Add a new OperationType handle type to the Transform dialect. This
transform type is parameterized by the name of the payload operation it
can point to. It is intended as a constraint on transformations that are
only applicable to a specific kind of payload operations. If a
transformation is applicable to a small set of operation classes, it can
be wrapped into a transform op by using a disjunctive constraint, such
as `Type<Or<[Transform_ConcreteOperation<"foo">.predicate,
Transform_ConcreteOperation<"bar">.predicate]>>` for its operand without
modifying this type. Broader sets of accepted operations should be
modeled as specific types.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D135586
This differential adjusts the numeric values for DimLevelType values: using the low-order two bits for recording the "No" and "Nu" properties, and the high-order bits for the formats per se. (The choice of encoding may seem a bit peculiar, since the bits are mapped to negative properties rather than positive properties. But this was done in order to preserve the collation order of DimLevelType values. If we don't care about collation order, then we may prefer to flip the semantics of the property bits, so that they're less surprising to readers.)
Using distinguished bits for the properties and formats enables faster implementation for the predicates detecting those properties/formats, which matters because this is in the runtime library itself (rather than on the codegen side of things). This differential pushes through the changes to the enum values, and optimizes the basic predicates. However it does not optimize all the places where we check compound predicates (e.g., "is compressed or singleton"), to help reduce rebasing conflict with D134933. Those optimizations will be done after this differential and D134933 are landed.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D135004
This extension to the sparse tensor type system in MLIR
opens up a whole new set of sparse storage schemes, such as
block sparse storage (e.g. BCSR) and ELL (aka jagged diagonals).
This revision merely introduces the type extension and
initial documentation. The actual interpretation of the type
(reading in tensors, lowering to code, etc.) will follow.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D135206
We recently removed the singleton dimension level type (see the revision
https://reviews.llvm.org/D131002) since it was unimplemented but also
incomplete (properties were missing). This revision add singleton back as
extra dimension level type, together with properties ordered/not-ordered
and unique/not-unique. Even though still not lowered to actual code, this
provides a complete way of defining many more sparse storage schemes (in
the long run, we want to support even dimension level types and properties
using the additional extensions proposed in [Chou]).
Note that the current solution of using suffixes for the properties is not
ideal, but keeps the extension relatively simple with respect to parsing and
printing. Furthermore, it is rather consistent with the TACO implementation
which uses things like Compressed-Unique as well. Nevertheless, we probably
want to separate dimension level types from properties when we add more types
and properties.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D132897
Since the very first commits, the Python and C MLIR APIs have had mis-placed registration/load functionality for dialects, extensions, etc. This was done pragmatically in order to get bootstrapped and then just grew in. Downstreams largely bypass and do their own thing by providing various APIs to register things they need. Meanwhile, the C++ APIs have stabilized around this and it would make sense to follow suit.
The thing we have observed in canonical usage by downstreams is that each downstream tends to have native entry points that configure its installation to its preferences with one-stop APIs. This patch leans in to this approach with `RegisterEverything.h` and `mlir._mlir_libs._mlirRegisterEverything` being the one-stop entry points for the "upstream packages". The `_mlir_libs.__init__.py` now allows customization of the environment and Context by adding "initialization modules" to the `_mlir_libs` package. If present, `_mlirRegisterEverything` is treated as such a module. Others can be added by downstreams by adding a `_site_initialize_{i}.py` module, where '{i}' is a number starting with zero. The number will be incremented and corresponding module loaded until one is not found. Initialization modules can:
* Perform load time customization to the global environment (i.e. registering passes, hooks, etc).
* Define a `register_dialects(registry: DialectRegistry)` function that can extend the `DialectRegistry` that will be used to bootstrap the `Context`.
* Define a `context_init_hook(context: Context)` function that will be added to a list of callbacks which will be invoked after dialect registration during `Context` initialization.
Note that the `MLIRPythonExtension.RegisterEverything` is not included by default when building a downstream (its corresponding behavior was prior). For downstreams which need the default MLIR initialization to take place, they must add this back in to their Python CMake build just like they add their own components (i.e. to `add_mlir_python_common_capi_library` and `add_mlir_python_modules`). It is perfectly valid to not do this, in which case, only the things explicitly depended on and initialized by downstreams will be built/packaged. If the downstream has not been set up for this, it is recommended to simply add this back for the time being and pay the build time/package size cost.
CMake changes:
* `MLIRCAPIRegistration` -> `MLIRCAPIRegisterEverything` (renamed to signify what it does and force an evaluation: a number of places were incidentally linking this very expensive target)
* `MLIRPythonSoure.Passes` removed (without replacement: just drop)
* `MLIRPythonExtension.AllPassesRegistration` removed (without replacement: just drop)
* `MLIRPythonExtension.Conversions` removed (without replacement: just drop)
* `MLIRPythonExtension.Transforms` removed (without replacement: just drop)
Header changes:
* `mlir-c/Registration.h` is deleted. Dialect registration functionality is now in `IR.h`. Registration of upstream features are in `mlir-c/RegisterEverything.h`. When updating MLIR and a couple of downstreams, I found that proper usage was commingled so required making a choice vs just blind S&R.
Python APIs removed:
* mlir.transforms and mlir.conversions (previously only had an __init__.py which indirectly triggered `mlirRegisterTransformsPasses()` and `mlirRegisterConversionPasses()` respectively). Downstream impact: Remove these imports if present (they now happen as part of default initialization).
