cont : return important tensors

ggml-ci

src/llama-context.cpp

@@ -255,7 +255,8 @@ void llama_context::init() {
         // reserve pp graph first so that buffers are only allocated once
         {
             llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
-            auto res_pp = graph_build(ubatch_pp, true);
+            auto ctx = graph_init();
+            auto res_pp = graph_build(ctx, ubatch_pp, true);
             auto & gf_pp = res_pp.gf;
             if (!ggml_backend_sched_reserve(sched.get(), gf_pp)) {
                 LLAMA_LOG_ERROR("%s: failed to allocate compute pp buffers\n", __func__);
@@ -269,7 +270,8 @@ void llama_context::init() {
         // reserve with tg graph to get the number of splits and nodes
         {
             llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
-            auto res_tg = graph_build(ubatch_tg, true);
+            auto ctx = graph_init();
+            auto res_tg = graph_build(ctx, ubatch_tg, true);
             auto & gf_tg = res_tg.gf;
             if (!ggml_backend_sched_reserve(sched.get(), gf_tg)) {
                 LLAMA_LOG_ERROR("%s: failed to allocate compute tg buffers\n", __func__);
@@ -282,7 +284,8 @@ void llama_context::init() {
         // reserve again with pp graph to avoid ggml-alloc reallocations during inference
         {
             llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
-            auto res_pp = graph_build(ubatch_pp, true);
+            auto ctx = graph_init();
+            auto res_pp = graph_build(ctx, ubatch_pp, true);
             auto & gf_pp = res_pp.gf;
             if (!ggml_backend_sched_reserve(sched.get(), gf_pp)) {
                 LLAMA_LOG_ERROR("%s: failed to allocate compute pp buffers\n", __func__);
@@ -569,6 +572,13 @@ ggml_context_ptr llama_context::graph_init() {
     return ggml_context_ptr { ggml_init(params) };
 }
 
+llama_graph_result llama_context::graph_build(
+        ggml_context_ptr & ctx,
+      const llama_ubatch & ubatch,
+                    bool   worst_case) {
+    return model.build_graph(ctx, *this, cparams, ubatch, worst_case);
+}
+
 enum ggml_status llama_context::graph_compute(
             ggml_cgraph * graph,
                    bool   batched) {
@@ -907,10 +917,6 @@ void llama_context::build_cb(
     }
 }
 
-llama_graph_result llama_context::graph_build(const llama_ubatch & ubatch, bool worst_case) {
-    return model.build_graph(*this, cparams, ubatch, graph_init(), worst_case);
-}
-
 llama_perf_context_data llama_context::perf_get_data() const {
     llama_perf_context_data data = {};
 
@@ -1831,7 +1837,8 @@ int llama_context_kv_self::decode(llama_batch & inp_batch) {
             llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
             llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
 
-            auto res = graph_build(ubatch, true);
+            auto ctx = graph_init();
+            auto res = graph_build(ctx, ubatch, true);
 
             // initialize scheduler with the worst-case graph
             ggml_backend_sched_reset(sched.get());
@@ -1845,7 +1852,8 @@ int llama_context_kv_self::decode(llama_batch & inp_batch) {
         ggml_backend_sched_reset(sched.get());
         ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
 
-        auto res = graph_build(ubatch, false);
+        auto ctx = graph_init();
+        auto res = graph_build(ctx, ubatch, false);
 
         auto & gf = res.gf;
 
@@ -2092,7 +2100,8 @@ int llama_context_kv_self::encode(llama_batch & inp_batch) {
     ggml_backend_sched_reset(sched.get());
     ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
 
-    auto res = graph_build(ubatch, false);
+    auto ctx = graph_init();
+    auto res = graph_build(ctx, ubatch, false);
 
     auto & gf = res.gf;
 

src/llama-context.h

@@ -96,7 +96,10 @@ struct llama_context : public llama_graph_i {
     virtual ggml_context_ptr graph_init();
 
     // TODO: add encode/decode graphs
-    virtual llama_graph_result graph_build(const llama_ubatch & ubatch, bool worst_case);
+    virtual llama_graph_result graph_build(
+            ggml_context_ptr & ctx,
+          const llama_ubatch & ubatch,
+                        bool   worst_case);
 
     // returns the result of ggml_backend_sched_graph_compute_async execution
     virtual enum ggml_status graph_compute(

src/llama-graph.h

@@ -13,8 +13,10 @@ struct llama_ubatch;
 struct llama_graph_result {
     ggml_cgraph * gf = nullptr;
 
-    ggml_tensor * t_logits = nullptr;
+    // important graph nodes
-    ggml_tensor * t_embd   = nullptr;
+    ggml_tensor * t_logits      = nullptr;
+    ggml_tensor * t_embd        = nullptr;
+    ggml_tensor * t_embd_pooled = nullptr;
 };
 
 // TODO: can become more granular in the future

src/llama-model.h

@@ -370,11 +370,11 @@ struct llama_model {
 
     // TODO: add encode/decode graphs
     llama_graph_result build_graph(
-             llama_graph_i &  lgf,
+          ggml_context_ptr & ctx,
-       const llama_cparams &  cparams,
+             llama_graph_i & lgf,
-       const llama_ubatch  &  ubatch,
+       const llama_cparams & cparams,
-          ggml_context_ptr && ctx,
+       const llama_ubatch  & ubatch,
-                      bool    worst_case) const;
+                      bool   worst_case) const;
 
 private:
     struct impl;