It's recommended practice that people calling MLIR in a loop
pre-create a LLVM ThreadPool and a dialect registry and then
explicitly pass those into a MLIRContext for each compilation.
However, the C API does not expose the functions needed to follow this
recommendation from a project that isn't calling MLIR's C++ dilectly.
Add the necessary APIs to mlir-c, including a wrapper around LLVM's
ThreadPool struct (so as to avoid having to amend or re-export parts
of the LLVM API).
Reviewed By: makslevental
Differential Revision: https://reviews.llvm.org/D153593
depends on D150839
This diff uses `MlirTypeID` to register `TypeCaster`s (i.e., `[](PyType pyType) -> DerivedTy { return pyType; }`) for all concrete types (i.e., `PyConcrete<...>`) that are then queried for (by `MlirTypeID`) and called in `struct type_caster<MlirType>::cast`. The result is that anywhere an `MlirType mlirType` is returned from a python binding, that `mlirType` is automatically cast to the correct concrete type. For example:
```
c0 = arith.ConstantOp(f32, 0.0)
# CHECK: F32Type(f32)
print(repr(c0.result.type))
unranked_tensor_type = UnrankedTensorType.get(f32)
unranked_tensor = tensor.FromElementsOp(unranked_tensor_type, [c0]).result
# CHECK: UnrankedTensorType
print(type(unranked_tensor.type).__name__)
# CHECK: UnrankedTensorType(tensor<*xf32>)
print(repr(unranked_tensor.type))
```
This functionality immediately extends to typed attributes (i.e., `attr.type`).
The diff also implements similar functionality for `mlir_type_subclass`es but in a slightly different way - for such types (which have no cpp corresponding `class` or `struct`) the user must provide a type caster in python (similar to how `AttrBuilder` works) or in cpp as a `py::cpp_function`.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D150927
This diff adds python bindings for `MlirTypeID`. It paves the way for returning accurately typed `Type`s from python APIs (see D150927) and then further along building type "conscious" `Value` APIs (see D150413).
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D150839
The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.
Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.
Context:
* https://mlir.llvm.org/deprecation/ at "Use the free function variants for dyn_cast/cast/isa/…"
* Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443
Implementation:
This follows a previous patch that updated calls
`op.cast<T>()-> cast<T>(op)`. However some cases could not handle an
unprefixed `cast` call due to occurrences of variables named cast, or
occurring inside of class definitions which would resolve to the method.
All C++ files that did not work automatically with `cast<T>()` are
updated here to `llvm::cast` and similar with the intention that they
can be easily updated after the methods are removed through a
find-replace.
See https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check
for the clang-tidy check that is used and then update printed
occurrences of the function to include `llvm::` before.
One can then run the following:
```
ninja -C $BUILD_DIR clang-tidy
run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
-export-fixes /tmp/cast/casts.yaml mlir/*\
-header-filter=mlir/ -fix
rm -rf $BUILD_DIR/tools/mlir/**/*.inc
```
Differential Revision: https://reviews.llvm.org/D150348
This new features enabled to dedicate custom storage inline within operations.
This storage can be used as an alternative to attributes to store data that is
specific to an operation. Attribute can also be stored inside the properties
storage if desired, but any kind of data can be present as well. This offers
a way to store and mutate data without uniquing in the Context like Attribute.
See the OpPropertiesTest.cpp for an example where a struct with a
std::vector<> is attached to an operation and mutated in-place:
struct TestProperties {
int a = -1;
float b = -1.;
std::vector<int64_t> array = {-33};
};
More complex scheme (including reference-counting) are also possible.
The only constraint to enable storing a C++ object as "properties" on an
operation is to implement three functions:
- convert from the candidate object to an Attribute
- convert from the Attribute to the candidate object
- hash the object
Optional the parsing and printing can also be customized with 2 extra
functions.
A new options is introduced to ODS to allow dialects to specify:
let usePropertiesForAttributes = 1;
When set to true, the inherent attributes for all the ops in this dialect
will be using properties instead of being stored alongside discardable
attributes.
The TestDialect showcases this feature.
