* Isolates the visibility controlled parts of its implementation to a detail namespace.
* Applies a struct level visibility attribute which applies to the static local within the get() functions.
* The prior version was not emitting a symbol for the static local "instance" fields when the user TU was compiled with -fvisibility=hidden.
Differential Revision: https://reviews.llvm.org/D89153
Without this PatternRewriting infrastructure does not know of modifications and
cannot properly legalize nor rollback changes.
Differential Revision: https://reviews.llvm.org/D89129
Async execute operation can take async arguments as dependencies.
Change `async.execute` custom parser/printer format to use `%value as %unwrapped: !async.value<!type>` sytax.
Reviewed By: mehdi_amini, herhut
Differential Revision: https://reviews.llvm.org/D88601
Without this, legalization might not recursively handle child ops properly.
Additionally, this is required for pattern rewriting to properly rollback conversions.
Differential Revision: https://reviews.llvm.org/D89122
The updated version of kernel outlining did not handle cases correctly
where an operand of a candidate for sinking itself was defined by an operation
that is a sinking candidate. In such cases, it could happen that sunk
operations were inserted in the wrong order, breaking ssa properties.
Differential Revision: https://reviews.llvm.org/D89112
When attempting to compute a differential orderIndex we were calculating the
bailout condition correctly, but then an errant "+ 1" meant the orderIndex we
created was invalid.
Added test.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D89115
This reverts commit 1ceaffd95a.
The build is broken with -DBUILD_SHARED_LIBS=ON ; seems like a possible
layering issue to investigate:
tools/mlir/lib/IR/CMakeFiles/obj.MLIRIR.dir/Operation.cpp.o: In function `mlir::MemoryEffectOpInterface::hasNoEffect(mlir::Operation*)':
Operation.cpp:(.text._ZN4mlir23MemoryEffectOpInterface11hasNoEffectEPNS_9OperationE[_ZN4mlir23MemoryEffectOpInterface11hasNoEffectEPNS_9OperationE]+0x9c): undefined reference to `mlir::MemoryEffectOpInterface::getEffects(llvm::SmallVectorImpl<mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect> >&)'
mlir-tblgen was incompatible with libLLVM, due to explicit linkage with
libLLVMSupport etc.
As it cannot link with libLLVM, make sure all lib it uses are not using libLLVM
either.
As a side effect, also remove some explicit references to LLVM libs and use
components instead.
Differential Revision: https://reviews.llvm.org/D88846
This change allows folds to be done on a newly introduced involution trait rather than having to manually rewrite this optimization for every instance of an involution
Reviewed By: rriddle, andyly, stephenneuendorffer
Differential Revision: https://reviews.llvm.org/D88809
* I believe this was done early on due to it being experimental/etc.
* Needed for dynamic linking in npcomp.
Differential Revision: https://reviews.llvm.org/D89081
When distributing a vector larger than the given multiplicity, we can
distribute it by block where each id gets a chunk of consecutive element
along the dimension distributed. This adds a test for this case and adds extra
checks to make sure we don't distribute for cases not multiple of multiplicity.
Differential Revision: https://reviews.llvm.org/D89061
The methods allow to check
- if an operation has dependencies,
- if there is a dependence from one operation to another.
Differential Revision: https://reviews.llvm.org/D88993
This revision also inserts an end-to-end test that lowers tensors to buffers all the way to executable code on CPU.
Differential revision: https://reviews.llvm.org/D88998
The simplest case is when the indexing maps are DimIds in every component. This covers cwise ops.
Also:
* Expose populateConvertLinalgOnTensorsToBuffersPatterns in Transforms.h
* Expose emitLoopRanges in Transforms.h
Differential Revision: https://reviews.llvm.org/D88781
Added missing strides check to verification method of rank reducing subview
which enforces strides specification for the resulting type.
Differential Revision: https://reviews.llvm.org/D88879
Rationale:
More consistent with the other names. Also forward looking to reading
in other kinds of matrices. Also fixes lint issue on hard-coded %llu.
