This CL introduces a generic attribute (called "encoding") on tensors.
The attribute currently does not carry any concrete information, but the type
system already correctly determines that tensor<8xi1,123> != tensor<8xi1,321>.
The attribute will be given meaning through an interface in subsequent CLs.
See ongoing discussion on discourse:
[RFC] Introduce a sparse tensor type to core MLIR
https://llvm.discourse.group/t/rfc-introduce-a-sparse-tensor-type-to-core-mlir/2944
A sparse tensor will look something like this:
```
// named alias with all properties we hold dear:
#CSR = {
// individual named attributes
}
// actual sparse tensor type:
tensor<?x?xf64, #CSR>
```
I see the following rough 5 step plan going forward:
(1) introduce this format attribute in this CL, currently still empty
(2) introduce attribute interface that gives it "meaning", focused on sparse in first phase
(3) rewrite sparse compiler to use new type, remove linalg interface and "glue"
(4) teach passes to deal with new attribute, by rejecting/asserting on non-empty attribute as simplest solution, or doing meaningful rewrite in the longer run
(5) add FE support, document, test, publicize new features, extend "format" meaning to other domains if useful
Reviewed By: stellaraccident, bondhugula
Differential Revision: https://reviews.llvm.org/D99548
This revision tightens up the handling of attributes for both named
and generic linalg ops.
To demonstrate the IR validity, a working e2e Linalg example is added.
Differential Revision: https://reviews.llvm.org/D99430
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
This exposes the ability to register Python functions with the JIT and
exposes them to the MLIR jitted code. The provided test case illustrates
the mechanism.
Differential Revision: https://reviews.llvm.org/D99562
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
Based on the following discussion:
https://llvm.discourse.group/t/rfc-memref-memory-shape-as-attribute/2229
The goal of the change is to make memory space property to have more
expressive representation, rather then "magic" integer values.
It will allow to have more clean ASM form:
```
gpu.func @test(%arg0: memref<100xf32, "workgroup">)
// instead of
gpu.func @test(%arg0: memref<100xf32, 3>)
```
Explanation for `Attribute` choice instead of plain `string`:
* `Attribute` classes allow to use more type safe API based on RTTI.
* `Attribute` classes provides faster comparison operator based on
pointer comparison in contrast to generic string comparison.
* `Attribute` allows to store more complex things, like structs or dictionaries.
It will allows to have more complex memory space hierarchy.
This commit preserve old integer-based API and implements it on top
of the new one.
Depends on D97476
Reviewed By: rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D96145
This offers the ability to create a JIT and invoke a function by passing
ctypes pointers to the argument and the result.
Differential Revision: https://reviews.llvm.org/D97523
This adds minimalistic bindings for the execution engine, allowing to
invoke the JIT from the C API. This is still quite early and
experimental and shouldn't be considered stable in any way.
Differential Revision: https://reviews.llvm.org/D96651
`verifyConstructionInvariants` is intended to allow for verifying the invariants of an attribute/type on construction, and `getChecked` is intended to enable more graceful error handling aside from an assert. There are a few problems with the current implementation of these methods:
* `verifyConstructionInvariants` requires an mlir::Location for emitting errors, which is prohibitively costly in the situations that would most likely use them, e.g. the parser.
This creates an unfortunate code duplication between the verifier code and the parser code, given that the parser operates on llvm::SMLoc and it is an undesirable overhead to pre-emptively convert from that to an mlir::Location.
* `getChecked` effectively requires duplicating the definition of the `get` method, creating a quite clunky workflow due to the subtle different in its signature.
This revision aims to talk the above problems by refactoring the implementation to use a callback for error emission. Using a callback allows for deferring the costly part of error emission until it is actually necessary.
Due to the necessary signature change in each instance of these methods, this revision also takes this opportunity to cleanup the definition of these methods by:
* restructuring the signature of `getChecked` such that it can be generated from the same code block as the `get` method.
* renaming `verifyConstructionInvariants` to `verify` to match the naming scheme of the rest of the compiler.
