The pattern generalizes a tensor::UnPackOp into a sequence of tensor +
Linalg ops, when the outer dims are all 1s. It uses the trick of
rank-reduced tensor.extract_slice to get the tile; transpose the tile;
extract sub tile for incomplete cases if needed; use tensor.insert_slice
to insert it to the destination tensor.
Reviewed By: tyb0807, chelini
Differential Revision: https://reviews.llvm.org/D140254
This is part of an effort to migrate from llvm::Optional to
std::optional. This patch changes the way mlir-tblgen generates .inc
files, and modifies tests and documentation appropriately. It is a "no
compromises" patch, and doesn't leave the user with an unpleasant mix of
llvm::Optional and std::optional.
A non-trivial change has been made to ControlFlowInterfaces to split one
constructor into two, relating to a build failure on Windows.
See also: https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716
Signed-off-by: Ramkumar Ramachandra <r@artagnon.com>
Differential Revision: https://reviews.llvm.org/D138934
The pattern generalizes a tensor::PackOp into a sequence of tensor +
Linalg ops, when the outer dims are all 1s. It uses the trick of
rank-reduced tensor.extract_slice to get the tile; transpose the tile;
use tensor.insert_slice to insert it to the destination of inner tile.
Reviewed By: pifon2a, tyb0807
Differential Revision: https://reviews.llvm.org/D140060
[RFC: EnumAttr for iterator types in Linalg](https://discourse.llvm.org/t/rfc-enumattr-for-iterator-types-in-linalg/64535)
This affect touches and probably breaks most of the code that creates `linalg.generic`. A fix would be to replace calls to `getParallelIteratorTypeName/getReductionIteratorTypeName` with `mlir::utils::IteratorType::parallel/reduction` and types from `StringRef` to `mlir::utils::IteratorType`.
Due to limitations of tablegen, shared C++ definition of IteratorType enum lives in StructuredOpsUtils.td, but each dialect should have it's own EnumAttr wrapper. To avoid conflict, all enums in a dialect are put into a separate file with a separate tablegen rule.
Test dialect td files are refactored a bit.
Printed format of `linalg.generic` temporarily remains unchanged to avoid breaking code and tests in the same change.
Differential Revision: https://reviews.llvm.org/D137658
Context: https://discourse.llvm.org/t/psa-retire-linalg-filter-based-patterns/63785
Uses of `LinalgTilingPattern::returningMatchAndRewrite` are replaced by a top-level `tileWithLinalgTilingOptions` function that is marked obsolete and serves
as a temporary means to transition away from `LinalgTilingOptions`-based tiling.
LinalgTilingOptions supports too many options that have been orthogonalized with the use of the transform dialect.
Additionally, the revision introduces a `transform.structured.tile_to_scf_for` structured transform operation that is needed to properly tile `tensor.pad`
via the TilingInterface. Uses of `transform.structured.tile` will be deprecated and replaced by this new op.
This will achieve the deprecation of `linalg::tileLinalgOp`.
Context: https://discourse.llvm.org/t/psa-retire-tileandfuselinalgops-method/63850
In the process of transitioning, tests that were performing tile and distribute on tensors are retired: transformations should be orthogonalized better in the future.
In particular, tiling to specific loop types and tileAndDistribute behavior are not available via the transform ops.
The behavior is still available as part of the `tileWithLinalgTilingOptions` method to allow downstream clients to transition without breakages but is meant to be retired soon.
As more tests are ported to the transform dialect, it became necessary to introduce a test-transform-dialect-erase-schedule-pass to discard the transform specification
once applied so that e2e lowering and execution is possible.
Lastly, a number of redundant tests that were testing composition of patterns are retired as they are available with a better mechanism via the transform dialect.
Differential Revision: https://reviews.llvm.org/D135573
Context: https://discourse.llvm.org/t/psa-retire-linalg-filter-based-patterns/63785
In the process, also retire `tileConsumerAndFuseProducers` that is now replaced by `tileConsumerAndFuseProducerGreedilyUsingSCFForOp`.
