llama : allow pooled embeddings on any model (#7477)

* create append_pooling operation; allow to specify attention_type; add last token pooling; update examples

* find result_norm/result_embd tensors properly; update output allocation logic

* only use embd output for pooling_type NONE

* get rid of old causal_attn accessor

* take out attention_type; add in llama_set_embeddings

* bypass logits when doing non-NONE pooling

1 files changed, 11 insertions, 10 deletions

diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index 244751e0..b4b73c01 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -17,9 +17,10 @@ static std::vector<std::string> split_lines(const std::string & s) {
     return lines;
 }
 
-static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
-    for (size_t i = 0; i < tokens.size(); i++) {
-        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
+    size_t n_tokens = tokens.size();
+    for (size_t i = 0; i < n_tokens; i++) {
+        llama_batch_add(batch, tokens[i], i, { seq_id }, true);
     }
 }
 
@@ -40,13 +41,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
 
         // try to get sequence embeddings - supported only when pooling_type is not NONE
         const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
-        if (embd == NULL) {
-            embd = llama_get_embeddings_ith(ctx, i);
-            if (embd == NULL) {
-                fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
-                continue;
-            }
-        }
+        GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
 
         float * out = output + batch.seq_id[i][0] * n_embd;
         //TODO: I would also add a parameter here to enable normalization or not.
@@ -97,6 +92,12 @@ int main(int argc, char ** argv) {
     const int n_ctx_train = llama_n_ctx_train(model);
     const int n_ctx = llama_n_ctx(ctx);
 
+    const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
+    if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
+        return 1;
+    }
+
     if (n_ctx > n_ctx_train) {
         fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
                 __func__, n_ctx_train, n_ctx);