Removes one element of the pointer union to make it work on 32-bit
systems.
This patch introduces a generic data-flow analysis framework to MLIR. The framework implements a fixed-point iteration algorithm and a dependency graph between lattice states and analysis. Lattice states and points are fully extensible to support highly-customizable analyses.
Reviewed By: phisiart, rriddle
Differential Revision: https://reviews.llvm.org/D126751
This patch introduces a generic data-flow analysis framework to MLIR. The framework implements a fixed-point iteration algorithm and a dependency graph between lattice states and analysis. Lattice states and points are fully extensible to support highly-customizable analyses.
Reviewed By: phisiart, rriddle
Differential Revision: https://reviews.llvm.org/D126751
This patch adds support for tiling operations that implement the
TilingInterface.
- It separates the loop constructs that are used to iterate over tile
from the implementation of the tiling itself. For example, the use
of destructive updates is more related to use of scf.for for
iterating over tiles that are tensors.
- To test the transformation, TilingInterface is implemented for
LinalgOps. The separation of the looping constructs used from the
implementation of tile code generation greatly simplifies the
latter.
- The implementation of TilingInterface for LinalgOp is kept as an
external model for now till this approach can be fully flushed out
to replace the existing tiling + fusion approaches in Linalg.
Differential Revision: https://reviews.llvm.org/D127133
This patch completes outstanding TODOs of removing aliases bazel target names.
This patch also renames and cosolidates some bazel targets to be more in line
with their CMake counterparts, e.g. combining `:LinalgOps` and `:LinalgInterfaces`
into `:LinalgDialect`.
Differential Revision: https://reviews.llvm.org/D127459
Add lowering of the vector.warp_execute_on_lane_0 into scf.if plus memory
transfer for the operands and yield values.
This also add an integration test running on GPU warp. The same tests can be
later re-used with different comment lines to tests distribution
transformations.
This is mostly from @springerm contribution.
Differential Revision: https://reviews.llvm.org/D125430
This dialect provides operations that can be used to control transformation of
the IR using a different portion of the IR. It refers to the IR being
transformed as payload IR, and to the IR guiding the transformation as
transform IR.
The main use case for this dialect is orchestrating fine-grain transformations
on individual operations or sets thereof. For example, it may involve finding
loop-like operations with specific properties (e.g., large size) in the payload
IR, applying loop tiling to those and only those operations, and then applying
loop unrolling to the inner loops produced by the previous transformations. As
such, it is not intended as a replacement for the pass infrastructure, nor for
the pattern rewriting infrastructure. In the most common case, the transform IR
will be processed and applied to payload IR by a pass. Transformations
expressed by the transform dialect may be implemented using the pattern
infrastructure or any other relevant MLIR component.
This dialect is designed to be extensible, that is, clients of this dialect are
allowed to inject additional operations into this dialect using the newly
introduced in this patch `TransformDialectExtension` mechanism. This allows the
dialect to avoid a dependency on the implementation of the transformation as
well as to avoid introducing dialect-specific transform dialects.
See https://discourse.llvm.org/t/rfc-interfaces-and-dialects-for-precise-ir-transformation-control/60927.
Reviewed By: nicolasvasilache, Mogball, rriddle
Differential Revision: https://reviews.llvm.org/D123135
Pass the padding options using arrays instead of lambdas. In particular pass the padding value as string and use the argument parser to create the padding value. Arrays are a more natural choice that matches the current use cases and avoids converting arrays to lambdas.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D122309
This improves the modularity of the bufferization.
From now on, all ops that do not implement BufferizableOpInterface are considered hoisting barriers. Previously, all ops that do not implement the interface were not considered barriers and such ops had to be marked as barriers explicitly. This was unsafe because we could've hoisted across unknown ops where it was not safe to hoist.
As a side effect, this allows for cleaning up AffineBufferizableOpInterfaceImpl. This build unit no longer needed and can be deleted.
Differential Revision: https://reviews.llvm.org/D121519
This transformation is useful to break dependency between consecutive loop
iterations by increasing the size of a temporary buffer. This is usually
combined with heavy software pipelining.
Differential Revision: https://reviews.llvm.org/D119406
This commit adds a pattern to wrap a tensor.pad op with
an scf.if op to separate the cases where we don't need padding
(all pad sizes are actually zeros) and where we indeed need
padding.
This pattern is meant to handle padding inside tiled loops.
Under such cases the padding sizes typically depend on the
loop induction variables. Splitting them would allow treating
perfect tiles and edge tiles separately.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D117018
This reduces the dependencies of the MLIRVector target and makes the dialect consistent with other dialects.
Differential Revision: https://reviews.llvm.org/D118533
Also reimplement `std-bufferize` in terms of BufferizableOpInterface-based bufferization. The old `std.select` bufferization pattern is no longer needed and deleted.
Differential Revision: https://reviews.llvm.org/D118559
This would have enabled me to notice the MLIR test file needed updating too, preventing the test file of 2074eef5db from being necessary.
layering_check is already enabled in mlir/BUILD.bazel. I don't know why I didn't see the other breakage there.
Differential Revision: https://reviews.llvm.org/D118125
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
This is in preparation of unifying the existing bufferization with One-Shot bufferization.
A subsequent commit will replace `tensor-bufferize`'s implementation with the BufferizableOpInterface-based implementation and move over missing test cases.
Differential Revision: https://reviews.llvm.org/D117984
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
This op is an example for how to deal with ops who's OpResult may aliasing with one of multiple OpOperands.
Differential Revision: https://reviews.llvm.org/D116868
This reverts commit a9e236bed8.
This broke the Windows build:
mlir\include\mlir/Dialect/X86Vector/Transforms.h(28): error C2061: syntax error: identifier 'uint'
Adrian Kuegel