Use wrappers around `std::accumulate` to make the code more concise and
less bug-prone: https://github.com/llvm/llvm-project/pull/162129.
With `std::accumulate`, it's the initial value that determines the
accumulator type. `llvm::sum_of` and `llvm::product_of` pick the right
accumulator type based on the range element type.
Found some funny bugs like a local accumulate helper that calculated a
sum with initial value of 1 -- we didn't hit the bug because the code
was actually dead...
This patch updates the following ops to use `source` (instead of
`vector`) as the name for their source argument:
* `vector.extract`
* `vector.scalable.extract`
* `vector.extract_strided_slice`
This change ensures naming consistency with the "builders" for these Ops
that already use the name `source` rather than `vector`. It also
addresses part of:
* https://github.com/llvm/llvm-project/issues/131602
Specifically, it ensures that we use `source` and `dest` for read and
write operations, respectively (as opposed to `vector` and `dest`).
[mlir][vector] Standardize base Naming Across Vector Ops (NFC)
This change standardizes the naming convention for the argument
representing the value to read from or write to in Vector ops that
interface with Tensors or MemRefs. Specifically, it ensures that all
such ops use the name `base` (i.e., the base address or location to
which offsets are applied).
Updated operations:
* `vector.transfer_read`,
* `vector.transfer_write`.
For reference, these ops already use `base`:
* `vector.load`, `vector.store`, `vector.scatter`, `vector.gather`,
`vector.expandload`, `vector.compressstore`, `vector.maskedstore`,
`vector.maskedload`.
This is a non-functional change (NFC) and does not alter the semantics of these
operations. However, it does require users of the XFer ops to switch from
`op.getSource()` to `op.getBase()`.
To ease the transition, this PR temporarily adds a `getSource()` interface
method for compatibility. This is intended for downstream use only and should
not be relied on upstream. The method will be removed prior to the LLVM 21
release.
Implements #131602
This PR removes one OpRewritePattern `shape_cast(shape_cast(x)) -> x`
that is already handled by `ShapeCastOp::fold`.
Note that this might affect downstream users who indirectly call
`populateShapeCastFoldingPatterns(RewritePatternSet &patterns,
PatternBenefit)` and then use `patterns` with a `GreedyRewriteConfig
config` that has `config.fold = false`. (only user I've checked is IREE,
that never uses config.fold = false).
This change standardises the naming convention for the argument
representing the value to store in various vector operations.
Specifically, it ensures that all vector ops storing a value—whether
into memory, a tensor, or another vector — use `valueToStore` for the
corresponding argument name.
Updated operations:
* `vector.transfer_write`, `vector.insert`, `vector.scalable_insert`,
`vector.insert_strided_slice`.
For reference, here are operations that currently use `valueToStore`:
* `vector.store` `vector.scatter`, `vector.compressstore`,
`vector.maskedstore`.
This change is non-functional (NFC) and does not affect the
functionality of these operations.
Implements #131602
In the LLVM style guide, we prefer not using braced initializer lists to
call a constructor. Also, we prefer using an equal before the open curly
brace if we use a braced initializer list when initializing a variable.
See
https://llvm.org/docs/CodingStandards.html#do-not-use-braced-initializer-lists-to-call-a-constructor
for more details.
The style guide does not explain the reason well. There is an article
from abseil, which mentions few benefits. E.g., we can avoid the most
vexing parse, etc. See https://abseil.io/tips/88 for more details.
Signed-off-by: hanhanW <hanhan0912@gmail.com>
Updates `castAwayContractionLeadingOneDim` to check for leading unit
dimensions before inserting `vector.transpose` ops.
Currently `castAwayContractionLeadingOneDim` removes all leading unit
dims based on the accumulator and transpose any subsequent operands to
match the accumulator indexing. This does not take into account if the
transpose is strictly necessary, for instance when given this
vector-matrix contract:
```mlir
%result = vector.contract {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d1, d2)>], iterator_types = ["parallel", "parallel", "parallel", "reduction"], kind = #vector.kind<add>} %lhs, %rhs, %acc : vector<1x1x8xi32>, vector<1x8x8xi32> into vector<1x8xi32>
```
Passing this through `castAwayContractionLeadingOneDim` pattern produces
the following:
```mlir
%0 = vector.transpose %arg0, [1, 0, 2] : vector<1x1x8xi32> to vector<1x1x8xi32>
%1 = vector.extract %0[0] : vector<1x8xi32> from vector<1x1x8xi32>
%2 = vector.extract %arg2[0] : vector<8xi32> from vector<1x8xi32>
%3 = vector.contract {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d1)>], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>} %1, %arg1, %2 : vector<1x8xi32>, vector<1x8x8xi32> into vector<8xi32>
%4 = vector.broadcast %3 : vector<8xi32> to vector<1x8xi32>
```
The `vector.transpose` introduced does not affect the underlying data
layout (effectively a no op), but it cannot be folded automatically.