Differential Revision: https://reviews.llvm.org/D97100
Replace MlirDialectRegistrationHooks with MlirDialectHandle, which under-the-hood is an opaque pointer to MlirDialectRegistrationHooks. Then we expose the functionality previously directly on MlirDialectRegistrationHooks, as functions which take the opaque MlirDialectHandle struct. This makes the actual structure of the registration hooks an implementation detail, and happens to avoid this issue: https://llvm.discourse.group/t/strange-swift-issues-with-dialect-registration-hooks/2759/3
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D96229
This follows up on the introduction of C API for the same object and is similar
to AffineExpr and AffineMap.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D95437
* Adds a flag to MlirOperationState to enable result type inference using the InferTypeOpInterface.
* I chose this level of implementation for a couple of reasons:
a) In the creation flow is naturally where generated and custom builder code will be invoking such a thing
b) it is a bit more efficient to share the data structure and unpacking vs having a standalone entry-point
c) we can always decide to expose more of these interfaces with first-class APIs, but that doesn't preclude that we will always want to use this one in this way (and less API surface area for common things is better for API stability and evolution).
* I struggled to find an appropriate way to test it since we don't link the test dialect into anything CAPI accessible at present. I opted instead for one of the simplest ops I found in a regular dialect which implements the interface.
* This does not do any trait-based type selection. That will be left to generated tablegen wrappers.
Differential Revision: https://reviews.llvm.org/D95283
* 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
This wasn't possible before because there was no support for affine expressions
as maps. Now that this support is available, provide the mechanism for
constructing maps with a layout and inspecting it.
Rework the `get` method on MemRefType in Python to avoid needing an explicit
memory space or layout map. Remove the `get_num_maps`, it is too low-level,
using the length of the now-avaiable pseudo-list of layout maps is more
pythonic.
Depends On D94297
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D94302
Now that the bindings for AffineExpr have been added, add more bindings for
constructing and inspecting AffineMap that consists of AffineExprs.
Depends On D94225
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D94297
This adds the Python bindings for AffineExpr and a couple of utility functions
to the C API. AffineExpr is a top-level context-owned object and is modeled
similarly to attributes and types. It is required, e.g., to build layout maps
of the built-in memref type.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D94225
- Add `PyAffineMap` to wrap around `MlirAffineMap`.
- Add `mlirPythonAffineMapToCapsule` and `mlirPythonCapsuleToAffineMap` to interoperate with python capsule.
- Add and test some simple bindings of `PyAffineMap`.
Differential Revision: https://reviews.llvm.org/D93200
This mirror the C++ API for NamedAttribute, and has the advantage or
internalizing earlier in the Context and not requiring the caller to
keep the StringRef alive beyong this call.
Differential Revision: https://reviews.llvm.org/D93133
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 reduces the chances of segfault. While it is a good practice to ensure
robust custom printers, it is unfortunately common to have them crash on
invalid input.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D92536
* Add capsule get/create for Attribute and Type, which already had capsule interop defined.
* Add capsule interop and get/create for Location.
* Add Location __eq__.
* Use get() and implicit cast to go from PyAttribute, PyType, PyLocation to MlirAttribute, MlirType, MlirLocation (bundled with this change because I didn't want to continue the pattern one more time).
Differential Revision: https://reviews.llvm.org/D92283
While this makes the unit tests a bit more verbose, this simplifies the creation of bindings because only the bidirectional mapping between the host language's string type and MlirStringRef need to be implemented.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D91905
Previously, there was no way to add context to the diagnostic engine via the C API. Adding this ability makes it much easier to reason about memory ownership, particularly in reference-counted languages such as Swift. There are more details in the review comments.
Reviewed By: ftynse, mehdi_amini
Differential Revision: https://reviews.llvm.org/D91738
These pointers do not need to be mutable. This has an affect that generated function signatures in the Swift bindings now use `UnsafePointer` instead of `UnsafeMutablePointer`.
Reviewed By: ftynse, mehdi_amini
Differential Revision: https://reviews.llvm.org/D91740
- Add `mlirElementsAttrGetType` C API.
- Add `def_buffer` binding to PyDenseElementsAttribute.
- Implement the protocol to access the buffer.
Differential Revision: https://reviews.llvm.org/D91021
Slicing, that is element access with `[being🔚step]` structure, is
a common Python idiom for sequence-like containers. It is also necessary
to support custom accessor for operations with variadic operands and
results (an operation an return a slice of its operands that correspond
to the given variadic group).
Add generic utility to support slicing in Python bindings and use it
for operation operands and results.