Another change is that we introduce new APIs on the Operation class
to access separately the inherent attributes from the discardable ones.
We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`,
and other similar method which don't make the distinction explicit, leading
to an entirely separate namespace for discardable attributes.
Recommit d572cd1b06 after fixing python bindings build.
Differential Revision: https://reviews.llvm.org/D141742
This new features enabled to dedicate custom storage inline within operations.
This storage can be used as an alternative to attributes to store data that is
specific to an operation. Attribute can also be stored inside the properties
storage if desired, but any kind of data can be present as well. This offers
a way to store and mutate data without uniquing in the Context like Attribute.
See the OpPropertiesTest.cpp for an example where a struct with a
std::vector<> is attached to an operation and mutated in-place:
struct TestProperties {
int a = -1;
float b = -1.;
std::vector<int64_t> array = {-33};
};
More complex scheme (including reference-counting) are also possible.
The only constraint to enable storing a C++ object as "properties" on an
operation is to implement three functions:
- convert from the candidate object to an Attribute
- convert from the Attribute to the candidate object
- hash the object
Optional the parsing and printing can also be customized with 2 extra
functions.
A new options is introduced to ODS to allow dialects to specify:
let usePropertiesForAttributes = 1;
When set to true, the inherent attributes for all the ops in this dialect
will be using properties instead of being stored alongside discardable
attributes.
The TestDialect showcases this feature.
Another change is that we introduce new APIs on the Operation class
to access separately the inherent attributes from the discardable ones.
We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`,
and other similar method which don't make the distinction explicit, leading
to an entirely separate namespace for discardable attributes.
Differential Revision: https://reviews.llvm.org/D141742
Can't return a well-formed IR output while enabling version to be bumped
up during emission. Previously it would return min version but
potentially invalid IR which was confusing, instead make it return
error and abort immediately instead.
Differential Revision: https://reviews.llvm.org/D149569
Add method to set a desired bytecode file format to generate. Change
write method to be able to return status including the minimum bytecode
version needed by reader. This enables generating an older version of
the bytecode (not dialect ops, attributes or types). But this does not
guarantee that an older version can always be generated, e.g., if a
dialect uses a new encoding only available at later bytecode version.
This clamps setting to at most current version.
Differential Revision: https://reviews.llvm.org/D146555
X. Sun et al. (https://dl.acm.org/doi/10.5555/3454287.3454728) published
a paper showing that an FP format with 4 bits of exponent, 3 bits of
significand and an exponent bias of 11 would work quite well for ML
applications.
Google hardware supports a variant of this format where 0x80 is used to
represent NaN, as in the Float8E4M3FNUZ format. Just like the
Float8E4M3FNUZ format, this format does not support -0 and values which
would map to it will become +0.
This format is proposed for inclusion in OpenXLA's StableHLO dialect: https://github.com/openxla/stablehlo/pull/1308
As part of inclusion in that dialect, APFloat needs to know how to
handle this format.
Differential Revision: https://reviews.llvm.org/D146441
`mlirPassManagerRun` is currently restricted to running on
`builtin.module` ops, but this restriction doesn't exist on the C++
side. This renames it to `mlirPassManagerRunOnOp` and updates it to take
`MlirOperation` instead of `MlirModule`.
Depends on D143352
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D143354
Currently the bindings only allow for parsing IR with a top-level
`builtin.module` op, since the parse APIs insert an implicit module op.
This change adds `Operation.parse`, which returns whatever top-level op
is actually in the source.
To simplify parsing of specific operations, `OpView.parse` is also
added, which handles the error checking for `OpView` subclasses.
Reviewed By: ftynse, stellaraccident
Differential Revision: https://reviews.llvm.org/D143352
The asm printer grew the ability to automatically fall back to the
generic format for invalid ops, so this logic doesn't need to be in the
bindings anymore. The printer already handles supressing diagnostics
that get emitted while checking if the op is valid.
Reviewed By: mehdi_amini, stellaraccident
Differential Revision: https://reviews.llvm.org/D144805
Float8E5M2FNUZ and Float8E4M3FNUZ have been added to APFloat in D141863.
