For context, `tensor.insert_slice` is vectorized using a
`vector.transfer_read` + `vector.transfer_write` pair.
An unmasked example is shown below:
```mlir
// BEFORE VECTORIZATION
%res = tensor.insert_slice
%slice into %dest[0, %c2]
[5, 1] [1, 1] : tensor<5x1xi32> into tensor<5x3xi32>
// AFTER VECTORIZATION
%read = vector.transfer_read %source[%c0, %c0], %pad
: tensor<5x1xi32>, vector<8x1xi32>
%res = vector.transfer_write %read, %dest[%c0, %c2]
: vector<8x1xi32>, tensor<5x3xi32>
```
This PR refactors `InsertSliceVectorizePattern` (which is used to
vectorize `tensor.extract_slice`) to enable masked vectorization. ATM,
only `vector.transfer_read` is masked. If `vector.transfer_write` also
requires masking, the vectorizer will bail out. This will be addressed
in a sub-sequent PR.
Summary of changes:
* Added an argument to specify vector sizes (behavior remains
unchanged if vector sizes are not specified).
* Renamed `InsertSliceVectorizePattern` to `vectorizeAsInsertSliceOp`
and integrated into (alongside other hooks for vectorization) in
`linalg::vectorize`.
* Removed `populateInsertSliceVectorizationPatterns`, as
`InsertSliceVectorizePattern` was its only pattern.
* Updated `vectorizeAsInsertSliceOp` to support masking for the
"read" operation.
* Updated `@pad_and_insert_slice_dest` in
"vectorization-pad-patterns.mlir" to reflect the removal of
`populateInsertSliceVectorizationPatterns` from
`ApplyPadVectorizationPatternsOps`.
d68a4b93d3