Reviewed By: penpornk
Differential Revision: https://reviews.llvm.org/D89005
* 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
Subtraction is a foundational arithmetic operation that is often used when computing, for example, data transfer sets or cache hits. Since the result of subtraction need not be a convex polytope, a new class `PresburgerSet` is introduced to represent unions of convex polytopes.
Reviewed By: ftynse, bondhugula
Differential Revision: https://reviews.llvm.org/D87068
The patch fixes the types used to access the elements of the kernel parameter structure from a pointer to the structure to a pointer to the actual parameter type.
Reviewed By: csigg
Differential Revision: https://reviews.llvm.org/D88959
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
Setting up input data for benchmarks and integration tests can be tedious in
pure MLIR. With more sparse tensor work planned, this convenience library
simplifies reading sparse matrices in the popular Matrix Market Exchange
Format (see https://math.nist.gov/MatrixMarket). Note that this library
is *not* part of core MLIR. It is merely intended as a convenience library
for benchmarking and integration testing.
Reviewed By: penpornk
Differential Revision: https://reviews.llvm.org/D88856
Add conversion pass for Vector dialect to SPIR-V dialect and add some simple
conversion pattern for vector.broadcast, vector.insert, vector.extract.
Differential Revision: https://reviews.llvm.org/D88761
This change replaces container used for storing temporary
strings for generated code to std::list.
SmallVector may reallocate internal data, which will invalidate
references when more than one extended instruction set is
generated.
Reviewed By: mravishankar, antiagainst
Differential Revision: https://reviews.llvm.org/D88626
Combine ExtractOp with scalar result with BroadcastOp source. This is useful to
be able to incrementally convert degenerated vector of one element into scalar.
Differential Revision: https://reviews.llvm.org/D88751
This revision adds init_tensors support to buffer allocation for Linalg on tensors.
Currently makes the assumption that the init_tensors fold onto the first output tensors.
This assumption is not currently enforced or cast in stone and requires experimenting with tiling linalg on tensors for ops **without reductions**.
Still this allows progress towards the end-to-end goal.
A pattern to convert `spv.CompositeInsert` and `spv.CompositeExtract`.
In LLVM, there are 2 ops that correspond to each instruction depending
on the container type. If the container type is a vector type, then
the result of conversion is `llvm.insertelement` or `llvm.extractelement`.
If the container type is an aggregate type (i.e. struct, array), the
result of conversion is `llvm.insertvalue` or `llvm.extractvalue`.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D88205
Adds support for SPIR-V composite speciailization constants to spv._reference_of.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D88732
The previous code did the lowering to alloca, malloc, and aligned_malloc
in a single class with different code paths that are somewhat difficult to
follow.
This change moves the common code to a base class and has a separte
derived class per lowering target that contains the specifics.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D88696
This canonicalization is the counterpart of MemRefCastOp -> LinalgOp but on tensors.
This is needed to properly canonicalize post linalg tiling on tensors.
Differential Revision: https://reviews.llvm.org/D88729
While affine maps are part of the builtin memref type, there is very
limited support for manipulating them in the standard dialect. Add
transpose to the set of ops to complement the existing view/subview ops.
This is a metadata transformation that encodes the transpose into the
strides of a memref.
I'm planning to use this when lowering operations on strided memrefs,
using the transpose to remove the stride without adding a dependency on
linalg dialect.
Differential Revision: https://reviews.llvm.org/D88651
This reverts commit e9b87f43bd.
There are issues with macros generating macros without an obvious simple fix
so I'm going to revert this and try something different.
This aligns the behavior with the standard call as well as the LLVM verifier.
Reviewed By: ftynse, dcaballe
Differential Revision: https://reviews.llvm.org/D88362
New projects (particularly out of tree) have a tendency to hijack the existing
llvm configuration options and build targets (add_llvm_library,
add_llvm_tool). This can lead to some confusion.
1) When querying a configuration variable, do we care about how LLVM was
configured, or how these options were configured for the out of tree project?
