kludge-y kv cache management of mtp layer

src/llama-context.cpp

@@ -7,6 +7,7 @@
 #include "llama-mmap.h"
 #include "llama-model.h"
 #include "llama-graph.h"
+#include "llama-kv-cache-unified.h"
 
 #include <cinttypes>
 #include <cstring>
@@ -3000,7 +3001,20 @@ void llama_build_and_execute_mtp_graph(struct llama_context * ctx,
     const auto * model = llama_get_model(ctx);
 
     auto res_mtp = std::make_unique<llm_graph_result>(ctx->graph_max_nodes());
-    llama_memory_context_ptr mctx = ctx->mtp_memory_batch(batch_inp);
+    std::unique_ptr<llama_memory_context_i> mctx = ctx->mtp_memory_batch(batch_inp);
+
+    std::vector<uint32_t> idxs;
+    idxs.push_back(n_past);
+    llama_kv_cache_unified::slot_info sinfo = {
+        /*.s0   =*/ 0,
+        /*.s1   =*/ 0,
+        /*.strm =*/ { 0 },
+        /*.idxs =*/ { idxs },
+    };
+    llama_kv_cache_unified::slot_info_vec_t sinfos;
+    sinfos.push_back(sinfo);
+
+    static_cast<llama_kv_cache_unified_context*>(mctx.get())->set_sinfos(sinfos);
     const auto& ubatch_mtp = mctx->get_ubatch();
 
     //llama_ubatch ubatch_mtp;
@@ -3012,9 +3026,10 @@ void llama_build_and_execute_mtp_graph(struct llama_context * ctx,
 
     auto * last_embd = ctx->get_embeddings_ith(last_tok_idx);
 
-    if (mctx && !mctx->apply()) {
-        LLAMA_LOG_ERROR("%s: failed to apply memory context\n", __func__);
-    }
+    //if (mctx && !mctx->set_n_kv()) {
+    //    LLAMA_LOG_ERROR("%s: failed to apply memory context\n", __func__);
+    //}
+    static_cast<llama_kv_cache_unified_context*>(mctx.get())->set_n_kv();
 
     auto * gf = model->build_mtp_graph(*params_mtp, last_token_id, n_past);
 

src/llama-kv-cache-unified.cpp

@@ -2322,6 +2322,11 @@ bool llama_kv_cache_unified_context::apply() {
     return true;
 }
 
+void llama_kv_cache_unified_context::set_n_kv() {
+    n_kv = kv->get_n_kv();
+}
+
+
 llama_memory_status llama_kv_cache_unified_context::get_status() const {
     return status;
 }
@@ -2384,6 +2389,10 @@ void llama_kv_cache_unified_context::set_input_pos_bucket(ggml_tensor * dst, con
     kv->set_input_pos_bucket(dst, ubatch);
 }
 
+void llama_kv_cache_unified_context::set_sinfos(llama_kv_cache_unified::slot_info_vec_t new_sinfos) {
+    sinfos = new_sinfos;
+}
+
 uint32_t llama_kv_cache_unified::get_padding(const llama_cparams & cparams) {
     // the FA kernels require padding to avoid extra runtime boundary checks
     return cparams.flash_attn ? 256u : 32u;

src/llama-kv-cache-unified.h

@@ -340,6 +340,7 @@ public:
     //
 
     uint32_t get_n_kv() const;
+    void set_n_kv();
 
     // TODO: temporary
     bool get_supports_set_rows() const;
@@ -362,6 +363,8 @@ public:
     void set_input_kq_mask   (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
     void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
 
+    void set_sinfos(slot_info_vec_t new_sinfos);
+
 private:
     llama_memory_status status;
 

tools/server/server.cpp

@@ -3545,7 +3545,7 @@ struct server_context {
 
                 llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx);
                 slot.last_tok_idx = tok_idx;
-                SRV_INF("main loop sampled token: '%s'\n", common_token_to_piece(ctx, id, true).c_str());
+                //SRV_INF("main loop sampled token: '%s'\n", common_token_to_piece(ctx, id, true).c_str());
 
                 slot.i_batch = -1;