From: Andrew Godfrey <redacted>
Date: Fri, 17 Nov 2023 10:23:11 +0000 (-0800)
Subject: finetune : zero the loraB initial vectors (#4082)
X-Git-Tag: upstream/0.0.4488~2964
X-Git-Url: https://git.djapps.eu/?a=commitdiff_plain;h=947f64f1630bb8b0b363a3bb5e29e11425312d57;p=pkg%2Fggml%2Fsources%2Fllama.cpp

finetune : zero the loraB initial vectors (#4082)

* finetune : zero the loraB initial vectors

Without this, the first iteration is starting out far from the base model, instead of exactly on it.
Zeroing loraB is what the paper recommends. loralib also zeroes at least one of the init vector pairs
(though it departs from the paper in using a different distribution for the other vector, in some cases).

* tabs to spaces

* Use ggml_set_zero instead of adding a new function
---

diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index 5a6cf22c..7fecce25 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -548,35 +548,35 @@ static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, fl
     struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);
 
     randomize_tensor_normal(lora->tok_embeddings_a, rnd);
-    randomize_tensor_normal(lora->tok_embeddings_b, rnd);
+    ggml_set_zero(lora->tok_embeddings_b);
     randomize_tensor_normal(lora->norm_a,           rnd);
-    randomize_tensor_normal(lora->norm_b,           rnd);
+    ggml_set_zero(lora->norm_b);
     randomize_tensor_normal(lora->output_a,         rnd);
-    randomize_tensor_normal(lora->output_b,         rnd);
+    ggml_set_zero(lora->output_b);
 
     for (uint32_t i = 0; i < n_layer; ++i) {
         auto & layer = lora->layers[i];
         randomize_tensor_normal(layer.attention_norm_a, rnd);
-        randomize_tensor_normal(layer.attention_norm_b, rnd);
+        ggml_set_zero(layer.attention_norm_b);
 
         randomize_tensor_normal(layer.wq_a, rnd);
-        randomize_tensor_normal(layer.wq_b, rnd);
+        ggml_set_zero(layer.wq_b);
         randomize_tensor_normal(layer.wk_a, rnd);
-        randomize_tensor_normal(layer.wk_b, rnd);
+        ggml_set_zero(layer.wk_b);
         randomize_tensor_normal(layer.wv_a, rnd);
-        randomize_tensor_normal(layer.wv_b, rnd);
+        ggml_set_zero(layer.wv_b);
         randomize_tensor_normal(layer.wo_a, rnd);
-        randomize_tensor_normal(layer.wo_b, rnd);
+        ggml_set_zero(layer.wo_b);
 
         randomize_tensor_normal(layer.ffn_norm_a, rnd);
-        randomize_tensor_normal(layer.ffn_norm_b, rnd);
+        ggml_set_zero(layer.ffn_norm_b);
 
         randomize_tensor_normal(layer.w1_a, rnd);
-        randomize_tensor_normal(layer.w1_b, rnd);
+        ggml_set_zero(layer.w1_b);
         randomize_tensor_normal(layer.w2_a, rnd);
-        randomize_tensor_normal(layer.w2_b, rnd);
+        ggml_set_zero(layer.w2_b);
         randomize_tensor_normal(layer.w3_a, rnd);
-        randomize_tensor_normal(layer.w3_b, rnd);
+        ggml_set_zero(layer.w3_b);
     }
 
     free_random_normal_distribution(rnd);