[MLIR] Add support for permutation_map

This CL hooks up and uses permutation_map in vector_transfer ops.
In particular, when going into the nuts and bolts of the implementation, it
became clear that cases arose that required supporting broadcast semantics.
Broadcast semantics are thus added to the general permutation_map.
The verify methods and tests are updated accordingly.

Examples of interest include.

Example 1:
The following MLIR snippet:
```mlir
   for %i3 = 0 to %M {
     for %i4 = 0 to %N {
       for %i5 = 0 to %P {
         %a5 = load %A[%i4, %i5, %i3] : memref<?x?x?xf32>
   }}}
```
may vectorize with {permutation_map: (d0, d1, d2) -> (d2, d1)} into:
```mlir
   for %i3 = 0 to %0 step 32 {
     for %i4 = 0 to %1 {
       for %i5 = 0 to %2 step 256 {
         %4 = vector_transfer_read %arg0, %i4, %i5, %i3
              {permutation_map: (d0, d1, d2) -> (d2, d1)} :
              (memref<?x?x?xf32>, index, index) -> vector<32x256xf32>
   }}}
````
Meaning that vector_transfer_read will be responsible for reading the 2-D slice:
`%arg0[%i4, %i5:%15+256, %i3:%i3+32]` into vector<32x256xf32>. This will
require a transposition when vector_transfer_read is further lowered.

Example 2:
The following MLIR snippet:
```mlir
   %cst0 = constant 0 : index
   for %i0 = 0 to %M {
     %a0 = load %A[%cst0, %cst0] : memref<?x?xf32>
   }
```
may vectorize with {permutation_map: (d0) -> (0)} into:
```mlir
   for %i0 = 0 to %0 step 128 {
     %3 = vector_transfer_read %arg0, %c0_0, %c0_0
          {permutation_map: (d0, d1) -> (0)} :
          (memref<?x?xf32>, index, index) -> vector<128xf32>
   }
````
Meaning that vector_transfer_read will be responsible of reading the 0-D slice
`%arg0[%c0, %c0]` into vector<128xf32>. This will require a 1-D vector
broadcast when vector_transfer_read is further lowered.

Additionally, some minor cleanups and refactorings are performed.

One notable thing missing here is the composition with a projection map during
materialization. This is because I could not find an AffineMap composition
that operates on AffineMap directly: everything related to composition seems
to require going through SSAValue and only operates on AffinMap at a distance
via AffineValueMap. I have raised this concern a bunch of times already, the
followup CL will actually do something about it.

In the meantime, the projection is hacked at a minimum to pass verification
and materialiation tests are temporarily incorrect.

PiperOrigin-RevId: 224376828