This change adds these types as MLIR builtin types alongside Float8E5M2
and Float8E4M3FN (added in D133823 and D138075).
Reviewed By: krzysz00
Differential Revision: https://reviews.llvm.org/D143744
This patch adds three functions to the C API:
- mlirAttributeIsALocation: returns true if the attribute is a LocationAttr,
false otherwise.
- mlirLocationGetAttribute: returns the underlying LocationAttr of a Location.
- mlirLocationFromAttribute: gets a Location from a LocationAttr.
Reviewed By: mikeurbach, Mogball
Differential Revision: https://reviews.llvm.org/D142182
This allows basic IR traversal via the C API, which is useful for
analyses in languages other than C++.
This starts by defining an MlirOpOperand struct to encapsulate a pair
of an owner operation and an operand number.
A new API is added for MlirValue, to return the first use of the Value
as an MlirOpOperand, or a "null" MlirOpOperand if there are no uses.
A couple APIs are added for MlirOpOperand. The first checks if an
MlirOpOperand is "null", by checking if its owner's pointer is
null. The second supports iteration along the use-def lists by
accepting an MlirOpOperand and returning the next use of the Value as
another MlirOpOperand, or a "null" MlirOpOperand if there are no more
uses.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D139596
This patch mechanically replaces None with std::nullopt where the
compiler would warn if None were deprecated. The intent is to reduce
the amount of manual work required in migrating from Optional to
std::optional.
This is part of an effort to migrate from llvm::Optional to
std::optional:
https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716
This adds an `enable` flag to OpPrintingFlags::enableDebugInfo
that allows for overriding any command line flags for debug printing,
and matches the format that we use for other `enableBlah` API.
The pipeline string must now include the pass manager's anchor op. This
makes the parse API properly roundtrip the printed form of a pass
manager. Since this is already an API break, I also added an extra
callback argument which is used for reporting errors.
The old functionality of appending to an existing pass manager is
available through `mlirOpPassManagerAddPipeline`.
Reviewed By: mehdi_amini, ftynse
Differential Revision: https://reviews.llvm.org/D136403
This adds a new function for creating pass managers that takes an
argument for the anchor string.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D136404
Currently any errors during pipeline parsing are reported to stderr.
This adds a new pipeline parsing function to the C api that reports
errors through a callback, and updates the python bindings to use it.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D136402
(Re-Apply with fixes to clang MicrosoftMangle.cpp)
This is a first step towards high level representation for fp8 types
that have been built in to hardware with near term roadmaps. Like the
BFLOAT16 type, the family of fp8 types are inspired by IEEE-754 binary
floating point formats but, due to the size limits, have been tweaked in
various ways in order to maximally use the range/precision in various
scenarios. The list of variants is small/finite and bounded by real
hardware.
This patch introduces the E5M2 FP8 format as proposed by Nvidia, ARM,
and Intel in the paper: https://arxiv.org/pdf/2209.05433.pdf
As the more conformant of the two implemented datatypes, we are plumbing
it through LLVM's APFloat type and MLIR's type system first as a
template. It will be followed by the range optimized E4M3 FP8 format
described in the paper. Since that format deviates further from the
IEEE-754 norms, it may require more debate and implementation
complexity.
Given that we see two parts of the FP8 implementation space represented
by these cases, we are recommending naming of:
* `F8M<N>` : For FP8 types that can be conceived of as following the
same rules as FP16 but with a smaller number of mantissa/exponent
bits. Including the number of mantissa bits in the type name is enough
to fully specify the type. This naming scheme is used to represent
the E5M2 type described in the paper.
* `F8M<N>F` : For FP8 types such as E4M3 which only support finite
values.
The first of these (this patch) seems fairly non-controversial. The
second is previewed here to illustrate options for extending to the
other known variant (but can be discussed in detail in the patch
which implements it).
Many conversations about these types focus on the Machine-Learning
ecosystem where they are used to represent mixed-datatype computations
at a high level. At that level (which is why we also expose them in
MLIR), it is important to retain the actual type definition so that when
lowering to actual kernels or target specific code, the correct
promotions, casts and rescalings can be done as needed. We expect that
most LLVM backends will only experience these types as opaque `I8`
values that are applicable to some instructions.
