q8_KV: be able to use it for K cache

This required quite a few fixes in ggml and llama.cpp:
* ggml: do not calculate row size as n/block_size*type_size. I had
  removed most of it when implementing the quants with per row scale,
  bit it was stull lurking in ggml_copy. Not sure if these were the last
  remnants of ggmil-style row sizes, or if there are still places left
* llama.cpp: get rid of the the 1d K cache assumption. Create and manage
  the K-cache as a 2D tensor so we can have per row meta data as needed
  by q8_KV.

Using q8_KV for K-cache results in non-negligible performance gains.
More details to follow, but for DeepSeek-Lite with MLA, we get
18% speedup for PP-8192 compared to q8_0 K-cache.

examples/llama-bench/llama-bench.cpp

@@ -339,6 +339,9 @@ static ggml_type ggml_type_from_name(const std::string & s) {
     if (s == "q6_0") {
         return GGML_TYPE_Q6_0;
     }
+    if (s == "q8_KV") {
+        return GGML_TYPE_Q8_KV;
+    }
 
     return GGML_TYPE_COUNT;
 }