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result/single dim of result. (#162924)
Current implementation of `reifyResultShapes` forces all
implementations to return all dimensions of all results. This can be
wasteful when you only require dimensions of one result, or a single
dimension of a result. Further this also creates issues with using
patterns to resolve the `tensor.dim` and `memref.dim` operations since
the extra operations created result in the pattern rewriter entering
an infinite loop (eventually breaking out of the loop due to the
iteration limit on the pattern rewriter). This is demonstrated by some
of the test cases added here that hit this limit when using
`--resolve-shaped-type-result-dims` and
`--resolve-ranked-shaped-type-result-dims`. To resolve this issue the
interface should allow for creating just the operations needed. This
change is the first step in resolving this.
The original implementation was done with the restriction in mind that
it might not always be possible to compute dimension of a single
result or one dimension of a single result in all cases. To account
for such cases, two additional interface methods are added
- `reifyShapeOfResult` (which allows reifying dimensions of
just one result), has a default implementation that calls
`reifyResultShapes` and returns the dimensions of a single result.
- `reifyDimOfResult` (which allows reifying a single dimension of a
single result) has a default implementation that calls
`reifyDimOfResult` and returns the value for the dimension of the
result (which in turn for the default case would call
`reifyDimOfResult`).
While this change sets up the interface, ideally most operations will
implement the `refiyDimOfResult` when possible. For almost all
operations in tree this is true. Subsequent commits will change those
incrementally.
Some of the tests added here that check that the default
implementations for the above method work as expected, also end up
hitting the pattern rewriter limit when using
`--resolve-ranked-shaped-type-result-dims`/
`--resolve-ranked-shaped-type-result-dims`. For testing purposes, a
flag is added to these passes that ignore the error returned by the
pattern application (this flag is left on by default to maintain
current state).
Changes required downstream to integrate this change
1. In operation definitions in .td files, for those operations that
implement the `ReifyRankedShapedTypeOpInterface`.
```
def <op-name> : Op<..., [...,
DeclareOpInterfaceMethods[ReifyRankedShapedTypeOpInterface]]>
```
should be changed to
```
def <op-name> : Op<..., [...,
DeclareOpInterfaceMethods[ReifyRankedShapedTypeOpInterface, [
"reifyResultShapes"]]]>
```
---------
Signed-off-by: MaheshRavishankar <mahesh.ravishankar@gmail.com>
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`tensor.expand_shape` op. (#113501)
The op carries the output-shape directly. This can be used directly.
Also adds a method to get the shape as a `SmallVector<OpFoldResult>`.
Signed-off-by: MaheshRavishankar <mahesh.ravishankar@gmail.com>
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Note that PointerUnion::{is,get} have been soft deprecated in
PointerUnion.h:
// FIXME: Replace the uses of is(), get() and dyn_cast() with
// isa<T>, cast<T> and the llvm::dyn_cast<T>
I'm not touching PointerUnion::dyn_cast for now because it's a bit
complicated; we could blindly migrate it to dyn_cast_if_present, but
we should probably use dyn_cast when the operand is known to be
non-null.
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(#112336)
Print an error such as the following one before terminating program
execution.
```
mlir/test/Dialect/SparseTensor/convert_dense2sparse.mlir:26:8: remark: location of op
%0 = sparse_tensor.convert %arg0 : tensor<?xi32> to tensor<?xi32, #SparseVector>
^
LLVM ERROR: Failed to infer result type(s):
"sparse_tensor.positions"(...) {} : (index) -> ( ??? )
(stack trace follows)
```
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This helps support generic manipulation of operations that don't (yet)
use properties to store inherent attributes.
Use this mechanism in type inference and operation equivalence.
Note that only minimal unit tests are introduced as all the upstream
dialects seem to have been updated to use properties and the
non-property behavior is essentially deprecated and untested.
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The change in c1eab57673ef3eb2842c0fbe454d7878854cf54c fixed the
behavior of `getDiscardableAttrDictionary` for ops that are not using
properties to only return discardable attributes. `InferTypeOpInterface`
was relying on the wrong behavior when constructing an adaptor and would
assume that all attributes were discardable, which is not the case.
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Support extra concrete class declarations and definitions under NativeTrait that get injected into the class that specifies the trait. Extra declarations and definitions can be passed in as template arguments for NativeOpTraitNativeAttrTrait and NativeTypeTrait.
Usage examples of this feature include:
- Creating a wrapper Trait for authoring inferReturnTypes with the OpAdaptor by specifying necessary Op specific declarations and definitions directly in the trait
- Refactoring the InferTensorType trait
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D154731
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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.