MLIR does not make it particularly easy to add new floating point types
(i.e. the FloatType hierarchy is not open). Given the need to fully
model FloatTypes and make them interop with tooling, such types will
always be "heavy-weight" and it is not expected that a highly open type
system will be particularly helpful. There are also a bounded number of
floating point types in use for current and upcoming hardware, and we
can just implement them like this (perhaps looking for some cosmetic
ways to reduce the number of places that need to change). Creating a
more generic mechanism for extending floating point types seems like it
wouldn't be worth it and we should just deal with defining them one by
one on an as-needed basis when real hardware implements a new scheme.
Hopefully, with some additional production use and complete software
stacks, hardware makers will converge on a set of such types that is not
terribly divergent at the level that the compiler cares about.
(I cleaned up some old formatting and sorted some items for this case:
If we converge on landing this in some form, I will NFC commit format
only changes as a separate commit)
Differential Revision: https://reviews.llvm.org/D133823
This is a first step towards high level representation for fp8 types
that have been built in to hardware with near term roadmaps. Like the
BFLOAT16 type, the family of fp8 types are inspired by IEEE-754 binary
floating point formats but, due to the size limits, have been tweaked in
various ways in order to maximally use the range/precision in various
scenarios. The list of variants is small/finite and bounded by real
hardware.
This patch introduces the E5M2 FP8 format as proposed by Nvidia, ARM,
and Intel in the paper: https://arxiv.org/pdf/2209.05433.pdf
As the more conformant of the two implemented datatypes, we are plumbing
it through LLVM's APFloat type and MLIR's type system first as a
template. It will be followed by the range optimized E4M3 FP8 format
described in the paper. Since that format deviates further from the
IEEE-754 norms, it may require more debate and implementation
complexity.
Given that we see two parts of the FP8 implementation space represented
by these cases, we are recommending naming of:
* `F8M<N>` : For FP8 types that can be conceived of as following the
same rules as FP16 but with a smaller number of mantissa/exponent
bits. Including the number of mantissa bits in the type name is enough
to fully specify the type. This naming scheme is used to represent
the E5M2 type described in the paper.
* `F8M<N>F` : For FP8 types such as E4M3 which only support finite
values.
The first of these (this patch) seems fairly non-controversial. The
second is previewed here to illustrate options for extending to the
other known variant (but can be discussed in detail in the patch
which implements it).
Many conversations about these types focus on the Machine-Learning
ecosystem where they are used to represent mixed-datatype computations
at a high level. At that level (which is why we also expose them in
MLIR), it is important to retain the actual type definition so that when
lowering to actual kernels or target specific code, the correct
promotions, casts and rescalings can be done as needed. We expect that
most LLVM backends will only experience these types as opaque `I8`
values that are applicable to some instructions.
MLIR does not make it particularly easy to add new floating point types
(i.e. the FloatType hierarchy is not open). Given the need to fully
model FloatTypes and make them interop with tooling, such types will
always be "heavy-weight" and it is not expected that a highly open type
system will be particularly helpful. There are also a bounded number of
floating point types in use for current and upcoming hardware, and we
can just implement them like this (perhaps looking for some cosmetic
ways to reduce the number of places that need to change). Creating a
more generic mechanism for extending floating point types seems like it
wouldn't be worth it and we should just deal with defining them one by
one on an as-needed basis when real hardware implements a new scheme.
Hopefully, with some additional production use and complete software
stacks, hardware makers will converge on a set of such types that is not
terribly divergent at the level that the compiler cares about.
(I cleaned up some old formatting and sorted some items for this case:
If we converge on landing this in some form, I will NFC commit format
only changes as a separate commit)
Differential Revision: https://reviews.llvm.org/D133823
This adds a `write_bytecode` method to the Operation class.
