[Stream] Attach layouts to tensor ops in encoding specialization pass. (#19649)

The revision attaches the layouts to Stream tensor ops, e.g.,
stream.tensor.sizeof, etc. Each layout is wrapped with an attribute
where the attribute should implement the `calculateStorageSizeInBytes`
interface method. Because only the device/target knows the details. It
provides a way to materialize the serialized layouts into actual storage
size.

To teach the pass about layouts, there are few core changes happening in
the revision.

1. The revision introduces `cloneWithSimplifiedConfig` interface method
to EncodingLayoutAttrInterface. Each backend encoding attribute can
filter needed configurations when making layout resolvers in
HALAffinityAnalysisDialectInterface. This is needed because attributes
are not mutable. We always need to create a new attribute if we want to
update the parameters. The main benifit is that it makes the IR dump
much less verbose.
2. The revision also introduces `getLayout` interface method, which
returns the encoded layouts into encoding attributes. The CPU and VMVX
encoding attributes implement it in the revision, and it returns the
serialized MaterializeEncodingInfo struct as a dictionary attribute. See
below example for more details.

```
... layouts = [#iree_cpu.vmvx_encoding_layout<configuration =
  {encoding_info =
    {innerDimsPos = [0, 1],
     innerTileSizes = [8, 4],
     outerDimsPerm = [0, 1]}}>
]
```

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Signed-off-by: hanhanW <hanhan0912@gmail.com>