This reverts commit 3875804a0725c6490b4c0e76e1c0e1e0dbccedf4.
This caused some test failures for the MLIR python bindings. Reverting
until those are addressed.
This patch splits the lowering of vector.print into first converting
an n-D print into a loop of scalar prints of the elements, then a second
pass that converts those scalar prints into the runtime calls. The
former is done in VectorToSCF and the latter in VectorToLLVM.
The main reason for this is to allow printing scalable vector types,
which are not possible to fully unroll at compile time, though this
also avoids fully unrolling very large vectors.
To allow VectorToSCF to add the necessary punctuation between vectors
and elements, a "punctuation" attribute has been added to vector.print.
This abstracts calling the runtime functions such as printNewline(),
without leaking the LLVM details into the higher abstraction levels.
For example:
vector.print <comma>
lowers to
llvm.call @printComma() : () -> ()
The output format and runtime functions remain the same, which avoids
the need to alter a large number of tests (aside from the pipelines).
Reviewed By: awarzynski, c-rhodes, aartbik
Differential Revision: https://reviews.llvm.org/D156519
This patch improves the lowering by changing target LLVM intrinsics from
`reduce.fmax` and `reduce.fmin`,
which have different semantic for handling NaN,
to `reduce.fmaximum` and `reduce.fminimum` ones.
Fixes#63969
Depends on D155869
Reviewed By: dcaballe
Differential Revision: https://reviews.llvm.org/D155877
`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
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
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
Love or hate it, but the vector.print operation was the very
first operation that actually made "end-to-end" CHECK integration
testing possible for MLIR. This revision adds support for
the -until recently- less common but important floating-point
types f16 and bf16.
This will become useful for accelerator specific testing (e.g. NVidia GPUs)
Reviewed By: wrengr
Differential Revision: https://reviews.llvm.org/D145207
This patch adds masking support for more contraction flavors including those
with any combiner operation (add, mul, min, max, and, or, etc.) and
regular matmul contractions.
Combiner operations that are performing vertical reductions (and,
therefore, they are not represented with a horizontal reduction
operation) can be executed unmasked. However, the previous value of
the accumulator must be propagated for lanes that shouldn't accumulate.
We achieve this goal by introducing a select operation after the
accumulator to choose between the combined and the previous accumulator
value. This design decision is made to avoid introducing masking support
to all the arithmetic and logical operations in the Arith dialect. VP
intrinsics do not support pass-thru values either so we would have to
generate the same sequence when lowering to LLVM. The op + select
pattern is peepholed by some backend with native masking support for those
operations.
Consequently, this patch removes masking support from the vector.fma
operation to follow the same approach for all the combiner operations.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D144239
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 patch adds support for masking vector.contract ops with the
vector.mask approach. This also includes the lowering of vector.contract
through the vector.outerproduct path to LLVM. For now, this only adds
support for one of the many potential flavors of
vector.contract/vector.outerproduct but unsupported cases will fail
gratefully.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D143965
This patch adds the conversion patterns to lower masked reduction
operations to the corresponding vp intrinsics in LLVM.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D142177
Remapping memory spaces is a function often needed in type
conversions, most often when going to LLVM or to/from SPIR-V (a future
commit), and it is possible that such remappings may become more
common in the future as dialects take advantage of the more generic
memory space infrastructure.
Currently, memory space remappings are handled by running a
special-purpose conversion pass before the main conversion that
changes the address space attributes. In this commit, this approach is
replaced by adding a notion of type attribute conversions
TypeConverter, which is then used to convert memory space attributes.
Then, we use this infrastructure throughout the *ToLLVM conversions.
This has the advantage of loosing the requirements on the inputs to
those passes from "all address spaces must be integers" to "all
memory spaces must be convertible to integer spaces", a looser
requirement that reduces the coupling between portions of MLIR.
ON top of that, this change leads to the removal of most of the calls
to getMemorySpaceAsInt(), bringing us closer to removing it.
(A rework of the SPIR-V conversions to use this new system will be in
a folowup commit.)
As a note, one long-term motivation for this change is that I would
eventually like to add an allocaMemorySpace key to MLIR data layouts
and then call getMemRefAddressSpace(allocaMemorySpace) in the
relevant *ToLLVM in order to ensure all alloca()s, whether incoming or
produces during the LLVM lowering, have the correct address space for
a given target.
I expect that the type attribute conversion system may be useful in
other contexts.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D142159
This patch mechanically replaces None with std::nullopt where the
compiler would warn if None were deprecated. The intent is to reduce
the amount of manual work required in migrating from Optional to
std::optional.
This is part of an effort to migrate from llvm::Optional to
std::optional:
https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716
Currently vector.gather only supports reading memory into a 1-D result vector.
This patch extends it to support an n-D result vector with the indices, masks,
and passthroughs in n-D vectors.
As we are trying to vectorize tensor.extract with vector.gather
(https://github.com/iree-org/iree/issues/9198), it will need to gather the
elements into an n-D vector. Having vector.gather with n-D results allows us
to avoid flatten and reshape at the vectorization stage. The backends can then
decide the optimal ways to lower the vector.gather op.
Note that this is different from n-D gathering, which is about reading n-D
memory with the n-D indices. The indices here are still only 1-D offsets on
the base.
