hparams : remove n_vocab

Author: ggerganov

src/llama-context.cpp

@@ -469,11 +469,12 @@ void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) {
 size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs) {
     const auto & cparams = lctx.cparams;
     const auto & hparams = lctx.model.hparams;
+    const auto & vocab   = lctx.model.vocab;
 
     const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
 
     const auto n_batch = cparams.n_batch;
-    const auto n_vocab = hparams.n_vocab;
+    const auto n_vocab = vocab.n_vocab();
     const auto n_embd  = hparams.n_embd;
 
     // TODO: use a per-batch flag for logits presence instead
@@ -540,7 +541,7 @@ size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs) {
 void llama_output_reorder(struct llama_context & ctx) {
     std::vector<size_t> & out_ids = ctx.sbatch.out_ids;
     if (!out_ids.empty()) {
-        const uint32_t n_vocab = ctx.model.hparams.n_vocab;
+        const uint32_t n_vocab = ctx.model.vocab.n_vocab();
         const uint32_t n_embd  = ctx.model.hparams.n_embd;
 
         const int32_t n_outputs = ctx.n_outputs;
@@ -724,7 +725,7 @@ float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
             throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
         }
 
-        return ctx->logits + j*ctx->model.hparams.n_vocab;
+        return ctx->logits + j*ctx->model.vocab.n_vocab();
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
 #ifndef NDEBUG
@@ -884,7 +885,7 @@ struct llama_data_write {
     }
 
     void write_logits(const struct llama_context * ctx) {
-        const uint64_t logits_size = std::min((uint64_t) ctx->logits_size, (uint64_t) ctx->n_outputs * ctx->model.hparams.n_vocab);
+        const uint64_t logits_size = std::min((uint64_t) ctx->logits_size, (uint64_t) ctx->n_outputs * ctx->model.vocab.n_vocab());
 
         write(&logits_size, sizeof(logits_size));
 

src/llama-hparams.h

@@ -30,7 +30,6 @@ struct llama_hparams {
     bool use_par_res;
     bool swin_norm;
 
-    uint32_t n_vocab = 0;
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
     uint32_t n_embd_features = 0;

src/llama-model.cpp

@@ -402,9 +402,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
     // get general kv
     ml.get_key(LLM_KV_GENERAL_NAME, name, false);
 
-    // get hparams kv
-    ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab, false);
-
     // everything past this point is not vocab-related
     if (hparams.vocab_only) {
         return;
@@ -500,6 +497,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
         hparams.n_embd_head_v = 0;
     }
 
+    uint32_t n_vocab = 0;
+
+    ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
+
     // arch-specific KVs
     switch (arch) {
         case LLM_ARCH_LLAMA:
@@ -519,7 +520,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                         case 26: type = LLM_TYPE_3B; break;
                         case 28: type = LLM_TYPE_3B; break; // Llama 3.2 3B
                         // granite uses a vocab with len 49152
-                        case 32: type = hparams.n_vocab == 49152 ? LLM_TYPE_3B : (hparams.n_vocab < 40000 ? LLM_TYPE_7B : LLM_TYPE_8B); break;
+                        case 32: type = n_vocab == 49152 ? LLM_TYPE_3B : (n_vocab < 40000 ? LLM_TYPE_7B : LLM_TYPE_8B); break;
                         case 36: type = LLM_TYPE_8B; break; // granite
                         case 40: type = LLM_TYPE_13B; break;
                         case 48: type = LLM_TYPE_34B; break;
@@ -1365,7 +1366,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
         const int64_t n_embd_head_v = hparams.n_embd_head_v;
         const int64_t n_ff          = hparams.n_ff();
         const int64_t n_embd_gqa    = n_embd_v_gqa;
-        const int64_t n_vocab       = hparams.n_vocab;
+        const int64_t n_vocab       = vocab.n_vocab();
         const int64_t n_vocab_type  = hparams.n_vocab_type;
         const int64_t n_rot         = hparams.n_rot;
         const int64_t n_expert      = hparams.n_expert;
@@ -3494,7 +3495,6 @@ void llama_model::print_info() const {
 
     // hparams
     LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, arch_name().c_str());
-    LLAMA_LOG_INFO("%s: n_vocab (hp)     = %u\n",     __func__, hparams.n_vocab);
     LLAMA_LOG_INFO("%s: vocab_only       = %d\n",     __func__, hparams.vocab_only);
 
     if (!hparams.vocab_only) {

src/llama.cpp

@@ -65,11 +65,6 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
         model.load_stats(ml);
         model.print_info();
 
-        if (model.vocab.get_type() != LLAMA_VOCAB_TYPE_NONE &&
-            model.hparams.n_vocab != model.vocab.n_vocab()) {
-            throw std::runtime_error("vocab size mismatch");
-        }
-
         if (params.vocab_only) {
             LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
             return 0;
@@ -8342,6 +8337,7 @@ static int llama_decode_impl(
     const uint32_t n_tokens_all = batch.n_tokens;
 
     const auto & model   = lctx.model;
+    const auto & vocab   = model.vocab;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
 
@@ -8369,7 +8365,7 @@ static int llama_decode_impl(
     llama_kv_slot_restorer kv_slot_restorer(kv_self);
 
     const int64_t n_embd  = hparams.n_embd;
-    const int64_t n_vocab = hparams.n_vocab;
+    const int64_t n_vocab = vocab.n_vocab();
 
     uint32_t n_outputs = 0;
     uint32_t n_outputs_prev = 0;