- Add `mlirElementsAttrGetType` C API.
- Add `def_buffer` binding to PyDenseElementsAttribute.
- Implement the protocol to access the buffer.
Differential Revision: https://reviews.llvm.org/D91021
* I had missed the note about "Standard size" in the docs. On Windows, the 'l' types are 32bit.
* This fixes the only failing MLIR-Python test on Windows.
Differential Revision: https://reviews.llvm.org/D91283
This utility function is helpful for dialect-specific builders that need
to access the context through location, and the location itself may be
either provided as an argument or expected to be recovered from the
implicit location stack.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D91623
In ODS, attributes of an operation can be provided as a part of the "arguments"
field, together with operands. Such attributes are accepted by the op builder
and have accessors generated.
Implement similar functionality for ODS-generated op-specific Python bindings:
the `__init__` method now accepts arguments together with operands, in the same
order as in the ODS `arguments` field; the instance properties are introduced
to OpView classes to access the attributes.
This initial implementation accepts and returns instances of the corresponding
attribute class, and not the underlying values since the mapping scheme of the
value types between C++, C and Python is not yet clear. Default-valued
attributes are not supported as that would require Python to be able to parse
C++ literals.
Since attributes in ODS are tightely related to the actual C++ type system,
provide a separate Tablegen file with the mapping between ODS storage type for
attributes (typically, the underlying C++ attribute class), and the
corresponding class name. So far, this might look unnecessary since all names
match exactly, but this is not necessarily the cases for non-standard,
out-of-tree attributes, which may also be placed in non-default namespaces or
Python modules. This also allows out-of-tree users to generate Python bindings
without having to modify the bindings generator itself. Storage type was
preferred over the Tablegen "def" of the attribute class because ODS
essentially encodes attribute _constraints_ rather than classes, e.g. there may
be many Tablegen "def"s in the ODS that correspond to the same attribute type
with additional constraints
The presence of the explicit mapping requires the change in the .td file
structure: instead of just calling the bindings generator directly on the main
ODS file of the dialect, it becomes necessary to create a new file that
includes the main ODS file of the dialect and provides the mapping for
attribute types. Arguably, this approach offers better separability of the
Python bindings in the build system as the main dialect no longer needs to know
that it is being processed by the bindings generator.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D91542
This only exposes the ability to round-trip a textual pipeline at the
moment.
To exercise it, we also bind the libTransforms in a new Python extension. This
does not include any interesting bindings, but it includes all the
mechanism to add separate native extensions and load them dynamically.
As such passes in libTransforms are only registered after `import
mlir.transforms`.
To support this global registration, the TableGen backend is also
extended to bind to the C API the group registration for passes.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D90819
Introduce an ODS/Tablegen backend producing Op wrappers for Python bindings
based on the ODS operation definition. Usage:
mlir-tblgen -gen-python-op-bindings -Iinclude <path/to/Ops.td> \
-bind-dialect=<dialect-name>
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D90960
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
Enumerating elements in these classes is necessary to enable custom
operand accessors for variadic operands.
Depends On D90919
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D90923
Operations in a MLIR have a dictionary of attributes attached. Expose
those to Python bindings through a pseudo-container that can be indexed
either by attribute name, producing a PyAttribute, or by a contiguous
index for enumeration purposes, producing a PyNamedAttribute.
Depends On D90917
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D90919
The PyOpOperands container was erroneously constructing objects for
individual operands as PyOpResult. Operands in fact are just values,
which may or may not be results of another operation. The code would
eventually crash if the operand was a block argument. Add a test that
exercises the behavior that previously led to crashes.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D90917
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 target will depend on each individual extension and represent "all"
Python bindings in the repo. User projects can get a finer grain control by
depending directly on some individual targets as needed.
The Python bindings now require -DLLVM_BUILD_LLVM_DYLIB=ON to build.
This change is needed to be able to build multiple Python native
extension without having each of them embedding a copy of MLIR, which
would make them incompatible with each other. Instead they should all
link to the same copy of MLIR.
Differential Revision: https://reviews.llvm.org/D90813
* All functions that return an Operation now return an OpView.
* All functions that accept an Operation now accept an _OperationBase, which both Operation and OpView extend and can resolve to the backing Operation.
* Moves user-facing instance methods from Operation -> _OperationBase so that both can have the same API.
* Concretely, this means that if there are custom op classes defined (i.e. in Python), any iteration or creation will return the appropriate instance (i.e. if you get/create an std.addf, you will get an instance of the mlir.dialects.std.AddFOp class, getting full access to any custom API it exposes).
* Refactors all __eq__ methods after realizing the proper way to do this for _OperationBase.
Differential Revision: https://reviews.llvm.org/D90584
* Finishes support for Context, InsertionPoint and Location to be carried by the thread using context managers.
* Introduces type casters and utilities so that DefaultPyMlirContext and DefaultPyLocation in method signatures does the right thing (allows explicit or gets from the thread context).