Depends On D90923
Reviewed By: stellaraccident, mehdi_amini
Differential Revision: https://reviews.llvm.org/D90936
We were discussing on discord regarding the need for extension-based systems like Python to dynamically link against MLIR (or else you can only have one extension that depends on it). Currently, when I set that up, I piggy-backed off of the flag that enables build libLLVM.so and libMLIR.so and depended on libMLIR.so from the python extension if shared library building was enabled. However, this is less than ideal.
In the current setup, libMLIR.so exports both all symbols from the C++ API and the C-API. The former is a kitchen sink and the latter is curated. We should be splitting them and for things that are properly factored to depend on the C-API, they should have the option to *only* depend on the C-API, and we should build that shared library no matter what. Its presence isn't just an optimization: it is a key part of the system.
To do this right, I needed to:
* Introduce visibility macros into mlir-c/Support.h. These should work on both *nix and windows as-is.
* Create a new libMLIRPublicAPI.so with just the mlir-c object files.
* Compile the C-API with -fvisibility=hidden.
* Conditionally depend on the libMLIR.so from libMLIRPublicAPI.so if building libMLIR.so (otherwise, also links against the static libs and will produce a mondo libMLIRPublicAPI.so).
* Disable re-exporting of static library symbols that come in as transitive deps.
This gives us a dynamic linked C-API layer that is minimal and should work as-is on all platforms. Since we don't support libMLIR.so building on Windows yet (and it is not very DLL friendly), this will fall back to a mondo build of libMLIRPublicAPI.so, which has its uses (it is also the most size conscious way to go if you happen to know exactly what you need).
Sizes (release/stripped, Ubuntu 20.04):
Shared library build:
libMLIRPublicAPI.so: 121Kb
_mlir.cpython-38-x86_64-linux-gnu.so: 1.4Mb
mlir-capi-ir-test: 135Kb
libMLIR.so: 21Mb
Static build:
libMLIRPublicAPI.so: 5.5Mb (since this is a "static" build, this includes the MLIR implementation as non-exported code).
_mlir.cpython-38-x86_64-linux-gnu.so: 1.4Mb
mlir-capi-ir-test: 44Kb
Things like npcomp and circt which bring their own dialects/transforms/etc would still need the shared library build and code that links against libMLIR.so (since it is all C++ interop stuff), but hopefully things that only depend on the public C-API can just have the one narrow dep.
I spot checked everything with nm, and it looks good in terms of what is exporting/importing from each layer.
I'm not in a hurry to land this, but if it is controversial, I'll probably split off the Support.h and API visibility macro changes, since we should set that pattern regardless.
Reviewed By: mehdi_amini, benvanik
Differential Revision: https://reviews.llvm.org/D90824
This is exposing the basic functionalities (create, nest, addPass, run) of
the PassManager through the C API in the new header: `include/mlir-c/Pass.h`.
In order to exercise it in the unit-test, a basic TableGen backend is
also provided to generate a simple C wrapper around the pass
constructor. It is used to expose the libTransforms passes to the C API.
Reviewed By: stellaraccident, ftynse
Differential Revision: https://reviews.llvm.org/D90667
This header was an initial early attempt at a crude C API for bindings,
but it isn't used and redundant with the new API. At this point it only
contributes to more confusion.
Differential Revision: https://reviews.llvm.org/D90643
Leaking macros isn't a good practice when defining headers. This
requires to duplicate the macro definition in every header though, but
that seems like a better tradeoff right now.
Differential Revision: https://reviews.llvm.org/D90633
* Removes index based insertion. All insertion now happens through the insertion point.
* Introduces thread local context managers for implicit creation relative to an insertion point.
* Introduces (but does not yet use) binding the Context to the thread local context stack. Intent is to refactor all methods to take context optionally and have them use the default if available.
* Adds C APIs for mlirOperationGetParentOperation(), mlirOperationGetBlock() and mlirBlockGetTerminator().
* Removes an assert in PyOperation creation that was incorrectly constraining. There is already a TODO to rework the keepAlive field that it was guarding and without the assert, it is no worse than the current state.
Differential Revision: https://reviews.llvm.org/D90368
Getting the body of a Module is a common need which justifies a
dedicated accessor instead of forcing users to go through the
region->blocks->front unwrapping manually.
Differential Revision: https://reviews.llvm.org/D90287
This patch provides C API for MLIR affine expression.
- Implement C API for methods of AffineExpr class.
- Implement C API for methods of derived classes (AffineBinaryOpExpr, AffineDimExpr, AffineSymbolExpr, and AffineConstantExpr).
