This will allow out-of-tree translation to register the dialects they expect
to see in their input, on the model of getDependentDialects() for passes.
Differential Revision: https://reviews.llvm.org/D86409
Refactor the way the reduction tree pass works in the MLIR Reduce tool by introducing a set of utilities that facilitate the implementation of new Reducer classes to be used in the passes.
This will allow for the fast implementation of general transformations to operate on all mlir modules as well as custom transformations for different dialects.
These utilities allow for the implementation of Reducer classes by simply defining a method that indexes the operations/blocks/regions to be transformed and a method to perform the deletion or transfomration based on the indexes.
Create the transformSpace class member in the ReductionNode class to keep track of the indexes that have already been transformed or deleted at a current level.
Delete the FunctionReducer class and replace it with the OpReducer class to reflect this new API while performing the same transformation and allowing the instantiation of a reduction pass for different types of operations at the module's highest hierarchichal level.
Modify the SinglePath Traversal method to reflect the use of the new API.
Reviewed: jpienaar
Differential Revision: https://reviews.llvm.org/D85591
PDL presents a high level abstraction for the rewrite pattern infrastructure available in MLIR. This abstraction allows for representing patterns transforming MLIR, as MLIR. This allows for applying all of the benefits that the general MLIR infrastructure provides, to the infrastructure itself. This means that pattern matching can be more easily verified for correctness, targeted by frontends, and optimized.
PDL abstracts over various different aspects of patterns and core MLIR data structures. Patterns are specified via a `pdl.pattern` operation. These operations contain a region body for the "matcher" code, and terminate with a `pdl.rewrite` that either dispatches to an external rewriter or contains a region for the rewrite specified via `pdl`. The types of values in `pdl` are handle types to MLIR C++ types, with `!pdl.attribute`, `!pdl.operation`, and `!pdl.type` directly mapping to `mlir::Attribute`, `mlir::Operation*`, and `mlir::Value` respectively.
An example pattern is shown below:
```mlir
// pdl.pattern contains metadata similarly to a `RewritePattern`.
pdl.pattern : benefit(1) {
// External input operand values are specified via `pdl.input` operations.
// Result types are constrainted via `pdl.type` operations.
%resultType = pdl.type
%inputOperand = pdl.input
%root, %results = pdl.operation "foo.op"(%inputOperand) -> %resultType
pdl.rewrite(%root) {
pdl.replace %root with (%inputOperand)
}
}
```
This is a culmination of the work originally discussed here: https://groups.google.com/a/tensorflow.org/g/mlir/c/j_bn74ByxlQ
Differential Revision: https://reviews.llvm.org/D84578
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>()
Create a reduction pass that accepts an optimization pass as argument
and only replaces the golden module in the pipeline if the output of the
optimization pass is smaller than the input and still exhibits the
interesting behavior.
Add a -test-pass option to test individual passes in the MLIR Reduce
tool.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D84783
This will help refactoring some of the tools to prepare for the explicit registration of
Dialects.
Differential Revision: https://reviews.llvm.org/D86023
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.
This exercises the corner case that was fixed in
https://reviews.llvm.org/rG8979a9cdf226066196f1710903d13492e6929563.
The bug can be reproduced when there is a @callee with a custom type argument and @caller has a producer of this argument passed to the @callee.
Example:
func @callee(!test.test_type) -> i32
func @caller() -> i32 {
%arg = "test.type_producer"() : () -> !test.test_type
%out = call @callee(%arg) : (!test.test_type) -> i32
return %out : i32
}
Even though there is a type conversion for !test.test_type, the output IR (before the fix) contained a DialectCastOp:
module {
llvm.func @callee(!llvm.ptr<i8>) -> !llvm.i32
llvm.func @caller() -> !llvm.i32 {
%0 = llvm.mlir.null : !llvm.ptr<i8>
%1 = llvm.mlir.cast %0 : !llvm.ptr<i8> to !test.test_type
%2 = llvm.call @callee(%1) : (!test.test_type) -> !llvm.i32
llvm.return %2 : !llvm.i32
}
}
instead of
module {
llvm.func @callee(!llvm.ptr<i8>) -> !llvm.i32
llvm.func @caller() -> !llvm.i32 {
%0 = llvm.mlir.null : !llvm.ptr<i8>
%1 = llvm.call @callee(%0) : (!llvm.ptr<i8>) -> !llvm.i32
llvm.return %1 : !llvm.i32
}
}
Differential Revision: https://reviews.llvm.org/D85914
This patch adds the translation of the proc_bind clause in a
parallel operation.
