This patch fixes:
mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp:76:46: error:
'multiplies' may not intend to support class template argument
deduction [-Werror,-Wctad-maybe-unsupported]
- Revamped lowering conversion pattern for `tosa.reshape` to handle previously unsupported combinations of dynamic dimensions in input and output tensors. The lowering strategy continues to rely on pairs `tensor.collapse_shape` + `tensor.expand_shape`, which allow for downstream fusion with surrounding `linalg.generic` ops.
- Fixed bug in canonicalization pattern `ReshapeOp::fold()` in `TosaCanonicalizations.cpp`. The input and result types being equal is not a sufficient condition for folding. If there is more than 1 dynamic dimension in the input and result types, a productive reshape could still occur.
- This work exposed the fact that bufferization does not properly handle a `tensor.collapse_shape` op producing a 0D tensor from a dynamically shaped one due to a limitation in `memref.collapse_shape`. While the proper way to address this would involve releasing the `memref.collapse_shape` restriction and verifying correct bufferization, this is left as possible future work. For now, this scenario is avoided by casting the `tosa.reshape` input tensor to a static shape if necessary (see `inferReshapeInputType()`.
- An extended set of tests are intended to cover relevant conversion paths. Tests are named using pattern `test_reshape_<rank>_{up|down|same}_{s2s|s2d|d2s|d2d}_{explicit|auto}[_empty][_identity]`, where:
- `<rank>` is the input rank (e.g., 3d, 6d)
- `{up|down|same}` indicates whether the reshape increases, decreases, or retains the input rank.
- `{s2s|s2d|d2s|d2d}` indicates whether reshape converts a statically shaped input to a statically shaped result (`s2s`), a statically shaped input to a dynamically shaped result (`s2d`), etc.
- `{explicit|auto}` is used to indicate that all values in the `new_shape` attribute are >=0 (`explicit`) or that a -1 placeholder value is used (`auto`).
- `empty` is used to indicate that `new_shape` includes a component set to 0.
- `identity` is used when the input and result shapes are the same.
Fixes#68481, In the following scenario, the conversion fails:
1. resultType of tosa.slice is UnrankedTensorType
2. tosa.slice.getsize().size() < resultType.getRank()
reshape(reshape(x)) -> reshape(x) can be directly written as a fold instead of a canonicalization,
to help other passes cleanup while they work.
This initially broke ReshapeConverterExpand/Collapse, which relies on creating foldable reshapes and a carefully crafted
benefit priority of patterns.
I turned this into a single pattern on reshapes, which does expand and/or collapse as needed in one go.
Differential Revision: https://reviews.llvm.org/D155266
The pattern would create reshape ops without a newShape attr.
This fails the verifier (which can be seen in the debug output;
but curiously doesn't abort compilation),
and can cause crashes in other code that expect to see valid
reshape ops, like ReshapeOp::fold.
Differential Revision: https://reviews.llvm.org/D154651
* Remove duplicate functions. `tensor::getMixedSize` and `tensor::getMixedSizes` should be used.
* Use `tensor::getMixedSize` instead of `createOrFold<tensor::DimOp>`. This is more efficient. `createOrFold` will create an op an immediately try to fold it. In case of a static dimension size, an attribute can be used directly.
Differential Revision: https://reviews.llvm.org/D153332
The existing lowering for tosa.concat fails in some instances when the
output shape contains more information the input shapes. The result is
an illegal tensor.empty operation.
This change bases the output shape on the original tosa.concat
operation, while querying the input tensor shapes to build the slicing
operations.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D151707
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
Currently conversions to interfaces may happen implicitly (e.g.
`Attribute -> TypedAttr`), failing a runtime assert if the interface
isn't actually implemented. This change marks the `Interface(ValueT)`
constructor as explicit so that a cast is required.
Where it was straightforward to I adjusted code to not require casts,
otherwise I just made them explicit.
Depends on D148491, D148492
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D148493
tosa.concat is lowered to tensor.insert_slice thus it should be in
TosaToTensor rather than in TosaToLinalg.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D145952
Converting tosa.reshape to tensor.expand_shape and
tensor.collapse_shape logically belongs in the tosa-to-tensor
conversion process. In addition, we (rocMLIR downstream) want to use
the reshape -> expand/collapse_shape logic to simplify parts of our
Tosa integration without using the full tosa-to-linalg flow, further
motivating moving these patterns.
The downside to this change is that it means you need to run
tosa-to-tensor after tosa-to-linalg, which is probably a breaking
change.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D145119
Since tosa.pad is lowered strictly to artih and tensor ops, move
ConvertPad from TosaToLinalg to TosaToTensor, benefitting non-Linalg
Tosa targets. TensorToLinalg exists, and is trivial, so nothing is lost.
Signed-off-by: Ramkumar Ramachandra <r@artagnon.com>
Differential Revision: https://reviews.llvm.org/D139091
Apply scale should be optionally disabled when lowering via TosaToStandard.
In most cases it should persist until the lowering to specific backend.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D122948
