With the introduction of IntegerPolyhedron and IntegerRelation in Presburger
directory, the purpose of FlatAffineConstraints becomes redundant. For users
requiring Presburger arithmetic without IR information, Presburger library can
directly be used. For users requiring IR information,
FlatAffineValueConstraints can be used.
This patch merges FAC and FACV to remove redundancy of FAC.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D122476
This patch adds supports for union of relations (PresburgerRelation). Along
with this, support for PresburgerSet is also maintained.
This patch is part of a series of patches to add support for relations in
Presburger library.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D121417
There is no reason for this file to be at the top-level, and
its current placement predates the Parser/ folder's existence.
Differential Revision: https://reviews.llvm.org/D121024
This patch moves the Presburger library to a new `presburger` namespace.
This allows to shorten some names, helps to avoid polluting the mlir namespace,
and also provides some structure.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120505
The current state of the top level Analysis/ directory is that it contains two libraries;
a generic Analysis library (free from dialect dependencies), and a LoopAnalysis library
that contains various analysis utilities that originated from Affine loop transformations.
This commit moves the LoopAnalysis to the more appropriate home of `Dialect/Affine/Analysis/`,
given the use and intention of the majority of the code within it. After the move, if there
are generic utilities that would fit better in the top-level Analysis/ directory, we can move
them.
Differential Revision: https://reviews.llvm.org/D117351
`getNumRegionInvocations` was originally added for the async reference counting, but turned out to be not useful, and currently is not used anywhere (couldn't find any uses in public github repos). Removing dead code.
Reviewed By: Mogball, mehdi_amini
Differential Revision: https://reviews.llvm.org/D117347
Implements the RegionBranchOpInterface method getNumRegionInvocations to `scf::IfOp` so that, when the condition is constant, the number of region executions can be analyzed by `NumberOfExecutions`.
Reviewed By: jpienaar, ftynse
Differential Revision: https://reviews.llvm.org/D115087
It's legal per the Vulkan / SPIR-V spec; still it's better to avoid
such duplication to have cleaner blob and reduce the binary size.
Reviewed By: scotttodd
Differential Revision: https://reviews.llvm.org/D115532
In SPIR-V, symbol names are encoded as `OpName` instructions.
They are not semantic impacting and can be omitted, which can
reduce the binary size.
Reviewed By: scotttodd
Differential Revision: https://reviews.llvm.org/D115531
Also remove `TooFewDims` test which tried to create an invalid AffineMap.
The creation of an invalid AffineMap is rejected by `willBeValidAffineMap`,
as a result we can deprecate the test.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D114657
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
Adds zero-preserving unary operators from std. Also adds xor.
Performs minor refactoring to remove "zero" node, and pushed
the irregular logic for negi (not support in std) into one place.
Reviewed By: gussmith23
Differential Revision: https://reviews.llvm.org/D105928
This is a fix of https://reviews.llvm.org/D104956, which broke the gcc5 build.
We opt to use unit tests rather than check tests as the lattice/merger code is a small C++ component with a well-defined API. Testing this API via check tests would be far less direct and readable. In addition, as the check tests will only be able to test the API indirectly, the tests may break based on unrelated changes; e.g. changes in linalg.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D105828
We opt to use unit tests rather than check tests as the lattice/merger code is a small C++ component with a well-defined API. Testing this API via check tests would be far less direct and readable. In addition, as the check tests will only be able to test the API indirectly, the tests may break based on unrelated changes; e.g. changes in linalg.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D104956
This allows us to remove the `spv.mlir.endmodule` op and
all the code associated with it.
Along the way, tightened the APIs for `spv.module` a bit
by removing some aliases. Now we use `getRegion` to get
the only region, and `getBody` to get the region's only
block.
Reviewed By: mravishankar, hanchung
Differential Revision: https://reviews.llvm.org/D103265
Extracts the relevant dimensions from the map under test to build up the
maps to test against in a permutation-invariant way.
This also includes a fix to the indexing maps used by
isColumnMajorMatmul. The maps as currently written do not describe a
column-major matmul. The linalg named op column_major_matmul has the
correct maps (and notably fails the current test).
If `C = matmul(A, B)` we want an operation that given A in column major
format and B in column major format produces C in column major format.
Given that for a matrix, faux column major is just transpose.
`column_major_matmul(transpose(A), transpose(B)) = transpose(C)`. If
`A` is `NxK` and `B` is `KxM`, then `C` is `NxM`, so `transpose(A)` is
`KxN`, `transpose(B)` is `MxK` and `transpose(C)` is `MxN`, not `NxM`
as these maps currently have.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D96984
This makes ignoring a result explicit by the user, and helps to prevent accidental errors with dropped results. Marking LogicalResult as no discard was always the intention from the beginning, but got lost along the way.
Differential Revision: https://reviews.llvm.org/D95841
This better matches the rest of the infrastructure, is much simpler, and makes it easier to move these types to being declaratively specified.
Differential Revision: https://reviews.llvm.org/D93432
This commit shuffles SPIR-V code around to better follow MLIR
convention. Specifically,
* Created IR/, Transforms/, Linking/, and Utils/ subdirectories and
moved suitable code inside.
* Created SPIRVEnums.{h|cpp} for SPIR-V C/C++ enums generated from
SPIR-V spec. Previously they are cluttered inside SPIRVTypes.{h|cpp}.
* Fixed include guards in various header files (both .h and .td).
* Moved serialization tests under test/Target/SPIRV.
* Renamed TableGen backend -gen-spirv-op-utils into -gen-spirv-attr-utils
as it is only generating utility functions for attributes.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D93407
This commit splits SPIR-V's serialization and deserialization code
into separate libraries. The motiviation being that the serializer
is used more often the deserializer and therefore lumping them
together unnecessarily increases binary size for the most common
case.
This commit also moves these libraries into the Target/ directory
to follow MLIR convention.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D91548
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
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
mlir::registerDialect<mlir::standalone::StandaloneDialect>();
mlir::registerDialect<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
Similar to OwningModuleRef, OwningSPIRVModuleRef signals ownership
transfer clearly. This is useful for APIs like spirv::deserialize,
where a spirv::ModuleOp is returned by deserializing SPIR-V binary
module.
This addresses the ASAN error as reported in
https://bugs.llvm.org/show_bug.cgi?id=46272
Differential Revision: https://reviews.llvm.org/D81652