This commit renames 4 pattern rewriter API functions:
* `updateRootInPlace` -> `modifyOpInPlace`
* `startRootUpdate` -> `startOpModification`
* `finalizeRootUpdate` -> `finalizeOpModification`
* `cancelRootUpdate` -> `cancelOpModification`
The term "root" is a misnomer. The root is the op that a rewrite pattern
matches against
(https://mlir.llvm.org/docs/PatternRewriter/#root-operation-name-optional).
A rewriter must be notified of all in-place op modifications, not just
in-place modifications of the root
(https://mlir.llvm.org/docs/PatternRewriter/#pattern-rewriter). The old
function names were confusing and have contributed to various broken
rewrite patterns.
Note: The new function names use the term "modify" instead of "update"
for consistency with the `RewriterBase::Listener` terminology
(`notifyOperationModified`).
`bufferization.materialize_in_destination` should be used instead. Both
ops bufferize to a memcpy. This change also conceptually cleans up the
memref dialect a bit: the memref dialect no longer contains ops that
operate on tensor values.
This revision adds support to
`transform.structured.bufferize_to_allocation` to bufferize
`bufferization.alloc_tensor()` ops.
This is useful as a means path to control the bufferization of
`tensor.empty` ops that have bene previously
`bufferization.empty_tensor_to_alloc_tensor`'ed.
This commit allows to omit insertion of the memref.dealloc operation
when linalg.structured.bufferize_to_allocation is run and makes this the
default behavior. This is desirable when the
buffer-deallocation-pipeline is run after bufferization to handle buffer
deallocation.
`bufferize_to_allocation` does not supports ops with regions, unless `bufferize_destination_only` is set. In that case, only the operand is replaced with an allocation and wrapped in a `to_tensor` op. The error checking was too strict.
Differential Revision: https://reviews.llvm.org/D159420
Add an option that does not bufferize the targeted op itself, but just materializes a buffer for the destination operands. This is useful for partial bufferization of complex ops such as `scf.forall`, which need special handling (and an analysis if the region).
Differential Revision: https://reviews.llvm.org/D155946
Add a new option that allows users to specify a memcpy op: "memref.tensor_store", "memref.copy" or "linalg.copy".
Differential Revision: https://reviews.llvm.org/D154968
Tensors/buffers that do not have any defined contents (e.g., `tensor.empty`) are no longer copied.
Differential Revision: https://reviews.llvm.org/D154081
This op needs special handling because the allocation for the masked op must be placed outside of the mask op.
Differential Revision: https://reviews.llvm.org/D154058
Until now, only `tensor.pad` ops could be bufferized to an allocation. This revision adds support for all bufferizable ops that do not already bufferize to an allocation. (Those still need special handling.)
Differential Revision: https://reviews.llvm.org/D153971
The `bufferize_to_allocation` transform op now operates on payload ops, not payload values. Only ops can be bufferized, not values.
Also remove the `replacement` result from the transform op.
Differential Revision: https://reviews.llvm.org/D153970
Add an additional result handle to the op. This new handle is mapped to the newly allocated buffer.
Differential Revision: https://reviews.llvm.org/D153514
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 change adds a new helper function `mlir::reifyResultShapes` that calls the corresponding interface method and also checks the result produced by the implementation when running in debug mode. Bugs due to incorrect interface implementations can be difficult to debug.
This helper function also reduces the amount of code needed at call sites: the cast to `ReifyRankedShapedTypeOpInterface` is done in the helper function.
Differential Revision: https://reviews.llvm.org/D145777
`reifyResultShapes` now returns `OpFoldResult`s instead of `Value`s. This is often more efficient because many transformations immediately attempt to extract a constant from the reified values.
Differential Revision: https://reviews.llvm.org/D145250
A new transform dialect op is introduced to perform the rewrite.
The test pass option is now obsolete and is removed in favor of the transform.
In the process I realized the tensor.pad nofold attribute was not taken into account
and added support to emit a bufferization.alloc_tensor + linalg.copy.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D143943
This transform materializes a buffer allocation for a given tensor value. All uses of the original value are replaced with the allocation.
Certain non-DPS ops may have an optimized lowering path that bufferizes the entire defining op. Such optimization is added for `tensor.pad` as part of this change.
The resulting IR can be further bufferized with One-Shot Bufferize.
Differential Revision: https://reviews.llvm.org/D144022
This can be a pre-processing for bufferization and allows for more efficient lowerings without an alloc.
Differential Revision: https://reviews.llvm.org/D142206
This can be a pre-processing for bufferization and allows for more efficient lowerings without an alloc.
Differential Revision: https://reviews.llvm.org/D142207
This can be a pre-processing for bufferization and allows for more efficient lowerings without an alloc.
Differential Revision: https://reviews.llvm.org/D142205