2) LLVM has lots of defaults, which are easy to miss
(e.g. LLVM_BUILD_TOOLS=ON). These options all need to be duplicated in the
CMakeLists.txt for the project.
In addition, with LLVM Incubators coming online, we need better ways for these
incubators to do things the "LLVM way" without alot of futzing. Ideally, this
would happen in a way that eases importing into the LLVM monorepo when
projects mature.
This patch creates some generic infrastructure in llvm/cmake/modules and
refactors MLIR to use this infrastructure. This should expand to include
add_xxx_library, which is by far the most complicated bit of building a
project correctly, since it has to deal with lots of shared library
configuration bits. (MLIR currently hijacks the LLVM infrastructure for
building libMLIR.so, so this needs to get refactored anyway.)
Differential Revision: https://reviews.llvm.org/D85140
Class simplifies keeping track of the indentation while emitting. For every new line the current indentation is simply prefixed (if not at start of line, then it just emits as normal). Add a simple Region helper that makes it easy to have the C++ scope match the emitted scope.
Use this in op doc generator and rewrite generator.
This reverts revert commit be185b6a73 addresses shared lib failure by fixing up cmake files.
Differential Revision: https://reviews.llvm.org/D84107
Class simplifies keeping track of the indentation while emitting. For every new line the current indentation is simply prefixed (if not at start of line, then it just emits as normal). Add a simple Region helper that makes it easy to have the C++ scope match the emitted scope.
Use this in op doc generator and rewrite generator.
Differential Revision: https://reviews.llvm.org/D84107
This commit adds support to SPIR-V's composite specialization constants.
These are specialization constants which are composed of other spec
constants (whehter scalar or composite), regular constatns, or undef
values.
This commit adds support for parsing, printing, verification, and
(De)serialization.
A few TODOs are still in order:
- Supporting more types of constituents; currently, only scalar spec constatns are supported.
- Extending `spv._reference_of` to support composite spec constatns.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D88568
Add basic canonicalization patterns for the extractMap/insertMap to allow them
to be folded into Transfer ops.
Also mark transferRead as memory read so that it can be removed by dead code.
Differential Revision: https://reviews.llvm.org/D88622
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
Previously the actual types were not shown, which makes the message
difficult to grok in the context of long lowering chains. Also, it
appears that there were no actual tests for this.
Differential Revision: https://reviews.llvm.org/D88318
We hit an llvm_unreachable related to unranked memrefs for call ops
with scalar types. Removing the llvm_unreachable since the conversion
should gracefully bail out in the presence of unranked memrefs. Adding
tests to verify that.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D88709
Instead of recursive helper method `topologicalSortImpl()`,
sort's implementation is moved to `topologicalSort()` function's
body directly. `llvm::ReversePostOrderTraversal` is used to create
a traversal of blocks in reverse post order.
Reviewed By: kiranchandramohan, rriddle
Differential Revision: https://reviews.llvm.org/D88544
This revision introduces a `subtensor` op, which is the counterpart of `subview` for a tensor operand. This also refactors the relevant pieces to allow reusing the `subview` implementation where appropriate.
This operation will be used to implement tiling for Linalg on tensors.
The documentation for the NormalizeMemRefs pass and the associated MemRefsNormalizable
traits was confusing and not on the website. This update clarifies the language
around the difference between a MemRef Type, an operation that accesses the value of
MemRef Type, and better documents the limitations of the current implementation.
This patch also includes some basic debugging information for the pass so people
might have a chance of figuring out why it doesn't work on their code.
Differential Revision: https://reviews.llvm.org/D88532
```
LinalgTilingOptions &setTileSizes(ValueRange ts)
```
makes it all too easy to create stack-use-after-return errors.
In particular, c694588fc5 introduced one such issue.
Instead just take a copy in the lambda and be done with it.
The current implementation uses a fold expression to add all of the operations at once. This is really nice, but apparently the lifetime of each of the AbstractOperation instances is for the entire expression which may lead to a stack overflow for large numbers of operations. This splits the method in two to allow for the lifetime of the AbstractOperation to be properly scoped.