Context: https://discourse.llvm.org/t/psa-retire-tileandfuselinalgops-method/63850
When performing this replacement, a change of behavior appeared: the older `tileConsumerAndFuseProducers` would split the parallel
and non-parallel dimensions automatically and perform a first level of tile-and-fuse on parallel dimensions only and then introduce a
second level of tiling-only on the reduction dimensions. The newer `tileConsumerAndFuseProducerGreedilyUsingSCFForOp` on the other hand
does not perform this breakdown. As a consequence, the transform specification is evolved to produce the same output.
Additionally, replace some uses of `unsigned` by `int64_t` where possible without pulling in larger interface changes (left for a future PR).
Context: https://www.youtube.com/watch?v=Puio5dly9N8
Lastly, tests that were performing tile and fuse and distribute on tensors are retired: the generated IR mixing scf.for, tensors and
distributed processor ids was racy at best ..
Differential Revision: https://reviews.llvm.org/D135559
tensor.empty/linalg.init_tensor produces an uninititalized tensor that can be used as a destination operand for destination-style ops (ops that implement `DestinationStyleOpInterface`).
This change makes it possible to implement `TilingInterface` for non-destination-style ops without depending on the Linalg dialect.
RFC: https://discourse.llvm.org/t/rfc-add-tensor-from-shape-operation/65101
Differential Revision: https://reviews.llvm.org/D135129
This revision retires the LinalgCodegenStrategy vectorization pattern. Please see the context: https://discourse.llvm.org/t/psa-retire-linalg-filter-based-patterns/63785.
This revision improves the transform dialect's VectorizeOp in different ways below:
- Adds LinalgDialect as a dependent dialect. When `transform.structured.vectorize` vectorizes `tensor.pad`, it generates `linalg.init_tensor`. In this case, linalg dialect must be registered.
- Inserts CopyVectorizationPattern in order to vectorize `memref.copy`.
- Creates two attributes: `disable_multi_reduction_to_contract_patterns` and `disable_transfer_permutation_map_lowering_patterns`. They are limiting the power of vectorization and are currently intended for testing purposes.
It also removes some of the "CHECK: vector.transfer_write" in the vectorization.mlir test. They are redundant writes, at the end of the code there is a rewrite to the same place. Transform dialect no longer generates them.
Depends on D133684 that retires the LinalgCodegenStrategy vectorization pass.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D133699
This patch removes the `type` field from `Attribute` along with the
`Attribute::getType` accessor.
Going forward, this means that attributes in MLIR will no longer have
types as a first-class concept. This patch lays the groundwork to
incrementally remove or refactor code that relies on generic attributes
being typed. The immediate impact will be on attributes that rely on
`Attribute` containing a type, such as `IntegerAttr`,
`DenseElementsAttr`, and `ml_program::ExternAttr`, which will now need
to define a type parameter on their storage classes. This will save
memory as all other attribute kinds will no longer contain a type.
Moreover, it will not be possible to generically query the type of an
attribute directly. This patch provides an attribute interface
`TypedAttr` that implements only one method, `getType`, which can be
used to generically query the types of attributes that implement the
interface. This interface can be used to retain the concept of a "typed
attribute". The ODS-generated accessor for a `type` parameter
automatically implements this method.
Next steps will be to refactor the assembly formats of certain operations
that rely on `parseAttribute(type)` and `printAttributeWithoutType` to
remove special handling of type elision until `type` can be removed from
the dialect parsing hook entirely; and incrementally remove uses of
`TypedAttr`.
Reviewed By: lattner, rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D130092
Combine the recently added utilities for folded-by-construction affine
operations with the attribute-based Range to enable more folding. This
decreases the amount of emitted code but has little effect on test
precisely because the tests are not checking for the spurious constants.
The difference in the shape of affine maps comes from the internals of
affine folding.
Depends on D129633
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D130167
The `tileAndFuseLinalgOps` is a legacy approach for tiling + fusion of
Linalg operations. Since it was also intended to work on operations
with buffer operands, this method had fairly complex logic to make
sure tile and fuse was correct even with side-effecting linalg ops.
While complex, it still wasnt robust enough. This patch deprecates
this method and thereby deprecating the tiling + fusion method for ops
with buffer semantics. Note that the core transformation to do fusion
of a producer with a tiled consumer still exists. The deprecation here
only removes methods that auto-magically tried to tile and fuse
correctly in presence of side-effects.
