Fixes#61871 and fixes#60581.
This PR fixes two small things. First and foremost, it throws a clear
error in the `-test-elements-attr-interface` when those tests are called
on elements which are not an integer. I've looked through the
introduction of the attribute interface
(https://reviews.llvm.org/D109190) and later commits and see no evidence
that the interface (`attr.tryGetValues<T>()`) is expected to handle
mismatching types.
For example, the case which is given in #61871 is:
```mlir
arith.constant sparse<[[0, 0, 5]], -2.0> : vector<1x1x10xf16>
```
So, a sparse vector containing `f16` elements. This will crash at
various locations when called in the test because the test introduces
integer types (`int64_t`, `uint64_t`, `APInt`, `IntegerAttr`), but as I
said in the previous paragraph: I see no reason to believe that the
implementation of the interface is wrong here. The interface just
assumes that clients don't do things like `attr.tryGetValues<APInt>()`
on a floating point `attr`.
Also I've added a test for the implementation of this interface by the
`sparse` dialect. There were no problems there. Still, probably good to
increase code coverage on that one.
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 patch removes the implementation of TypedAttr and ElementsAttr
from DenseArrayAttr and, in doing so, removes the need store a shaped
type. The attribute now stores a size (number of elements), an MLIR type
as a discriminator, and a raw byte array.
The intent of DenseArrayAttr was not to be a drop-in replacement for DenseElementsAttr. It was meant to be a simple container of integers or floats that map to C++ types. The ElementsAttr implementation on DenseArrayAttr had many holes in it, and fixing those holes would require evolving DenseArrayAttr in a way that is incompatible with its original purpose.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D137606
This patch changes `value_begin_impl` to a faillable
`try_value_begin_impl` so that specific cases can fail iteration if the
type doesn't match the internal storage.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D132904
This patch turns `DenseArrayBaseAttr` into a fully-functional attribute by
adding a generic parser and printer, supporting bool or integer and floating
point element types with bitwidths divisible by 8. It has been renamed
to `DenseArrayAttr`. The patch maintains the specialized subclasses,
e.g. `DenseI32ArrayAttr`, which remain the preferred API for accessing
elements in C++.
This allows `DenseArrayAttr` to hold signed and unsigned integer elements:
```
array<si8: -128, 127>
array<ui8: 255>
```
"Exotic" floating point elements:
```
array<bf16: 1.2, 3.4>
```
And integers of other bitwidths:
```
array<i24: 8388607>
```
Reviewed By: rriddle, lattner
Differential Revision: https://reviews.llvm.org/D132758
The element type enum is not needed to differentiate dense array kinds
because the element type of the shaped type can be used instead.
Reviewed By: mehdi_amini, rriddle
Differential Revision: https://reviews.llvm.org/D132535
This patch adds a DenseI1ArrayAttr to support arrays of i1. Importantly,
the implementation is as a simple `ArrayRef<bool>` instead of using bit
compression, which was problematic in DenseElementsAttr.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D130957
This attribute is similar to DenseElementsAttr but does not support
splat. As such it has a much simpler API and does not need any smart
iterator: it exposes direct ArrayRef access.
A new syntax is introduced so that the generic printing/parsing looks
like:
[:i64 1, -2, 3]
This attribute beings like an ArrayAttr but has a `:` token after the
opening square brace to introduce the element type (supported are I8,
I16, I32, I64, F32, F64) and the comma separated list for the data.
This is particularly convenient for attributes intended to be small,
like those referring to shapes.
