The implementation only allows to bit-cast between two 0-D vectors. We could
probably support casting from/to vectors like `vector<1xf32>`, but I wasn't
convinced that this would be important and it would require breaking the
invariant that `BitCastOp` works only on vectors with equal rank.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D114854
This change provides `BufferizableOpInterface` implementations for ops from the Bufferization dialects. These ops are needed at the bufferization boundaries for partial bufferization.
Differential Revision: https://reviews.llvm.org/D114618
Affine maps and integer sets previously relied on a single lock for creating unique instances. In a multi-threaded setting, this lock becomes a contention point. This commit updates AffineMap and IntegerSet to use StorageUniquer instead. StorageUniquer internally uses sharded locks and thread-local caches to reduce contention. It is already used for affine expressions, types and attributes. On my local machine, this gives me a 5X speedup for an application that manipulates a lot of affine maps and integer sets.
This commit also removes the integer set uniquer threshold. The threshold was used to avoid adding integer sets with a lot of constraints to the hash_map containing unique instances, but the constraints and the integer set were still allocated in the same allocator and never freed, thus not saving any space expect for the hash-map entry.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D114942
This patch implements detecting duplicate local identifiers by extracting their
division representation while merging local identifiers.
For example, given the FACs A, B:
```
A: (x, y)[s0] : (exists d0 = [x / 4], d1 = [y / 4]: d0 <= s0, d1 <= s0, x + y >= 2)
B: (x, y)[s0] : (exists d0 = [x / 4], d1 = [y / 4]: d0 <= s0, d1 <= s0, x + y >= 5)
```
The intersection of A and B without this patch would lead to the following FAC:
```
(x, y)[s0] : (exists d0 = [x / 4], d1 = [y / 4], d2 = [x / 4], d3 = [x / 4]: d0 <= s0, d1 <= s0, d2 <= s0, d3 <= s0, x + y >= 2, x + y >= 5)
```
after this patch, merging of local ids will detect that `d0 = d2` and `d1 = d3`,
and the intersection of these two FACs will be (after removing duplicate constraints):
```
(x, y)[s0] : (exists d0 = [x / 4], d1 = [y / 4] : d0 <= s0, d1 <= s0, x + y >= 2, x + y >= 5)
```
This reduces the number of constraints by 2 (constraints) + 4 (2 constraints for each extra division) for this case.
This is used to reduce the output size representation of operations like
PresburgerSet::subtract, PresburgerSet::intersect which require merging local
variables.
Reviewed By: arjunp, bondhugula
Differential Revision: https://reviews.llvm.org/D112867
Revert changes that were meant to be sent as a single commit with
summary for the differential review, but were accidently sent directly.
This reverts commit 3bc5353fc6.
When an attribute is optional & is given an additional constraint in
rewrite pattern that could lead to dereferencing null Attribute. Avoid
cases where the constraints checks attribute but has no check if null.
This should be improved to be more uniformly guarded.
This is a lightweight operation, useful for writing unit tests. It will be utilized for testing in subsequent commits.
Differential Revision: https://reviews.llvm.org/D114693
This patch fixes the build by removing
extractVectorTypeFromShapedValue. The last use was removed Dec 1,
2021 in commit extractVectorTypeFromShapedValue.
This revision adds 0-d vector support to vector.transfer ops.
In the process, numerous cleanups are applied, in particular around normalizing
and reducing the number of builders.
Reviewed By: ThomasRaoux, springerm
Differential Revision: https://reviews.llvm.org/D114803
RootOrderingTest is a low-level unit test that creates values and uses them as vertices in a directed graph. These vertices were created using `builder.create`, but never freed, due to my insufficient understanding of the MLIR infrastructure.
Reviewed By: mehdi_amini, bondhugula, rriddle
Differential Revision: https://reviews.llvm.org/D114745
However, since CallOps have no aliasing OpResults, their OpOperands always bufferize out-of-place.
This change removes `bufferizesToMemoryWrite` from `CallOpInterface`. This method was called, but its return value did not matter.
Differential Revision: https://reviews.llvm.org/D114616
The new affine map generated by linearizeCollapsedDims should not drop
dimensions. We need to make sure we create a map with at least as many
dimensions as the source map. This prevents
FoldProducerReshapeOpByLinearization from generating invalid IR.
This solves regression in IREE due to e4e4da86af
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D114838
This reverts commit 9a844c2a9b.
The new affine map generated by linearizeCollapsedDims should not drop
dimensions. We need to make sure we create a map with at least as many
dimensions as the source map. This prevents
FoldProducerReshapeOpByLinearization from generating invalid IR.
This solves regression in IREE due to e4e4da86af
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D114838
This reverts commit 29a50c5864.
After LLVM lowering, the original patch incorrectly moved alignment
information across an unconstrained GEP operation. This is only correct
for some index offsets in the GEP. It seems that the best approach is,
in fact, to rely on LLVM to propagate information from the llvm.assume()
to users.
Thanks to Thomas Raoux for catching this.
Proper test for sparse tensor outputs is a single condition throughout
the whole tensor index expression (not a general conjunction, since this
may include other conditions that cause cancellation).
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D114810
This revision reintroduces tensor.insert_slice verification which seems
to have vanished over time: a verifier was initially introduced in cf9503c1b7
but for some reason the invalid.mlir was not properly updated; as time passed the verifier was not called anymore and later the code was deleted.
As a consequence, a non-negligible portion of tests has run astray using invalid
tensor.insert_slice semantics and needed to be fixed.
Also, extract isRankReducedType from TensorOps for better reuse
Originally, this facility was used by both tensor and memref forms but
it got copied around as dialects were split.
Differential Revision: https://reviews.llvm.org/D114715
The canonical type of the result of the `memref.subview` needs to make
sure that the previously dropped unit-dimensions are the ones dropped
for the canonicalized type as well. This means the generic
`inferRankReducedResultType` cannot be used. Instead the current
dropped dimensions need to be querried and the same need to be dropped.
Reviewed By: nicolasvasilache, ThomasRaoux
Differential Revision: https://reviews.llvm.org/D114751
For a 1x1 weight and stride of 1, the input/weight can be reshaped and passed into a fully connected op then reshaped back
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D114757
Add the decompose patterns that lower higher dimensional convolutions to lower dimensional ones to CodegenStrategy and use CodegenStrategy to test the decompose patterns. Additionally, remove the assertion that checks the anchor op name is set in the CodegenStrategyTest pass. Removing the assertion allows us to simplify the pipelines used in the interchange and decompose tests.
Depends On D114797
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D114798
The revision updates the convolution decomposition patterns to take a linalg transformation filter. The transformation filter in a later revision allows use the patterns from CodegenStrategy.
Depends On D114690
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D114797
Add support for an empty anchor op string in vectorization. An empty anchor op string is useful after fusion when there are multiple different operations to vectorize.
Depends On D114689
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D114690
Pad the operation using a top down traversal. The top down traversal unlocks folding opportunities and dim op canonicalizations due to the introduced extract slice operation after the padded operation.
Depends On D114585
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D114689
Michal Terepeta