This patch moves `LLVM::ShuffleVectorOp` to assembly format and in the
process drops the extra type that can be inferred (both operand types
are required to be the same) and switches to a dense integer array.
The syntax change:
```
// Before
%0 = llvm.shufflevector %0, %1 [0 : i32, 0 : i32, 0 : i32, 0 : i32] : vector<4xf32>, vector<4xf32>
// After
%0 = llvm.shufflevector %0, %1 [0, 0, 0, 0] : vector<4xf32>
```
Reviewed By: dcaballe
Differential Revision: https://reviews.llvm.org/D132038
This fixes a bug in SCF/AffineCanonicalizationUtils.cpp. Loop lb/ub were previously considered dimensions, which caused a crash when a (non-optimizable) affine.min / affine.max expression was processed (due to multiplication of two dims). Lb/ub are now considered symbols and symbols may be multiplied. (The scope of the analysis is "within the loop body", at which point lb/ub are constants.)
Differential Revision: https://reviews.llvm.org/D132021
bufferization.to_memref ops are not supported in One-Shot Analysis. They often trigger a failed assertion that can be confusing. Instead, scan for to_memref ops before running the analysis and immediately abort with a proper error message.
Differential Revision: https://reviews.llvm.org/D132027
Arrange the operations in alphabetical order, rather than what
appears to be the order they were added. This was suggested in
a review when adding new operations.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D132046
A number of mlir tests `FAIL` on Solaris/sparcv9 with `Target has no JIT
support`. This patch fixes that by mimicing `clang/test/lit.cfg.py` which
implements a `host-supports-jit` keyword for this. The gtest-based unit
tests don't support `REQUIRES:`, so lack of support needs to be hardcoded
there.
Tested on `amd64-pc-solaris2.11` (`check-mlir` results unchanged) and
`sparcv9-sun-solaris2.11` (only one unrelated failure left).
Differential Revision: https://reviews.llvm.org/D131151
Remove hardcoded usage of memref.alloc/dealloc ops in affine utils; use memory
effects instead. This is NFC for existing test cases, but strictly more
general/powerful in terms of functionality: it allows other allocation and
freeing ops (like gpu.alloc/dealloc) to work in conjunction with affine ops and
utilities.
Differential Revision: https://reviews.llvm.org/D132011
This commit adds support for 0-D vectors in ReductionOp.
Reviewed By: nicolasvasilache, dcaballe
Differential Revision: https://reviews.llvm.org/D131896
This change omits default values from the sparse tensor type,
saving considerable text real estate for the common cases.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D132083
OptionalAttr does the wrapping with Optional explicitly in ODS while
default valued optional attribute doesn't (and follows DefaultValuedAttr
in this behavior/meant as drop-in for updating old behavior), update
when to emit check for non-null to account for this. Also add variant
for optional default valued string attribute to have same convenience as
default valued string attribute.
The `arith.addi_carry` op implements integer addition with overflows. The carry is returned via the second result, as `i1`.
Reviewed By: antiagainst, bondhugula
Differential Revision: https://reviews.llvm.org/D131893
These are useful in builders of Ops taking DenseArrayAttrs or for use in Rewriter, to create constant instances.
Differential Revision: https://reviews.llvm.org/D132067
Adding more supported storage classes in pointer type
Removing support for arithmetic ops as this is not available on the tensor hardware
Reviwed By: antiagainst
Differential Revision: https://reviews.llvm.org/D132039
Implement the new sparse_tensor.reduce operation which
accepts a starting identity value and a code block
describing how to perform the reduction.
Reviewed by: aartbik
Differential Revision: https://reviews.llvm.org/D130573
There are some of this supported in various places, but the
basic conversion of single operations to LLVM was not supported.
Adding this to allow Flang to use these.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D131912
Simdloop collapse clause is supported in the same way
as colllapse clause for worksharing loops.
Reviewed By: kiranchandramohan
Differential Revision: https://reviews.llvm.org/D131674
Signed-off-by: Dominik Adamski <dominik.adamski@amd.com>
This reverts commit b8ecf32f81.
This commit introduces undefined behavior according to UBSan:
UndefinedBehaviorSanitizer: nullptr-with-nonzero-offset third_party/llvm/llvm-project/mlir/include/mlir/ExecutionEngine/CRunnerUtils.h:377:5
This patch remove the Operation *op from the argument list in utility functions, and directly pass the Location instead of calling op->getLoc().
This should make the code more clear, as the utility function (logically) does not relies on the operation that we are currently rewriting, and they behave the same regardless of the operation.
Reviewed By: aartbik, wrengr
Differential Revision: https://reviews.llvm.org/D131991
The operation computes pow(b, p), where 'b' is floating point
and 'p' is a signed integer. The result's type matches 'b' type.