The values that can be specified for the proc_bind clause are
specified in the OMP.td tablegen file in the llvm/Frontend/OpenMP
directory. From this single source of truth enumeration for
proc_bind is generated in llvm and mlir (used in specification of
the parallel Operation in the OpenMP dialect). A function to return
the enum value from the string representation is also generated.
A new header file (DirectiveEmitter.h) containing definitions of
classes directive, clause, clauseval etc is created so that it can
be used in mlir as well.
Reviewers: clementval, jdoerfert, DavidTruby
Differential Revision: https://reviews.llvm.org/D84347
- Fix ODS framework to suppress build methods that infer result types and are
ambiguous with collective variants. This applies to operations with a single variadic
inputs whose result types can be inferred.
- Extended OpBuildGenTest to test these kinds of ops.
Differential Revision: https://reviews.llvm.org/D85060
Implement the Reduction Tree Pass framework as part of the MLIR Reduce tool. This is a parametarizable pass that allows for the implementation of custom reductions passes in the tool.
Implement the FunctionReducer class as an example of a Reducer class parameter for the instantiation of a Reduction Tree Pass.
Create a pass pipeline with a Reduction Tree Pass with the FunctionReducer class specified as parameter.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D83969
This patch moves the registration to a method in the MLIRContext: getOrCreateDialect<ConcreteDialect>()
This method requires dialect to provide a static getDialectNamespace()
and store a TypeID on the Dialect itself, which allows to lazyily
create a dialect when not yet loaded in the context.
As a side effect, it means that duplicated registration of the same
dialect is not an issue anymore.
To limit the boilerplate, TableGen dialect generation is modified to
emit the constructor entirely and invoke separately a "init()" method
that the user implements.
Differential Revision: https://reviews.llvm.org/D85495
Due to the original type system implementation, LLVMDialect in MLIR contains an
LLVMContext in which the relevant objects (types, metadata) are created. When
an MLIR module using the LLVM dialect (and related intrinsic-based dialects
NVVM, ROCDL, AVX512) is converted to LLVM IR, it could only live in the
LLVMContext owned by the dialect. The type system no longer relies on the
LLVMContext, so this limitation can be removed. Instead, translation functions
now take a reference to an LLVMContext in which the LLVM IR module should be
constructed. The caller of the translation functions is responsible for
ensuring the same LLVMContext is not used concurrently as the translation no
longer uses a dialect-wide context lock.
As an additional bonus, this change removes the need to recreate the LLVM IR
module in a different LLVMContext through printing and parsing back, decreasing
the compilation overhead in JIT and GPU-kernel-to-blob passes.
Reviewed By: rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D85443
The RewritePattern will become one of several, and will be part of the LLVM conversion pass (instead of a separate pass following LLVM conversion).
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D84946
Previous type model in the LLVM dialect did not support identified structure
types properly and therefore could use stateless translations implemented as
free functions. The new model supports identified structs and must keep track
of the identified structure types present in the target context (LLVMContext or
MLIRContext) to avoid creating duplicate structs due to LLVM's type
auto-renaming. Expose the stateful type translation classes and use them during
translation, storing the state as part of ModuleTranslation.
Drop the test type translation mechanism that is no longer necessary and update
the tests to exercise type translation as part of the main translation flow.
Update the code in vector-to-LLVM dialect conversion that relied on stateless
translation to use the new class in a stateless manner.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85297
This dialect was introduced during the bring-up of the new LLVM dialect type
system for testing purposes. The main LLVM dialect now uses the new type system
and the test dialect is no longer necessary, so remove it.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85224
With new LLVM dialect type modeling, the dialect types no longer wrap LLVM IR
types. Therefore, they need to be translated to and from LLVM IR during export
and import. Introduce the relevant functionality for translating types. It is
currently exercised by an ad-hoc type translation roundtripping test that will
be subsumed by the actual translation test when the type system transition is
complete.
Depends On D84339
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D85019
Unit attributes are given meaning by their existence, and thus have no meaningful value beyond "is it present". As such, in the format of an operation unit attributes are generally used to guard the printing of other elements and aren't generally printed themselves; as the presence of the group when parsing means that the unit attribute should be added. This revision adds support to the declarative format for eliding unit attributes in situations where they anchor an optional group, but aren't the first element.
For example,
```
let assemblyFormat = "(`is_optional` $unit_attr^)? attr-dict";
```
would print `foo.op is_optional` when $unit_attr is present, instead of the current `foo.op is_optional unit`.
