+++ b/examples/grammar-parser.cpp
@@ -405,7 +405,7 @@ namespace grammar_parser {
             for (size_t i = 0, end = state.rules.size(); i < end; i++) {
                 // fprintf(file, "%zu: ", i);
                 // print_rule_binary(file, state.rules[i]);
-                print_rule(file, i, state.rules[i], symbol_id_names);
+                print_rule(file, uint32_t(i), state.rules[i], symbol_id_names);
                 // fprintf(file, "\n");
             }
         } catch (const std::exception & err) {
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 6870a11b9..62433e983 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -153,7 +153,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     }
 
     size_t hs_task_count = prompt_lines.size()/6;
-    fprintf(stderr, "%s : loaded %lu tasks from prompt.\n", __func__, hs_task_count);
+    fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
 
     // This is needed as usual for LLaMA models
     bool prepend_bos = true;
@@ -178,7 +178,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         double ending_logprob[4];
     };
 
-    fprintf(stderr, "%s : selecting %lu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first")  );
+    fprintf(stderr, "%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first")  );
 
     // Select and read data from prompt lines
     hs_data_t *hs_data = new hs_data_t[hs_task_count];
@@ -223,7 +223,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
 
             // Stop if query wont fit the ctx window
             if (query_size > (size_t)params.n_ctx) {
-                fprintf(stderr, "%s : number of tokens in query %lu > n_ctxl\n", __func__, query_size);
+                fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
                 return;
             }
 
@@ -284,7 +284,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         }
 
         // Print the accumulated accuracy mean x 100
-        printf("%li\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0);
+        printf("%zu\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0);
         fflush(stdout);
     }
 
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index aa2c4352d..97137a658 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -123,7 +123,7 @@ int main(int argc, char ** argv)
         // Evaluate the tokens :
         //---------------------------------
 
-        if ( llama_eval( ctx , tokens_list.data() , tokens_list.size() , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
+        if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
         {
             fprintf( stderr,  "%s : failed to eval\n" , __func__ );
             return 1;