Reviewed By: dcaballe
Differential Revision: https://reviews.llvm.org/D131905
This patch moves `LLVM::ShuffleVectorOp` to assembly format and in the
process drops the extra type that can be inferred (both operand types
are required to be the same) and switches to a dense integer array.
The syntax change:
```
// Before
%0 = llvm.shufflevector %0, %1 [0 : i32, 0 : i32, 0 : i32, 0 : i32] : vector<4xf32>, vector<4xf32>
// After
%0 = llvm.shufflevector %0, %1 [0, 0, 0, 0] : vector<4xf32>
```
Reviewed By: dcaballe
Differential Revision: https://reviews.llvm.org/D132038
This patch "modernizes" the LLVM `insertvalue` and `extractvalue`
operations to use DenseI64ArrayAttr, since they only require an array of
indices and previously there was confusion about whether to use i32 or
i64 arrays, and to use assembly format.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D131537
In addition to memref, accept ranked tensor as the base operand of vector.gather, similar to vector.trasnfer_read.
This will allow us to vectorize noncontiguous tensor.extract into vector.gather. Full discussion can be found here: https://github.com/iree-org/iree/issues/9198
Reviewed By: hanchung, dcaballe
Differential Revision: https://reviews.llvm.org/D130097
Adding the accumulator value after the `vector.contract` changes the
precision of the operation. This makes sure the accumulator is carried
through to `vector.reduce` (and down to LLVM).
Differential Revision: https://reviews.llvm.org/D128674
We are using "enable-index-optimizations" and "indexOptimizations" as
names for an optimization that consists of using i32 for indices within
a vector. For instance, when building a vector comparison for mask
generation. The name is confusing and suggests a scope beyond these
vector indices. This change makes the function of the option explicit
in its name.
Differential Revision: https://reviews.llvm.org/D122415
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
The way vector.create_mask is currently lowered is
vector-length-dependent, and therefore incompatible with scalable vector
types. This patch adds an alternative lowering path for create_mask
operations that return a scalable vector mask.
Differential Revision: https://reviews.llvm.org/D118248
The Func has a large number of legacy dependencies carried over from the old
Standard dialect, which was pervasive and contained a large number of varied
operations. With the split of the standard dialect and its demise, a lot of lingering
dead dependencies have survived to the Func dialect. This commit removes a
large majority of then, greatly reducing the dependence surface area of the
Func dialect.
The last remaining operations in the standard dialect all revolve around
FuncOp/function related constructs. This patch simply handles the initial
renaming (which by itself is already huge), but there are a large number
of cleanups unlocked/necessary afterwards:
* Removing a bunch of unnecessary dependencies on Func
* Cleaning up the From/ToStandard conversion passes
* Preparing for the move of FuncOp to the Func dialect
See the discussion at https://discourse.llvm.org/t/standard-dialect-the-final-chapter/6061
Differential Revision: https://reviews.llvm.org/D120624
This is part of the larger effort to split the standard dialect. This will also allow for pruning some
additional dependencies on Standard (done in a followup).
Differential Revision: https://reviews.llvm.org/D118202
This reduces the dependencies of the MLIRVector target and makes the dialect consistent with other dialects.
Differential Revision: https://reviews.llvm.org/D118533
With VectorType supporting scalable dimensions, we don't need many of
the operations currently present in ArmSVE, like mask generation and
basic arithmetic instructions. Therefore, this patch also gets
rid of those.
Having built-in scalable vector support also simplifies the lowering of
scalable vector dialects down to LLVMIR.
Scalable dimensions are indicated with the scalable dimensions
between square brackets:
vector<[4]xf32>
Is a scalable vector of 4 single precission floating point elements.
More generally, a VectorType can have a set of fixed-length dimensions
followed by a set of scalable dimensions:
vector<2x[4x4]xf32>
Is a vector with 2 scalable 4x4 vectors of single precission floating
point elements.
The scale of the scalable dimensions can be obtained with the Vector
operation:
%vs = vector.vscale
This change is being discussed in the discourse RFC:
https://llvm.discourse.group/t/rfc-add-built-in-support-for-scalable-vector-types/4484
Differential Revision: https://reviews.llvm.org/D111819
The implementation only allows to bit-cast between two 0-D vectors. We could
probably support casting from/to vectors like `vector<1xf32>`, but I wasn't
convinced that this would be important and it would require breaking the
invariant that `BitCastOp` works only on vectors with equal rank.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D114854
This reverts commit 29a50c5864ddab283c1ff38694fb5926ce37b39a.
After LLVM lowering, the original patch incorrectly moved alignment
information across an unconstrained GEP operation. This is only correct
for some index offsets in the GEP. It seems that the best approach is,
in fact, to rely on LLVM to propagate information from the llvm.assume()
to users.
Thanks to Thomas Raoux for catching this.
This revision makes concrete use of 0-d vectors to extend the semantics of
InsertElementOp.
Reviewed By: dcaballe, pifon2a
Differential Revision: https://reviews.llvm.org/D114388
This revision starts making concrete use of 0-d vectors to extend the semantics of
ExtractElementOp.
In the process a new VectorOfAnyRank Tablegen OpBase.td is added to allow progressive transition to supporting 0-d vectors by gradually opting in.
Differential Revision: https://reviews.llvm.org/D114387
