Fix all warnings

ggml/examples/unity/fairseq2.cpp

@@ -1223,11 +1223,11 @@ void _bootstrap_seqs_and_scores(
     // Fetch scores of next steps from "lprobs"
     float p_score = 0;
     for (int i = 1; i < prefix_seq_len; ++i) {
-        int p;
+        int p = 0;
         if (ggml_get_i32_1d(job.prefix_seq, i) == model.vocab.token_to_id["<unk>"]) {
             // If tgt_lang is unk, use the most probable lang tag predicted by model
             int max_value = std::numeric_limits<float>::min();
-            for (int j = 0; j < lang_ids.size(); j++) {
+            for (size_t j = 0; j < lang_ids.size(); j++) {
                 if(ggml_get_f32_1d(lprobs, lang_ids[j]) > max_value) {
                     max_value = ggml_get_f32_1d(lprobs, lang_ids[j]);
                     p = lang_ids[j];
@@ -1354,7 +1354,7 @@ void _finalize_hypothesis(
 
 ggml_context* ctx_from_buffer(std::vector<uint8_t>& buffer) {
     return ggml_init({
-        /*.mem_size   =*/ static_cast<int64_t>(buffer.capacity()),
+        /*.mem_size   =*/ static_cast<size_t>(buffer.capacity()),
         /*.mem_buffer =*/ buffer.data(),
         /*.no_alloc   =*/ false,
     });
@@ -1438,7 +1438,7 @@ extern "C" Hypothesis* generate_sequence(
     ggml_context* step_ctx = ctx_from_buffer(local_bufs[start_step % 2]);
     GGML_ASSERT(step_ctx != search_ctx);
     model.enc_kv_cache_ctx = search_ctx;
-    ggml_tensor* lid_scores;
+    ggml_tensor* lid_scores = ggml_new_tensor_1d(result_ctx, GGML_TYPE_F32, 1); // Dummy initialization to get rid of warnings
     if (lang_ids.size()) {
         lid_scores = ggml_new_tensor_1d(result_ctx, GGML_TYPE_F32, lang_ids.size());
     } 
@@ -1463,20 +1463,19 @@ extern "C" Hypothesis* generate_sequence(
     for (int step_nr = start_step; step_nr < max_seq_len - 1; ++step_nr) {
         model.ctx = step_ctx;
         ggml_set_no_alloc(step_ctx, true); // Use allocr for the model forward pass
-        float max_lprob;
-        int p;
+        int p = 0;
         if (step_nr == start_step) {
             // Find the most probable lang_tok and assign it to all beams, when prefix_seq[1] is <unk>
             if (lang_ids.size() && ggml_get_i32_1d(job.prefix_seq, 1) == model.vocab.token_to_id["<unk>"]) {
                 float max_lprob = std::numeric_limits<float>::min();
-                for(int j = 0; j < lang_ids.size(); j++) {
+                for(size_t j = 0; j < lang_ids.size(); j++) {
                     auto val = ggml_get_f32_1d(lid_scores, j);
                     if (val > max_lprob) {
                         max_lprob = val;
                         p = lang_ids[j];
                     }
                 }
-                for (int k = 0; k < beam_size; k++) {
+                for (std::size_t k = 0; k < beam_size; k++) {
                     ggml_set_i32_1d(seqs, k * vocab_size + step_nr, p);
                 }
             }

ggml/examples/unity/model_loader.cpp

@@ -47,7 +47,7 @@ model_loader::load_model_weights(fairseq2_model &model, std::ifstream &fin)
     // Note this require changing the on disk format
     bool as_float32 = true;
     struct ggml_init_params params = {
-        /*.mem_size   =*/ f32_tensor_size + (num_tensor + 1) * (int64_t)ggml_tensor_overhead(),
+        /*.mem_size   =*/ static_cast<size_t>(f32_tensor_size + (num_tensor + 1) * (int64_t)ggml_tensor_overhead()),
         /*.mem_buffer =*/ NULL,
         /*.no_alloc   =*/ false,
     };
@@ -144,7 +144,7 @@ void model_loader::load_vocab(llama_vocab& vocab, std::ifstream &fin)
     std::string packed_vocab = get_name(fin);
     std::int64_t ctx_size = vocab_size * sizeof(float) + vocab_size + 2 * ggml_tensor_overhead();
     ctx_size *= 2;
-    ggml_context* ctx = ggml_init(ggml_init_params{ctx_size, nullptr, false});
+    ggml_context* ctx = ggml_init(ggml_init_params{static_cast<size_t>(ctx_size), nullptr, false});
     ggml_tensor* lengths_tensor = load_tensor_value(fin, ctx, true);
     std::int8_t* lengths = (std::int8_t*)lengths_tensor->data;
     ggml_tensor* scores_tensor = load_tensor_value(fin, ctx, true);

ggml/examples/unity/unity.cpp

@@ -102,8 +102,6 @@ int main(int argc, char ** argv) {
     int ctx_size_mb = params.opts.mem_mb;
     auto encoder_buf = std::vector<uint8_t>(8 * 1024 * 1024); // Only tensor metadata goes in there
     auto encoder_fwd_buf = std::vector<uint8_t>(ctx_size_mb * 1024 * 1024 / 2);
-    ggml_allocr* fwd_alloc = ggml_allocr_new(encoder_fwd_buf.data(), encoder_fwd_buf.capacity(), 8);
-    char result_str[4096];
 
     while (true) {
         // S2ST