Differential Revision: https://reviews.llvm.org/D89856
* Adds a new MlirOpPrintingFlags type and supporting accessors.
* Adds a new mlirOperationPrintWithFlags function.
* Adds a full featured python Operation.print method with all options and the ability to print directly to files/stdout in text or binary.
* Adds an Operation.get_asm which delegates to print and returns a str or bytes.
* Reworks Operation.__str__ to be based on get_asm.
Differential Revision: https://reviews.llvm.org/D89848
Values are ubiquitous in the IR, in particular block argument and operation
results are Values. Define Python classes for BlockArgument, OpResult and their
common ancestor Value. Define pseudo-container classes for lists of block
arguments and operation results, and use these containers to access the
corresponding values in blocks and operations.
Differential Revision: https://reviews.llvm.org/D89778
The Value hierarchy consists of BlockArgument and OpResult, both of which
derive Value. Introduce IsA functions and functions specific to each class,
similarly to other class hierarchies. Also, introduce functions for
pointer-comparison of Block and Operation that are necessary for testing and
are generally useful.
Reviewed By: stellaraccident, mehdi_amini
Differential Revision: https://reviews.llvm.org/D89714
* Also fixes the const-ness of the various DenseElementsAttr construction functions.
* Both issues identified when trying to use the DenseElementsAttr functions.
Differential Revision: https://reviews.llvm.org/D89517
* Extends Context/Operation interning to cover Module as well.
* Implements Module.context, Attribute.context, Type.context, and Location.context back-references (facilitated testing and also on the TODO list).
* Adds method to create an empty Module.
* Discovered missing in npcomp.
Differential Revision: https://reviews.llvm.org/D89294
* New functions: mlirOperationSetAttributeByName, mlirOperationRemoveAttributeByName
* Also adds some *IsNull checks and standardizes the rest to use "static inline" form, which makes them all non-opaque and not part of the ABI (which is desirable).
* Changes needed to resolve TODOs in npcomp PyTorch capture.
Differential Revision: https://reviews.llvm.org/D88946
Add basic support for registering diagnostic handlers with the context
(actually, the diagnostic engine contained in the context) and processing
diagnostic messages from the C API.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D88736
Based on PyAttribute and PyConcreteAttribute classes, this patch implements the bindings of Float Attribute, Integer Attribute and Bool Attribute subclasses.
This patch also defines the `mlirFloatAttrDoubleGetChecked` C API which is bound with the `FloatAttr.get_typed` python method.
Differential Revision: https://reviews.llvm.org/D88531
* Providing stable, C-accessible definitions for bridging MLIR Python<->C APIs, we eliminate inter-extension dependencies (i.e. they can all share a diamond dependency on the MLIR C-API).
* Just provides accessors for context and module right now.
* Needed in NPComp in ~a week or so for high level Torch APIs.
Differential Revision: https://reviews.llvm.org/D88426
- Add a minimalist C API for mlir::Dialect.
- Allow one to query the context about registered and loaded dialects.
- Add API for loading dialects.
- Provide functions to register the Standard dialect.
When used naively, this will require to separately register each dialect. When
we have more than one exposed, we can add variadic macros that expand to
individual calls.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D88162
Blocks in a region and operations in a block are organized in a linked list.
The C API only provides functions to append or to insert elements at the
specified numeric position in the list. The latter is expensive since it
requires to traverse the list. Add insert before/after functionality with low
cost that relies on the iplist elements being convertible to iterators.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D88148
* Removes the half-completed prior attempt at region/block mutation in favor of new approach to ownership.
* Will re-add mutation more correctly in a follow-on.
* Eliminates the detached state on blocks and regions, simplifying the ownership hierarchy.
* Adds both iterator and index based access at each level.
Differential Revision: https://reviews.llvm.org/D87982
* Per thread https://llvm.discourse.group/t/revisiting-ownership-and-lifetime-in-the-python-bindings/1769
* Reworks contexts so it is always possible to get back to a py::object that holds the reference count for an arbitrary MlirContext.
* Retrofits some of the base classes to automatically take a reference to the context, elimintating keep_alives.
* More needs to be done, as discussed, when moving on to the operations/blocks/regions.
Differential Revision: https://reviews.llvm.org/D87886
This patch provides C API for MLIR affine map.
- Implement C API for AffineMap class.
