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

While we figure out how to best add Standard support for scalable
vectors, these instructions provide a workaround for basic arithmetic
between scalable vectors.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D100837

Differential Revision: https://reviews.llvm.org/D101798

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

See: https://reviews.llvm.org/D101710

Differential Revision: https://reviews.llvm.org/D101801

Differential Revision: https://reviews.llvm.org/D101710

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

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

TransferReadOps that are a scalar read + broadcast are handled by TransferReadToVectorLoadLowering.

Differential Revision: https://reviews.llvm.org/D101808

This fixes a performance regression in vec-mat vectorization

Reviewed By: asaadaldien

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

This approximation matches the one in Eigen.

```
name                      old cpu/op  new cpu/op  delta
BM_mlir_Log1p_f32/10      83.2ns ± 7%  34.8ns ± 5%  -58.19%    (p=0.000 n=84+71)
BM_mlir_Log1p_f32/100      664ns ± 4%   129ns ± 4%  -80.57%    (p=0.000 n=82+82)
BM_mlir_Log1p_f32/1k      6.75µs ± 4%  0.81µs ± 3%  -88.07%    (p=0.000 n=88+79)
BM_mlir_Log1p_f32/10k     76.5µs ± 3%   7.8µs ± 4%  -89.84%    (p=0.000 n=80+80)
BM_eigen_s_Log1p_f32/10   70.1ns ±14%  72.6ns ±14%   +3.49%  (p=0.000 n=116+112)
BM_eigen_s_Log1p_f32/100   706ns ± 9%   717ns ± 3%   +1.60%   (p=0.018 n=117+80)
BM_eigen_s_Log1p_f32/1k   8.26µs ± 1%  8.26µs ± 1%     ~       (p=0.567 n=84+86)
BM_eigen_s_Log1p_f32/10k  92.1µs ± 5%  92.6µs ± 6%   +0.60%  (p=0.047 n=115+115)
BM_eigen_v_Log1p_f32/10   31.8ns ±24%  34.9ns ±17%   +9.72%    (p=0.000 n=98+96)
BM_eigen_v_Log1p_f32/100   169ns ±10%   177ns ± 5%   +4.66%   (p=0.000 n=119+81)
BM_eigen_v_Log1p_f32/1k   1.42µs ± 4%  1.46µs ± 8%   +2.70%   (p=0.000 n=93+113)
BM_eigen_v_Log1p_f32/10k  14.4µs ± 5%  14.9µs ± 8%   +3.61%  (p=0.000 n=115+110)
```

Reviewed By: ezhulenev, ftynse

Differential Revision: https://reviews.llvm.org/D101765

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

Differential Revision: https://reviews.llvm.org/D101637

Differential Revision: https://reviews.llvm.org/D101643

Move TransposeOp lowering in its own populate function as in some cases
it is better to keep it during ContractOp lowering to better
canonicalize it rather than emiting scalar insert/extract.

Differential Revision: https://reviews.llvm.org/D101647

Differential Revision: https://reviews.llvm.org/D101747

Differential Revision: https://reviews.llvm.org/D101705

1) Canonicalize IndexCast(SExt(x)) => IndexCast(x)
2) Provide constant folds of sign_extend and truncate

Differential Revision: https://reviews.llvm.org/D101714

Added canonicalization for vector_load and vector_store. An existing
pattern SimplifyAffineOp can be reused to compose maps that supplies
result into them. Added AffineVectorStoreOp and AffineVectorLoadOp
into static_assert of SimplifyAffineOp to allow operation to use it.

This fixes the bug filed: https://bugs.llvm.org/show_bug.cgi?id=50058

Reviewed By: bondhugula

Differential Revision: https://reviews.llvm.org/D101691

(1) migrates the encoding from TensorDialect into the new SparseTensorDialect
(2) replaces dictionary-based storage and builders with struct-like data

Reviewed By: mehdi_amini

Differential Revision: https://reviews.llvm.org/D101669

Three patterns are added to convert into vector.multi_reduction into a
sequence of vector.reduction as the following:

- Transpose the inputs so inner most dimensions are always reduction.
- Reduce rank of vector.multi_reduction into 2d with inner most
reduction dim (get the 2d canical form)
- 2D canonical form is converted into a sequence of vector.reduction.

