build : fix several cast and printf warnings (#2499)

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Borislav Stanimirov 2023-08-04 13:07:21 +03:00 committed by GitHub
parent 8183159cf3
commit ff966e7ca6
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 7 additions and 7 deletions

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@ -30,7 +30,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed == LLAMA_DEFAULT_SEED) { 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); fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);

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@ -405,7 +405,7 @@ namespace grammar_parser {
for (size_t i = 0, end = state.rules.size(); i < end; i++) { for (size_t i = 0, end = state.rules.size(); i < end; i++) {
// fprintf(file, "%zu: ", i); // fprintf(file, "%zu: ", i);
// print_rule_binary(file, state.rules[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"); // fprintf(file, "\n");
} }
} catch (const std::exception & err) { } catch (const std::exception & err) {

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@ -153,7 +153,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
} }
size_t hs_task_count = prompt_lines.size()/6; 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 // This is needed as usual for LLaMA models
bool prepend_bos = true; bool prepend_bos = true;
@ -178,7 +178,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
double ending_logprob[4]; 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 // Select and read data from prompt lines
hs_data_t *hs_data = new hs_data_t[hs_task_count]; 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 // Stop if query wont fit the ctx window
if (query_size > (size_t)params.n_ctx) { 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; return;
} }
@ -284,7 +284,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
} }
// Print the accumulated accuracy mean x 100 // 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); fflush(stdout);
} }

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@ -123,7 +123,7 @@ int main(int argc, char ** argv)
// Evaluate the tokens : // 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__ ); fprintf( stderr, "%s : failed to eval\n" , __func__ );
return 1; return 1;