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.
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