ggml : change ggml_scale to take a float instead of tensor (#4573)

* ggml : change ggml_scale to take a float instead of tensor

* ggml : fix CPU implementation

* tests : fix test-grad0

ggml-ci

1 files changed, 20 insertions, 22 deletions

diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index 6a668d76..7b1333a9 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -269,7 +269,7 @@ static void load_model_hparams_gguf(struct gguf_context * ctx, struct my_llama_h
     float rope_freq_scale = 1.0f;
     GGUF_GET_KEY(ctx, hparams->f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
     GGUF_GET_KEY(ctx, hparams->rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
-    GGUF_GET_KEY(ctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
+    GGUF_GET_KEY(ctx, rope_freq_scale,         gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
     if (rope_freq_scale != 1.0f) {
         hparams->rope_freq_scale = 1.0f / rope_freq_scale;
     }
@@ -612,6 +612,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
     const int n_rot       = hparams.n_embd_head();
     const int n_embd_head = hparams.n_embd_head();
     const int n_embd_gqa  = hparams.n_embd_gqa();
+
     const float rms_norm_eps    = hparams.f_norm_rms_eps;
     const float rope_freq_base  = hparams.rope_freq_base;
     const float rope_freq_scale = hparams.rope_freq_scale;
@@ -680,10 +681,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
         checkpoints.push_back(t01);
     }
 
-    struct ggml_tensor * kv_scale = NULL;
-    if (!enable_flash_attn) {
-        kv_scale = ggml_new_f32(ctx, 1.0f/sqrtf(float(n_embd)/n_head));
-    }
+    const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head);
 
     for (int il = 0; il < n_layer; ++il) {
         struct my_llama_layer & layer = model->layers[il];
@@ -781,32 +779,32 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
     // make sure some tensors are not reallocated by inserting new temporary nodes depending on them
     int n_leafs_before = gb->n_leafs;
     int n_nodes_before = gb->n_nodes;
-    struct ggml_tensor * one = ggml_new_f32(ctx, 1.0f);
+
     // output tensors
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, one));
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one));
+    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f));
+    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f));
     // input gradient
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one));
+    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f));
     GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
     ggml_allocr_alloc(alloc, t36->grad);
     // KQ_pos
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, one));
+    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
 
     // make sure base model tensors data cannot be used in viewable operations
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, one));
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, one));
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, one));
+    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, 1.0f));
+    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, 1.0f));
+    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, 1.0f));
     for (int il = 0; il < n_layer; ++il) {
         struct my_llama_layer & layer = model->layers[il];
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, one));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, one));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, one));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, one));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, one));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, one));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, one));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, one));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, one));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, 1.0f));
     }
 
     // allocating checkpoints in one block to reduce memory fragmentation