@@ -2958,6 +2958,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
29582958 float *dst = (float *)layer.wk_b ->data ;
29592959 int src_stride = wkv_b->ne [0 ]; // 原始张量每行的元素数
29602960
2961+ LLAMA_LOG_DEBUG (" 222\n " , 0 );
29612962 for (int h = 0 ; h < n_head_kv; ++h) {
29622963 int k_start = h * (n_embd_head_qk_nope + n_embd_head_v);
29632964 for (int row = 0 ; row < kv_lora_rank; ++row) {
@@ -2968,6 +2969,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
29682969 int dst_row = h * kv_lora_rank + row;
29692970 int dst_col = col;
29702971 dst[dst_row * n_embd_head_qk_nope + dst_col] = src[src_idx];
2972+ LLAMA_LOG_DEBUG (" 333 row: %d, col: %d\n " , row, col);
29712973 }
29722974 }
29732975 }
@@ -2979,11 +2981,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
29792981 n_head_kv * n_embd_head_v, // 行数:合并头和特征维度
29802982 kv_lora_rank // 列数:LoRA 秩
29812983 );
2984+ LLAMA_LOG_DEBUG (" 444\n " , 0 );
29822985 {
29832986 float *src = (float *)wkv_b->data ;
29842987 float *dst = (float *)layer.wv_b ->data ;
29852988 int src_stride = wkv_b->ne [0 ]; // 原始张量每行的元素数
29862989
2990+ LLAMA_LOG_DEBUG (" 555\n " , 0 );
29872991 for (int h = 0 ; h < n_head_kv; ++h) {
29882992 int v_start = h * (n_embd_head_qk_nope + n_embd_head_v) + n_embd_head_qk_nope;
29892993 for (int row = 0 ; row < kv_lora_rank; ++row) {
@@ -2996,6 +3000,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
29963000 int dst_row = h * n_embd_head_v + col; // 合并头和特征维度
29973001 int dst_col = row; // LoRA 秩维度
29983002 dst[dst_row * kv_lora_rank + dst_col] = src[src_idx];
3003+ LLAMA_LOG_DEBUG (" 666 row: %d, col: %d\n " , row, col);
29993004 }
30003005 }
30013006 }
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