The method takes a file handle and writes the binary blob to it.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D133210
This patch turns `DenseArrayBaseAttr` into a fully-functional attribute by
adding a generic parser and printer, supporting bool or integer and floating
point element types with bitwidths divisible by 8. It has been renamed
to `DenseArrayAttr`. The patch maintains the specialized subclasses,
e.g. `DenseI32ArrayAttr`, which remain the preferred API for accessing
elements in C++.
This allows `DenseArrayAttr` to hold signed and unsigned integer elements:
```
array<si8: -128, 127>
array<ui8: 255>
```
"Exotic" floating point elements:
```
array<bf16: 1.2, 3.4>
```
And integers of other bitwidths:
```
array<i24: 8388607>
```
Reviewed By: rriddle, lattner
Differential Revision: https://reviews.llvm.org/D132758
This attribute is technical debt from the early stages of MLIR, before
ElementsAttr was an interface and when it was more difficult for
dialects to define their own types of attributes. At present it isn't
used at all in tree (aside from being convenient for eliding other
ElementsAttr), and has had little to no evolution in the past three years.
Differential Revision: https://reviews.llvm.org/D129917
This patch removes the `type` field from `Attribute` along with the
`Attribute::getType` accessor.
Going forward, this means that attributes in MLIR will no longer have
types as a first-class concept. This patch lays the groundwork to
incrementally remove or refactor code that relies on generic attributes
being typed. The immediate impact will be on attributes that rely on
`Attribute` containing a type, such as `IntegerAttr`,
`DenseElementsAttr`, and `ml_program::ExternAttr`, which will now need
to define a type parameter on their storage classes. This will save
memory as all other attribute kinds will no longer contain a type.
Moreover, it will not be possible to generically query the type of an
attribute directly. This patch provides an attribute interface
`TypedAttr` that implements only one method, `getType`, which can be
used to generically query the types of attributes that implement the
interface. This interface can be used to retain the concept of a "typed
attribute". The ODS-generated accessor for a `type` parameter
automatically implements this method.
Next steps will be to refactor the assembly formats of certain operations
that rely on `parseAttribute(type)` and `printAttributeWithoutType` to
remove special handling of type elision until `type` can be removed from
the dialect parsing hook entirely; and incrementally remove uses of
`TypedAttr`.
Reviewed By: lattner, rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D130092
The current Parser library is solely focused on providing API for
the textual MLIR format, but MLIR will soon also provide a binary
format. This commit renames the current Parser library to AsmParser to
better correspond to what the library is actually intended for. A new
Parser library is added which will act as a unified parser interface
between both text and binary formats. Most parser clients are
unaffected, given that the unified interface is essentially the same as
the current interface. Only clients that rely on utilizing the
AsmParserState, or those that want to parse Attributes/Types need to be
updated to point to the AsmParser library.
Differential Revision: https://reviews.llvm.org/D129605
This patch adds a new function mlirDenseElementsAttrFloat16Get(),
which accepts the shaped type, the number of Float16 values, and a
pointer to an array of Float16 values, each of which is a uint16_t
value.
This commit is repeating https://reviews.llvm.org/D123981 + #761 but for Float16
Differential Revision: https://reviews.llvm.org/D130069
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
Introduce a new transformation on structured ops that splits the iteration
space into two parts along the specified dimension. The index at which the
splitting happens may be static or dynamic. This transformation can be seen as
a rudimentary form of index-set splitting that only supports the splitting
along hyperplanes parallel to the iteration space hyperplanes, and is therefore
decomposable into per-dimension application.
It is a key low-level transformation that enables independent scheduling for
different parts of the iteration space of the same op, which hasn't been
possible previously. It may be used to implement, e.g., multi-sized tiling. In
future, peeling can be implemented as a combination of split-off amount
computation and splitting.
The transformation is conceptually close to tiling in its separation of the
iteration and data spaces, but cannot be currently implemented on top of
TilingInterface as the latter does not properly support `linalg.index`
offsetting.
Note that the transformation intentionally bypasses folding of
`tensor.extract_slice` operations when creating them as this folding was found
to prevent repeated splitting of the same operation because due to internal
assumptions about extract/insert_slice combination in dialect utilities.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D129090
Implement the C-API and Python bindings for the builtin opaque type, which was previously missing.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D127303
