Drop the remaining EDSC subdirectories and update all uses.

Differential Revision: https://reviews.llvm.org/D102911

Drop the Affine dialect EDSC subdirectory and update all uses.

Differential Revision: https://reviews.llvm.org/D102878

Drop the MemRef dialect EDSC subdirectory and update all uses.

Differential Revision: https://reviews.llvm.org/D102868

Drop the Linalg dialect EDSC subdirectory and update all uses.

Differential Revision: https://reviews.llvm.org/D102848

Instead, use createOrFold builders which result in more static information available.

Differential Revision: https://reviews.llvm.org/D102832

Drop the SCF dialect EDSC subdirectory and update all uses.

Differential Revision: https://reviews.llvm.org/D102780

Drop the vector dialect EDSC subdirectory and update all uses.

LinalgOps that are all parallel do not use the value of `outs`
tensor. The semantics is that the `outs` tensor is fully
overwritten. Using anything other than `init_tensor` can add false
dependencies between operations, when the use is just for the shape of
the tensor. Adding a canonicalization to always use `init_tensor` in
such cases, breaks this dependence.

Differential Revision: https://reviews.llvm.org/D102561

Replace the templated linalgLowerOpToLoops method by three specialized methods linalgOpToLoops, LinalgOpToParallelLoops, and linalgOpToAffineLoops.

Differential Revision: https://reviews.llvm.org/D102324

BEGIN_PUBLIC
Add support for complex constants to MLIR core.
END_PUBLIC

Differential Revision: https://reviews.llvm.org/D101908

[mlir][Linalg] NFC - More gracefully degrade lookup into failure during comprehensive bufferization (4/n)

Differential revsion: https://reviews.llvm.org/D102420

Differential revision: https://reviews.llvm.org/D102417

Differential Revision: https://reviews.llvm.org/D102458

Differential revision: https://reviews.llvm.org/D102395

This is the first step towards upstreaming comprehensive bufferization following the
discourse post: https://llvm.discourse.group/t/rfc-linalg-on-tensors-update-and-comprehensive-bufferization-rfc/3373/6.

This first commit introduces a basic pass for bufferizing within function boundaries,
assuming that the inplaceable function boundaries have been marked as such.

Differential revision: https://reviews.llvm.org/D101693

This covers the extremely common case of replacing all uses of a Value
with a new op that is itself a user of the original Value.

This should also be a little bit more efficient than the
`SmallPtrSet<Operation *, 1>{op}` idiom that was being used before.

Differential Revision: https://reviews.llvm.org/D102373

after introducing the IndexedGenericOp to GenericOp canonicalization (https://reviews.llvm.org/D101612).

Differential Revision: https://reviews.llvm.org/D102163

after introducing the IndexedGenericOp to GenericOp canonicalization (https://reviews.llvm.org/D101612).

Differential Revision: https://reviews.llvm.org/D102235

after introducing the IndexedGenericOp to GenericOp canonicalization (https://reviews.llvm.org/D101612).

Differential Revision: https://reviews.llvm.org/D102245

after introducing the IndexedGenericOp to GenericOp canonicalization (https://reviews.llvm.org/D101612).

Differential Revision: https://reviews.llvm.org/D102308

after introducing the IndexedGenericOp to GenericOp canonicalization (https://reviews.llvm.org/D101612).

Differential Revision: https://reviews.llvm.org/D102187

after introducing the IndexedGenericOp to GenericOp canonicalization (https://reviews.llvm.org/D101612).

Differential Revision: https://reviews.llvm.org/D102174

after introducing the IndexedGenericOp to GenericOp canonicalization (https://reviews.llvm.org/D101612).

Differential Revision: https://reviews.llvm.org/D102176

All glue and clutter in the linalg ops has been replaced by proper
sparse tensor type encoding. This code is no longer needed. Thanks
to ntv@ for giving us a temporary home in linalg.

So long, and thanks for all the fish.

