Although we now have semi-rings to deal with arbitrary ops,
it is still good to convey zero-preserving semantics of
ops to the sparse compiler.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D125043
Adding lowering for Unary and Binary required several changes due to
their unique nature of containing custom code for different "regions"
of the sparse structure being operated on. Along with a Kind, a pointer
to the Operation is passed along to be merged once the lattice
structure is figured out.
The original operation is maintained, as it is required for subsequent
lattice decisions. However, sparse_tensor.binary has some branches
are considered as fully handled and therefore are marked with as
kBinaryBranch to distinguish them.
A unique aspect of the custom code is that sometimes the desired result
is no result at all -- i.e. a user wants overlapping sparse entries to
become empty in the output. The solution to this is to return an
uninitialized Value(), which is checked and handled elsewhere in the
code and results in nothing being written to the output tensor for that
case.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D123057
This allows for inferring the result types of operations in certain situations by using the type of
an operand. This commit allowed for automatically supporting type inference for many more
operations with no additional effort, e.g. nearly all Arithmetic operations now support
result type inferrence with no additional changes.
Differential Revision: https://reviews.llvm.org/D124581
Current index value generation uses fixed-length vector ops, this patch
adds an alterantive codegen path compatible with scalable vectors by
using `LLVM::StepVectorOp`.
Differential Revision: https://reviews.llvm.org/D124454
The SparseTensor passes currently use opaque numbers for the CLI, despite using an enum internally. This patch exposes the enums instead of numbered items that are matched back to the enum.
Fixes GitHub issue #53389
Reviewed by: aartbik, mehdi_amini
Differential Revision: https://reviews.llvm.org/D123876
Now that dialect constructors are generated in the .cpp file, we can
drop all of the dependent dialect includes from the .h file.
Differential Revision: https://reviews.llvm.org/D124298
Rationale:
Allocating the temporary buffers for access pattern expansion on the stack
(using alloca) is a bit too agressive, since it easily runs out of stack space
for large enveloping tensor dimensions. This revision changes the dynamic
allocation of these buffers with explicit alloc/dealloc pairs.
Reviewed By: bixia, wrengr
Differential Revision: https://reviews.llvm.org/D123253
Prior to this change there were a number of places where the allocation and deallocation of SparseTensorCOO objects were not cleanly paired, leading to inconsistencies regarding whether each function released its tensor/coo arguments or not, as well as making it easy to run afoul of memory leaks, use-after-free, or double-free errors. This change cleans up the codegen vs runtime boundary to resolve those issues. Now, the only time the runtime library frees an object is either (a) because it's a function explicitly designed to do so, or (b) because the allocated object is entirely local to the function and would be a memory leak if not released. Thus, now the codegen takes complete responsibility for releasing any objects it caused to be allocated.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D122435
Use "enable-vla-vectorization=vla" to generate a vector length agnostic
loops during vectorization. This option works for vectorization strategy 2.
Differential Revision: https://reviews.llvm.org/D118379
This is work towards: https://github.com/llvm/llvm-project/issues/51652
This differential sets up the options and threads them through everywhere, but doesn't actually use them yet. The differential that finally makes use of them is D122061, which is the final differential in the chain that fixes bug 51652.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D122054
When the sparse_tensor dialect lowers linalg.generic,
it makes inferences about how the operations should
affect the looping logic. For example, multiplication
is an intersection while addition is a union of two
sparse tensors.
The new binary and unary op separate the looping logic
from the computation by nesting the computation code
inside a block which is merged at the appropriate level
in the lowered looping code.
The binary op can have custom computation code for the
overlap, left, and right sparse overlap regions. The
unary op can have custom computation code for the
present and absent values.
Reviewed by: aartbik
Differential Revision: https://reviews.llvm.org/D121018
This removes any potential confusion with the `getType` accessors
which correspond to SSA results of an operation, and makes it
clear what the intent is (i.e. to represent the type of the function).
