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
```
OpBuilder OpBuilder::Listener
^ ^
| |
RewriterBase RewriterBase::Listener
```
* Clients can listen to IR modifications with `RewriterBase::Listener`.
* `RewriterBase` no longer inherits from `OpBuilder::Listener`.
* Only a single listener can be registered at the moment (same as `OpBuilder`).
RFC: https://discourse.llvm.org/t/rfc-listeners-for-rewriterbase/68198
Differential Revision: https://reviews.llvm.org/D143339
This change is needed in order to set the flag when running the pass not via the command line.
It also allows simplifying the signature of some functions.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D143416
OneShotModuleBufferize fails if the input IR cannot be analyzed.
One can set CopyBeforeWrite=true in order to skip analysis.
In that case, a buffer copy is inserted on every write.
This leads to many copies, also in FuncOps that could be analyzed.
This change aims to copy buffers only when it is a must.
When running OneShotModuleBufferize with CopyBeforeWrite=false,
FuncOps whose names are specified in noAnalysisFuncFilter will not be
analyzed. Ops in these FuncOps will not be analyzed as well.
They will be bufferized with CopyBeforeWrite=true,
while the other ops will be bufferized with CopyBeforeWrite=false.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D142631
The masked op can currently not bufferize out-of-place. Such IR would be rejected by the One-Shot Bufferize because it would mean that a new buffer allocation is yielded from a block. Furthermore, only one operation is currently allowed inside `vector.mask`.
Differential Revision: https://reviews.llvm.org/D141686
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
MemRef has been accepting a general Attribute as memory space for
a long time. This commits updates bufferization side to catch up,
which allows downstream users to plugin customized symbolic memory
space. This also eliminates quite a few `getMemorySpaceAsInt`
calls, which is deprecated.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D138330
The methods in `SideEffectUtils.h` (and their implementations in
`SideEffectUtils.cpp`) seem to have similar intent to methods already
existing in `SideEffectInterfaces.h`. Move the decleration (and
implementation) from `SideEffectUtils.h` (and `SideEffectUtils.cpp`)
into `SideEffectInterfaces.h` (and `SideEffectInterface.cpp`).
Also drop the `SideEffectInterface::hasNoEffect` method in favor of
`mlir::isMemoryEffectFree` which actually recurses into the operation
instead of just relying on the `hasRecursiveMemoryEffectTrait`
exclusively.
Differential Revision: https://reviews.llvm.org/D137857
Expose `function-boundary-type-conversion` in `OneShotBufferizeOp`. To
reuse options between passes and transform operations, create a
`BufferizationEnums.td`.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D137833
This fixes an issue in One-Shot Bufferize that could lead to missing buffer copies in the future. This bug can currently not be triggered because of the order in which ops are analyzed (always bottom-to-top). However, if we consider different traversal orders for the analysis in the future, this bug can cause subtle issues that are difficult to debug.
Example:
```
%0 = ...
%1 = tensor.insert ... into %0
%2 = tensor.extract_slice %0
tensor.extract %2[...]
```
In case of a top-to-bottom analysis of the above IR, the `tensor.insert` is analyzed before the `tensor.extract_slice`. In that case, the `tensor.insert` will bufferize in-place because %2 is not yet known to become an alias of %0 (and therefore causing a conflict).
With this change, the `tensor.insert` will bufferize out-of-place, regardless of the traversal order.
Differential Revision: https://reviews.llvm.org/D135049
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.
Reviewed By: mehdi_amini, rriddle
Differential Review: https://reviews.llvm.org/D132838
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.
Reviewed By: mehdi_amini, rriddle
Differential Review: https://reviews.llvm.org/D132838
tensor.pad is lowered to tensor.generate + tensor.insert_slice during bufferization. For best performance with constant padding values, users should vectorize the IR before bufferizing it.
This change also relaxes tje restriction that no new ops that bufferize to a memory write should be added during bufferization. Since bufferization has been split into two steps a while ago (tensor copy insertion + bufferization), it is reasonable to allow this now.
Differential Revision: https://reviews.llvm.org/D132355
The `unknownTypeConversion` bufferization option (enum) is now a type converter function option. Some logic of `getMemRefType` is now handled by that function.
This change makes type conversion more controllable. Previously, there were only two options when generating memref types for non-bufferizable ops: Static identity layout or fully dynamic layout. With this change, users of One-Shot Bufferize can provide a function with custom logic.
