cont : use returend tensors from the graph build

ggml-ci

Author: ggerganov

src/llama-context.cpp

@@ -1855,37 +1855,14 @@ int llama_context_kv_self::decode(llama_batch & inp_batch) {
         auto ctx = graph_init();
         auto res = graph_build(ctx, ubatch, false);
 
-        auto & gf = res.gf;
+        auto * gf = res.gf;
 
         // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
 
         ggml_backend_sched_alloc_graph(sched.get(), gf);
 
         input_set(ubatch);
 
-        // the output is always the last tensor in the graph
-        struct ggml_tensor * t_logits = ggml_graph_node(gf, -1);
-        struct ggml_tensor * t_embd   = ggml_graph_node(gf, -2);
-
-        if (n_outputs == 0) {
-            // no output
-            t_logits  = nullptr;
-            t_embd = nullptr;
-        } else if (cparams.embeddings) {
-            t_logits  = nullptr; // do not extract logits for embedding case
-            t_embd = nullptr;
-            for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
-                if (strcmp(ggml_graph_node(gf, i)->name, "result_embd_pooled") == 0) {
-                    t_embd = ggml_graph_node(gf, i);
-                    break;
-                }
-            }
-            GGML_ASSERT(t_embd != nullptr && "missing embeddings tensor");
-        } else {
-            t_embd = nullptr; // do not extract embeddings when not needed
-            GGML_ASSERT(strcmp(t_logits->name, "result_output") == 0 && "missing result_output tensor");
-        }
-
         const auto compute_status = graph_compute(gf, ubatch.n_tokens > 1);
         if (compute_status != GGML_STATUS_SUCCESS) {
             switch (compute_status) {
@@ -1914,8 +1891,15 @@ int llama_context_kv_self::decode(llama_batch & inp_batch) {
         //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
         //}
 
+        auto * t_logits = cparams.embeddings ? nullptr    : res.t_logits;
+        auto * t_embd   = cparams.embeddings ? res.t_embd : nullptr;
+
+        if (t_embd && res.t_embd_pooled) {
+            t_embd = res.t_embd_pooled;
+        }
+
         // extract logits
-        if (t_logits) {
+        if (t_logits && n_outputs > 0) {
             ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
             GGML_ASSERT(backend_res != nullptr);
             GGML_ASSERT(logits != nullptr);
@@ -1930,7 +1914,7 @@ int llama_context_kv_self::decode(llama_batch & inp_batch) {
         }
 
         // extract embeddings
-        if (t_embd) {
+        if (t_embd && n_outputs > 0) {
             ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
             GGML_ASSERT(backend_embd != nullptr);
 
@@ -2103,32 +2087,12 @@ int llama_context_kv_self::encode(llama_batch & inp_batch) {
     auto ctx = graph_init();
     auto res = graph_build(ctx, ubatch, false);
 
-    auto & gf = res.gf;
+    auto * gf = res.gf;
 
     ggml_backend_sched_alloc_graph(sched.get(), gf);
 
     input_set(ubatch);
 
-    // the output embeddings after the final encoder normalization
-    struct ggml_tensor * t_embd = nullptr;
-
-    // there are two cases here
-    if (llama_model_has_decoder(&model)) {
-        // first case is an encoder-decoder T5 model where embeddings are passed to decoder
-        t_embd = ggml_graph_node(gf, -1);
-        GGML_ASSERT(strcmp(t_embd->name, "result_norm") == 0 && "missing result_output tensor");
-    } else {
-        // second case is an encoder-only T5 model
-        if (cparams.embeddings) {
-            // only output embeddings if required
-            t_embd = ggml_graph_node(gf, -1);
-            if (strcmp(t_embd->name, "result_embd_pooled") != 0) {
-                t_embd = ggml_graph_node(gf, -2);
-            }
-            GGML_ASSERT(strcmp(t_embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
-        }
-    }
-
     const auto compute_status = graph_compute(gf, n_tokens > 1);
     switch (compute_status) {
         case GGML_STATUS_SUCCESS:
@@ -2142,6 +2106,8 @@ int llama_context_kv_self::encode(llama_batch & inp_batch) {
             return -3;
     }
 
+    auto * t_embd = res.t_embd_pooled ? res.t_embd_pooled : res.t_embd;
+
     // extract embeddings
     if (t_embd) {
         ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);