34 Commits

Author SHA1 Message Date
Maksim Levental
eaa67a3cf0
[mlir][NFC] update Conversion create APIs (5/n) (#149887)
See https://github.com/llvm/llvm-project/pull/147168 for more info.
2025-07-22 10:40:45 -04:00
Peiyong Lin
04ad8d4900
Emit inbounds and nuw attributes in memref. (#138984)
Now that MLIR accepts nuw and nusw in getelementptr, this patch emits
the inbounds and nuw attributes when lower memref to LLVM in load and
store operators.

This patch also strengthens the memref.load and memref.store spec about
undefined behaviour during lowering.

This patch also lifts the |rewriter| parameter in getStridedElementPtr
ahead so that LLVM::GEPNoWrapFlags can be added at the end with a
default value and grouped together with other operators' parameters.

Signed-off-by: Lin, Peiyong <linpyong@gmail.com>
2025-05-20 14:16:22 -07:00
Matthias Springer
4defac91db
[mlir][GPUToNVVM] Add benefit to populate functions (#128484)
Certain GPU->NVVM patterns compete with Arith->LLVM patterns. (The ones
that lower to libdevice.) Add an optional `benefit` parameter to all
`populate` functions so that users can give preference to GPU->NVVM
patterns.
2025-02-24 17:27:55 +01:00
Krzysztof Drewniak
f4e3b8783c
[mlir][LLVM] Switch undef for poison for uninitialized values (#125629)
LLVM itself is generally moving away from using `undef` and towards
using `poison`, to the point of having a lint that caches new uses of
`undef` in tests.

In order to not trip the lint on new patterns and to conform to the
evolution of LLVM
- Rename valious ::undef() methods on StructBuilder subclasses to
::poison()
- Audit the uses of UndefOp in the MLIR libraries and replace almost all
of them with PoisonOp

The remaining uses of `undef` are initializing `uninitialized` memrefs,
explicit conversions to undef from SPIR-V, and a few cases in
AMDGPUToROCDL where usage like

    %v = insertelement <M x iN> undef, iN %v, i32 0
    %arg = bitcast <M x iN> %v to i(M * N)

is used to handle "i32" arguments that are are really packed vectors of
smaller types that won't always be fully initialized.
2025-02-06 12:49:30 -06:00
Matthias Springer
206fad0e21
[mlir][NFC] Mark type converter in populate... functions as const (#111250)
This commit marks the type converter in `populate...` functions as
`const`. This is useful for debugging.

Patterns already take a `const` type converter. However, some
`populate...` functions do not only add new patterns, but also add
additional type conversion rules. That makes it difficult to find the
place where a type conversion was added in the code base. With this
change, all `populate...` functions that only populate pattern now have
a `const` type converter. Programmers can then conclude from the
function signature that these functions do not register any new type
conversion rules.

Also some minor cleanups around the 1:N dialect conversion
infrastructure, which did not always pass the type converter as a
`const` object internally.
2024-10-05 21:32:40 +02:00
Kazu Hirata
dec8055a1e
[mlir] Use StringRef::operator== instead of StringRef::equals (NFC) (#91560)
I'm planning to remove StringRef::equals in favor of
StringRef::operator==.

- StringRef::operator==/!= outnumber StringRef::equals by a factor of
  10 under mlir/ in terms of their usage.

- The elimination of StringRef::equals brings StringRef closer to
  std::string_view, which has operator== but not equals.

- S == "foo" is more readable than S.equals("foo"), especially for
  !Long.Expression.equals("str") vs Long.Expression != "str".
2024-05-08 23:52:22 -07:00
Tres Popp
5550c82189 [mlir] Move casting calls from methods to function calls
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
2023-05-12 11:21:25 +02:00
Quinn Dawkins
5205c7126b [mlir][gpu] Add support for unsigned integer extend in vector to gpu.subgroup_mma lowering
Unsigned integer types are supported in subgroup mma ops by matching
against arith.extui ops. This allows for subgroup_mma_compute ops with
mixed signedness which requires later conversions to handle this. SPIR-V
cooperative matrix ops support this while the lowering to WMMA does not.

Differential Revision: https://reviews.llvm.org/D143922
2023-02-14 13:09:46 -05:00
Quinn Dawkins
985f7ff632 [mlir][gpu] Add support for integer types in gpu.subgroup_mma ops
The signedness is carried by `!gpu.mma_matrix` types to most closely
match the Cooperative Matrix specification which determines signedness
with the type (and sometimes the operation).

See: https://htmlpreview.github.io/?https://github.com/KhronosGroup/SPIRV-Registry/blob/master/extensions/NV/SPV_NV_cooperative_matrix.html

To handle the lowering from vector to gpu, ops such as arith.extsi are
pattern matched next to `vector.transfer_read` and `vector.contract` to
determine the signedness of the matrix type.