Differential Revision: https://reviews.llvm.org/D121762
This commit moves FuncOp out of the builtin dialect, and into the Func
dialect. This move has been planned in some capacity from the moment
we made FuncOp an operation (years ago). This commit handles the
functional aspects of the move, but various aspects are left untouched
to ease migration: func::FuncOp is re-exported into mlir to reduce
the actual API churn, the assembly format still accepts the unqualified
`func`. These temporary measures will remain for a little while to
simplify migration before being removed.
Differential Revision: https://reviews.llvm.org/D121266
The revision removes the linalg.fill operation and renames the OpDSL generated linalg.fill_tensor operation to replace it. After the change, all named structured operations are defined via OpDSL and there are no handwritten operations left.
A side-effect of the change is that the pretty printed form changes from:
```
%1 = linalg.fill(%cst, %0) : f32, tensor<?x?xf32> -> tensor<?x?xf32>
```
changes to
```
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
```
Additionally, the builder signature now takes input and output value ranges as it is the case for all other OpDSL operations:
```
rewriter.create<linalg::FillOp>(loc, val, output)
```
changes to
```
rewriter.create<linalg::FillOp>(loc, ValueRange{val}, ValueRange{output})
```
All other changes remain minimal. In particular, the canonicalization patterns are the same and the `value()`, `output()`, and `result()` methods are now implemented by the FillOpInterface.
Depends On D120726
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120728
This revision adds support for the linalg.index to the sparse compiler
pipeline. In essence, this adds the ability to refer to indices in
the tensor index expression, as illustrated below:
Y[i, j, k, l, m] = T[i, j, k, l, m] * i * j
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D121251
The current StandardToLLVM conversion patterns only really handle
the Func dialect. The pass itself adds patterns for Arithmetic/CFToLLVM, but
those should be/will be split out in a followup. This commit focuses solely
on being an NFC rename.
Aside from the directory change, the pattern and pass creation API have been renamed:
* populateStdToLLVMFuncOpConversionPattern -> populateFuncToLLVMFuncOpConversionPattern
* populateStdToLLVMConversionPatterns -> populateFuncToLLVMConversionPatterns
* createLowerToLLVMPass -> createConvertFuncToLLVMPass
Differential Revision: https://reviews.llvm.org/D120778
The Func has a large number of legacy dependencies carried over from the old
Standard dialect, which was pervasive and contained a large number of varied
operations. With the split of the standard dialect and its demise, a lot of lingering
dead dependencies have survived to the Func dialect. This commit removes a
large majority of then, greatly reducing the dependence surface area of the
Func dialect.
The last remaining operations in the standard dialect all revolve around
FuncOp/function related constructs. This patch simply handles the initial
renaming (which by itself is already huge), but there are a large number
of cleanups unlocked/necessary afterwards:
* Removing a bunch of unnecessary dependencies on Func
* Cleaning up the From/ToStandard conversion passes
* Preparing for the move of FuncOp to the Func dialect
See the discussion at https://discourse.llvm.org/t/standard-dialect-the-final-chapter/6061
Differential Revision: https://reviews.llvm.org/D120624
It is time to compose Linalg related optimizations with SparseTensor
related optimizations. This is a careful first start by adding some
general Linalg optimizations "upstream" of the sparse compiler in the
full sparse compiler pipeline. Some minor changes were needed to make
those optimizations aware of sparsity.
Note that after this, we will add a sparse specific fusion rule,
just to demonstrate the power of the new composition.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D119971
A very small refactoring, but a big impact on tests that expect an exact order.
This revision fixes the tests, but also makes them less brittle for similar
minor changes in the future!
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D119992
Rationale:
empty line between main include for this file
moved include that actually defines code into right section
Note that this revision started as breaking up ops/attrs even more
(for bug https://github.com/llvm/llvm-project/issues/52748), but due
the the connection in Dialect.initalize(), this cannot be split further).