* mlir._mlir_libs._all_passes_registration, mlir._mlir_libs._mlirTransforms, mlir._mlir_libs._mlirConversions. Downstream impact: None expected (these were internally used).
C-APIs changed:
* mlirRegisterAllDialects(MlirContext) now takes an MlirDialectRegistry instead. It also used to trigger loading of all dialects, which was already marked with a TODO to remove -- it no longer does, and for direct use, dialects must be explicitly loaded. Downstream impact: Direct C-API users must ensure that needed dialects are loaded or call `mlirContextLoadAllAvailableDialects(MlirContext)` to emulate the prior behavior. Also see the `ir.c` test case (e.g. ` mlirContextGetOrLoadDialect(ctx, mlirStringRefCreateFromCString("func"));`).
* mlirDialectHandle* APIs were moved from Registration.h (which now is restricted to just global/upstream registration) to IR.h, arguably where it should have been. Downstream impact: include correct header (likely already doing so).
C-APIs added:
* mlirContextLoadAllAvailableDialects(MlirContext): Corresponds to C++ API with the same purpose.
Python APIs added:
* mlir.ir.DialectRegistry: Mapping for an MlirDialectRegistry.
* mlir.ir.Context.append_dialect_registry(MlirDialectRegistry)
* mlir.ir.Context.load_all_available_dialects()
* mlir._mlir_libs._mlirAllRegistration: New native extension that exposes a `register_dialects(MlirDialectRegistry)` entry point and performs all upstream pass/conversion/transforms registration on init. In this first step, we eagerly load this as part of the __init__.py and use it to monkey patch the Context to emulate prior behavior.
* Type caster and capsule support for MlirDialectRegistry
This should make it possible to build downstream Python dialects that only depend on a subset of MLIR. See: https://github.com/llvm/llvm-project/issues/56037
Here is an example PR, minimally adapting IREE to these changes: https://github.com/iree-org/iree/pull/9638/files In this situation, IREE is opting to not link everything, since it is already configuring the Context to its liking. For projects that would just like to not think about it and pull in everything, add `MLIRPythonExtension.RegisterEverything` to the list of Python sources getting built, and the old behavior will continue.
Reviewed By: mehdi_amini, ftynse
Differential Revision: https://reviews.llvm.org/D128593
Add basic C API for the ControlFlow dialect. Follows the format of the other dialects.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D121867
The last remaining operations in the standard dialect all revolve around
FuncOp/function related constructs. This patch simply handles the initial
renaming (which by itself is already huge), but there are a large number
of cleanups unlocked/necessary afterwards:
* Removing a bunch of unnecessary dependencies on Func
* Cleaning up the From/ToStandard conversion passes
* Preparing for the move of FuncOp to the Func dialect
See the discussion at https://discourse.llvm.org/t/standard-dialect-the-final-chapter/6061
Differential Revision: https://reviews.llvm.org/D120624
This change adds full python bindings for PDL, including types and operations
with additional mixins to make operation construction more similar to the PDL
syntax.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D117458
Previously, the Python bindings for the Linalg dialect relied on the internal
implementation of core bindings. Most of that functionality was moved, and the
remaining one does not need access to the implementation: it used to accept a
dialect pointer as argument, but it can always be extracted from the operation
that it also accepts; operations are available through PybindAdaptors in an
opaque way. Change the bindings in that direction.
This enables the decoupling of the Linalg dialect Python extension from the
core IR Python extension.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D116649
First set of "boilerplate" to get sparse tensor
passes available through CAPI and Python.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D102362
* Adds dialect registration, hand coded 'encoding' attribute and test.
* An MLIR CAPI tablegen backend for attributes does not exist, and this is a relatively complicated case. I opted to hand code it in a canonical way for now, which will provide a reasonable blueprint for building out the tablegen version in the future.
* Also added a (local) CMake function for declaring new CAPI tests, since it was getting repetitive/buggy.
Differential Revision: https://reviews.llvm.org/D102141
When Linalg named ops support was added, captures were omitted
from the body builder. This revision adds support for captures
which allows us to write FillOp in a more idiomatic fashion using
the _linalg_ops_ext mixin support.
This raises an issue in the generation of `_linalg_ops_gen.py` where
```
@property
def result(self):
return self.operation.results[0] if len(self.operation.results) > 1 else None
```.
The condition should be `== 1`.
This will be fixed in a separate commit.
Differential Revision: https://reviews.llvm.org/D100363
This revision adds support to properly add the body of registered
builtin named linalg ops.
At this time, indexing_map and iterator_type support is still
missing so the op is not executable yet.
Differential Revision: https://reviews.llvm.org/D99578
Provide a registration mechanism for Linalg dialect-specific passes in C
API and Python bindings. These are being built into the dialect library
but exposed in separate headers (C) or modules (Python).
Differential Revision: https://reviews.llvm.org/D99431
* Registers a small set of sample dialects.
* NFC with respect to existing C-API symbols but some headers have been moved down a level to the Dialect/ sub-directory.
* Adds an additional entry point per dialect that is needed for dynamic discovery/loading.
* See discussion: https://llvm.discourse.group/t/dialects-and-the-c-api/2306/16
Differential Revision: https://reviews.llvm.org/D94370