The operands must have the same shape.
Differential Revision: https://reviews.llvm.org/D129811
This fixes some fallout from D131282. Currently, add_tablegen() will add the tablegen target to LLVM_EXPORTS and associates the install with LLVMExports. For non-standalone builds, this means that you end up with mlir-tblgen and clang-tblgen in LLVMExports.
However, these projects should instead be using MLIR_EXPORTS/MLIRTargets and CLANG_EXPORTS/ClangTargets. To fix this, add an extra EXPORT option and make use of get_target_export_arg() to create the correct export argument.
Reviewed By: ashay-github
Differential Revision: https://reviews.llvm.org/D131565
The latter has accuracy issues around 0. The lowering in MathToLLVM is kept for now.
Reviewed By: bkramer
Differential Revision: https://reviews.llvm.org/D131676
This change add a helper function for computing a topological sorting of a list of ops. E.g. this can be useful in transforms where a subset of ops should be cloned without dominance errors.
The analysis reuses the existing implementation in TopologicalSortUtils.cpp.
Differential Revision: https://reviews.llvm.org/D131669
D130092 removed types from attributes. This patch fixes a minor
issues what was exposed when integrating that change in IREE. An
explicit cast is needed so that the template type of `makeArrayRef`
is automatically deduced.
Co-authored-by: Lei Zhang <antiagainst@google.com>
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D131604
We were hitting issues on Linux where this was only being caught at runtime, and different linkers (BFD vs LLD) are differently strict in such situations. Such libraries will also fail to build properly on Windows (but test coverage of that is limited, so it is better to enforce globally).
Differential Revision: https://reviews.llvm.org/D131911
Depends On D131660
`defaultInitialize()` was introduced for the "nudging" behavior, which has been deleted.
Reviewed By: Mogball, rriddle
Differential Revision: https://reviews.llvm.org/D131746
Currently, in the MLIR `{Sparse,Dense}DataFlowAnalysis` API, there is a small optimization:
Before running a transfer function, if the "out state" is already at the pessimistic fixpoint (bottom lattice value), then we know that it cannot possibly be changed, therefore we can skip the transfer function.
I benchmarked and found that this optimization is ineffective, so we can remove it and simplify `{Sparse,Dense}DataFlowAnalysis`. In a subsequent patch, I plan to change/remove the concept of the pessimistic fixpoint so that the API is further simplified.
Benchmark: I ran the following tests 5 times (after 3 warmup runs), and timed the `initializeAndRun()` function.
| Test | Before (us) | After (us) |
| mlir-opt -test-dead-code-analysis mlir/test/Analysis/DataFlow/test-dead-code-analysis.mlir | 181.2536 | 187.7074 |
| mlir-opt -- -test-dead-code-analysis mlir/test/Analysis/DataFlow/test-last-modified-callgraph.mlir | 109.5504 | 105.0654 |
| mlir-opt -- -test-dead-code-analysis mlir/test/Analysis/DataFlow/test-last-modified.mlir | 333.3646 | 322.4224 |
| mlir-opt -- -allow-unregistered-dialect -sccp mlir/test/Analysis/DataFlow/test-combined-sccp.mlir | 1027.1492 | 1081.818 |
Note: `test-combined-sccp.mlir` is crafted by combining `mlir/test/Transforms/sccp.mlir`, `mlir/test/Transforms/sccp-structured.mlir` and `mlir/test/Transforms/sccp-callgraph.mlir`.
Reviewed By: aartbik, Mogball
Differential Revision: https://reviews.llvm.org/D131660
This patch cleans up the way `LinalgLoopDistributionOptions` are meant
to be used. The option just contains a call back that takes the list
of loop ranges that represent the loops that are to be distributed.
These loops are the outer parallel loops of the tiled operation which
have non-zero tile sizes specified. The call back returns for each of
the loops,
- The procId to use,
- The number of processors,
- The distribution method to use for that loop.
Reviewed By: antiagainst, hanchung
Differential Revision: https://reviews.llvm.org/D131232
The methods to perform such operations have been implemented for the DynamicMemRefType in a way that is similar to the implementation for StridedMemRefType. Up until here one could pass an unranked memref to the library, and thus obtain a “dynamic” memref descriptor, but then there would have been no possibility to operate on its content.
Differential Revision: https://reviews.llvm.org/D131359
AllocTensorElimination does currently not support chains where the type is
changing. AllocTensorElimination used to generate invalid IR for such
inputs. With this commit, AllocTensorElimination does no longer apply to
such inputs. (It can be extended to support such IR if needed.)
Differential Revision: https://reviews.llvm.org/D131880
Vitaly Buka