All heavy lifting refactoring was already done by River in previous cleanup.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D119617
This reduces the dependencies of the MLIRVector target and makes the dialect consistent with other dialects.
Differential Revision: https://reviews.llvm.org/D118533
The bufferization of arith.constant ops is also switched over to BufferizableOpInterface-based bufferization. The old implementation is deleted. Both implementations utilize GlobalCreator, now renamed to just `getGlobalFor`.
GlobalCreator no longer maintains a set of all created allocations to avoid duplicate allocations of the same constant. Instead, `getGlobalFor` scans the module to see if there is already a global allocation with the same constant value.
For compatibility reasons, it is still possible to create a pass that bufferizes only `arith.constant`. This pass (createConstantBufferizePass) could be deleted once all users were switched over to One-Shot bufferization.
Differential Revision: https://reviews.llvm.org/D118483
A lot of dialects have dependencies that are unnecessary, either because of copy/paste
of files when creating things or some other means. This commit cleans up a bunch of
the simple ones:
* Copy/Paste or missed during refactoring
Most of the dependencies cleaned up here look like copy/paste errors when creating
new dialects/transformations, or because the dependency wasn't removed during a
refactoring (e.g. when splitting the standard dialect).
* Unnecessary hard coding of constant operations in matchers
There are a few instances where a dialect had a dependency because it
was hardcoding checks for constant operations instead of using the better m_Constant
approach.
Differential Revision: https://reviews.llvm.org/D118062
No longer go through an external model. Also put BufferizableOpInterface into the same build target as the BufferizationDialect. This allows for some code reuse between BufferizationOps canonicalizers and BufferizableOpInterface implementations.
Differential Revision: https://reviews.llvm.org/D117987
Rationale:
Although file I/O is a bit alien to MLIR itself, we provide two convenient ways
for sparse tensor I/O. The input part was already there (behind the swiss army
knife sparse_tensor.new). Now we have a sparse_tensor.out to write out data. As
before, the ops are kept vague and may change in the future. For now this
allows us to compare TACO vs MLIR very easily.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D117850
This commit is the first step towards unifying core bufferization and One-Shot Bufferize.
This commit does not move over the implementations of BufferizableOpInterface yet. This will be done in separate commits. This change does also not move the unit tests yet. The tests will be moved together with op interface implementations and split into separate files.
Differential Revision: https://reviews.llvm.org/D117641
BlockArguments gained the ability to have locations attached a while ago, but they
have always been optional. This goes against the core tenant of MLIR where location
information is a requirement, so this commit updates the API to require locations.
Fixes#53279
Differential Revision: https://reviews.llvm.org/D117633
This commit refactors the FunctionLike trait into an interface (FunctionOpInterface).
FunctionLike as it is today is already a pseudo-interface, with many users checking the
presence of the trait and then manually into functionality implemented in the
function_like_impl namespace. By transitioning to an interface, these accesses are much
cleaner (ideally with no direct calls to the impl namespace outside of the implementation
of the derived function operations, e.g. for parsing/printing utilities).
I've tried to maintain as much compatability with the current state as possible, while
also trying to clean up as much of the cruft as possible. The general migration plan for
current users of FunctionLike is as follows:
* function_like_impl -> function_interface_impl
Realistically most user calls should remove references to functions within this namespace
outside of a vary narrow set (e.g. parsing/printing utilities). Calls to the attribute name
accessors should be migrated to the `FunctionOpInterface::` equivalent, most everything
else should be updated to be driven through an instance of the interface.
* OpTrait::FunctionLike -> FunctionOpInterface
`hasTrait` checks will need to be moved to isa, along with the other various Trait vs
Interface API differences.
* populateFunctionLikeTypeConversionPattern -> populateFunctionOpInterfaceTypeConversionPattern
Fixes#52917
Differential Revision: https://reviews.llvm.org/D117272
Mehdi Amini