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 patch updates all remaining uses of the deprecated functionality in
mlir/. This was done with clang-tidy as described below and further
modifications to GPUBase.td and OpenMPOpsInterfaces.td.
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:
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.
```
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
```
Differential Revision: https://reviews.llvm.org/D151542
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getDiscardableAttrDictionary() when possible
This also speeds up some benchmarks in compiling simple fortan file by 2x!
Fixes #62687
Differential Revision: https://reviews.llvm.org/D150540
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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
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This new features enabled to dedicate custom storage inline within operations.
This storage can be used as an alternative to attributes to store data that is
specific to an operation. Attribute can also be stored inside the properties
storage if desired, but any kind of data can be present as well. This offers
a way to store and mutate data without uniquing in the Context like Attribute.
See the OpPropertiesTest.cpp for an example where a struct with a
std::vector<> is attached to an operation and mutated in-place:
struct TestProperties {
int a = -1;
float b = -1.;
std::vector<int64_t> array = {-33};
};
More complex scheme (including reference-counting) are also possible.
The only constraint to enable storing a C++ object as "properties" on an
operation is to implement three functions:
- convert from the candidate object to an Attribute
- convert from the Attribute to the candidate object
- hash the object
Optional the parsing and printing can also be customized with 2 extra
functions.
A new options is introduced to ODS to allow dialects to specify:
let usePropertiesForAttributes = 1;
When set to true, the inherent attributes for all the ops in this dialect
will be using properties instead of being stored alongside discardable
attributes.
The TestDialect showcases this feature.
Another change is that we introduce new APIs on the Operation class
to access separately the inherent attributes from the discardable ones.
We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`,
and other similar method which don't make the distinction explicit, leading
to an entirely separate namespace for discardable attributes.
Recommit d572cd1b067f after fixing python bindings build.
Differential Revision: https://reviews.llvm.org/D141742
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This reverts commit d572cd1b067f1177a981a4711bf2e501eaa8117b.
Some bots are broken and investigation is needed before relanding.
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This new features enabled to dedicate custom storage inline within operations.
This storage can be used as an alternative to attributes to store data that is
specific to an operation. Attribute can also be stored inside the properties
storage if desired, but any kind of data can be present as well. This offers
a way to store and mutate data without uniquing in the Context like Attribute.
See the OpPropertiesTest.cpp for an example where a struct with a
std::vector<> is attached to an operation and mutated in-place:
struct TestProperties {
int a = -1;
float b = -1.;
std::vector<int64_t> array = {-33};
};
More complex scheme (including reference-counting) are also possible.
The only constraint to enable storing a C++ object as "properties" on an
operation is to implement three functions:
- convert from the candidate object to an Attribute
- convert from the Attribute to the candidate object
- hash the object
Optional the parsing and printing can also be customized with 2 extra
functions.
A new options is introduced to ODS to allow dialects to specify:
let usePropertiesForAttributes = 1;
When set to true, the inherent attributes for all the ops in this dialect
will be using properties instead of being stored alongside discardable
attributes.
The TestDialect showcases this feature.
Another change is that we introduce new APIs on the Operation class
to access separately the inherent attributes from the discardable ones.
We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`,
and other similar method which don't make the distinction explicit, leading
to an entirely separate namespace for discardable attributes.
Differential Revision: https://reviews.llvm.org/D141742
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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
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InferTypeOpInterface.cpp (NFC)
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This is part of an effort to migrate from llvm::Optional to
std::optional. This patch changes the way mlir-tblgen generates .inc
files, and modifies tests and documentation appropriately. It is a "no
compromises" patch, and doesn't leave the user with an unpleasant mix of
llvm::Optional and std::optional.
A non-trivial change has been made to ControlFlowInterfaces to split one
constructor into two, relating to a build failure on Windows.
See also: https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716
Signed-off-by: Ramkumar Ramachandra <r@artagnon.com>
Differential Revision: https://reviews.llvm.org/D138934
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Originally, inferReturnTensorTypes didn't support shaped type components
containing an attribute just because there wasn't any motivating use-case.
Removing that limitation and using it to set the encoding attribute for
RankedTensorType.
Updated the existing test to set result attribute based on the first operand,
if available.
Signed-off-by: Smit Hinsu <smittvhinsu@gmail.com>
Differential Revision: https://reviews.llvm.org/D139271
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refineReturnType method shares the same parameters as inferReturnTypes
but gets passed in the return types of the op if known that can be used
during refinement passes or for more op specific error reporting.
