Since
ddf2d62c7d
, 0-d vectors are supported in VectorType. This patch removes 0-d vector
handling with scalars for the TransferOpReduceRank pattern. This pattern
specifically introduces tensor.extract_slice during vectorization,
causing vectorization to not fold transfer_read/transfer_write slices
properly. The changes in vectorization test files reflect this.
There are other places where lowering patterns are still side-stepping
from handling 0-d vectors properly, by turning them into scalars, but
this patch only focuses on the vector.transfer_x patterns.
At the moment, the in_bounds attribute has two confusing/contradicting
properties:
1. It is both optional _and_ has an effective default-value.
2. The default value is "out-of-bounds" for non-broadcast dims, and
"in-bounds" for broadcast dims.
(see the `isDimInBounds` vector interface method for an example of this
"default" behaviour [1]).
This PR aims to clarify the logic surrounding the `in_bounds` attribute
by:
* making the attribute mandatory (i.e. it is always present),
* always setting the default value to "out of bounds" (that's
consistent with the current behaviour for the most common cases).
#### Broadcast dimensions in tests
As per [2], the broadcast dimensions requires the corresponding
`in_bounds` attribute to be `true`:
```
vector.transfer_read op requires broadcast dimensions to be in-bounds
```
The changes in this PR mean that we can no longer rely on the
default value in cases like the following (dim 0 is a broadcast dim):
```mlir
%read = vector.transfer_read %A[%base1, %base2], %f, %mask
{permutation_map = affine_map<(d0, d1) -> (0, d1)>} :
memref<?x?xf32>, vector<4x9xf32>
```
Instead, the broadcast dimension has to explicitly be marked as "in
bounds:
```mlir
%read = vector.transfer_read %A[%base1, %base2], %f, %mask
{in_bounds = [true, false], permutation_map = affine_map<(d0, d1) -> (0, d1)>} :
memref<?x?xf32>, vector<4x9xf32>
```
All tests with broadcast dims are updated accordingly.
#### Changes in "SuperVectorize.cpp" and "Vectorization.cpp"
The following patterns in "Vectorization.cpp" are updated to explicitly
set the `in_bounds` attribute to `false`:
* `LinalgCopyVTRForwardingPattern` and `LinalgCopyVTWForwardingPattern`
Also, `vectorizeAffineLoad` (from "SuperVectorize.cpp") and
`vectorizeAsLinalgGeneric` (from "Vectorization.cpp") are updated to
make sure that xfer Ops created by these hooks set the dimension
corresponding to broadcast dims as "in bounds". Otherwise, the Op
verifier would complain
Note that there is no mechanism to verify whether the corresponding
memory access are indeed in bounds. Still, this is consistent with the
current behaviour where the broadcast dim would be implicitly assumed
to be "in bounds".
[1]
4145ad2bac/mlir/include/mlir/Interfaces/VectorInterfaces.td (L243-L246)
[2]
https://mlir.llvm.org/docs/Dialects/Vector/#vectortransfer_read-vectortransferreadop
Implements `TransferReadToVectorLoadLowering` and
`TransferWriteToVectorStoreLowering` as a `MaskableOpRewritePattern`.
Allowing to exit gracefully when run on an xferOp located inside a
`vector::MaskOp` instead of breaking because the pattern generated
multiple ops in the MaskOp with `error: 'vector.mask' op expects only
one operation to mask`.
Split of https://github.com/llvm/llvm-project/pull/90835
Implements `TransferOpReduceRank` as a `MaskableOpRewritePattern`.
Allowing to exit gracefully when run on a `vector::transfer_read`
located inside a `vector::MaskOp` instead of generating `error: 'vector.mask'
op expects only one operation to mask` because the
pattern generated multiple ops inside the MaskOp.
Split of https://github.com/llvm/llvm-project/pull/90835
* Implements `TransferWritePermutationLowering`,
`TransferReadPermutationLowering` and
`TransferWriteNonPermutationLowering` as a MaskableOpRewritePattern.
