@@ -786,7 +786,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
786786 const auto & hparams = model.hparams ;
787787
788788 const int64_t n_embd = hparams.n_embd ;
789- const int32_t n_vocab = model.vocab .n_tokens ();
789+ const int64_t n_vocab = model.vocab .n_tokens ();
790790
791791 // note: during encode, we always pass the full sequence starting from pos = 0
792792 if (!balloc->init (batch_inp, model.vocab , nullptr , n_embd, cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max , true )) {
@@ -959,7 +959,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
959959 const auto & vocab = model.vocab ;
960960 const auto & hparams = model.hparams ;
961961
962- const int32_t n_vocab = vocab.n_tokens ();
962+ const int64_t n_vocab = vocab.n_tokens ();
963963 const int64_t n_embd = hparams.n_embd ;
964964
965965 // when computing embeddings, all tokens are output
@@ -1328,21 +1328,21 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
13281328}
13291329
13301330void llama_context::output_reorder () {
1331- const uint32_t n_vocab = model.vocab .n_tokens ();
1331+ const uint64_t n_vocab = model.vocab .n_tokens ();
13321332 const uint64_t n_embd = model.hparams .n_embd ;
13331333
1334- for (uint32_t s = 0 ; s < output_swaps.size (); ++s) {
1335- const uint32_t i0 = output_swaps[s].i0 ;
1336- const uint32_t i1 = output_swaps[s].i1 ;
1334+ for (size_t s = 0 ; s < output_swaps.size (); ++s) {
1335+ const uint64_t i0 = output_swaps[s].i0 ;
1336+ const uint64_t i1 = output_swaps[s].i1 ;
13371337
13381338 if (logits_size > 0 ) {
1339- for (uint32_t k = 0 ; k < n_vocab; k++) {
1339+ for (uint64_t k = 0 ; k < n_vocab; k++) {
13401340 std::swap (logits[i0*n_vocab + k], logits[i1*n_vocab + k]);
13411341 }
13421342 }
13431343
13441344 if (embd_size > 0 ) {
1345- for (uint32_t k = 0 ; k < n_embd; k++) {
1345+ for (uint64_t k = 0 ; k < n_embd; k++) {
13461346 std::swap (embd[i0*n_embd + k], embd[i1*n_embd + k]);
13471347 }
13481348 }
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