From fcc1b43c06ca90a10ce2b5b563b4d2f066b6f392 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 28 May 2025 23:24:33 +0800
Subject: [PATCH] embeddings change to encode

---
 otherarch/embeddings_adapter.cpp | 22 ++++++++--------------
 1 file changed, 8 insertions(+), 14 deletions(-)

diff --git a/otherarch/embeddings_adapter.cpp b/otherarch/embeddings_adapter.cpp
index 9ea7e37d5..97d21633f 100644
--- a/otherarch/embeddings_adapter.cpp
+++ b/otherarch/embeddings_adapter.cpp
@@ -34,7 +34,7 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke
     }
 }
 
-static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
+static void batch_encode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
     const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
     const struct llama_model * model = llama_get_model(ctx);
 
@@ -46,16 +46,10 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
     {
         printf("\n%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
     }
-    if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) {
-        // encoder-only model
-        if (llama_encode(ctx, batch) < 0) {
-            printf("\n%s : failed to encode\n", __func__);
-        }
-    } else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
-        // decoder-only model
-        if (llama_decode(ctx, batch) < 0) {
-            printf("\n%s : failed to decode\n", __func__);
-        }
+
+    // run model
+    if (llama_encode(ctx, batch) < 0) {
+        printf("%s : failed to process\n", __func__);
     }
 
     for (int i = 0; i < batch.n_tokens; i++) {
@@ -150,7 +144,7 @@ bool embeddingstype_load_model(const embeddings_load_model_inputs inputs)
 
     std::vector<int> tmp = {1, 2, 3, 4};
     llama_kv_self_clear(embeddings_ctx);
-    auto er = llama_decode(embeddings_ctx, llama_batch_get_one(tmp.data(), tmp.size()));
+    auto er = llama_encode(embeddings_ctx, llama_batch_get_one(tmp.data(), tmp.size()));
     if(er!=0)
     {
         printf("\nEmbeddings Model Eval returned nonzero: %d\n",er);
@@ -265,7 +259,7 @@ embeddings_generation_outputs embeddingstype_generate(const embeddings_generatio
         // encode if at capacity
         if (batch.n_tokens + n_toks > n_batch) {
             float * out = emb + e * n_embd;
-            batch_decode(embeddings_ctx, batch, out, s, n_embd, embd_normalize);
+            batch_encode(embeddings_ctx, batch, out, s, n_embd, embd_normalize);
             e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
             s = 0;
             common_batch_clear(batch);
@@ -277,7 +271,7 @@ embeddings_generation_outputs embeddingstype_generate(const embeddings_generatio
 
     // final batch
     float * out = emb + e * n_embd;
-    batch_decode(embeddings_ctx, batch, out, s, n_embd, embd_normalize);
+    batch_encode(embeddings_ctx, batch, out, s, n_embd, embd_normalize);
 
     std::string outputarray = "[";
     for (int i = 0; i < n_embd; i++) {