Allowing to exit gracefully when such use of a xferOp is inside a
`vector::MaskOp`
* Updates MaskableOpRewritePattern to handle MemRefs and buffer
semantics providing empty `Value()` as a return value for
`matchAndRewriteMaskableOp` now represents successful rewriting without
value to replace the original op.
Split of https://github.com/llvm/llvm-project/pull/90835
Updates `extendVectorRank` so that scalability in patterns
that use it (in particular, `TransferWriteNonPermutationLowering`),
is correctly propagated.
Closed related previous PR
https://github.com/llvm/llvm-project/pull/85270
---------
Signed-off-by: Crefeda Rodrigues <crefeda.rodrigues@arm.com>
Co-authored-by: Benjamin Maxwell <macdue@dueutil.tech>
The following patterns
- TransferReadToVectorLoadLowering
- TransferWriteToVectorStoreLowering
attempt to generate invalid vector.maskedload and vector.maskedstore ops
for non rank-1 vector types. These ops operate on 1-D vectors. This
patch adds a check to prevent this.
This is a follow-on to D158753, and allows the lowering of a
transfer read/write of n-D vectors with a single trailing scalable dimension
to primitive vector ops.
The final conversion to LLVM depends on D158517 and D158752, without
these patches type conversion will fail (or an assert is hit in the LLVM
backend) if the final IR contains an array of scalable vectors.
This patch adds `transform.apply_patterns.vector.lower_create_mask`
which allows the lowering of vector.create_mask/constant_mask to be
tested independently of --convert-vector-to-llvm.
Reviewed By: c-rhodes, awarzynski, dcaballe
Differential Revision: https://reviews.llvm.org/D159482
This patch adds the missing logic so that the
`TransferReadPermutationLowering` can be used for scalable vectors. To
this end:
* TransferOp custom C++ builder is updated to support scalable
vectors,
* `TransferOpReduceRank` is also updated to support scalable vectors.
This pattern is relevant when lowering `linalg.matmul` via
`vector_multi_reduction` for scalable vectors.
I've also updated relevant code in `TransferOpReduceRank` not to use
`llvm::to_vector` for constructing `SmallVector` from `ArrayRef`. That
hook doesn't work for `ArraryRef<bool>` (*), so for consistency I
switched to an explicit constructor (so that both `newShape` and
`newScalableDim` are constructed in a similar fashion).
(*) IIUC, that's due how implicit narrowing conversions between `bool`
and `*bool` work. Note that these narrowing conversions change when
using initializer lists, see
* https://en.cppreference.com/w/cpp/language/list_initialization.
Depends on D157092
Differential Revision: https://reviews.llvm.org/D157268
There was a bug in `TransferWriteNonPermutationLowering`, a pattern that extends the permutation map of a TransferWriteOp with leading transfer dimensions of size ones. These newly added transfer dimensions are always in-bounds, because the starting point of any dimension is in-bounds. VectorToSCF inserts out-of-bounds checks based on the "in_bounds" attribute and dims that are marked as out-of-bounds but that are actually always in-bounds lead to unnecessary "scf.if" ops.
Differential Revision: https://reviews.llvm.org/D155196
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 revision adds vector transform operations that allow us to better inspect the composition
of various lowerings that were previously very opaque.
This commit is NFC in that it does not change patterns beyond adding `rewriter.notifyFailure` messages
and it does not change the tests beyond breaking them into pieces and using transforms instead of
throwaway opaque test passes.
Reviewed By: ftynse, springerm
Co-authored-by: Alex Zinenko <zinenko@google.com>
Differential Revision: https://reviews.llvm.org/D146755
Vector dialect patterns have grown enormously in the past year to a point where they are now impenetrable.
Start reorganizing them towards finer-grained control.
Differential Revision: https://reviews.llvm.org/D146736
