@@ -2948,12 +2948,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
29482948
29492949 ggml_context * ctx = ctx_for_buft (buft);
29502950 layer.wk_b = ggml_new_tensor_2d (ctx,
2951- layer. wkv_b ->type ,
2951+ wkv_b->type ,
29522952 n_head_kv * kv_lora_rank,
29532953 n_embd_head_qk_nope
29542954 );
2955+ LLAMA_LOG_DEBUG (" 111\n " , 0 );
29552956 {
2956- float *src = (float *)layer. wkv_b ->data ;
2957+ float *src = (float *)wkv_b->data ;
29572958 float *dst = (float *)layer.wk_b ->data ;
29582959 int src_stride = wkv_b->ne [0 ]; // 原始张量每行的元素数
29592960
@@ -2962,7 +2963,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
29622963 for (int row = 0 ; row < kv_lora_rank; ++row) {
29632964 for (int col = 0 ; col < n_embd_head_qk_nope; ++col) {
29642965 int src_idx = row * src_stride + k_start + col;
2965- GGML_ASSERT (src_idx < ggml_nelements (layer. wkv_b ));
2966+ GGML_ASSERT (src_idx < ggml_nelements (wkv_b));
29662967
29672968 int dst_row = h * kv_lora_rank + row;
29682969 int dst_col = col;
@@ -2974,12 +2975,12 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
29742975
29752976 layer.wv_b = ggml_new_tensor_2d (
29762977 ctx,
2977- layer. wkv_b ->type ,
2978+ wkv_b->type ,
29782979 n_head_kv * n_embd_head_v, // 行数:合并头和特征维度
29792980 kv_lora_rank // 列数:LoRA 秩
29802981 );
29812982 {
2982- float *src = (float *)layer. wkv_b ->data ;
2983+ float *src = (float *)wkv_b->data ;
29832984 float *dst = (float *)layer.wv_b ->data ;
29842985 int src_stride = wkv_b->ne [0 ]; // 原始张量每行的元素数
29852986
@@ -2989,7 +2990,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
29892990 for (int col = 0 ; col < n_embd_head_v; ++col) {
29902991 // 源索引计算
29912992 int src_idx = row * src_stride + v_start + col;
2992- GGML_ASSERT (src_idx < ggml_nelements (layer. wkv_b ));
2993+ GGML_ASSERT (src_idx < ggml_nelements (wkv_b));
29932994
29942995 // 目标索引计算
29952996 int dst_row = h * n_embd_head_v + col; // 合并头和特征维度
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