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 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.
Summary: It is a very common user trap to think that the location printed along with the diagnostic is the same as the current operation that caused the error. This revision changes the behavior to always print the current operation, except for when diagnostics are being verified. This is achieved by moving the command line flags in IR/ to be options on the MLIRContext.
Differential Revision: https://reviews.llvm.org/D77095
Summary:
This file only contains references to test passes, and was never removed when the test passes were moved to the test/ directory.
Differential Revision: https://reviews.llvm.org/D76553
A printer refactoring removed automatic newline printing in the printer
of a ModuleOp. As a consequence, mlir-opt no longer printed a newline
after the closing brace of a module, which made it hard to distinguish
when used from command line. Print the newline character explicitly in
mlir-opt.
This allows individual passes to define options structs and for these options to be parsed per instance of the pass while building the pass pipeline from the command line provided textual specification.
The user can specify these per-instance pipeline options like so:
```
struct MyPassOptions : public PassOptions<MyPassOptions> {
Option<int> exampleOption{*this, "flag-name", llvm:🆑:desc("...")};
List<int> exampleListOption{*this, "list-flag-name", llvm:🆑:desc("...")};
};
static PassRegistration<MyPass, MyPassOptions> pass("my-pass", "description");
```
PiperOrigin-RevId: 273650140
Similar to mlir-opt, having a -split-input-file mode is quite useful
in mlir-translate. It allows to put logically related tests in the
same test file for better organization.
PiperOrigin-RevId: 270805467
This allows for explicitly specifying the pipeline to add to the pass manager. This includes the nesting structure, as well as the passes/pipelines to run. A textual pipeline string is defined as a series of names, each of which may in itself recursively contain a nested pipeline description. A name is either the name of a registered pass, or pass pipeline, (e.g. "cse") or the name of an operation type (e.g. "func").
For example, the following pipeline:
$ mlir-opt foo.mlir -cse -canonicalize -lower-to-llvm
Could now be specified as:
$ mlir-opt foo.mlir -pass-pipeline='func(cse, canonicalize), lower-to-llvm'
This will allow for running pipelines on nested operations, like say spirv modules. This does not remove any of the current functionality, and in fact can be used in unison. The new option is available via 'pass-pipeline'.
PiperOrigin-RevId: 268954279
This change generalizes the structure of the pass manager to allow arbitrary nesting pass managers for other operations, at any level. The only user visible change to existing code is the fact that a PassManager must now provide an MLIRContext on construction. A new class `OpPassManager` has been added that represents a pass manager on a specific operation type. `PassManager` will remain the top-level entry point into the pipeline, with OpPassManagers being nested underneath. OpPassManagers will still be implicitly nested if the operation type on the pass differs from the pass manager. To explicitly build a pipeline, the 'nest' methods on OpPassManager may be used:
// Pass manager for the top-level module.
PassManager pm(ctx);
// Nest a pipeline operating on FuncOp.
OpPassManager &fpm = pm.nest<FuncOp>();
fpm.addPass(...);
// Nest a pipeline under the FuncOp pipeline that operates on spirv::ModuleOp
OpPassManager &spvModulePM = pm.nest<spirv::ModuleOp>();
// Nest a pipeline on FuncOps inside of the spirv::ModuleOp.
OpPassManager &spvFuncPM = spvModulePM.nest<FuncOp>();
To help accomplish this a new general OperationPass is added that operates on opaque Operations. This pass can be inserted in a pass manager of any type to operate on any operation opaquely. An example of this opaque OperationPass is a VerifierPass, that simply runs the verifier opaquely on the current operation.
/// Pass to verify an operation and signal failure if necessary.
class VerifierPass : public OperationPass<VerifierPass> {
void runOnOperation() override {
Operation *op = getOperation();
if (failed(verify(op)))
signalPassFailure();
markAllAnalysesPreserved();
}
};
PiperOrigin-RevId: 266840344
As with Functions, Module will soon become an operation, which are value-typed. This eases the transition from Module to ModuleOp. A new class, OwningModuleRef is provided to allow for owning a reference to a Module, and will auto-delete the held module on destruction.
PiperOrigin-RevId: 256196193
Enable reusing the real mlir-opt main from unit tests and in case where
additional initialization needs to happen before main is invoked (e.g., when
using different command line flag libraries).
PiperOrigin-RevId: 254764575
Mehdi Amini