For example a `transpose` operation with a `dims` attribute could be
defined as such:
let arguments = (ins AnyTensor:$input, DenseI64ArrayAttr:$dims);
let assemblyFormat = "$input `dims` `=` $dims attr-dict : type($input)";
And printed this way (the element type is elided in this case):
transpose %input dims = [0, 2, 1] : tensor<2x3x4xf32>
The C++ API for dims would just directly return an ArrayRef<int64>
RFC: https://discourse.llvm.org/t/rfc-introduce-a-new-dense-array-attribute/63279
Recommit with a custom DenseArrayBaseAttrStorage class to ensure
over-alignment of the storage to the largest type.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D123774
This attribute is similar to DenseElementsAttr but does not support
splat. As such it has a much simpler API and does not need any smart
iterator: it exposes direct ArrayRef access.
A new syntax is introduced so that the generic printing/parsing looks
like:
[:i64 1, -2, 3]
This attribute beings like an ArrayAttr but has a `:` token after the
opening square brace to introduce the element type (supported are I8,
I16, I32, I64, F32, F64) and the comma separated list for the data.
This is particularly convenient for attributes intended to be small,
like those referring to shapes.
For example a `transpose` operation with a `dims` attribute could be
defined as such:
let arguments = (ins AnyTensor:$input, DenseI64ArrayAttr:$dims);
let assemblyFormat = "$input `dims` `=` $dims attr-dict : type($input)";
And printed this way (the element type is elided in this case):
transpose %input dims = [0, 2, 1] : tensor<2x3x4xf32>
The C++ API for dims would just directly return an ArrayRef<int64>
RFC: https://discourse.llvm.org/t/rfc-introduce-a-new-dense-array-attribute/63279
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D123774
This commit restructures how TypeID is implemented to ideally avoid
the current problems related to shared libraries. This is done by changing
the "implicit" fallback path to use the name of the type, instead of using
a static template variable (which breaks shared libraries). The major downside to this
is that it adds some additional initialization costs for the implicit path. Given the
use of type names for uniqueness in the fallback, we also no longer allow types
defined in anonymous namespaces to have an implicit TypeID. To simplify defining
an ID for these classes, a new `MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID` macro
was added to allow for explicitly defining a TypeID directly on an internal class.
To help identify when types are using the fallback, `-debug-only=typeid` can be
used to log which types are using implicit ids.
This change generally only requires changes to the test passes, which are all defined
in anonymous namespaces, and thus can't use the fallback any longer.
Differential Revision: https://reviews.llvm.org/D122775
NamedAttribute is currently represented as an std::pair, but this
creates an extremely clunky .first/.second API. This commit
converts it to a class, with better accessors (getName/getValue)
and also opens the door for more convenient API in the future.
Differential Revision: https://reviews.llvm.org/D113956
This revision refactors ElementsAttr into an Attribute Interface.
This enables a common interface with which to interact with
element attributes, without needing to modify the builtin
dialect. It also removes a majority (if not all?) of the need for
the current OpaqueElementsAttr, which was originally intended as
a way to opaquely represent data that was not representable by
the other builtin constructs.
The new ElementsAttr interface not only allows for users to
natively represent their data in the way that best suits them,
it also allows for efficient opaque access and iteration of the
underlying data. Attributes using the ElementsAttr interface
can directly expose support for interacting with the held
elements using any C++ data type they claim to support. For
example, DenseIntOrFpElementsAttr supports iteration using
various native C++ integer/float data types, as well as
APInt/APFloat, and more. ElementsAttr instances that refer to
DenseIntOrFpElementsAttr can use all of these data types for
iteration:
```c++
DenseIntOrFpElementsAttr intElementsAttr = ...;
ElementsAttr attr = intElementsAttr;
for (uint64_t value : attr.getValues<uint64_t>())
...;
for (APInt value : attr.getValues<APInt>())
...;
for (IntegerAttr value : attr.getValues<IntegerAttr>())
...;
```
ElementsAttr also supports failable range/iterator access,
allowing for selective code paths depending on data type
support:
```c++
ElementsAttr attr = ...;
if (auto range = attr.tryGetValues<uint64_t>()) {
for (uint64_t value : *range)
...;
}
```
Differential Revision: https://reviews.llvm.org/D109190