The pattern is structured similar to other patterns like
LinalgTilingPattern. The fusion patterns takes options that allows you
to fuse with producers of multiple operands at once.
- The pattern fuses only at the level that is known to be legal, i.e
if a reduction loop in the consumer is tiled, then fusion should
happen "before" this loop. Some refactoring of the fusion code is
needed to fuse only where it is legal.
- Since the fusion on buffers uses the LinalgDependenceGraph that is
not mutable in place the fusion pattern keeps the original
operations in the IR, but are tagged with a marker that can be later
used to find the original operations.
This change also fixes an issue with tiling and
distribution/interchange where if the tile size of a loop were 0 it
wasnt account for in these.
Differential Revision: https://reviews.llvm.org/D88435
This is the first of several steps to support distributing large vectors. This
adds instructions extract_map and insert_map that allow us to do incremental
lowering. Right now the transformation only apply to simple pointwise operation
with a vector size matching the multiplicity of the IDs used to distribute the
vector.
This can be used to distribute large vectors to loops or SPMD.
Differential Revision: https://reviews.llvm.org/D88341
Switch to a dummy op in the test dialect so we can remove the -allow-unregistred-dialect
on ops.mlir and invalid.mlir. Change after comment on D88272.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D88587
while folding tensor_reshape op.
While folding reshapes that introduce unit extent dims, the logic to
compute the reassociation maps can be generalized to handle some
corner cases, for example, when the folded shape still has unit-extent
dims but corresponds to folded unit extent dims of the expanded shape.
Differential Revision: https://reviews.llvm.org/D88521
AffineMapAttr is already part of base, it's just impossible to refer to
it from ODS without pulling in the definition from Affine dialect.
Differential Revision: https://reviews.llvm.org/D88555
Current setup for conv op vectorization does not enable user to specify tile
sizes as well as dimensions for vectorization. In this commit we change that by
adding tile sizes as pass arguments. Every dimension with corresponding tile
size > 1 is automatically vectorized.
Differential Revision: https://reviews.llvm.org/D88533
This commit adds support for subviews which enable to reduce resulting rank
by dropping static dimensions of size 1.
Differential Revision: https://reviews.llvm.org/D88534
Added support for different function control
in serialization and deserialization.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D88280
Also add a verifier pass to ExecutionEngine.
It's hard to come up with a test case, since mlir-opt always add location info after parsing it (?)
Differential Revision: https://reviews.llvm.org/D88135
This patch adds support for the 'return' and 'call' ops to the bare-ptr
calling convention. These changes also align the bare-ptr calling
convention code with the latest changes in the default calling convention
and reduce the amount of customization code needed.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87724
* 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
This patch introduces the acc.shutdown operation that represents an OpenACC shutdown directive.
Clauses are derived from the spec 2.14.2
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D88272
This patch introduces the init operation that represents the init executable directive
from the OpenACC 3.0 specifications.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D88254
This patch introduce the wait operation that represent the OpenACC wait directive.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D88125
- 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
This patch introduce the update operation that represent the OpenACC update directive.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D88102
Manually-defined named ops do not currently support `init_tensors` or return values and may never support them. Add extra interface to the StructuredOpInterface so that we can still write op-agnostic transformations based on StructuredOpInterface.
This is an NFC extension in preparation for tiling on tensors.
Differential Revision: https://reviews.llvm.org/D88481
This revision changes the signatures of helper function that Linalg uses to create loops so that they can also take iterArgs.
iterArgs are asserted empty to ensure no functional change.
This is a mechanical change in preparation of tiling on linalg on tensors to avoid polluting the implementation with an NFC change.
Differential Revision: https://reviews.llvm.org/D88480
The previous implementation did not support sinking simple expressions. In particular,
it is often beneficial to sink dim operations.
Differential Revision: https://reviews.llvm.org/D88439
Nicolas Vasilache