Prior to this change, the "ExtractSliceFromReshape" pattern would transform
```
%collapsed = tensor.collapse_shape %input [[0, 1], [2]]
: tensor<1x11x100xf32> into tensor<11x100xf32>
%slice = tensor.extract_slice %collapsed [%offt, 0] [%size, 100] [1, 1]
: tensor<11x100xf32> to tensor<?x100xf32>
```
into a loop that iterated over the range `%size - %offt`, that pieces
together multiple sub-slices of `%input` along the first dimension. This
is correct but obviously inefficient. The technical condition is that
collapsing at-most-one non-unit dimension of `%src` will not result in a
subsequent slice along the corresponding dimension of `%collapsed`
mapping across discontinuities in the index space of `%src`. Thus, the
definition of a "linearized dimension" (from the perspective of
`tensor.collapse_shape`) is updated to reflect this condition.
The transform will now generate
```
%slice = tensor.extract_slice %input [0, %offt, 0][1, %size, 100] [1, 1]
: tensor<1x11x100xf32> to tensor<1x?x100xf32>
%result = tensor.collapse_shape [[0, 1], [2]]
: tensor<1x?x100xf32> to tensor<?x100xf32>
```
which can be further canonicalized.
Additional tests are added to check this family of edge cases.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D135726
446981bdb6