Differential Revision: https://reviews.llvm.org/D84577
The current modeling of LLVM IR types in MLIR is based on the LLVMType class
that wraps a raw `llvm::Type *` and delegates uniquing, printing and parsing to
LLVM itself. This model makes thread-safe type manipulation hard and is being
progressively replaced with a cleaner MLIR model that replicates the type
system. Introduce a set of classes reflecting the LLVM IR type system in MLIR
instead of wrapping the existing types. These are currently introduced as
separate classes without affecting the dialect flow, and are exercised through
a test dialect. Once feature parity is reached, the old implementation will be
gradually substituted with the new one.
Depends On D84171
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D84339
The current output is a bit clunky and requires including files+macros everywhere, or manually wrapping the file inclusion in a registration function. This revision refactors the pass backend to automatically generate `registerFooPass`/`registerFooPasses` functions that wrap the pass registration. `gen-pass-decls` now takes a `-name` input that specifies a tag name for the group of passes that are being generated. For each pass, the generator now produces a `registerFooPass` where `Foo` is the name of the definition specified in tablegen. It also generates a `registerGroupPasses`, where `Group` is the tag provided via the `-name` input parameter, that registers all of the passes present.
Differential Revision: https://reviews.llvm.org/D84983
The patch fixes minor issues in the rocm runtime wrapper due to api differences between CUDA and HIP.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D84861
The current modeling of LLVM IR types in MLIR is based on the LLVMType class
that wraps a raw `llvm::Type *` and delegates uniquing, printing and parsing to
LLVM itself. This is model makes thread-safe type manipulation hard and is
being progressively replaced with a cleaner MLIR model that replicates the type
system. In the new model, LLVMType will no longer have an underlying LLVM IR
type. Restrict access to this type in the current model in preparation for the
change.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D84389
functions.
This allows using command line flags to lowere from GPU to SPIR-V. The
pass added is only for testing/example purposes. Most uses cases will
need more fine-grained control on setting workgroup sizes for kernel
functions.
Differential Revision: https://reviews.llvm.org/D84619
Do not return error code, instead return created resource handles or void. Error reporting is done by the library function.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D84660
Introduce support for mutable storage in the StorageUniquer infrastructure.
This makes MLIR have key-value storage instead of just uniqued key storage. A
storage instance now contains a unique immutable key and a mutable value, both
stored in the arena allocator that belongs to the context. This is a
preconditio for supporting recursive types that require delayed initialization,
in particular LLVM structure types. The functionality is exercised in the test
pass with trivial self-recursive type. So far, recursive types can only be
printed in parsed in a closed type system. Removing this restriction is left
for future work.
Differential Revision: https://reviews.llvm.org/D84171
- Added more default values for `attributes` parameter for 2 more build methods
- Extend the op-decls.td unit test to test these build methods.
Differential Revision: https://reviews.llvm.org/D83839
This adds a `parseOptionalAttribute` method to the OpAsmParser that allows for parsing optional attributes, in a similar fashion to how optional types are parsed. This also enables the use of attribute values as the first element of an assembly format optional group.
Differential Revision: https://reviews.llvm.org/D83712
Summary: Currently forward decls are included with all the op classes. But there are cases (say when splitting up headers) where one wants the forward decls but not all the classes. Add an option to enable this. This does not change any current behavior (some further refactoring is probably due here).
Differential Revision: https://reviews.llvm.org/D83727
- Provide default value for `ArrayRef<NamedAttribute> attributes` parameter of
the collective params build method.
- Change the `genSeparateArgParamBuilder` function to not generate build methods
that may be ambiguous with the new collective params build method.
- This change should help eliminate passing empty NamedAttribue ArrayRef when the
collective params build method is used
- Extend op-decl.td unit test to make sure the ambiguous build methods are not
generated.
Differential Revision: https://reviews.llvm.org/D83517
The namespace can be specified using the `cppNamespace` field. This matches the functionality already present on dialects, enums, etc. This fixes problems with using interfaces on operations in a different namespace than the interface was defined in.
Differential Revision: https://reviews.llvm.org/D83604
Introduce pass to convert parallel affine.for op into 1-D affine.parallel op.
Run using --affine-parallelize. Removes test-detect-parallel: pass for checking
parallel affine.for ops.
Signed-off-by: Yash Jain <yash.jain@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D83193
- Create a pass that generates bugs based on trivially defined behavior for the purpose of testing the MLIR Reduce Tool.
- Implement the functionality inside the pass to crash mlir-opt in the presence of an operation with the name "crashOp".
- Register the pass as a test pass in the mlir-opt tool.
Reviewed by: jpienaar
Differential Revision: https://reviews.llvm.org/D83422
Create the framework and testing environment for MLIR Reduce - a tool
with the objective to reduce large test cases into smaller ones while
preserving their interesting behavior.
Implement the functionality to parse command line arguments, parse the
MLIR test cases into modules and run the interestingness tests on
the modules.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D82803
Mehdi Amini