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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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4 changed files with 7 additions and 7 deletions
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2
examples/embd-input/embd-input-lib.cpp
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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);
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);

2
examples/grammar-parser.cpp
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@ -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) {

8
examples/perplexity/perplexity.cpp
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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;
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);
}

2
examples/simple/simple.cpp
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@ -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;