llama : reserve graphs with samplers

src/llama-context.cpp

@@ -1903,6 +1903,15 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u
     llama_batch_allocr balloc(model.hparams.n_pos_per_embd());
     llama_ubatch ubatch = balloc.ubatch_reserve(n_tokens/n_seqs, n_seqs);
 
+    // set one output token per sequence in order to activate all backend samplers
+    std::vector<llama_seq_id> seq_ids(n_seqs);
+    for (uint32_t i = 0; i < n_seqs; ++i) {
+        seq_ids[i] = i;
+        ubatch.n_seq_id[i] = 1;
+        ubatch.seq_id[i] = &seq_ids[i];
+        ubatch.output[i] = true;
+    }
+
     auto * res = gf_res_reserve.get();
 
     const auto gparams = graph_params(res, ubatch, mctx, LLM_GRAPH_TYPE_DEFAULT);

src/llama-graph.cpp

@@ -2063,18 +2063,18 @@ void llm_graph_context::build_sampling() const {
             logit_row_idx++;
         }
     }
+
     if (seq_to_logit_row.empty()) {
         return;
     }
 
-    // res->t_logits will contain logits for all tokens that specied that want
+    // res->t_logits will contain logits for all tokens that want the logits calculated (logits=1 or output=1)
-    // logits calculated (logits=1 or output=1)
     ggml_tensor * logits_t = res->t_logits;
     GGML_ASSERT(res->t_logits != nullptr && "missing t_logits tensor");
 
     const int64_t n_vocab = logits_t->ne[0];
 
-    std::unordered_map<llama_seq_id, llama_sampler*> active_samplers;
+    std::unordered_map<llama_seq_id, llama_sampler *> active_samplers;
 
     for (const auto & [seq_id, sampler] : samplers) {
         // Only process samplers for sequences that are in the current batch