Differential Revision: https://reviews.llvm.org/D129273
This is useful because the result type of an op can sometimes be inferred from its body (e.g., `scf.if`). This will be utilized in subsequent changes.
Also introduces a new `getBufferType` interface method on BufferizableOpInterface. This method is useful for computing a bufferized block argument type with respect to OpOperand types of the parent op.
Differential Revision: https://reviews.llvm.org/D128420
This attribute is currently supported on AllocTensorOp only. Future changes will add support to other ops. Furthermore, the memory space is not propagated properly in all bufferization patterns and some of the core bufferization infrastructure. This will be addressed in a subsequent change.
Differential Revision: https://reviews.llvm.org/D128274
With the recent refactorings, this class is no longer needed. We can use BufferizationOptions in all places were BufferizationState was used.
Differential Revision: https://reviews.llvm.org/D127653
This change changes the bufferization so that it utilizes the new TensorCopyInsertion pass. One-Shot Bufferize no longer calls the One-Shot Analysis. Instead, it relies on the TensorCopyInsertion pass to make the entire IR fully inplacable. The `bufferize` implementations of all ops are simplified; they no longer have to account for out-of-place bufferization decisions. These were already materialized in the IR in the form of `bufferization.alloc_tensor` ops during the TensorCopyInsertion pass.
Differential Revision: https://reviews.llvm.org/D127652
This simplifies the bufferization itself and is in preparation of connecting with the sparse compiler.
Differential Revision: https://reviews.llvm.org/D126814
Users should explicitly run `-buffer-results-to-out-params` instead.
The purpose of this change is to remove `finalizeBuffers`, which made it difficult to extend the bufferization to custom buffer types.
Differential Revision: https://reviews.llvm.org/D126253
The buffer deallocation pass must now be run explicitly when `allow-return-alloc` is set.
This results in a few extra buffer copies in unoptimized test cases. The proper way to avoid such copies is to relax the OpOperand/OpResult aliasing contract on ops such as scf.for. Some of these copies can also be avoided by improving the buffer deallocation pass.
Differential Revision: https://reviews.llvm.org/D126252
Now that analysis and bufferization are better separated, post-analysis steps are no longer needed. Users can directly interleave analysis and bufferization as needed.
Differential Revision: https://reviews.llvm.org/D126571
Also fixes integration of the pass into One-Shot Bufferize and adds additional test cases.
BufferResultsToOutParams can be used with "identity-layout-map" and "fully-dynamic-layout-map". "infer-layout-map" is not supported.
Differential Revision: https://reviews.llvm.org/D125636
This change adds a new op `alloc_tensor` to the bufferization dialect. During bufferization, this op is always lowered to a buffer allocation (unless it is "eliminated" by a pre-processing pass). It is useful to have such an op in tensor land, because it allows users to model tensor SSA use-def chains (which drive bufferization decisions) and because tensor SSA use-def chains can be analyzed by One-Shot Bufferize, while memref values cannot.
This change also replaces all uses of linalg.init_tensor in bufferization-related code with bufferization.alloc_tensor.
linalg.init_tensor and bufferization.alloc_tensor are similar, but the purpose of the former one is just to carry a shape. It does not indicate a memory allocation.
linalg.init_tensor is not suitable for modelling SSA use-def chains for bufferization purposes, because linalg.init_tensor is marked as not having side effects (in contrast to alloc_tensor). As such, it is legal to move linalg.init_tensor ops around/CSE them/etc. This is not desirable for alloc_tensor; it represents an explicit buffer allocation while still in tensor land and such allocations should not suddenly disappear or get moved around when running the canonicalizer/CSE/etc.
BEGIN_PUBLIC
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END_PUBLIC
Differential Revision: https://reviews.llvm.org/D126003
This changes replaces the `fully-dynamic-layout-maps` options (which was badly named) with two new options:
* `unknown-type-conversion` controls the layout maps on buffer types for which no layout map can be inferred.
* `function-boundary-type-conversion` controls the layout maps on buffer types inside of function signatures.
Differential Revision: https://reviews.llvm.org/D125615
This change integrates the BufferResultsToOutParamsPass into One-Shot Module Bufferization. This improves memory management (deallocation) when buffers are returned from a function.
Note: This currently only works with statically-sized tensors. The generated code is not very efficient yet and there are opportunities for improvment (fewer copies). By default, this new functionality is deactivated.
Differential Revision: https://reviews.llvm.org/D125376
