This is mostly a copy of the existing tensor.from_elements bufferization. Once TensorInterfaceImpl.cpp is moved to the tensor dialect, the existing rewrite pattern can be deleted.
Differential Revision: https://reviews.llvm.org/D117775
This is mostly a copy of the existing tensor.generate bufferization. Once TensorInterfaceImpl.cpp is moved to the tensor dialect, the existing rewrite pattern can be deleted.
Differential Revision: https://reviews.llvm.org/D117770
This patch supports the atomic construct (capture) following section 2.17.7 of OpenMP 5.0 standard. Also added tests for the same.
Reviewed By: peixin, kiranchandramohan
Differential Revision: https://reviews.llvm.org/D115851
This is a pretty important debugging option to stay hidden. Also,
improve its cmd-line description; the current description gives no hint
that this is the one to use to have locations printed inline.
Out-of-line locations are also unproductive to work with in many cases
where the locations are actually compact, which is also why this option
should be more visible. This revision doesn't change the default on it
though.
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D117186
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
This has been a major TODO for a very long time, and is necessary for establishing a proper
dialect-free dependency layering for the Transforms library. Code was moved to effectively
two main locations:
* Affine/
There was quite a bit of affine dialect related code in Transforms/ do to historical reasons
(of a time way into MLIR's past). The following headers were moved to:
Transforms/LoopFusionUtils.h -> Dialect/Affine/LoopFusionUtils.h
Transforms/LoopUtils.h -> Dialect/Affine/LoopUtils.h
Transforms/Utils.h -> Dialect/Affine/Utils.h
The following transforms were also moved:
AffineLoopFusion, AffinePipelineDataTransfer, LoopCoalescing
* SCF/
Only one SCF pass was in Transforms/ (likely accidentally placed here): ParallelLoopCollapsing
The SCF specific utilities in LoopUtils have been moved to SCF/Utils.h
* Misc:
mlir::moveLoopInvariantCode was also moved to LoopLikeInterface.h given
that it is a simple utility defined in terms of LoopLikeOpInterface.
Differential Revision: https://reviews.llvm.org/D117848
Transforms/ should only contain transformations that are dialect-independent and
this pass interacts with MemRef operations (making it a better fit for living in that
dialect).
Differential Revision: https://reviews.llvm.org/D117841
Transforms/ should only contain dialect-independent transformations,
and these files are a much better fit for the bufferization dialect anyways.
Differential Revision: https://reviews.llvm.org/D117839
The current lowering from GPU to NVVM does
not correctly handle the following cases when
lowering the gpu shuffle op.
1. When the active width is set to 32 (all lanes),
then the current approach computes (1 << 32) -1 which
results in poison values in the LLVM IR. We fix this by
defining the active mask as (-1) >> (32 - width).
2. In the case of shuffle up, the computation of the third
operand c has to be different from the other 3 modes due to
the op definition in the ISA reference.
(https://docs.nvidia.com/cuda/parallel-thread-execution/index.html)
Specifically, the predicate value is computed as j >= maxLane
for up and j <= maxLane for all other modes. We fix this by
computing maskAndClamp as 32 - width for this mode.
TEST: We modify the existing test and add more checks for the up mode.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D118086
Adding a similar decomposition for exponential minus one to the SPIRV
backends along with the necessary tests.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D118081
Control-Flow Sink moves operations whose only uses are in conditionally-executed regions into those regions so that paths in which their results are not needed do not perform unnecessary computation.
Depends on D115087
Reviewed By: jpienaar, rriddle, bondhugula
Differential Revision: https://reviews.llvm.org/D115088
Add a transpose option to hoist padding to transpose the padded tensor before storing it into the packed tensor. The early transpose improves the memory access patterns of the actual compute kernel. The patch introduces a transpose right after the hoisted pad tensor and a second transpose inside the compute loop. The second transpose can either be fused into the compute operation or will canonicalize away when lowering to vector instructions.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D117893
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
Both insertion points are valid. This is to make BufferizableOpInteface-based bufferization compatible with existing partial bufferization test cases. (So less changes are necessary to unit tests.)
Differential Revision: https://reviews.llvm.org/D117986
This is the only op that is not supported via BufferizableOpInterfaceImpl bufferization. Once this op is supported we can switch `tensor-bufferize` over to the new unified bufferization.
Differential Revision: https://reviews.llvm.org/D117985
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
Only using that change in StringRef already decreases the number of
preoprocessed lines from 7837621 to 7776151 for LLVMSupport
Perhaps more interestingly, it shows that many files were relying on the
inclusion of StringRef.h to have the declaration from STLExtras.h. This
patch tries hard to patch relevant part of llvm-project impacted by this
hidden dependency removal.
Potential impact:
- "llvm/ADT/StringRef.h" no longer includes <memory>,
"llvm/ADT/Optional.h" nor "llvm/ADT/STLExtras.h"
Related Discourse thread:
https://llvm.discourse.group/t/include-what-you-use-include-cleanup/5831
This patch moves merging of duplicate divisions to presburger utility
functions. This is required to support division merging in structures other
than IntegerPolyhedron.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D118001
Pass a ValueRange instead of an ArrayRef<Value> for better compatibility. Also provide an additional function overload that automatically deallocates the buffer if specified.
Differential Revision: https://reviews.llvm.org/D118025
This patch changes names of identifiers and their corresponding getters in
PresburgerSet to match those of IntegerPolyhedron.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D117998
When the coefficients of dividend are negative, the gcd may be negative
which will change the sign of dividend and overflow denominator.
Reviewed By: Groverkss
Differential Revision: https://reviews.llvm.org/D117911
When 2 clamp ops are in a row, they can be canonicalized into a single clamp
that uses the most constrained range
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D117934
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
Implement a taylor series approximation for atan and add an atan2 lowering
that uses atan's appromation. This includes tests for edge cases and tests
for each quadrant.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D115682
Matthias Springer