Kv optimization - up to 3x performance on larger context

[cmp-nct]

KV cache is now cyclic split into permuted V variant
The ggml_tensor_print function has been completely reworked to output proper 1-4dim tensors with data.

Example:

+======================+======================+======================+======================+
| :0
| V                                [f32 type]
+----------------------+----------------------+----------------------+----------------------+
| Dimensions           | Strides              | Layer id             | Backend              |
| 3                    | 4x16x1024            | 0                    | CPU                  |
+----------------------+----------------------+----------------------+----------------------+
| Elements             | Src0                 | Src1                 | Operation            |
| 4 x 64 x 2           | 4 x 64 x 2           | N/A                  | CONT                 |
+----------------------+----------------------+----------------------+----------------------+
| Transposed:      No  | Permuted:        No  | Contiguous:      Yes | Size:        0.00 MB |
| Src0 name:           | cache_v (view) (permuted)                                          |
+----------------------+----------------------+----------------------+----------------------+

+-------------------------------------------------------------------------------------------+
| Content of src0 "cache_v (view) (permuted)" (3 dim)

+-------------------------------------------------------------------------------------------+
| Content of src0 "cache_v (view) (permuted)" (3 dim)
| Total Elements : [ Row:4   Col:64  Layer:2   ]
+-------------------------------------------------------------------------------------------+
| Row 1: [0.302  , 0.010  ] [-0.238 , 0.680  ] [0.305  , 0.206  ] [-0.013 , 0.436  ] [-0.074 , -0.698 ] [-0.153 , -0.067 ]
| Row 2: [0.091  , 0.199  ] [0.253  , 0.151  ] [-0.557 , 0.089  ] [0.298  , -0.272 ] [-0.149 , 0.232  ] [-0.217 , 0.193  ]
| Row 3: [-0.085 , -0.014 ] [0.225  , 0.089  ] [-0.338 , 0.072  ] [0.416  , -0.186 ] [-0.071 , 0.110  ] [0.467  , 0.497  ]
| Row 4: [-0.336 , 0.471  ] [-0.144 , 0.070  ] [-0.062 , 0.520  ] [0.093  , 0.217  ] [-0.332 , -0.205 ] [0.012  , 0.335  ]
+-------------------------------------------------------------------------------------------+
+-------------------------------------------------------------------------------------------+
| Content of dst "V" (3 dim)

+-------------------------------------------------------------------------------------------+
| Content of dst "V" (3 dim)
| Total Elements : [ Row:4   Col:64  Layer:2   ]
+-------------------------------------------------------------------------------------------+
| Row 1: [0.302  , 0.010  ] [-0.238 , 0.680  ] [0.305  , 0.206  ] [-0.013 , 0.436  ] [-0.074 , -0.698 ] [-0.153 , -0.067 ]
| Row 2: [0.091  , 0.199  ] [0.253  , 0.151  ] [-0.557 , 0.089  ] [0.298  , -0.272 ] [-0.149 , 0.232  ] [-0.217 , 0.193  ]
| Row 3: [-0.085 , -0.014 ] [0.225  , 0.089  ] [-0.338 , 0.072  ] [0.416  , -0.186 ] [-0.071 , 0.110  ] [0.467  , 0.497  ]
| Row 4: [-0.336 , 0.471  ] [-0.144 , 0.070  ] [-0.062 , 0.520  ] [0.093  , 0.217  ] [-0.332 , -0.205 ] [0.012  , 0.335  ]
+-------------------------------------------------------------------------------------------+
+======================+======================+======================+======================+