Currently the error reporting on failure is generic and doesn't allow
for specializing the returned result based on failure, with this change
what would previously have been a separate trait with specialized
verification can just be handled as part of inferrence rather than
duplicated.
refineReturnTypes behaves like inferReturnTypes if no result types are fed in,
while the current verification is recast as the default implementation for
refineReturnTypes with it calling inferReturnTypes (and so the default type
verification now goes through refine and allows for more op specific inference
mismatch errors).
Differential Revision: https://reviews.llvm.org/D129955
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This would have assert before during tensor type construction with
opaque error, assert and fail earlier now.
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There are several aspects of the API that either aren't easy to use, or are
deceptively easy to do the wrong thing. The main change of this commit
is to remove all of the `getValue<T>`/`getFlatValue<T>` from ElementsAttr
and instead provide operator[] methods on the ranges returned by
`getValues<T>`. This provides a much more convenient API for the value
ranges. It also removes the easy-to-be-inefficient nature of
getValue/getFlatValue, which under the hood would construct a new range for
the type `T`. Constructing a range is not necessarily cheap in all cases, and
could lead to very poor performance if used within a loop; i.e. if you were to
naively write something like:
```
DenseElementsAttr attr = ...;
for (int i = 0; i < size; ++i) {
// We are internally rebuilding the APFloat value range on each iteration!!
APFloat it = attr.getFlatValue<APFloat>(i);
}
```
Differential Revision: https://reviews.llvm.org/D113229
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Currently DenseElementsAttr only exposes the ability to get the full range of values for a given type T, but there are many situations where we just want the beginning/end iterator. This revision adds proper value_begin/value_end methods for all of the supported T types, and also cleans up a bit of the interface.
Differential Revision: https://reviews.llvm.org/D104173
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This enables querying shapes/values as shapes without mutating the IR
directly (e.g., towards enabling doing inference in analysis &
application steps, inferring function shape with constant from callsite,
...). Add a new ShapeAdaptor that abstracts over whether shape is from
Type or ShapedTypeComponents or DenseIntElementsAttribute. This adds new
accessors to ValueShapeRange to get Shape and value as shape, but
doesn't restrict or remove the previous way of accessing Type via the
Value for now, that does mean a less refined shape could be accidentally
queried and will be restricted in follow up.
Currently restricted Value query to what can be represented as Shape. So
only supports cases where constant subgraph evaluation's output is a
shape. I had considered making it more general, but without TBD extern
attribute concept or some such a user cannot today uniformly avoid
overhead.
Update TOSA ops and also the shape inference pass.
Differential Revision: https://reviews.llvm.org/D107768
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Missed this one in the first go.
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A new `InterfaceMethod` is added to `InferShapedTypeOpInterface` that
allows an operation to return the `Value`s for each dim of its
results. It is intended for the case where the `Value` returned for
each dim is computed using the operands and operation attributes. This
interface method is for cases where the result dim of an operation can
be computed independently, and it avoids the need to aggregate all
dims of a result into a single shape value. This also implies that
this is not suitable for cases where the result type is unranked (for
which the existing interface methods is to be used).
Also added is a canonicalization pattern that uses this interface and
resolves the shapes of the output in terms of the shapes of the
inputs. Moving Linalg ops to use this interface, so that many
canonicalization patterns implemented for individual linalg ops to
achieve the same result can be removed in favor of the added
canonicalization pattern.
Differential Revision: https://reviews.llvm.org/D97887
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Include the types into the error message.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95854
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This is part of a larger refactoring the better congregates the builtin structures under the BuiltinDialect. This also removes the problematic "standard" naming that clashes with the "standard" dialect, which is not defined within IR/. A temporary forward is placed in StandardTypes.h to allow time for downstream users to replaced references.
Differential Revision: https://reviews.llvm.org/D92435
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This is a wrapper around vector of NamedAttributes that keeps track of whether sorted and does some minimal effort to remain sorted (doing more, e.g., appending attributes in sorted order, could be done in follow up). It contains whether sorted and if a DictionaryAttr is queried, it caches the returned DictionaryAttr along with whether sorted.
Change MutableDictionaryAttr to always return a non-null Attribute even when empty (reserve null cases for errors). To this end change the getter to take a context as input so that the empty DictionaryAttr could be queried. Also create one instance of the empty dictionary attribute that could be reused without needing to lock context etc.
Update infer type op interface to use DictionaryAttr and use NamedAttrList to avoid incurring multiple conversion costs.
Fix bug in sorting helper function.
Differential Revision: https://reviews.llvm.org/D79463
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Interfaces/ directory.
The interfaces themselves aren't really analyses, they may be used by analyses though. Having them in Analysis can also create cyclic dependencies if an analysis depends on a specific dialect, that also provides one of the interfaces.
Differential Revision: https://reviews.llvm.org/D75867
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