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D102098

The current design uses a unique entry for each argument/result attribute, with the name of the entry being something like "arg0". This provides for a somewhat sparse design, but ends up being much more expensive (from a runtime perspective) in-practice. The design requires building a string every time we lookup the dictionary for a specific arg/result, and also requires N attribute lookups when collecting all of the arg/result attribute dictionaries.

This revision restructures the design to instead have an ArrayAttr that contains all of the attribute dictionaries for arguments and another for results. This design reduces the number of attribute name lookups to 1, and allows for O(1) lookup for individual element dictionaries. The major downside is that we can end up with larger memory usage, as the ArrayAttr contains an entry for each element even if that element has no attributes. If the memory usage becomes too problematic, we can experiment with a more sparse structure that still provides a lot of the wins in this revision.

This dropped the compilation time of a somewhat large TensorFlow model from ~650 seconds to ~400 seconds.

Differential Revision: https://reviews.llvm.org/D102035

Differential Revision: https://reviews.llvm.org/D102089

Remove the builder signature taking a signed dimension identifier.

Reviewed By: ergawy

Differential Revision: https://reviews.llvm.org/D102055

The pattern to convert subtensor ops to their rank-reduced versions
(by dropping unit-dims in the result) can also convert to a zero-rank
tensor. Handle that case.
This also fixes a OOB access bug in the existing pattern for such
cases.

Differential Revision: https://reviews.llvm.org/D101949

This expose a lambda control instead of just a boolean to control unit
dimension folding.
This however gives more control to user to pick a good heuristic.
Folding reshapes helps fusion opportunities but may generate sub-optimal
generic ops.

Differential Revision: https://reviews.llvm.org/D101917

Fixing a minor bug which lead to element type of the output being
modified when folding reshapes with generic op.

Differential Revision: https://reviews.llvm.org/D101942

The old index op handling let the new index operations point back to the
producer block. As a result, after fusion some index operations in the
fused block had back references to the old producer block resulting in
illegal IR. The patch now relies on a block and value mapping to avoid
such back references.

Differential Revision: https://reviews.llvm.org/D101887

Differential Revision: https://reviews.llvm.org/D101861

This revision migrates more code from Linalg into the new permanent home of
SparseTensor. It replaces the test passes with proper compiler passes.

NOTE: the actual removal of the last glue and clutter in Linalg will follow

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D101811

Ensure the index operations are lowered on all linalg loop lowering paths.

Differential Revision: https://reviews.llvm.org/D101827

This fixes a performance regression in vec-mat vectorization

Reviewed By: asaadaldien

Differential Revision: https://reviews.llvm.org/D101795

Given the source and destination shapes, if they are static, or if the
expanded/collapsed dimensions are unit-extent, it is possible to
compute the reassociation maps that can be used to reshape one type
into another. Add a utility method to return the reassociation maps
when possible.

This utility function can be used to fuse a sequence of reshape ops,
given the type of the source of the producer and the final result
type. This pattern supercedes a more constrained folding pattern added
to DropUnitDims pass.

Differential Revision: https://reviews.llvm.org/D101343

Convert subtensor and subtensor_insert operations to use their
rank-reduced versions to drop unit dimensions.

Differential Revision: https://reviews.llvm.org/D101495

The current implementation had a bug as it was relying on the target vector
dimension sizes to calculate where to insert broadcast. If several dimensions
have the same size we may insert the broadcast on the wrong dimension. The
correct broadcast cannot be inferred from the type of the source and
destination vector.

Instead when we want to extend transfer ops we calculate an "inverse" map to the
projected permutation and insert broadcast in place of the projected dimensions.

Differential Revision: https://reviews.llvm.org/D101738

Differential Revision: https://reviews.llvm.org/D101771

Add dedicated pass `convert-linalg-tiled-loops-to-scf` to lower
`linalg.tiled_loop`s.

Differential Revision: https://reviews.llvm.org/D101768