Enables s8 and u8 WMMA types in NVVM for the GPUToNVVM conversion.

Reviewed By: ThomasRaoux

Differential Revision: https://reviews.llvm.org/D143223
2023-02-07 17:58:01 -05:00
Navdeep Katel
3d35546cd1 Support transpose mode for gpu.subgroup WMMA ops
Add support for loading, computing, and storing `gpu.subgroup` WMMA ops
in transpose mode as well. Update the GPU to NVVM lowerings to support
`transpose` mode and update integration tests as well.

Reviewed By: ThomasRaoux

Differential Revision: https://reviews.llvm.org/D139021
2022-12-05 22:37:02 +05:30
Quinn Dawkins
c0321edc26 [mlir][gpu] Adding support for transposed mma_load_matrix
Enables transposed gpu.subgroup_mma_load_matrix and updates the lowerings in Vector to GPU and GPU to SPIRV. Needed to enable B transpose matmuls lowering to wmma ops.

Taken over from author: stanley-nod <stanley@nod-labs.com>

Reviewed By: ThomasRaoux, antiagainst

Differential Revision: https://reviews.llvm.org/D138770
2022-11-29 03:35:49 +00:00
Nirvedh Meshram
c441070665 [mlir][spirv] Add conversion from GPU WMMA ops to SPIRV Cooperative matrix
Reviewed By: ThomasRaoux

Differential Revision: https://reviews.llvm.org/D136521
2022-10-22 18:29:40 -07:00
River Riddle
10c04f4641 [mlir:GPU][NFC] Update GPU API to use prefixed accessors
This doesn't flip the switch for prefix generation yet, that'll be
done in a followup.
2022-09-30 15:27:10 -07:00
River Riddle
986b5c56ea [mlir] Flip Async/GPU/OpenACC/OpenMP to use Both accessors
This allows for incrementally updating the old API usages without
needing to update everything at once. These will be left on Both
for a little bit and then flipped to prefixed when all APIs have been
updated.

Differential Revision: https://reviews.llvm.org/D134386
2022-09-21 17:36:13 -07:00
Jeff Niu
5c5af910fe [mlir][LLVMIR] "Modernize" Insert/ExtractValueOp
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
2022-08-10 12:51:11 -04:00
Jeff Niu
0af643f3ce [mlir][LLVMIR] (NFC) Add convenience builders for ConstantOp
And clean up some of the user code
2022-08-09 15:34:36 -04:00
Thomas Raoux
a6f2c2291e [mlir][GPUToNVVM] Fix bug in mma elementwise lowering
The maxf implementation of wmma elementwise op was incorrect as the
operands of the select to check for Nan were swapped.

Differential Revision: https://reviews.llvm.org/D127879
2022-06-15 17:23:17 +00:00
Mogball
d7ef488bb6 [mlir][gpu] Move GPU headers into IR/ and Transforms/
Depends on D127350

Reviewed By: rriddle

Differential Revision: https://reviews.llvm.org/D127352
2022-06-09 22:49:03 +00:00
Tres Popp
b4e0507ce0 Rename PatternRewriteSet::insert to add
insert is soft deprecated, so remove all references so it's less likely
to be used and can be easily removed in the future.

Differential Revision: https://reviews.llvm.org/D120021
2022-02-18 12:18:41 +01:00
River Riddle
38abdddf6f [mlir][NFC] Update AMX/LLVM/NVVM/X86 vector operations to use hasVerifier instead of verifier
The verifier field is deprecated, and slated for removal.

Differential Revision: https://reviews.llvm.org/D118819
2022-02-02 13:34:29 -08:00
Mehdi Amini
be0a7e9f27 Adjust "end namespace" comment in MLIR to match new agree'd coding style
See D115115 and this mailing list discussion:
https://lists.llvm.org/pipermail/llvm-dev/2021-December/154199.html

Differential Revision: https://reviews.llvm.org/D115309
2021-12-08 06:05:26 +00:00
Thomas Raoux
e7969240dc [mlir][VectorToGPU] Support more cases in conversion to MMA ops
Support load with broadcast, elementwise divf op and remove the
hardcoded restriction on the vector size. Picking the right size should
be enfored by user and will fail conversion to llvm/spirv if it is not
supported.

Differential Revision: https://reviews.llvm.org/D113618
2021-11-11 13:10:38 -08:00
thomasraoux
f309939d06 [mlir][nvvm] Remove special case ptr arithmetic lowering in gpu to nvvm
Use existing helper instead of handling only a subset of indices lowering
arithmetic. Also relax the restriction on the memref rank for the GPU mma ops
as we can now support any rank.

