From 29ae62d2ae163e2b68aa0ad3bf2ab4636de0c957 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 4 Mar 2024 22:31:20 +0200
Subject: llama : fix embeddings (#5796)

* llama : fix embeddings

ggml-ci

* llama : do not use KV cache for non-causal models

ggml-ci

* embeddings : fix llama_batch_init arg

* llama : add pooling switch

* llama : distinguish token vs sequence embeddings

ggml-ci

* llama : assert pooling tensor

* llama : simplify causal mask condition

ggml-ci

* llama : assert input batch with pooling enabled

* readme : update API changes list
---
 examples/embedding/embedding.cpp | 28 +++++++++++++++++++++-------
 1 file changed, 21 insertions(+), 7 deletions(-)

(limited to 'examples/embedding/embedding.cpp')

diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index acff715e..ff5883da 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -19,11 +19,11 @@ static std::vector<std::string> split_lines(const std::string & s) {
 
 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 }, false);
+        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
     }
 }
 
-static void normalize(float * vec, float * out, int n) {
+static void normalize(const float * vec, float * out, int n) {
     float norm = 0;
     for (int i = 0; i < n; i++) {
         norm += vec[i] * vec[i];
@@ -45,10 +45,23 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
     }
 
     // normalize on copy
-    for (int k = 0; k < n_seq; k++) {
-        float * emb = llama_get_embeddings_ith(ctx, k);
-        float * out = output + k * n_embd;
-        normalize(emb, out, n_embd);
+    for (int i = 0; i < batch.n_tokens; i++) {
+        if (!batch.logits[i]) {
+            continue;
+        }
+
+        // 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;
+            }
+        }
+
+        float * out = output + batch.seq_id[i][0] * n_embd;
+        normalize(embd, out, n_embd);
     }
 }
 
@@ -132,7 +145,7 @@ int main(int argc, char ** argv) {
 
     // initialize batch
     const int n_prompts = prompts.size();
-    struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts);
+    struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
 
     // allocate output
     const int n_embd = llama_n_embd(model);
@@ -145,6 +158,7 @@ int main(int argc, char ** argv) {
     for (int k = 0; k < n_prompts; k++) {
         // clamp to n_batch tokens
         auto & inp = inputs[k];
+
         const uint64_t n_toks = inp.size();
 
         // encode if at capacity
-- 
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