There are two things we might worth in a follow up diff:

- An scf.for (maybe optionally) around vector.reduction instead of unrolling it.
- Breakdown the vector.reduction into a sequence of vector.reduction
(e.g tree-based reduction) instead of relying on how downstream dialects
handle it.
  Note: this will requires passing target-vector-length

Differential Revision: https://reviews.llvm.org/D101570

This is the very first step toward removing the glue and clutter from linalg and
replace it with proper sparse tensor types. This revision migrates the LinalgSparseOps
into SparseTensorOps of a sparse tensor dialect. This also provides a new home for
sparse tensor related transformation.

NOTE: the actual replacement with sparse tensor types (and removal of linalg glue/clutter)
will follow but I am trying to keep the amount of changes per revision manageable.

Differential Revision: https://reviews.llvm.org/D101573

This reverts commit a6d92a97.

The build with -DBUILD_SHARED_LIBS=ON is broken.

This is the very first step toward removing the glue and clutter from linalg and
replace it with proper sparse tensor types. This revision migrates the LinalgSparseOps
into SparseTensorOps of a sparse tensor dialect. This also provides a new home for
sparse tensor related transformation.

NOTE: the actual replacement with sparse tensor types (and removal of linalg glue/clutter)
will follow but I am trying to keep the amount of changes per revision manageable.

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D101488

This enables to express more complex parallel loops in the affine framework,
for example, in cases of tiling by sizes not dividing loop trip counts perfectly
or inner wavefront parallelism, among others. One can't use affine.max/min
and supply values to the nested loop bounds since the results of such
affine.max/min operations aren't valid symbols. Making them valid symbols
isn't an option since they would introduce selection trees into memref
subscript arithmetic as an unintended and undesired consequence. Also
add support for converting such loops to SCF. Drop some API that isn't used in
the core repo from AffineParallelOp since its semantics becomes ambiguous in
presence of max/min bounds. Loop normalization is currently unavailable for
such loops.

Depends On D101171

Reviewed By: bondhugula

Differential Revision: https://reviews.llvm.org/D101172

Introduce a basic support for parallelizing affine loops with reductions
expressed using iteration arguments. Affine parallelism detector now has a flag
to assume such reductions are parallel. The transformation handles a subset of
parallel reductions that are can be expressed using affine.parallel:
integer/float addition and multiplication. This requires to detect the
reduction operation since affine.parallel only supports a fixed set of
reduction operators.

Reviewed By: chelini, kumasento, bondhugula

Differential Revision: https://reviews.llvm.org/D101171

FillOp allows complex ops, and filling a properly sized buffer with
a default zero complex number is implemented.

Differential Revision: https://reviews.llvm.org/D99939

This revision adds support for vectorizing more general linalg operations with projected permutation maps.

This is achieved by eagerly broadcasting the intermediate vector to the common size
of the iteration domain of the linalg op. This allows a much more natural expression of
generalized vectorization but may introduce additional computations until all the
proper canonicalizations are implemented.

This generalization modifies the vector.transfer_read/write permutation logic and
exposes the fact that the logic employed in vector.contract was too ad-hoc.

As a consequence, changes occur in the permutation / transposition logic for contraction. In turn this prompts supporting more cases in the lowering of contract
to matrix intrinsics, which is required to make the corresponding tests pass.

Differential revision: https://reviews.llvm.org/D101165

Canonicalizations for subtensor operations defaulted to use the
rank-reduced version of the operation, but the cast inserted to get
back the original type would be illegal if the rank was actually
reduced. Instead make the canonicalization not reduce the rank of the
operation.

Differential Revision: https://reviews.llvm.org/D101258

The current canonicalization did not remap operation results correctly
and attempted to erase tiledLoop, which is incorrect if not all tensor
results are folded.

This reverts commit dca53610.

Tensor inputs, if not used in the body of TiledLoopOp, can be removed.
memref::CastOp can be folded into TiledLoopOp as well.

Differential Revision: https://reviews.llvm.org/D101445

As a canonicalization, infer the resulting shape rank if possible.

Differential Revision: https://reviews.llvm.org/D101377