Tiling can be enabled with `linalg-tile-pad-tensor-ops`. Only scf::ForOp can be generated at the moment.
Differential Revision: https://reviews.llvm.org/D105460
Factor out the functionality into a new function, so that it can be used for creating PadTensorOp tiles.
Differential Revision: https://reviews.llvm.org/D105458
Remove `getDynOperands` and `createOrFoldDimOp` from MemRef.h to decouple MemRef a bit from Tensor. These two functions are used in other dialects/transforms.
Differential Revision: https://reviews.llvm.org/D105260
* Split memref.dim into two operations: memref.dim and tensor.dim. Both ops have the same builder interface and op argument names, so that they can be used with templates in patterns that apply to both tensors and memrefs (e.g., some patterns in Linalg).
* Add constant materializer to TensorDialect (needed for folding in affine.apply etc.).
* Remove some MemRefDialect dependencies, make some explicit.
Differential Revision: https://reviews.llvm.org/D105165
The main goal of this commit is to remove the dependency of Standard dialect on the Tensor dialect.
* Rename SubTensorOp -> tensor.extract_slice, SubTensorInsertOp -> tensor.insert_slice.
* Some helper functions are (already) duplicated between the Tensor dialect and the MemRef dialect. To keep this commit smaller, this will be cleaned up in a separate commit.
* Additional dialect dependencies: Shape --> Tensor, Tensor --> Standard
* Remove dialect dependencies: Standard --> Tensor
* Move canonicalization test cases to correct dialect (Tensor/MemRef).
Note: This is a fixed version of https://reviews.llvm.org/D104499, which was reverted due to a missing update to two CMakeFile.txt.
Differential Revision: https://reviews.llvm.org/D104676
The main goal of this commit is to remove the dependency of Standard dialect on the Tensor dialect.
* Rename ops: SubTensorOp --> ExtractTensorOp, SubTensorInsertOp --> InsertTensorOp
* Some helper functions are (already) duplicated between the Tensor dialect and the MemRef dialect. To keep this commit smaller, this will be cleaned up in a separate commit.
* Additional dialect dependencies: Shape --> Tensor, Tensor --> Standard
* Remove dialect dependencies: Standard --> Tensor
* Move canonicalization test cases to correct dialect (Tensor/MemRef).
Differential Revision: https://reviews.llvm.org/D104499
* Remove dependency: Standard --> MemRef
* Add dependencies: GPUToNVVMTransforms --> MemRef, Linalg --> MemRef, MemRef --> Tensor
* Note: The `subtensor_insert_propagate_dest_cast` test case in MemRef/canonicalize.mlir will be moved to Tensor/canonicalize.mlir in a subsequent commit, which moves over the remaining Tensor ops from the Standard dialect to the Tensor dialect.
Differential Revision: https://reviews.llvm.org/D104506
According to the API contract, LinalgLoopDistributionOptions
expects to work on parallel iterators. When getting processor
information, only loop ranges for parallel dimensions should
be fed in. But right now after generating scf.for loop nests,
we feed in *all* loops, including the ones materialized for
reduction iterators. This can cause unexpected distribution
of reduction dimensions. This commit fixes it.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D102079
Until now Linalg fusion only allow fusing producers whose operands
are all permutation indexing maps. It's easier to deduce the
subtensor/subview but it is an unnecessary constraint, as in tiling
we have more advanced logic to deduce the subranges even when the
operand is not of permutation indexing maps, e.g., the input operand
for convolution ops.
This patch uses the logic on tiling side to deduce subranges for
fusion. This enables fusing convolution with its consumer ops
when possible.
Along the way, we are now generating proper affine.min ops to guard
against size boundaries, if we cannot be certain they won't be
out of bounds.
Differential Revision: https://reviews.llvm.org/D99014
This is a preparation step to reuse makeTiledShapes in tensor
fusion. Along the way, did some lightweight cleanups.
Differential Revision: https://reviews.llvm.org/D99013
It is to use the methods in LinalgInterfaces.cpp for additional static shape verification to match the shaped operands and loop on linalgOps. If I used the existing methods, I would face circular dependency linking issue. Now we can use them as methods of LinalgOp.
Reviewed By: hanchung
Differential Revision: https://reviews.llvm.org/D98163
This revision drops init_tensor arguments from Linalg on tensors and instead uniformizes the output buffers and output tensors to be consistent.
This significantly simplifies the usage of Linalg on tensors and is a stepping stone for
its evolution towards a mixed tensor and shape abstraction discussed in https://llvm.discourse.group/t/linalg-and-shapes/2421/19.