The `tileAndFuseLinalgOps` also works with operations with tensor
semantics. There are at least two other ways the same functionality
exists.
1) The `tileConsumerAndFuseProducers` method. This does a similar
transformation, but using a slightly different logic to
automatically figure out the legal tile + fuse code. Note that this
is also to be deprecated soon.
2) The prefered way uses the `TilingInterface` for tile + fuse, and
relies on the caller to set the tiling options correctly to ensure
that the generated code is correct.
As proof that (2) is equivalent to the functionality provided by
`tileAndFuseLinalgOps`, relevant tests have been moved to use the
interface, where the test driver sets the tile sizes appropriately to
generate the expected code.
Differential Revision: https://reviews.llvm.org/D129901
This revision removes the LinalgPromotion pattern and adds a `transform.structured.promotion` op.
Since the LinalgPromotion transform allows the injection of arbitrary C++ via lambdas, the current
transform op does not handle it.
It is left for future work to decide what the right transform op control is for those cases.
Note the underlying implementation remains unchanged and the mechanism is still controllable by
lambdas from the API.
During this refactoring it was also determined that the `dynamicBuffers` option does not actually
connect to a change of behavior in the algorithm.
This also exhibits that the related test is wrong (and dangerous).
Both the option and the test are therefore removed.
Lastly, a test that connects patterns using the filter-based mechanism is removed: all the independent
pieces are already tested separately.
Context: https://discourse.llvm.org/t/psa-retire-linalg-filter-based-patterns/63785
Differential Revision: https://reviews.llvm.org/D129649
This aligns the SCF dialect file layout with the majority of the dialects.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D128049
This patch adds the knobs to use peeling in the codegen strategy
infrastructure.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D126842
These ops complement the tiling/padding transformations by transforming
higher-level named structured operations such as depthwise convolutions into
lower-level and/or generic equivalents that are better handled by some
downstream transformations.
Differential Revision: https://reviews.llvm.org/D126698
Now that dialect constructors are generated in the .cpp file, we can
drop all of the dependent dialect includes from the .h file.
Differential Revision: https://reviews.llvm.org/D124298
This revision supports padding only a subset of the iteration dimensions via an additional padding-dimensions parameter. This control allows us to pad an operation in multiple steps. For example, one may want to pad only the output dimensions of a producer matmul fused into a consumer loop nest, before tiling and padding its reduction dimension.
Depends On D122309
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D122560
Pass the padding options using arrays instead of lambdas. In particular pass the padding value as string and use the argument parser to create the padding value. Arrays are a more natural choice that matches the current use cases and avoids converting arrays to lambdas.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D122309
Previously, only LinalgOps whose operands are defined by an ExtractSliceOp could be padded. The revision supports walking a use-def chain of LinalgOps to find an ExtractSliceOp.
Reviewed By: hanchung
Differential Revision: https://reviews.llvm.org/D122116
This commit moves FuncOp out of the builtin dialect, and into the Func
dialect. This move has been planned in some capacity from the moment
we made FuncOp an operation (years ago). This commit handles the
functional aspects of the move, but various aspects are left untouched
to ease migration: func::FuncOp is re-exported into mlir to reduce
the actual API churn, the assembly format still accepts the unqualified
`func`. These temporary measures will remain for a little while to
simplify migration before being removed.
Differential Revision: https://reviews.llvm.org/D121266
The revision removes the linalg.fill operation and renames the OpDSL generated linalg.fill_tensor operation to replace it. After the change, all named structured operations are defined via OpDSL and there are no handwritten operations left.
A side-effect of the change is that the pretty printed form changes from:
```
%1 = linalg.fill(%cst, %0) : f32, tensor<?x?xf32> -> tensor<?x?xf32>
```
changes to
```
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
```
Additionally, the builder signature now takes input and output value ranges as it is the case for all other OpDSL operations:
```
rewriter.create<linalg::FillOp>(loc, val, output)
```
changes to
```
rewriter.create<linalg::FillOp>(loc, ValueRange{val}, ValueRange{output})
```
All other changes remain minimal. In particular, the canonicalization patterns are the same and the `value()`, `output()`, and `result()` methods are now implemented by the FillOpInterface.
Depends On D120726
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120728
