Qwen3ForCausalLM reranking (pre-support)

src/llama-context.cpp

@@ -893,7 +893,12 @@ int llama_context::decode(llama_batch & inp_batch) {
     const int32_t n_vocab = vocab.n_tokens();
 
     const int64_t n_tokens_all = batch.n_tokens;
-    const int64_t n_embd       = hparams.n_embd;
+    int64_t n_embd       = hparams.n_embd;
+
+    if (model.arch == LLM_ARCH_QWEN3 || model.arch == LLM_ARCH_QWEN3MOE) {
+        // Qwen3 uses a different embedding size
+        n_embd = n_vocab;
+    }
 
     GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
 
@@ -1067,7 +1072,15 @@ int llama_context::decode(llama_batch & inp_batch) {
 
             if (n_outputs) {
                 GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
+
+                if (model.arch == LLM_ARCH_QWEN3 && cparams.embeddings) {
+                    // For Qwen3 with embeddings enabled, we share the tensor between logits and embeddings
+                    GGML_ASSERT(n_outputs * n_vocab <= (int64_t) logits_size);
+                } else {
+                    // Standard check for other model architectures
+                    GGML_ASSERT((n_outputs_prev + n_outputs) * n_vocab <= (int64_t) logits_size);
+                }
+
                 ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
             }
         }
@@ -1213,7 +1226,12 @@ int32_t llama_context::output_reserve(int32_t n_outputs) {
 
     const auto n_batch = cparams.n_batch;
     const auto n_vocab = vocab.n_tokens();
-    const auto n_embd  = hparams.n_embd;
+    int64_t n_embd  = hparams.n_embd;
+
+    // For Qwen3, n_embd is equal to n_vocab
+    if (model.arch == LLM_ARCH_QWEN3) {
+        n_embd = n_vocab;
+    }
 
     // TODO: use a per-batch flag for logits presence instead
     bool has_logits = !cparams.embeddings;
@@ -1225,8 +1243,19 @@ int32_t llama_context::output_reserve(int32_t n_outputs) {
         has_embd   = true;
     }
 
-    logits_size = has_logits ? n_vocab*n_outputs_max : 0;
-    embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
+    // For Qwen3 models, both logits and embeddings point to the same tensor
+    bool shared_tensor = (model.arch == LLM_ARCH_QWEN3);
+
+    // Adjust buffer sizes for the case where both tensors are shared
+    if (shared_tensor && has_logits && has_embd) {
+        // For Qwen3, we only need one buffer since logits and embeddings share the same tensor
+        logits_size = n_vocab * n_outputs_max;
+        embd_size = 0; // No need for a separate embedding buffer
+    } else {
+        // Normal case - separate buffers
+        logits_size = has_logits ? n_vocab * n_outputs_max : 0;
+        embd_size = has_embd ? n_embd * n_outputs_max : 0;
+    }
 
     if (output_ids.empty()) {
         // init, never resized afterwards

src/llama-model.cpp

@@ -7077,13 +7077,13 @@ struct llm_build_qwen3 : public llm_graph_context {
                 LLM_NORM_RMS, -1);
 
         cb(cur, "result_norm", -1);
-        res->t_embd = cur;
 
         // lm_head
         cur = build_lora_mm(model.output, cur);
 
         cb(cur, "result_output", -1);
         res->t_logits = cur;
+        res->t_embd = cur;
 
         ggml_build_forward_expand(gf, cur);
     }
@@ -7205,13 +7205,13 @@ struct llm_build_qwen3moe : public llm_graph_context {
                 LLM_NORM_RMS, -1);
 
         cb(cur, "result_norm", -1);
-        res->t_embd = cur;
 
         // lm_head
         cur = build_lora_mm(model.output, cur);
 
         cb(cur, "result_output", -1);
         res->t_logits = cur;
+        res->t_embd = cur;
 
         ggml_build_forward_expand(gf, cur);
     }

tools/server/server.cpp

@@ -2540,7 +2540,18 @@ struct server_context {
         res->n_tokens  = slot.n_prompt_tokens;
         res->oaicompat = slot.params.oaicompat;
 
-        const int n_embd = llama_model_n_embd(model);
+        int n_embd = llama_model_n_embd(model);
+        // For Qwen3 specific handling
+        bool is_qwen3 = false;
+        char arch_name[128] = {0};
+        if (llama_model_meta_val_str(model, "general.architecture", arch_name, sizeof(arch_name)) > 0) {
+            is_qwen3 = (strcmp(arch_name, "qwen3") == 0 || strcmp(arch_name, "qwen3moe") == 0);
+            if (is_qwen3) {
+                // Get vocabulary size for Qwen3 models - they use n_vocab as embedding size
+                n_embd = llama_vocab_n_tokens(vocab);
+                SLT_INF(slot, "Qwen3 model embedding size: %d\n", n_embd);
+            }
+        }
 
         std::vector<float> embd_res(n_embd, 0.0f);
 
@@ -2551,6 +2562,7 @@ struct server_context {
 
             const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
             if (embd == NULL) {
+                fprintf(stderr, "Failed to get embeddings\n");
                 embd = llama_get_embeddings_ith(ctx, i);
             }