Differential Revision: https://reviews.llvm.org/D93469
In the past, the reshape op can be folded only if the indexing map is
permutation in consumer's usage. We can relax to condition to be projected
permutation.
This patch still limits the fusion for scalar cases. Scalar case is a corner
case, because we need to decide where to put extra dims.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D92466
This revision refactors code used in various Linalg transformations and makes it a first class citizen to the LinalgStructureOpInterface. This is in preparation to allowing more advanced Linalg behavior but is otherwise NFC.
Differential revision: https://reviews.llvm.org/D91863
scf.parallel is currently not a good fit for tiling on tensors.
Instead provide a path to parallelism directly through scf.for.
For now, this transformation ignores the distribution scheme and always does a block-cyclic mapping (where block is the tile size).
Differential revision: https://reviews.llvm.org/D90475
Linalg "tile-and-fuse" is currently exposed as a Linalg pass "-linalg-fusion" but only the mechanics of the transformation are currently relevant.
Instead turn it into a "-test-linalg-greedy-fusion" pass which performs canonicalizations to enable more fusions to compose.
This allows dropping the OperationFolder which is not meant to be used with the pattern rewrite infrastructure.
Differential Revision: https://reviews.llvm.org/D90394
This revision introduces a `subtensor` op, which is the counterpart of `subview` for a tensor operand. This also refactors the relevant pieces to allow reusing the `subview` implementation where appropriate.
This operation will be used to implement tiling for Linalg on tensors.
This revision changes the signatures of helper function that Linalg uses to create loops so that they can also take iterArgs.
iterArgs are asserted empty to ensure no functional change.
This is a mechanical change in preparation of tiling on linalg on tensors to avoid polluting the implementation with an NFC change.
Differential Revision: https://reviews.llvm.org/D88480
Also refactor the getViewSizes method to work on LinalgOp instead of
being a templated version. Keeping the templated version for
compatibility.
Differential Revision: https://reviews.llvm.org/D87303
LinalgDistribution options to allow more general distributions.
Changing the signature of the callback to send in the ranges for all
the parallel loops and expect a vector with the Value to use for the
processor-id and number-of-processors for each of the parallel loops.
Differential Revision: https://reviews.llvm.org/D86095
Linalg to processors.
This changes adds infrastructure to distribute the loops generated in
Linalg to processors at the time of generation. This addresses use
case where the instantiation of loop is done just to distribute
them. The option to distribute is added to TilingOptions for now and
will allow specifying the distribution as a transformation option,
just like tiling and promotion are specified as options.
Differential Revision: https://reviews.llvm.org/D85147
Loop bound inference is right now very limited as it supports only permutation maps and thus
it is impossible to implement convolution with linalg.generic as it requires more advanced
loop bound inference. This commits solves it for the convolution case.
Depends On D83158
Differential Revision: https://reviews.llvm.org/D83191
Recent work has introduced support for constructing loops via `::build` with
callbacks that construct loop bodies using only the core OpBuilder. This is now
supported on all loop types that Linalg lowers to. Refactor LoopNestBuilder in
Linalg to rely on this functionality instead of using a custom EDSC-based
approach to creating loop nests.
The specialization targeting parallel loops is also simplified by factoring out
the recursive call into a separate static function and considering only two
alternatives: top-level loop is parallel or sequential.
This removes the last remaining in-tree use of edsc::LoopBuilder, which is now
deprecated and will be removed soon.
Differential Revision: https://reviews.llvm.org/D81873
Modifying the loop nest builder for generating scf.parallel loops to
not generate scf.parallel loops for non-parallel iterator types in
Linalg operations. The existing implementation incorrectly generated
scf.parallel for all tiled loops. It is rectified by refactoring logic
used while lowering to loops that accounted for this.
Differential Revision: https://reviews.llvm.org/D80188
This dialect contains various structured control flow operaitons, not only
loops, reflect this in the name. Drop the Ops suffix for consistency with other
dialects.
Note that this only moves the files and changes the C++ namespace from 'loop'
to 'scf'. The visible IR prefix remains the same and will be updated
separately. The conversions will also be updated separately.
Differential Revision: https://reviews.llvm.org/D79578
This revision moves the various range utilities present in MLIR to LLVM to enable greater reuse. This revision moves the following utilities:
* indexed_accessor_*
This is set of utility iterator/range base classes that allow for building a range class where the iterators are represented by an object+index pair.
* make_second_range
Given a range of pairs, returns a range iterating over the `second` elements.
* hasSingleElement
Returns if the given range has 1 element. size() == 1 checks end up being very common, but size() is not always O(1) (e.g., ilist). This method provides O(1) checks for those cases.
Differential Revision: https://reviews.llvm.org/D78064
Tobias Gysi