* Extend the rules for the thread context stack to handle nesting, appropriately inheriting and clearing depending on whether the context is the same.
* Refactors all method signatures to follow the new convention on trailing parameters for defaulting parameters (loc, ip, context). When the objects are carried in the thread context, this allows most explicit uses of these values to be elided.
* Removes the style guide section on putting accessors to construct global objects on the PyMlirContext: this style fails to make good use of the new facility since it is often the only thing remaining needing an MlirContext.
* Moves Module parse/creation from mlir.ir.Context to static methods on mlir.ir.Module.
* Moves Context.create_operation to a static Operation.create method.
* Moves Type parsing from mlir.ir.Context to static methods on mlir.ir.Type.
* Moves Attribute parsing from mlir.ir.Context to static methods on mlir.ir.Attribute.
* Move Location factory methods from mlir.ir.Context to static methods on mlir.ir.Location.
* Refactors the std dialect fake "ODS" generated code to take advantage of the new scheme.
Differential Revision: https://reviews.llvm.org/D90547
* 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
* Still rough edges that need more sugar but the bones are there. Notes left in the test case for things that can be improved.
* Does not actually yield custom OpViews yet for traversing. Will rework that in a followup.
Differential Revision: https://reviews.llvm.org/D89932
* 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
Docstrings for `__str__` method in many classes was recycling the constant
string defined for `Type`, without being types themselves. Use proper
docstrings instead. Since they are succint, use string literals instead of
top-level constants to avoid further mistakes.
Differential Revision: https://reviews.llvm.org/D89780
The pybind class typedef for concrete attribute classes was erroneously
deriving all of them from PyAttribute instead of the provided base class. This
has not been triggering any error because only one level of the hierarchy is
currently exposed.
Differential Revision: https://reviews.llvm.org/D89779
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
* Interops with Python buffers/numpy arrays to create.
* Also cleans up 'get' factory methods on some types to be consistent.
* Adds mlirAttributeGetType() to C-API to facilitate error handling and other uses.
* Punts on a lot of features of the ElementsAttribute hierarchy for now.
* Does not yet support bool or string attributes.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D89363
* 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
* Links against libMLIR.so if the project is built for DYLIBs.
* Puts things in the right place in build and install time python/ trees so that RPaths line up.
* Adds install actions to install both the extension and sources.
* Copies py source files to the build directory to match (consistent layout between build/install time and one place to point a PYTHONPATH for tests and interactive use).
* Finally, "import mlir" from an installed LLVM just works.
Differential Revision: https://reviews.llvm.org/D89167
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
* Added mlirRegisterAllDialects() to the python API until a more complete registration design emerges for it.
Differential Revision: https://reviews.llvm.org/D88155
* The API is a bit more verbose than I feel like it needs to be. In a follow-up I'd like to abbreviate some things and look in to creating aliases for common accessors.
* There is a lingering lifetime hazard between the module and newly added operations. We have the facilities now to solve for this but I will do that in a follow-up.
* We may need to craft a more limited API for safely referencing successors when creating operations. We need more facilities to really prove that out and should defer for now.
Differential Revision: https://reviews.llvm.org/D87996
* 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
* Fixes a rather egregious bug with respect to the inability to return arbitrary objects from py::init (was causing aliasing of multiple py::object -> native instance).
* Makes Modules and Operations referencable types so that they can be reliably depended on.
* Uniques python operation instances within a context. Opens the door for further accounting.
* Next I will retrofit region and block to be dependent on the operation, and I will attempt to model the API to avoid detached regions/blocks, which will simplify things a lot (in that world, only operations can be detached).
* Added quite a bit of test coverage to check for leaks and reference issues.
* Supercedes: https://reviews.llvm.org/D87213
Differential Revision: https://reviews.llvm.org/D87958
* 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
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
* Resolves todos from D87091.
* Also modifies PyConcreteAttribute to follow suite (should be useful for ElementsAttr and friends).
* Adds a test to ensure that the ShapedType base class functions as expected.
Differential Revision: https://reviews.llvm.org/D87208
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
Based on the PyType and PyConcreteType classes, this patch implements the bindings of Complex Type, Vector Type and Tuple Type subclasses.
For the convenience of type checking, this patch defines a `mlirTypeIsAIntegerOrFloat` function to check whether the given type is an integer or float type.
These three subclasses in this patch have similar binding strategy:
- The function pointer `isaFunction` points to `mlirTypeIsA***`.
- The `mlir***TypeGet` C API is bound with the `get_***` method in the python side.
- The Complex Type and Vector Type check whether the given type is an integer or float type.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D86785
* 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
Based on the PyType and PyConcreteType classes, this patch implements the bindings of Index Type, Floating Point Type and None Type subclasses.
These three subclasses share the same binding strategy:
- The function pointer `isaFunction` points to `mlirTypeIsA***`.
- The `mlir***TypeGet` C API is bound with the `***Type` constructor in the python side.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86466
* 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
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