- Add Utils.h to include/mlir/CAPI/, and move the definition of the CallbackOstream to Utils.h to make sure mlirAffineMapPrint work correct.
- Add TODO for exposing the C API related to AffineExpr and mutable affine map.
Differential Revision: https://reviews.llvm.org/D87617
Numerous MLIR functions return instances of `StringRef` to refer to a
non-owning fragment of a string (usually owned by the context). This is a
relatively simple class that is defined in LLVM. Provide a simple wrapper in
the MLIR C API that contains the pointer and length of the string fragment and
use it for Standard attribute functions that return StringRef instead of the
previous, callback-based mechanism.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D87677
Based on the PyType and PyConcreteType classes, this patch implements the bindings of Shaped Type, Tensor Type and MemRef Type subclasses.
The Tensor Type and MemRef Type are bound as ranked and unranked separately.
This patch adds the ***GetChecked C API to make sure the python side can get a valid type or a nullptr.
Shaped type is not a kind of standard types, it is the base class for vectors, memrefs and tensors, this patch binds the PyShapedType class as the base class of Vector Type, Tensor Type and MemRef Type subclasses.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D87091
* This is just enough to create regions/blocks and iterate over them.
* Does not yet implement the preferred iteration strategy (python pseudo containers).
* Refinements need to come after doing basic mappings of operations and values so that the whole hierarchy can be used.
Differential Revision: https://reviews.llvm.org/D86683
* Generic mlir.ir.Attribute class.
* First standard attribute (mlir.ir.StringAttr), following the same pattern as generic vs standard types.
* NamedAttribute class.
Differential Revision: https://reviews.llvm.org/D86250
Provide C API for MLIR standard attributes. Since standard attributes live
under lib/IR in core MLIR, place the C APIs in the IR library as well (standard
ops will go in a separate library).
Affine map and integer set attributes are only exposed as placeholder types
with IsA support due to the lack of C APIs for the corresponding types.
Integer and floating point attribute APIs expecting APInt and APFloat are not
exposed pending decision on how to support APInt and APFloat.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86143
* The binding for Type is trivial and should be non-controversial.
* The way that I define the IntegerType should serve as a pattern for what I want to do next.
* I propose defining the rest of the standard types in this fashion and then generalizing for dialect types as necessary.
* Essentially, creating/accessing a concrete Type (vs interacting with the string form) is done by "casting" to the concrete type (i.e. IntegerType can be constructed with a Type and will throw if the cast is illegal).
* This deviates from some of our previous discussions about global objects but I think produces a usable API and we should go this way.
Differential Revision: https://reviews.llvm.org/D86179
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>()
Provide C API for MLIR standard types. Since standard types live under lib/IR
in core MLIR, place the C APIs in the IR library as well (standard ops will go
into a separate library). This also defines a placeholder for affine maps that
are necessary to construct a memref, but are not yet exposed to the C API.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86094
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.
Provide printing functions for most IR objects in C API (except Region that
does not have a `print` function, and Module that is expected to be printed as
Operation instead). The printing is based on a callback that is called with
chunks of the string representation and forwarded user-defined data.
Reviewed By: stellaraccident, Jing, mehdi_amini
Differential Revision: https://reviews.llvm.org/D85748
Using intptr_t is a consensus for MLIR C API, but the change was missing
from 75f239e975 (that was using unsigned initially) due to a
misrebase.
Reviewed By: stellaraccident, mehdi_amini
Differential Revision: https://reviews.llvm.org/D85751
Introduce an initial version of C API for MLIR core IR components: Value, Type,
Attribute, Operation, Region, Block, Location. These APIs allow for both
inspection and creation of the IR in the generic form and intended for wrapping
in high-level library- and language-specific constructs. At this point, there
is no stability guarantee provided for the API.
Reviewed By: stellaraccident, lattner
Differential Revision: https://reviews.llvm.org/D83310
Python bindings currently currently provide a makeScalarType function that
constructs one of the predefined types. It was implemented in the bindings
directly to circumvent the absence of standalone type parsing function. Now
that mlir::parseType has been made available, rely on the core parsing
procedure to construct types from strings in the bindings.
This changes includes a library reshuffling that splits out "CoreAPIs"
implementing the binding helper APIs into a separate library and makes that
dependent on the Parser library.
PiperOrigin-RevId: 274794516
Aart Bik