* "init" operands are specified with `MutableOperandRange` (which gives
access to the underlying `OpOperand *`). No more magic numbers.
* Remove most interface methods and make them helper functions. Only
`getInitsMutable` should be implemented.
* Provide separate helper functions for accessing mutable/immutable
operands (`OpOperand`/`Value`, in line with #66515): `getInitsMutable`
and `getInits` (same naming convention as auto-generated op accessors).
`getInputOperands` was not renamed because this function cannot return a
`MutableOperandRange` (because the operands are not necessarily
consecutive). `OpOperandVector` is no longer needed.
* The new `getDpsInits`/`getDpsInitsMutable` is more efficient than the
old `getDpsInitOperands` because no `SmallVector` is created. The new
functions return a range of operands.
* Fix a bug in `getDpsInputOperands`: out-of-bounds operands were
potentially returned.
This consolidates where this kind of implementations lives and
refactor the code to have more code sharing.
NFC
Differential Revision: https://reviews.llvm.org/D154362
The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.
Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.
Caveats include:
- This clang-tidy script probably has more problems.
- This only touches C++ code, so nothing that is being generated.
Context:
- https://mlir.llvm.org/deprecation/ at "Use the free function variants
for dyn_cast/cast/isa/…"
- Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443
Implementation:
This first patch was created with the following steps. The intention is
to only do automated changes at first, so I waste less time if it's
reverted, and so the first mass change is more clear as an example to
other teams that will need to follow similar steps.
Steps are described per line, as comments are removed by git:
0. Retrieve the change from the following to build clang-tidy with an
additional check:
https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check
1. Build clang-tidy
2. Run clang-tidy over your entire codebase while disabling all checks
and enabling the one relevant one. Run on all header files also.
3. Delete .inc files that were also modified, so the next build rebuilds
them to a pure state.
4. Some changes have been deleted for the following reasons:
- Some files had a variable also named cast
- Some files had not included a header file that defines the cast
functions
- Some files are definitions of the classes that have the casting
methods, so the code still refers to the method instead of the
function without adding a prefix or removing the method declaration
at the same time.
```
ninja -C $BUILD_DIR clang-tidy
run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
-header-filter=mlir/ mlir/* -fix
rm -rf $BUILD_DIR/tools/mlir/**/*.inc
git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\
mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\
mlir/lib/**/IR/\
mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\
mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\
mlir/test/lib/Dialect/Test/TestTypes.cpp\
mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\
mlir/test/lib/Dialect/Test/TestAttributes.cpp\
mlir/unittests/TableGen/EnumsGenTest.cpp\
mlir/test/python/lib/PythonTestCAPI.cpp\
mlir/include/mlir/IR/
```
Differential Revision: https://reviews.llvm.org/D150123
Currently conversions to interfaces may happen implicitly (e.g.
`Attribute -> TypedAttr`), failing a runtime assert if the interface
isn't actually implemented. This change marks the `Interface(ValueT)`
constructor as explicit so that a cast is required.
Where it was straightforward to I adjusted code to not require casts,
otherwise I just made them explicit.
Depends on D148491, D148492
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D148493
Replace references to enumerate results with either result_pairs
(reference wrapper type) or structured bindings. I did not use
structured bindings everywhere as it wasn't clear to me it would
improve readability.
This is in preparation to the switch to zip semantics which won't
support non-const lvalue reference to elements:
https://reviews.llvm.org/D144503.
I chose to use values instead of const lvalue-refs because MLIR is
biased towards avoiding `const` local variables. This won't degrade
performance because currently `result_pair` is cheap to copy (size_t
+ iterator), and in the future, the enumerator iterator dereference
will return temporaries anyway.
Reviewed By: dblaikie
Differential Revision: https://reviews.llvm.org/D146006
IREE's passes depend on the behavior of SplitReduction's introduced
parallel loop being the same as the introduced dimension in the
intermediate tensor (the order of loops was changed in
https://reviews.llvm.org/D137478).
Differential Revision: https://reviews.llvm.org/D138972
dimension where the extra parallel dimension is inserted
Currently, the innerParallel and non innerParallel strategies use two
different ways to fix for where the extra loop is inserted and where the
extra dimension for the intermediate result is inserted - innerParallel
adds the extra (parallel) loop right after the pre-existing reduction
loop, whereas non innerParallel adds the reduction loop in the successor
to the index supplied by control, and the parallel loop in the index
supplied by the control. The semantics of the index supplied by the
control is supposed to only control where the extra tensor dimension is
inserted in the intermediate tensor. Conflating this index with where
the reduction (and parallel) loops are inserted leads to more complex
(and confusing) logic overall. This differential removes conflating the
two uses of the index, and keeps the reduction and parallel loops in the
same vicinity and uses the supplied index to only determine the position
of the extra tensor dimension. It also simplifies the code by merging
the two strategies in a lot more places.
Differential Revision: https://reviews.llvm.org/D137478
[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
Add a transformation to tile reduction ops into a parallel operation
followed by a merge operation. This is equivalent to the existing
reduction spliting transformation but using loops instead of using
higher dimensions linalg.
Differential Revision: https://reviews.llvm.org/D136586
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
Previously, splitReduction transformation added the split parallel dimension
*before* the reduction dimension, leading to tiling for reduction. This
commit creates an option to create the parallel dimension *after* the
reduction dimension, allowing us to transform the op into vertical reduction
with SIMD parallelism.
Reviewed By: ThomasRaoux, dcaballe
Differential Revision: https://reviews.llvm.org/D134764
This revision merges the 2 split_reduction transforms and adds extra control by using attributes.
SplitReduction is known to require a concrete additional buffer to store tempoaray information.
Add an option to introduce a `bufferization.alloc_tensor` instead of `linalg.init_tensor`.
This behaves better with subset-based tiling and bufferization.
Differential Revision: https://reviews.llvm.org/D128722
This patch fixes:
llvm-project/mlir/lib/Dialect/Linalg/Transforms/SplitReduction.cpp:300:26:
error: comparison of integers of different signs: 'int64_t' (aka
'long') and 'size_t' (aka 'unsigned long') [-Werror,-Wsign-compare]
This revision proposes a different implementation of the SplitReductoin transformation that does
not rely on tensor::ExpandShapeOp.
Previously, a dimension `[k]` would be split into `[k][kk]` via an ExpandShapeOp.
Instead, this revision proposes to rewrite `[k]` into `[factor * k + kk]`.
There are different tradeoffs involved but the proposed implementation is more general because
the affine rewrite is well-defined. In particular, it works naturally with `?` parallel dimensions and
non-trivial indexing maps.
A further rewrite of `[factor * k + kk]` + ExpandShapeOp is possible as a followup.
Differential Revision: https://reviews.llvm.org/D128266
This revision separates the `LinalgSplitReduction` pattern, whose application is based on attributes,
from its implementation.
A transform dialect op extension is added to control the application of the transformation at a finer granularity.
Differential Revision: https://reviews.llvm.org/D128165
It is very wrong if the ranges can't be infered. It's also checked in
verifyStructuredOpInterface, so we don't need the Optional return type.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D124596
This transformation allow to break up a reduction dimension in a
parallel and a reduction dimension. This is followed by a separate
reduction op. This allows to generate tree reduction which is beneficial
on target allowing to take advantage parallelism.
Differential Revision: https://reviews.llvm.org/D122045