Differential Revision: https://reviews.llvm.org/D113383
2021-11-10 10:00:12 -08:00
thomasraoux
d88cc07943 [mlir][gpuTonvvm] Remove hardcoded values in MMAType to llvm struct
Also relax the types allowed in GPU wmma ops

Differential Revision: https://reviews.llvm.org/D112969
2021-11-02 08:12:27 -07:00
thomasraoux
8a992b20db [mlir][gpu] Add basic support to do elementwise ops on mma matrix type
In order to support fusion with mma matrix type we need to be able to
execute elementwise operations on them. This add an op to be able to
support some basic elementwise operations. This is a is not a full
solution as it only supports a limited scope or operations. Ideally we would
want to be able to fuse with more kind of operations.

Differential Revision: https://reviews.llvm.org/D112857
2021-11-01 11:51:19 -07:00
thomasraoux
77eafb8430 [mlir][nvvm] Generalize wmma ops to handle more types and shapes
wmma intrinsics have a large number of combinations, ideally we want to be able
to target all the different variants. To avoid a combinatorial explosion in the
number of mlir op we use attributes to represent the different variation of
load/store/mma ops. We also can generate with tablegen helpers to know which
combinations are available. Using this we can avoid having too hardcode a path
for specific shapes and can support more types.
This patch also adds boiler plates for tf32 op support.

Differential Revision: https://reviews.llvm.org/D112689
2021-11-01 10:27:26 -07:00
thomasraoux
eacd6e1ebe [mlir][GPUtoNVVM] Relax restriction on wmma op lowering
Allow lowering of wmma ops with 64bits indexes. Change the default
version of the test to use default layout.

Differential Revision: https://reviews.llvm.org/D112479
2021-10-27 21:31:55 -07:00
River Riddle
ef976337f5 [mlir:OpConversion] Remove the remaing usages of the deprecated matchAndRewrite methods
This commits updates the remaining usages of the ArrayRef<Value> based
matchAndRewrite/rewrite methods in favor of the new OpAdaptor
overload.

Differential Revision: https://reviews.llvm.org/D110360
2021-09-24 17:51:41 +00:00
Alex Zinenko
75e5f0aac9 [mlir] factor memref-to-llvm lowering out of std-to-llvm
After the MemRef has been split out of the Standard dialect, the
conversion to the LLVM dialect remained as a huge monolithic pass.
This is undesirable for the same complexity management reasons as having
a huge Standard dialect itself, and is even more confusing given the
existence of a separate dialect. Extract the conversion of the MemRef
dialect operations to LLVM into a separate library and a separate
conversion pass.

Reviewed By: herhut, silvas

Differential Revision: https://reviews.llvm.org/D105625
2021-07-09 14:49:52 +02:00
thomasraoux
0298f2cfb1 [mlir] Fix wrong type in WmmaConstantOpToNVVMLowering
InsertElement takes a scalar integer attribute not an array of integer.

Differential Revision: https://reviews.llvm.org/D105174
2021-06-30 09:10:02 -07:00
thomasraoux
428a62f65f [mlir][gpu] Add op to create MMA constant matrix
This allow creating a matrix with all elements set to a given value. This is
needed to be able to implement a simple dot op.

Differential Revision: https://reviews.llvm.org/D103870
2021-06-10 08:34:04 -07:00
thomasraoux
9b496c2373 [mlir][gpu][NFC] Simplify conversion of MMA type to NVVM
Consolidate the type conversion in a single function to make it simpler
to use. This allow to re-use the type conversion for up coming ops.

Differential Revision: https://reviews.llvm.org/D103868
2021-06-09 09:33:38 -07:00
thomasraoux
b44007bec2 [mlir][gpu] Relax restriction on MMA store op to allow chain of mma ops.
In order to allow large matmul operations using the MMA ops we need to chain
operations this is not possible unless "DOp" and "COp" type have matching
layout so remove the "DOp" layout and force accumulator and result type to
match.
Added a test for the case where the MMA value is accumulated.

Differential Revision: https://reviews.llvm.org/D103023
2021-05-27 09:13:51 -07:00
Navdeep Kumar
eaaf7a6a09 [MLIR][GPU][NVVM] Add conversion of warp synchronous matrix-multiply accumulate GPU ops
Add conversion of warp synchronous matrix-multiply
accumulate GPU ops
Add conversion of warp synchronous matrix-multiply accumulate GPU ops to
NVVM ops. The following conversions are added :-
  1.) subgroup_mma_load_matrix -> wmma.m16n16k16.load.[a,b,c]..row.stride
  2.) subgroup_mma_store_matrix -> wmma.m16n16k16.store.d.[f16,f32].row.stride
  3.) subgroup_mma_compute -> wmma.m16n16k16.mma.row.row.[f16,f32].[f16,f32]

Reviewed By: bondhugula, ftynse

Differential Revision: https://reviews.llvm.org/D95331
2021-05-21 21:20:33 +05:30