[mlir][linalg] Enable scalable vectorization of linalg.unpack (#149293)

This patch updates `vectorizeAsTensorUnpackOp` to support scalable
vectorization by requiring user-specified vector sizes for the _read_ operation
(rather than the _write_ operation) in `linalg.unpack`. 

Conceptually, `linalg.unpack` consists of these high-level steps:
  * **Read** from the source tensor using `vector.transfer_read`.
  * **Transpose** the read value according to the permutation in the
    `linalg.unpack` op (via `vector.transpose`).
  * **Re-associate** dimensions of the transposed value, as specified by the op
    (via `vector.shape_cast`)
  * **Write** the result into the destination tensor via
    `vector.transfer_write`.

Previously, the vector sizes provided by the user were interpreted as
write-vector sizes. These were used to:
  * Infer read-vector sizes using the `inner_tiles` attribute of the unpack op.
  * Deduce vector sizes for the transpose and shape cast operations.
  * Ultimately determine the vector shape for the write.

However, this logic breaks when one or more tile sizes are dynamic. In such
cases, `vectorizeUnPackOpPrecondition` fails, and vectorization is rejected.

This patch switches the contract: users now directly specify the
"read-vector-sizes", which inherently encode all inner tile sizes - including
dynamic ones. It becomes the user's responsibility to provide valid sizes.

In practice, since `linalg.unpack` is typically constructed, tiled, and
vectorized by the same transformation pipeline, the necessary
"read-vector-sizes" should be recoverable.