This change avoids inserting transposes when only leading unit
dimensions are involved.
Fixes#85691
Updates `castAwayContractionLeadingOneDim` to inherit from
`MaskableOpRewritePattern` so that this pattern can support masking.
Builds on top of #83827
Previously the pattern only worked when the permutation map was a minor
identity. Infer the new mask type from the new transfer map after
dropping leading unit dims.
This handles `vector.transfer_read`, `vector.transfer_write`, and
`vector.constant_mask`. The unit dims are only relevant for masks
created by `create_mask` and `constant_mask` if the mask size for the
unit dim is non-one, in which case all subsequent sizes must also be
zero. From the perspective of the vector transfers, however, these unit
dims can just be dropped directly.
This patch effectively enables the CastAwayElementwiseLeadingOneDim
rewrite pattern for scalable vectors. To this end,
`ExtractOp::inferReturnTypes` is updated so that scalable dimensions are
correctly recognised.
The change to ExtractOp will likely make also other conversion patterns
valid for scalable vectors, but this patch focuses on just one case.
Other conversion patterns will be enabled in the forthcoming patches.
Depends on D157993
Differential Revision: https://reviews.llvm.org/D158335
This patch updates one specific hook in "VectorDropLeadUnitDim.cpp" to
make sure that "scalable dims" are handled correctly. While this change
affects multiple patterns, I am only adding one regression tests that
captures one specific case that affects me right now.
I am also adding Vector dialect to the list of dependencies of
`-test-vector-to-vector-lowering`. Otherwise my test case won't work as
a standalone test.
Differential Revision: https://reviews.llvm.org/D157993
`DenseI64ArrayAttr` provides a better API than `I64ArrayAttr`. E.g., accessors returning `ArrayRef<int64_t>` (instead of `ArrayAttr`) are generated.
Differential Revision: https://reviews.llvm.org/D156684
Certain functions were declared in `VectorOps.h` instead of `VectorTransforms.h` or `VectorRewritePatterns.h`.
Differential Revision: https://reviews.llvm.org/D152146
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
This commit exposes the transformation behind the pattern.
It is useful for more targeted application on a specific op
for once.
Reviewed By: kuhar
Differential Revision: https://reviews.llvm.org/D148758
This was incorrect when the number of dropped source dims was smaller
than the number of dropped dst dims. We still need to insert zeros if
there is anything dropped from the src.
Differential Revision: https://reviews.llvm.org/D148636
When dropping leading unit dims of vector.insert's operands and creating
a new vector.insert, its new position rank should be computed explicitly
in two steps: first based on the numbers of leading unit dims dropped
from the vector.insert's destination, then based on the numbers of
leading unit dims dropped from its source.
Reviewed By: pifon2a
Differential Revision: https://reviews.llvm.org/D147280
This patch removes the historical lhs and rhs masks in vector.contract,
now that vector.mask supports vector.contract and the lhs and rhs masks
are barely supported by all the vector.contract lowerings and
transformations.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D144430
Plain `getVectorType()` can be quite confusing and error-prone
given that, well, vector ops always work on vector types, and
it can commonly involve both source and result vectors. So this
commit makes various such accessor methods to be explicit w.r.t.
source or result vectors.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D144159
This one required more changes than ideal due to overlapping generated name
with different return types. Changed getIndexingMaps to getIndexingMapsArray to
move it out of the way/highlight that it returns (more expensively) a
SmallVector and uses the prefixed name for the Attribute.
Differential Revision: https://reviews.llvm.org/D129919
This has been on _Both for a couple of weeks. Flip usages in core with
intention to flip flag to _Prefixed in follow up. Needed to add a couple
of helper methods in AffineOps and Linalg to facilitate a pure flag flip
in follow up as some of these classes are used in templates and so
sensitive to Vector dialect changes.
Differential Revision: https://reviews.llvm.org/D122151
This provides a way to create an operation without manipulating
OperationState directly. This is useful for creating unregistered ops.
Reviewed By: rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D120787
If the result operand has a unit leading dim it is removed from all operands.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D119206
This reduces the dependencies of the MLIRVector target and makes the dialect consistent with other dialects.
Differential Revision: https://reviews.llvm.org/D118533
