[mlir][Linalg] Add a Transform dialect NavigationOp op to match a list of ops or an interface.

This operation is a NavigationOp that simplifies the writing of transform IR.
Since there is no way of

[mlir][Linalg] Add a Transform dialect NavigationOp op to match a list of ops or an interface.

This operation is a NavigationOp that simplifies the writing of transform IR.
Since there is no way of refering to an interface by name, the current implementation uses
an EnumAttr and depends on the interfaces it supports.
In the future, it would be worthwhile to remove this dependence and generalize.

Differential Revision: https://reviews.llvm.org/D130267

show more ...

[mlir] fold more eagerly in structured op splitting

Existing implementation of structured op splitting creates several
affine.apply and affine.min operations in its subshape computation.
As these sh

[mlir] fold more eagerly in structured op splitting

Existing implementation of structured op splitting creates several
affine.apply and affine.min operations in its subshape computation.
As these shapes are further used in data slice extraction, this may lead
to slice shapes being dynamic even when the original shapes and the
splitting point are static. This is particularly visible when splitting
is combined with further subsetting transformations such as tiling. Use
composition and folding more aggressively in splitting to avoid this.

In particular, introduce a `createComposedAffineMin` function that the
affine map used in "min" with the maps used by any `affine.apply` that
may be feeding the operands to the "min". This enables production of
more static shapes. Also introduce a `createComposedFoldedAffineApply`
function that combines the existing `createComposedAffineApply` with
in-place folding to propagate constants produced by zero-input affine
maps. Using these when splitting allows the subsequent canonicalizer
pass to recover static shapes for structured ops.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D129379

show more ...

[mlir] Structured transforms: introduce op splitting

Introduce a new transformation on structured ops that splits the iteration
space into two parts along the specified dimension. The index at which

[mlir] Structured transforms: introduce op splitting

Introduce a new transformation on structured ops that splits the iteration
space into two parts along the specified dimension. The index at which the
splitting happens may be static or dynamic. This transformation can be seen as
a rudimentary form of index-set splitting that only supports the splitting
along hyperplanes parallel to the iteration space hyperplanes, and is therefore
decomposable into per-dimension application.

It is a key low-level transformation that enables independent scheduling for
different parts of the iteration space of the same op, which hasn't been
possible previously. It may be used to implement, e.g., multi-sized tiling. In
future, peeling can be implemented as a combination of split-off amount
computation and splitting.

The transformation is conceptually close to tiling in its separation of the
iteration and data spaces, but cannot be currently implemented on top of
TilingInterface as the latter does not properly support `linalg.index`
offsetting.

Note that the transformation intentionally bypasses folding of
`tensor.extract_slice` operations when creating them as this folding was found
to prevent repeated splitting of the same operation because due to internal
assumptions about extract/insert_slice combination in dialect utilities.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D129090

show more ...